Merge remote-tracking branch 'dmlc/master'

.gitignore

@@ -58,3 +58,12 @@ R-package.Rproj
 *.cache*
 R-package/inst
 R-package/src
+#java
+java/xgboost4j/target
+java/xgboost4j/tmp
+java/xgboost4j-demo/target
+java/xgboost4j-demo/data/
+java/xgboost4j-demo/tmp/
+java/xgboost4j-demo/model/
+nb-configuration*
+dmlc-core

.travis.yml

@@ -0,0 +1,47 @@
+sudo: true
+
+# Use Build Matrix to do lint and build seperately
+env:
+  matrix:
+    - TASK=lint LINT_LANG=cpp
+    - TASK=lint LINT_LANG=python
+    - TASK=R-package CXX=g++
+    - TASK=python-package CXX=g++
+    - TASK=java-package CXX=g++
+    - TASK=build CXX=g++
+    - TASK=build-with-dmlc CXX=g++
+
+# dependent apt packages
+addons:
+  apt:
+    packages:
+      - doxygen
+      - libopenmpi-dev
+      - wget
+      - libcurl4-openssl-dev
+      - unzip
+      - python-numpy
+      - python-scipy
+      - python-nose
+
+before_install:
+  - git clone https://github.com/dmlc/dmlc-core
+  - export TRAVIS=dmlc-core/scripts/travis/
+  - export PYTHONPATH=${PYTHONPATH}:${PWD}/wrapper
+  - source ${TRAVIS}/travis_setup_env.sh
+
+install:
+  - pip install cpplint pylint --user `whoami`
+
+script: scripts/travis_script.sh
+
+
+after_failure:
+  - scripts/travis_after_failure.sh
+
+
+notifications:
+  email:
+    on_success: change
+    on_failure: always
+

Makefile

@@ -1,8 +1,10 @@
 export CC  = gcc
 export CXX = g++
 export MPICXX = mpicxx
-export LDFLAGS= -pthread -lm 
+export LDFLAGS= -pthread -lm
 export CFLAGS = -Wall -O3 -msse2  -Wno-unknown-pragmas -funroll-loops
+# java include path
+export JAVAINCFLAGS = -I${JAVA_HOME}/include -I${JAVA_HOME}/include/linux -I./java
 
 ifeq ($(OS), Windows_NT)
 	export CXX = g++ -m64
@@ -10,8 +12,8 @@ ifeq ($(OS), Windows_NT)
 endif
 
 ifeq ($(no_omp),1)
-	CFLAGS += -DDISABLE_OPENMP 
+	CFLAGS += -DDISABLE_OPENMP
-else 
+else
 	CFLAGS += -fopenmp
 endif
 
@@ -27,7 +29,7 @@ ifdef dmlc
 			config = $(dmlc)/config.mk
 		else
 			config = $(dmlc)/make/config.mk
-		endif	
+		endif
 	endif
 	include $(config)
 	include $(dmlc)/make/dmlc.mk
@@ -41,7 +43,7 @@ ifndef WITH_FPIC
 	WITH_FPIC = 1
 endif
 ifeq ($(WITH_FPIC), 1)
-	CFLAGS += -fPIC	
+	CFLAGS += -fPIC
 endif
 
 
@@ -53,6 +55,9 @@ else
 	SLIB = wrapper/libxgboostwrapper.so
 endif
 
+# java lib
+JLIB = java/libxgboostjavawrapper.so
+
 # specify tensor path
 BIN = xgboost
 MOCKBIN = xgboost.mock
@@ -64,7 +69,11 @@ else
 	TARGET = $(BIN)
 endif
 
-.PHONY: clean all mpi python Rpack
+ifndef LINT_LANG
+	LINT_LANG= "all"
+endif
+
+.PHONY: clean all mpi python Rpack lint
 
 all: $(TARGET)
 mpi: $(MPIBIN)
@@ -73,12 +82,15 @@ python: wrapper/libxgboostwrapper.so
 # now the wrapper takes in two files. io and wrapper part
 updater.o: src/tree/updater.cpp  src/tree/*.hpp src/*.h src/tree/*.h src/utils/*.h
 dmlc_simple.o: src/io/dmlc_simple.cpp src/utils/*.h
-gbm.o: src/gbm/gbm.cpp src/gbm/*.hpp src/gbm/*.h 
+gbm.o: src/gbm/gbm.cpp src/gbm/*.hpp src/gbm/*.h
 io.o: src/io/io.cpp src/io/*.hpp src/utils/*.h src/learner/dmatrix.h src/*.h
-main.o: src/xgboost_main.cpp src/utils/*.h src/*.h src/learner/*.hpp src/learner/*.h 
+main.o: src/xgboost_main.cpp src/utils/*.h src/*.h src/learner/*.hpp src/learner/*.h
 xgboost:  updater.o gbm.o io.o main.o $(LIBRABIT) $(LIBDMLC)
 wrapper/xgboost_wrapper.dll wrapper/libxgboostwrapper.so: wrapper/xgboost_wrapper.cpp src/utils/*.h src/*.h src/learner/*.hpp src/learner/*.h  updater.o gbm.o io.o $(LIBRABIT) $(LIBDMLC)
 
+java: java/libxgboostjavawrapper.so
+java/libxgboostjavawrapper.so: java/xgboost4j_wrapper.cpp wrapper/xgboost_wrapper.cpp src/utils/*.h src/*.h src/learner/*.hpp src/learner/*.h  updater.o gbm.o io.o $(LIBRABIT) $(LIBDMLC)
+
 # dependency on rabit
 subtree/rabit/lib/librabit.a: subtree/rabit/src/engine.cc
 	+	cd subtree/rabit;make lib/librabit.a; cd ../..
@@ -89,23 +101,26 @@ subtree/rabit/lib/librabit_mock.a: subtree/rabit/src/engine_mock.cc
 subtree/rabit/lib/librabit_mpi.a: subtree/rabit/src/engine_mpi.cc
 	+	cd subtree/rabit;make lib/librabit_mpi.a; cd ../..
 
-$(BIN) : 
+$(BIN) :
-	$(CXX) $(CFLAGS) -fPIC -o $@ $(filter %.cpp %.o %.c %.cc %.a, $^) $(LDFLAGS) 
+	$(CXX) $(CFLAGS) -fPIC -o $@ $(filter %.cpp %.o %.c %.cc %.a, $^) $(LDFLAGS)
 
-$(MOCKBIN) : 
+$(MOCKBIN) :
-	$(CXX) $(CFLAGS) -o $@ $(filter %.cpp %.o %.c %.cc %.a, $^) $(LDFLAGS) 
+	$(CXX) $(CFLAGS) -o $@ $(filter %.cpp %.o %.c %.cc %.a, $^) $(LDFLAGS)
 
 $(SLIB) :
 	$(CXX) $(CFLAGS) -fPIC -shared -o $@ $(filter %.cpp %.o %.c %.a %.cc, $^) $(LDFLAGS) $(DLLFLAGS)
 
-$(OBJ) : 
+$(JLIB) :
+	$(CXX) $(CFLAGS) -fPIC -shared -o $@ $(filter %.cpp %.o %.c %.a %.cc, $^) $(LDFLAGS)  $(JAVAINCFLAGS)
+
+$(OBJ) :
 	$(CXX) -c $(CFLAGS) -o $@ $(firstword $(filter %.cpp %.c %.cc, $^) )
 
-$(MPIOBJ) : 
+$(MPIOBJ) :
-	$(MPICXX) -c $(CFLAGS) -o $@ $(firstword $(filter %.cpp %.c, $^) ) 
+	$(MPICXX) -c $(CFLAGS) -o $@ $(firstword $(filter %.cpp %.c, $^) )
 
-$(MPIBIN) : 
+$(MPIBIN) :
-	$(MPICXX) $(CFLAGS) -o $@ $(filter %.cpp %.o %.c %.cc %.a, $^) $(LDFLAGS) 
+	$(MPICXX) $(CFLAGS) -o $@ $(filter %.cpp %.o %.c %.cc %.a, $^) $(LDFLAGS)
 
 install:
 	cp -f -r $(BIN)  $(INSTALL_PATH)
@@ -133,10 +148,23 @@ Rpack:
 	cat R-package/src/Makevars|sed '2s/.*/PKGROOT=./' > xgboost/src/Makevars
 	cp xgboost/src/Makevars xgboost/src/Makevars.win
 	# R CMD build --no-build-vignettes xgboost
+	# R CMD build xgboost
+	# rm -rf xgboost
+	# R CMD check --as-cran xgboost*.tar.gz
+
+Rbuild:
+	make Rpack
 	R CMD build xgboost
 	rm -rf xgboost
+
+Rcheck:
+	make Rbuild
 	R CMD check --as-cran xgboost*.tar.gz
 
+# lint requires dmlc to be in current folder
+lint:
+	dmlc-core/scripts/lint.py xgboost $(LINT_LANG) src wrapper R-package
+
 clean:
 	$(RM) -rf $(OBJ) $(BIN) $(MPIBIN) $(MPIOBJ) $(SLIB) *.o  */*.o */*/*.o *~ */*~ */*/*~
 	cd subtree/rabit; make clean; cd ..

R-package/R/utils.R

@@ -220,7 +220,8 @@ xgb.cv.mknfold <- function(dall, nfold, param, stratified, folds) {
     stop("nfold must be bigger than 1")
   }
   if(is.null(folds)) {
-    if (exists('objective', where=param) && strtrim(param[['objective']], 5) == 'rank:') {
+    if (exists('objective', where=param) && is.character(param$objective) &&
+        strtrim(param[['objective']], 5) == 'rank:') {
       stop("\tAutomatic creation of CV-folds is not implemented for ranking!\n",
            "\tConsider providing pre-computed CV-folds through the folds parameter.")
     }
@@ -234,7 +235,7 @@ xgb.cv.mknfold <- function(dall, nfold, param, stratified, folds) {
       # For classification, need to convert y labels to factor before making the folds,
       # and then do stratification by factor levels.
       # For regression, leave y numeric and do stratification by quantiles.
-      if (exists('objective', where=param)) {
+      if (exists('objective', where=param) && is.character(param$objective)) {
         # If 'objective' provided in params, assume that y is a classification label
         # unless objective is reg:linear
         if (param[['objective']] != 'reg:linear') y <- factor(y)

R-package/R/xgb.cv.R

@@ -95,152 +95,160 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
                    prediction = FALSE, showsd = TRUE, metrics=list(), 
                    obj = NULL, feval = NULL, stratified = TRUE, folds = NULL, verbose = T, print.every.n=1L,
                    early.stop.round = NULL, maximize = NULL, ...) {
-  if (typeof(params) != "list") {
+    if (typeof(params) != "list") {
-    stop("xgb.cv: first argument params must be list")
+        stop("xgb.cv: first argument params must be list")
-  }
-  if(!is.null(folds)) {
-    if(class(folds)!="list" | length(folds) < 2) {
-      stop("folds must be a list with 2 or more elements that are vectors of indices for each CV-fold")
     }
-    nfold <- length(folds)
+    if(!is.null(folds)) {
-  }
+        if(class(folds)!="list" | length(folds) < 2) {
-  if (nfold <= 1) {
+            stop("folds must be a list with 2 or more elements that are vectors of indices for each CV-fold")
-    stop("nfold must be bigger than 1")
+        }
-  }
+        nfold <- length(folds)
-  if (is.null(missing)) {
-    dtrain <- xgb.get.DMatrix(data, label)
-  } else {
-    dtrain <- xgb.get.DMatrix(data, label, missing)
-  }
-  params <- append(params, list(...))
-  params <- append(params, list(silent=1))
-  for (mc in metrics) {
-    params <- append(params, list("eval_metric"=mc))
-  }
-  
-  # customized objective and evaluation metric interface
-  if (!is.null(params$objective) && !is.null(obj))
-    stop("xgb.cv: cannot assign two different objectives")
-  if (!is.null(params$objective))
-    if (class(params$objective)=='function') {
-      obj = params$objective
-      params$objective = NULL
     }
-  if (!is.null(params$eval_metric) && !is.null(feval))
+    if (nfold <= 1) {
-    stop("xgb.cv: cannot assign two different evaluation metrics")
+        stop("nfold must be bigger than 1")
-  if (!is.null(params$eval_metric))
-    if (class(params$eval_metric)=='function') {
-      feval = params$eval_metric
-      params$eval_metric = NULL
     }
-  
+    if (is.null(missing)) {
-  # Early Stopping
+        dtrain <- xgb.get.DMatrix(data, label)
-  if (!is.null(early.stop.round)){
-    if (!is.null(feval) && is.null(maximize))
-      stop('Please set maximize to note whether the model is maximizing the evaluation or not.')
-    if (is.null(maximize) && is.null(params$eval_metric))
-      stop('Please set maximize to note whether the model is maximizing the evaluation or not.')
-    if (is.null(maximize))
-    {
-      if (params$eval_metric %in% c('rmse','logloss','error','merror','mlogloss')) {
-        maximize = FALSE
-      } else {
-        maximize = TRUE
-      }
-    }
-    
-    if (maximize) {
-      bestScore = 0
     } else {
-      bestScore = Inf
+        dtrain <- xgb.get.DMatrix(data, label, missing)
+    }
+    dot.params = list(...)
+    nms.params = names(params)
+    nms.dot.params = names(dot.params)
+    if (length(intersect(nms.params,nms.dot.params))>0)
+        stop("Duplicated defined term in parameters. Please check your list of params.")
+    params <- append(params, dot.params)
+    params <- append(params, list(silent=1))
+    for (mc in metrics) {
+        params <- append(params, list("eval_metric"=mc))
     }
-    bestInd = 0
-    earlyStopflag = FALSE
     
-    if (length(metrics)>1)
+    # customized objective and evaluation metric interface
-      warning('Only the first metric is used for early stopping process.')
+    if (!is.null(params$objective) && !is.null(obj))
-  }
+        stop("xgb.cv: cannot assign two different objectives")
-  
+    if (!is.null(params$objective))
-  xgb_folds <- xgb.cv.mknfold(dtrain, nfold, params, stratified, folds)
+        if (class(params$objective)=='function') {
-  obj_type = params[['objective']]
+            obj = params$objective
-  mat_pred = FALSE
+            params[['objective']] = NULL
-  if (!is.null(obj_type) && obj_type=='multi:softprob')
+        }
-  {
+    # if (!is.null(params$eval_metric) && !is.null(feval))
-    num_class = params[['num_class']]
+    #  stop("xgb.cv: cannot assign two different evaluation metrics")
-    if (is.null(num_class))
+    if (!is.null(params$eval_metric))
-      stop('must set num_class to use softmax')
+        if (class(params$eval_metric)=='function') {
-    predictValues <- matrix(0,xgb.numrow(dtrain),num_class)
+            feval = params$eval_metric
-    mat_pred = TRUE
+            params[['eval_metric']] = NULL
-  }
-  else
-    predictValues <- rep(0,xgb.numrow(dtrain))
-  history <- c()
-  print.every.n = max(as.integer(print.every.n), 1L)
-  for (i in 1:nrounds) {
-    msg <- list()
-    for (k in 1:nfold) {
-      fd <- xgb_folds[[k]]
-      succ <- xgb.iter.update(fd$booster, fd$dtrain, i - 1, obj)
-      if (i<nrounds) {
-          msg[[k]] <- xgb.iter.eval(fd$booster, fd$watchlist, i - 1, feval) %>% str_split("\t") %>% .[[1]]
-      } else {
-        if (!prediction) {
-          msg[[k]] <- xgb.iter.eval(fd$booster, fd$watchlist, i - 1, feval) %>% str_split("\t") %>% .[[1]]
-        } else {
-          res <- xgb.iter.eval(fd$booster, fd$watchlist, i - 1, feval, prediction)
-          if (mat_pred) {
-            pred_mat = matrix(res[[2]],num_class,length(fd$index))
-            predictValues[fd$index,] <- t(pred_mat)
-          } else {
-            predictValues[fd$index] <- res[[2]]
-          }
-          msg[[k]] <- res[[1]] %>% str_split("\t") %>% .[[1]]
         }
-      }
-    }
-    ret <- xgb.cv.aggcv(msg, showsd)
-    history <- c(history, ret)
-    if(verbose)
-      if (0==(i-1L)%%print.every.n)
-        cat(ret, "\n", sep="")
     
-    # early_Stopping
+    # Early Stopping
     if (!is.null(early.stop.round)){
-      score = strsplit(ret,'\\s+')[[1]][1+length(metrics)+2]
+        if (!is.null(feval) && is.null(maximize))
-      score = strsplit(score,'\\+|:')[[1]][[2]]
+            stop('Please set maximize to note whether the model is maximizing the evaluation or not.')
-      score = as.numeric(score)
+        if (is.null(maximize) && is.null(params$eval_metric))
-      if ((maximize && score>bestScore) || (!maximize && score<bestScore)) {
+            stop('Please set maximize to note whether the model is maximizing the evaluation or not.')
-        bestScore = score
+        if (is.null(maximize))
-        bestInd = i
+        {
-      } else {
+            if (params$eval_metric %in% c('rmse','logloss','error','merror','mlogloss')) {
-        if (i-bestInd>=early.stop.round) {
+                maximize = FALSE
-          earlyStopflag = TRUE
+            } else {
-          cat('Stopping. Best iteration:',bestInd)
+                maximize = TRUE
-          break
+            }
         }
-      }
+        
+        if (maximize) {
+            bestScore = 0
+        } else {
+            bestScore = Inf
+        }
+        bestInd = 0
+        earlyStopflag = FALSE
+        
+        if (length(metrics)>1)
+            warning('Only the first metric is used for early stopping process.')
     }
     
-  }
+    xgb_folds <- xgb.cv.mknfold(dtrain, nfold, params, stratified, folds)
-  
+    obj_type = params[['objective']]
-  colnames <- str_split(string = history[1], pattern = "\t")[[1]] %>% .[2:length(.)] %>% str_extract(".*:") %>% str_replace(":","") %>% str_replace("-", ".")
+    mat_pred = FALSE
-  colnamesMean <- paste(colnames, "mean")
+    if (!is.null(obj_type) && obj_type=='multi:softprob')
-  if(showsd) colnamesStd <- paste(colnames, "std")
+    {
-  
+        num_class = params[['num_class']]
-  colnames <- c()
+        if (is.null(num_class))
-  if(showsd) for(i in 1:length(colnamesMean)) colnames <- c(colnames, colnamesMean[i], colnamesStd[i])
+            stop('must set num_class to use softmax')
-  else colnames <- colnamesMean
+        predictValues <- matrix(0,xgb.numrow(dtrain),num_class)
-  
+        mat_pred = TRUE
-  type <- rep(x = "numeric", times = length(colnames))
+    }
-  dt <- read.table(text = "", colClasses = type, col.names = colnames) %>% as.data.table
+    else
-  split <- str_split(string = history, pattern = "\t")
+        predictValues <- rep(0,xgb.numrow(dtrain))
-  
+    history <- c()
-  for(line in split) dt <- line[2:length(line)] %>% str_extract_all(pattern = "\\d*\\.+\\d*") %>% unlist %>% as.numeric %>% as.list %>% {rbindlist(list(dt, .), use.names = F, fill = F)}
+    print.every.n = max(as.integer(print.every.n), 1L)
-  
+    for (i in 1:nrounds) {
-  if (prediction) {
+        msg <- list()
-    return(list(dt = dt,pred = predictValues))
+        for (k in 1:nfold) {
-  }
+            fd <- xgb_folds[[k]]
-  return(dt)
+            succ <- xgb.iter.update(fd$booster, fd$dtrain, i - 1, obj)
+            msg[[k]] <- xgb.iter.eval(fd$booster, fd$watchlist, i - 1, feval) %>% str_split("\t") %>% .[[1]]
+        }
+        ret <- xgb.cv.aggcv(msg, showsd)
+        history <- c(history, ret)
+        if(verbose)
+            if (0==(i-1L)%%print.every.n)
+                cat(ret, "\n", sep="")
+        
+        # early_Stopping
+        if (!is.null(early.stop.round)){
+            score = strsplit(ret,'\\s+')[[1]][1+length(metrics)+2]
+            score = strsplit(score,'\\+|:')[[1]][[2]]
+            score = as.numeric(score)
+            if ((maximize && score>bestScore) || (!maximize && score<bestScore)) {
+                bestScore = score
+                bestInd = i
+            } else {
+                if (i-bestInd>=early.stop.round) {
+                    earlyStopflag = TRUE
+                    cat('Stopping. Best iteration:',bestInd)
+                    break
+                }
+            }
+        }
+        
+    }
+    
+    if (prediction) {
+        for (k in 1:nfold) {
+            fd = xgb_folds[[k]]
+            if (!is.null(early.stop.round) && earlyStopflag) {
+              res = xgb.iter.eval(fd$booster, fd$watchlist, bestInd - 1, feval, prediction)
+            } else {
+              res = xgb.iter.eval(fd$booster, fd$watchlist, nrounds - 1, feval, prediction)
+            }
+            if (mat_pred) {
+                pred_mat = matrix(res[[2]],num_class,length(fd$index))
+                predictValues[fd$index,] = t(pred_mat)
+            } else {
+                predictValues[fd$index] = res[[2]]
+            }
+        }
+    }
+    
+    
+    colnames <- str_split(string = history[1], pattern = "\t")[[1]] %>% .[2:length(.)] %>% str_extract(".*:") %>% str_replace(":","") %>% str_replace("-", ".")
+    colnamesMean <- paste(colnames, "mean")
+    if(showsd) colnamesStd <- paste(colnames, "std")
+    
+    colnames <- c()
+    if(showsd) for(i in 1:length(colnamesMean)) colnames <- c(colnames, colnamesMean[i], colnamesStd[i])
+    else colnames <- colnamesMean
+    
+    type <- rep(x = "numeric", times = length(colnames))
+    dt <- read.table(text = "", colClasses = type, col.names = colnames) %>% as.data.table
+    split <- str_split(string = history, pattern = "\t")
+    
+    for(line in split) dt <- line[2:length(line)] %>% str_extract_all(pattern = "\\d*\\.+\\d*") %>% unlist %>% as.numeric %>% as.list %>% {rbindlist(list(dt, .), use.names = F, fill = F)}
+    
+    if (prediction) {
+        return(list(dt = dt,pred = predictValues))
+    }
+    return(dt)
 }
 
 # Avoid error messages during CRAN check.

R-package/R/xgb.train.R

@@ -137,7 +137,13 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
   if (length(watchlist) != 0 && verbose == 0) {
     warning('watchlist is provided but verbose=0, no evaluation information will be printed')
   }
-  params = append(params, list(...))
+  
+  dot.params = list(...)
+  nms.params = names(params)
+  nms.dot.params = names(dot.params)
+  if (length(intersect(nms.params,nms.dot.params))>0)
+    stop("Duplicated term in parameters. Please check your list of params.")
+  params = append(params, dot.params)
   
   # customized objective and evaluation metric interface
   if (!is.null(params$objective) && !is.null(obj))

R-package/README.md

@@ -1,6 +1,8 @@
-# R package for xgboost.
+R package for xgboost
+=====================
 
-## Installation
+Installation
+------------
 
 For up-to-date version (which is recommended), please install from github. Windows user will need to install [RTools](http://cran.r-project.org/bin/windows/Rtools/) first.
 
@@ -8,8 +10,26 @@ For up-to-date version (which is recommended), please install from github. Windo
 devtools::install_github('dmlc/xgboost',subdir='R-package')
 ```
 
-
+Examples
-## Examples
+--------
 
 * Please visit [walk through example](demo).
 * See also the [example scripts](../demo/kaggle-higgs) for Kaggle Higgs Challenge, including [speedtest script](../demo/kaggle-higgs/speedtest.R) on this dataset and the one related to [Otto challenge](../demo/kaggle-otto), including a [RMarkdown documentation](../demo/kaggle-otto/understandingXGBoostModel.Rmd).
+
+Notes
+-----
+
+If you face an issue installing the package using  ```devtools::install_github```, something like this (even after updating libxml and RCurl as lot of forums say) -
+
+```
+devtools::install_github('dmlc/xgboost',subdir='R-package')
+Downloading github repo dmlc/xgboost@master
+Error in function (type, msg, asError = TRUE)  : 
+  Peer certificate cannot be authenticated with given CA certificates
+```
+To get around this you can build the package locally as mentioned [here](https://github.com/dmlc/xgboost/issues/347) - 
+```
+1. Clone the current repository and set your workspace to xgboost/R-package/
+2. Run R CMD INSTALL --build . in terminal to get the tarball.
+3. Run install.packages('path_to_the_tarball',repo=NULL) in R to install.
+```

R-package/demo/create_sparse_matrix.R

@@ -1,8 +1,10 @@
 require(xgboost)
 require(Matrix)
 require(data.table)
-if (!require(vcd)) install.packages('vcd') #Available in Cran. Used for its dataset with categorical values.
+if (!require(vcd)) {
-
+  install.packages('vcd') #Available in Cran. Used for its dataset with categorical values.
+  require(vcd)
+}
 # According to its documentation, Xgboost works only on numbers.
 # Sometimes the dataset we have to work on have categorical data. 
 # A categorical variable is one which have a fixed number of values. By exemple, if for each observation a variable called "Colour" can have only "red", "blue" or "green" as value, it is a categorical variable.

R-package/src/xgboost_R.cpp

@@ -1,9 +1,10 @@
+// Copyright (c) 2014 by Contributors
 #include <vector>
 #include <string>
 #include <utility>
 #include <cstring>
 #include <cstdio>
-#include <sstream> 
+#include <sstream>
 #include "wrapper/xgboost_wrapper.h"
 #include "src/utils/utils.h"
 #include "src/utils/omp.h"
@@ -34,7 +35,7 @@ bool CheckNAN(double v) {
 bool LogGamma(double v) {
   return lgammafn(v);
 }
-} // namespace utils
+}  // namespace utils
 
 namespace random {
 void Seed(unsigned seed) {
@@ -58,25 +59,30 @@ inline void _WrapperEnd(void) {
   PutRNGstate();
 }
 
+// do nothing, check error
+inline void CheckErr(int ret) {
+}
+
 extern "C" {
   SEXP XGCheckNullPtr_R(SEXP handle) {
     return ScalarLogical(R_ExternalPtrAddr(handle) == NULL);
   }
-  void _DMatrixFinalizer(SEXP ext) {    
+  void _DMatrixFinalizer(SEXP ext) {
     if (R_ExternalPtrAddr(ext) == NULL) return;
     XGDMatrixFree(R_ExternalPtrAddr(ext));
     R_ClearExternalPtr(ext);
   }
   SEXP XGDMatrixCreateFromFile_R(SEXP fname, SEXP silent) {
     _WrapperBegin();
-    void *handle = XGDMatrixCreateFromFile(CHAR(asChar(fname)), asInteger(silent));
+    DMatrixHandle handle;
+    CheckErr(XGDMatrixCreateFromFile(CHAR(asChar(fname)), asInteger(silent), &handle));
     _WrapperEnd();
     SEXP ret = PROTECT(R_MakeExternalPtr(handle, R_NilValue, R_NilValue));
     R_RegisterCFinalizerEx(ret, _DMatrixFinalizer, TRUE);
     UNPROTECT(1);
     return ret;
   }
-  SEXP XGDMatrixCreateFromMat_R(SEXP mat, 
+  SEXP XGDMatrixCreateFromMat_R(SEXP mat,
                                 SEXP missing) {
     _WrapperBegin();
     SEXP dim = getAttrib(mat, R_DimSymbol);
@@ -90,12 +96,13 @@ extern "C" {
         data[i * ncol +j] = din[i + nrow * j];
       }
     }
-    void *handle = XGDMatrixCreateFromMat(BeginPtr(data), nrow, ncol, asReal(missing));
+    DMatrixHandle handle;
+    CheckErr(XGDMatrixCreateFromMat(BeginPtr(data), nrow, ncol, asReal(missing), &handle));
     _WrapperEnd();
     SEXP ret = PROTECT(R_MakeExternalPtr(handle, R_NilValue, R_NilValue));
     R_RegisterCFinalizerEx(ret, _DMatrixFinalizer, TRUE);
     UNPROTECT(1);
-    return ret;    
+    return ret;
   }
   SEXP XGDMatrixCreateFromCSC_R(SEXP indptr,
                                 SEXP indices,
@@ -118,8 +125,10 @@ extern "C" {
       indices_[i] = static_cast<unsigned>(p_indices[i]);
       data_[i] = static_cast<float>(p_data[i]);
     }
-    void *handle = XGDMatrixCreateFromCSC(BeginPtr(col_ptr_), BeginPtr(indices_),
+    DMatrixHandle handle;
-                                          BeginPtr(data_), nindptr, ndata);
+    CheckErr(XGDMatrixCreateFromCSC(BeginPtr(col_ptr_), BeginPtr(indices_),
+                                    BeginPtr(data_), nindptr, ndata,
+                                    &handle));
     _WrapperEnd();
     SEXP ret = PROTECT(R_MakeExternalPtr(handle, R_NilValue, R_NilValue));
     R_RegisterCFinalizerEx(ret, _DMatrixFinalizer, TRUE);
@@ -133,17 +142,20 @@ extern "C" {
     for (int i = 0; i < len; ++i) {
       idxvec[i] = INTEGER(idxset)[i] - 1;
     }
-    void *res = XGDMatrixSliceDMatrix(R_ExternalPtrAddr(handle), BeginPtr(idxvec), len);
+    DMatrixHandle res;
+    CheckErr(XGDMatrixSliceDMatrix(R_ExternalPtrAddr(handle),
+                                   BeginPtr(idxvec), len,
+                                   &res));
     _WrapperEnd();
     SEXP ret = PROTECT(R_MakeExternalPtr(res, R_NilValue, R_NilValue));
     R_RegisterCFinalizerEx(ret, _DMatrixFinalizer, TRUE);
     UNPROTECT(1);
-    return ret;        
+    return ret;
   }
   void XGDMatrixSaveBinary_R(SEXP handle, SEXP fname, SEXP silent) {
     _WrapperBegin();
-    XGDMatrixSaveBinary(R_ExternalPtrAddr(handle),
+    CheckErr(XGDMatrixSaveBinary(R_ExternalPtrAddr(handle),
-                        CHAR(asChar(fname)), asInteger(silent));
+                                 CHAR(asChar(fname)), asInteger(silent)));
     _WrapperEnd();
   }
   void XGDMatrixSetInfo_R(SEXP handle, SEXP field, SEXP array) {
@@ -152,28 +164,31 @@ extern "C" {
     const char *name = CHAR(asChar(field));
     if (!strcmp("group", name)) {
       std::vector<unsigned> vec(len);
-      #pragma omp parallel for schedule(static)      
+      #pragma omp parallel for schedule(static)
       for (int i = 0; i < len; ++i) {
         vec[i] = static_cast<unsigned>(INTEGER(array)[i]);
       }
-      XGDMatrixSetGroup(R_ExternalPtrAddr(handle), BeginPtr(vec), len);
+      CheckErr(XGDMatrixSetGroup(R_ExternalPtrAddr(handle), BeginPtr(vec), len));
     } else {
       std::vector<float> vec(len);
       #pragma omp parallel for schedule(static)
       for (int i = 0; i < len; ++i) {
         vec[i] = REAL(array)[i];
       }
-      XGDMatrixSetFloatInfo(R_ExternalPtrAddr(handle), 
+      CheckErr(XGDMatrixSetFloatInfo(R_ExternalPtrAddr(handle),
-                            CHAR(asChar(field)),
+                                     CHAR(asChar(field)),
-                            BeginPtr(vec), len);
+                                     BeginPtr(vec), len));
     }
     _WrapperEnd();
   }
   SEXP XGDMatrixGetInfo_R(SEXP handle, SEXP field) {
     _WrapperBegin();
     bst_ulong olen;
-    const float *res = XGDMatrixGetFloatInfo(R_ExternalPtrAddr(handle),
+    const float *res;
-                                             CHAR(asChar(field)), &olen);
+    CheckErr(XGDMatrixGetFloatInfo(R_ExternalPtrAddr(handle),
+                                   CHAR(asChar(field)),
+                                   &olen,
+                                   &res));
     _WrapperEnd();
     SEXP ret = PROTECT(allocVector(REALSXP, olen));
     for (size_t i = 0; i < olen; ++i) {
@@ -183,23 +198,25 @@ extern "C" {
     return ret;
   }
   SEXP XGDMatrixNumRow_R(SEXP handle) {
-    bst_ulong nrow = XGDMatrixNumRow(R_ExternalPtrAddr(handle));
+    bst_ulong nrow;
+    CheckErr(XGDMatrixNumRow(R_ExternalPtrAddr(handle), &nrow));
     return ScalarInteger(static_cast<int>(nrow));
   }
   // functions related to booster
-  void _BoosterFinalizer(SEXP ext) {    
+  void _BoosterFinalizer(SEXP ext) {
     if (R_ExternalPtrAddr(ext) == NULL) return;
-    XGBoosterFree(R_ExternalPtrAddr(ext));
+    CheckErr(XGBoosterFree(R_ExternalPtrAddr(ext)));
     R_ClearExternalPtr(ext);
   }
   SEXP XGBoosterCreate_R(SEXP dmats) {
     _WrapperBegin();
     int len = length(dmats);
     std::vector<void*> dvec;
-    for (int i = 0; i < len; ++i){
+    for (int i = 0; i < len; ++i) {
       dvec.push_back(R_ExternalPtrAddr(VECTOR_ELT(dmats, i)));
     }
-    void *handle = XGBoosterCreate(BeginPtr(dvec), dvec.size());
+    BoosterHandle handle;
+    CheckErr(XGBoosterCreate(BeginPtr(dvec), dvec.size(), &handle));
     _WrapperEnd();
     SEXP ret = PROTECT(R_MakeExternalPtr(handle, R_NilValue, R_NilValue));
     R_RegisterCFinalizerEx(ret, _BoosterFinalizer, TRUE);
@@ -208,16 +225,16 @@ extern "C" {
   }
   void XGBoosterSetParam_R(SEXP handle, SEXP name, SEXP val) {
     _WrapperBegin();
-    XGBoosterSetParam(R_ExternalPtrAddr(handle),
+    CheckErr(XGBoosterSetParam(R_ExternalPtrAddr(handle),
-                      CHAR(asChar(name)),
+                               CHAR(asChar(name)),
-                      CHAR(asChar(val)));
+                               CHAR(asChar(val))));
     _WrapperEnd();
   }
   void XGBoosterUpdateOneIter_R(SEXP handle, SEXP iter, SEXP dtrain) {
     _WrapperBegin();
-    XGBoosterUpdateOneIter(R_ExternalPtrAddr(handle),
+    CheckErr(XGBoosterUpdateOneIter(R_ExternalPtrAddr(handle),
-                           asInteger(iter),
+                                    asInteger(iter),
-                           R_ExternalPtrAddr(dtrain));
+                                    R_ExternalPtrAddr(dtrain)));
     _WrapperEnd();
   }
   void XGBoosterBoostOneIter_R(SEXP handle, SEXP dtrain, SEXP grad, SEXP hess) {
@@ -230,9 +247,10 @@ extern "C" {
       tgrad[j] = REAL(grad)[j];
       thess[j] = REAL(hess)[j];
     }
-    XGBoosterBoostOneIter(R_ExternalPtrAddr(handle),
+    CheckErr(XGBoosterBoostOneIter(R_ExternalPtrAddr(handle),
-                          R_ExternalPtrAddr(dtrain),
+                                   R_ExternalPtrAddr(dtrain),
-                          BeginPtr(tgrad), BeginPtr(thess), len);
+                                   BeginPtr(tgrad), BeginPtr(thess),
+                                   len));
     _WrapperEnd();
   }
   SEXP XGBoosterEvalOneIter_R(SEXP handle, SEXP iter, SEXP dmats, SEXP evnames) {
@@ -249,21 +267,24 @@ extern "C" {
     for (int i = 0; i < len; ++i) {
       vec_sptr.push_back(vec_names[i].c_str());
     }
-    const char *ret =
+    const char *ret;
-        XGBoosterEvalOneIter(R_ExternalPtrAddr(handle),
+    CheckErr(XGBoosterEvalOneIter(R_ExternalPtrAddr(handle),
-                             asInteger(iter),
+                                  asInteger(iter),
-                             BeginPtr(vec_dmats), BeginPtr(vec_sptr), len);  
+                                  BeginPtr(vec_dmats),
+                                  BeginPtr(vec_sptr),
+                                  len, &ret));
     _WrapperEnd();
     return mkString(ret);
   }
   SEXP XGBoosterPredict_R(SEXP handle, SEXP dmat, SEXP option_mask, SEXP ntree_limit) {
     _WrapperBegin();
     bst_ulong olen;
-    const float *res = XGBoosterPredict(R_ExternalPtrAddr(handle),
+    const float *res;
-                                        R_ExternalPtrAddr(dmat),
+    CheckErr(XGBoosterPredict(R_ExternalPtrAddr(handle),
-                                        asInteger(option_mask),
+                              R_ExternalPtrAddr(dmat),
-                                        asInteger(ntree_limit),
+                              asInteger(option_mask),
-                                        &olen);
+                              asInteger(ntree_limit),
+                              &olen, &res));
     _WrapperEnd();
     SEXP ret = PROTECT(allocVector(REALSXP, olen));
     for (size_t i = 0; i < olen; ++i) {
@@ -274,15 +295,15 @@ extern "C" {
   }
   void XGBoosterLoadModel_R(SEXP handle, SEXP fname) {
     _WrapperBegin();
-    XGBoosterLoadModel(R_ExternalPtrAddr(handle), CHAR(asChar(fname)));
+    CheckErr(XGBoosterLoadModel(R_ExternalPtrAddr(handle), CHAR(asChar(fname))));
     _WrapperEnd();
   }
   void XGBoosterSaveModel_R(SEXP handle, SEXP fname) {
     _WrapperBegin();
-    XGBoosterSaveModel(R_ExternalPtrAddr(handle), CHAR(asChar(fname)));
+    CheckErr(XGBoosterSaveModel(R_ExternalPtrAddr(handle), CHAR(asChar(fname))));
     _WrapperEnd();
   }
-  void XGBoosterLoadModelFromRaw_R(SEXP handle, SEXP raw) {    
+  void XGBoosterLoadModelFromRaw_R(SEXP handle, SEXP raw) {
     _WrapperBegin();
     XGBoosterLoadModelFromBuffer(R_ExternalPtrAddr(handle),
                                  RAW(raw),
@@ -292,28 +313,29 @@ extern "C" {
   SEXP XGBoosterModelToRaw_R(SEXP handle) {
     bst_ulong olen;
     _WrapperBegin();
-    const char *raw = XGBoosterGetModelRaw(R_ExternalPtrAddr(handle), &olen);
+    const char *raw;
+    CheckErr(XGBoosterGetModelRaw(R_ExternalPtrAddr(handle), &olen, &raw));
     _WrapperEnd();
     SEXP ret = PROTECT(allocVector(RAWSXP, olen));
     if (olen != 0) {
       memcpy(RAW(ret), raw, olen);
     }
-    UNPROTECT(1);    
+    UNPROTECT(1);
     return ret;
   }
   SEXP XGBoosterDumpModel_R(SEXP handle, SEXP fmap, SEXP with_stats) {
     _WrapperBegin();
     bst_ulong olen;
-    const char **res =
+    const char **res;
-        XGBoosterDumpModel(R_ExternalPtrAddr(handle),
+    CheckErr(XGBoosterDumpModel(R_ExternalPtrAddr(handle),
-                           CHAR(asChar(fmap)),
+                                CHAR(asChar(fmap)),
-                           asInteger(with_stats),
+                                asInteger(with_stats),
-                           &olen);
+                                &olen, &res));
     _WrapperEnd();
-    SEXP out = PROTECT(allocVector(STRSXP, olen));    
+    SEXP out = PROTECT(allocVector(STRSXP, olen));
-    for (size_t i = 0; i < olen; ++i) {     
+    for (size_t i = 0; i < olen; ++i) {
       stringstream stream;
-      stream <<  "booster["<<i<<"]\n" << res[i];
+      stream <<  "booster[" << i <<"]\n" << res[i];
       SET_STRING_ELT(out, i, mkChar(stream.str().c_str()));
     }
     UNPROTECT(1);

R-package/src/xgboost_R.h

@@ -1,10 +1,12 @@
-#ifndef XGBOOST_WRAPPER_R_H_
-#define XGBOOST_WRAPPER_R_H_
 /*!
+ * Copyright 2014 (c) by Contributors
  * \file xgboost_wrapper_R.h
  * \author Tianqi Chen
  * \brief R wrapper of xgboost
  */
+#ifndef XGBOOST_WRAPPER_R_H_ // NOLINT(*)
+#define XGBOOST_WRAPPER_R_H_ // NOLINT(*)
+
 extern "C" {
 #include <Rinternals.h>
 #include <R_ext/Random.h>
@@ -19,7 +21,7 @@ extern "C" {
    */
   SEXP XGCheckNullPtr_R(SEXP handle);
   /*!
-   * \brief load a data matrix 
+   * \brief load a data matrix
    * \param fname name of the content
    * \param silent whether print messages
    * \return a loaded data matrix
@@ -32,9 +34,9 @@ extern "C" {
    * \param missing which value to represent missing value
    * \return created dmatrix
    */
-  SEXP XGDMatrixCreateFromMat_R(SEXP mat, 
+  SEXP XGDMatrixCreateFromMat_R(SEXP mat,
                                 SEXP missing);
-  /*! 
+  /*!
    * \brief create a matrix content from CSC format
    * \param indptr pointer to column headers
    * \param indices row indices
@@ -70,26 +72,26 @@ extern "C" {
    * \param handle a instance of data matrix
    * \param field field name
    * \return info vector
-   */  
+   */
   SEXP XGDMatrixGetInfo_R(SEXP handle, SEXP field);
   /*!
    * \brief return number of rows
    * \param handle a instance of data matrix
    */
   SEXP XGDMatrixNumRow_R(SEXP handle);
-  /*! 
+  /*!
-   * \brief create xgboost learner 
+   * \brief create xgboost learner
    * \param dmats a list of dmatrix handles that will be cached
-   */  
+   */
   SEXP XGBoosterCreate_R(SEXP dmats);
-  /*! 
+  /*!
-   * \brief set parameters 
+   * \brief set parameters
    * \param handle handle
    * \param name  parameter name
    * \param val value of parameter
    */
   void XGBoosterSetParam_R(SEXP handle, SEXP name, SEXP val);
-  /*! 
+  /*!
    * \brief update the model in one round using dtrain
    * \param handle handle
    * \param iter current iteration rounds
@@ -132,12 +134,12 @@ extern "C" {
    * \brief save model into existing file
    * \param handle handle
    * \param fname file name
-   */    
+   */
   void XGBoosterSaveModel_R(SEXP handle, SEXP fname);
   /*!
    * \brief load model from raw array
    * \param handle handle
-   */    
+   */
   void XGBoosterLoadModelFromRaw_R(SEXP handle, SEXP raw);
   /*!
    * \brief save model into R's raw array
@@ -153,4 +155,4 @@ extern "C" {
    */
   SEXP XGBoosterDumpModel_R(SEXP handle, SEXP fmap, SEXP with_stats);
 }
-#endif  // XGBOOST_WRAPPER_R_H_
+#endif  // XGBOOST_WRAPPER_R_H_ // NOLINT(*)

R-package/src/xgboost_assert.c

@@ -1,3 +1,4 @@
+// Copyright (c) 2014 by Contributors
 #include <stdio.h>
 #include <stdarg.h>
 #include <Rinternals.h>
@@ -6,17 +7,17 @@
 void XGBoostAssert_R(int exp, const char *fmt, ...) {
   char buf[1024];
   if (exp == 0) {
-    va_list args;  
+    va_list args;
     va_start(args, fmt);
     vsprintf(buf, fmt, args);
     va_end(args);
     error("AssertError:%s\n", buf);
-  }  
+  }
 }
 void XGBoostCheck_R(int exp, const char *fmt, ...) {
   char buf[1024];
   if (exp == 0) {
-    va_list args;  
+    va_list args;
     va_start(args, fmt);
     vsprintf(buf, fmt, args);
     va_end(args);
@@ -25,7 +26,7 @@ void XGBoostCheck_R(int exp, const char *fmt, ...) {
 }
 int XGBoostSPrintf_R(char *buf, size_t size, const char *fmt, ...) {
   int ret;
-  va_list args;  
+  va_list args;
   va_start(args, fmt);
   ret = vsnprintf(buf, size, fmt, args);
   va_end(args);

R-package/vignettes/xgboostPresentation.Rmd

@@ -337,6 +337,17 @@ err <- as.numeric(sum(as.integer(pred > 0.5) != label))/length(label)
 print(paste("test-error=", err))
 ```
 
+View feature importance/influence from the learnt model
+-------------------------------------------------------
+
+Feature importance is similar to R gbm package's relative influence (rel.inf). 
+
+```
+importance_matrix <- xgb.importance(model = bst)
+print(importance_matrix)
+xgb.plot.importance(importance_matrix)
+```
+
 View the trees from a model
 ---------------------------
 
@@ -346,6 +357,12 @@ You can dump the tree you learned using `xgb.dump` into a text file.
 xgb.dump(bst, with.stats = T)
 ```
 
+You can plot the trees from your model using ```xgb.plot.tree``
+
+```
+xgb.plot.tree(model = bst)
+```
+
 > if you provide a path to `fname` parameter you can save the trees to your hard drive.
 
 Save and load models

README.md

@@ -1,12 +1,14 @@
-XGBoost: eXtreme Gradient Boosting 
+DMLC/XGBoost
 ==================================
 
+[![Build Status](https://travis-ci.org/dmlc/xgboost.svg?branch=master)](https://travis-ci.org/dmlc/xgboost)
+
 An optimized general purpose gradient boosting library. The library is parallelized, and also provides an optimized distributed version.
-It implements machine learning algorithm under gradient boosting framework, including generalized linear model and gradient boosted regression tree (GBDT). XGBoost can also be distributed and scale to Terascale data
+It implements machine learning algorithms under the [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting) framework, including [Generalized Linear Model](https://en.wikipedia.org/wiki/Generalized_linear_model) (GLM) and [Gradient Boosted Decision Trees](https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting) (GBDT). XGBoost can also be [distributed](#features) and scale to Terascale data
 
 Contributors: https://github.com/dmlc/xgboost/graphs/contributors
 
-Documentations: [Documentation of xgboost](doc/README.md)
+Documentations: [Documentation of dmlc/xgboost](doc/README.md)
 
 Issues Tracker: [https://github.com/dmlc/xgboost/issues](https://github.com/dmlc/xgboost/issues?q=is%3Aissue+label%3Aquestion)
 
@@ -24,11 +26,19 @@ XGBoost is part of [Distributed Machine Learning Common](http://dmlc.github.io/)
 
 What's New
 ==========
+* XGBoost helps Chenglong Chen to win [Kaggle CrowdFlower Competition](https://www.kaggle.com/c/crowdflower-search-relevance)
+  - Checkout the winning solution at [Highlight links](doc/README.md#highlight-links)
 * XGBoost-0.4 release, see [CHANGES.md](CHANGES.md#xgboost-04)
-* XGBoost wins [WWW2015  Microsoft Malware Classification Challenge (BIG 2015)](http://www.kaggle.com/c/malware-classification/forums/t/13490/say-no-to-overfitting-approaches-sharing)
+* XGBoost helps three champion teams to win [WWW2015  Microsoft Malware Classification Challenge (BIG 2015)](http://www.kaggle.com/c/malware-classification/forums/t/13490/say-no-to-overfitting-approaches-sharing)
   - Checkout the winning solution at [Highlight links](doc/README.md#highlight-links)
 * [External Memory Version](doc/external_memory.md)
 
+Contributing to XGBoost
+=========
+XGBoost has been developed and used by a group of active community. Everyone is more than welcomed to is a great way to make the project better and more accessible to more users.
+* Checkout [Feature Wish List](https://github.com/dmlc/xgboost/labels/Wish-List) to see what can be improved, or open an issue if you want something.
+* Contribute to the [documents and examples](https://github.com/dmlc/xgboost/blob/master/doc/) to share your experience with other users.
+
 Features
 ========
 * Easily accessible in python, R, Julia, CLI

demo/binary_classification/README.md

@@ -147,7 +147,7 @@ Run the command again, we can find the log file becomes
 ```
 The rule is eval[name-printed-in-log] = filename, then the file will be added to monitoring process, and evaluated each round.
 
-xgboost also support monitoring multiple metrics, suppose we also want to monitor average log-likelihood of each prediction during training, simply add ```eval_metric=logloss``` to configure. Run again, we can find the log file becomes
+xgboost also supports monitoring multiple metrics, suppose we also want to monitor average log-likelihood of each prediction during training, simply add ```eval_metric=logloss``` to configure. Run again, we can find the log file becomes
 ```
 [0]     test-error:0.016139     test-negllik:0.029795   trainname-error:0.014433        trainname-negllik:0.027023
 [1]     test-error:0.000000     test-negllik:0.000000   trainname-error:0.001228        trainname-negllik:0.002457
@@ -162,11 +162,15 @@ If you want to continue boosting from existing model, say 0002.model, use
 ```
 xgboost will load from 0002.model continue boosting for 2 rounds, and save output to continue.model. However, beware that the training and evaluation data specified in mushroom.conf should not change when you use this function.
 #### Use Multi-Threading
-When you are working with a large dataset, you may want to take advantage of parallelism. If your compiler supports OpenMP, xgboost is naturally multi-threaded, to set number of parallel running threads to 10, add ```nthread=10``` to your configuration.
+When you are working with a large dataset, you may want to take advantage of parallelism. If your compiler supports OpenMP, xgboost is naturally multi-threaded, to set number of parallel running add ```nthread``` parameter to you configuration. 
+Eg. ```nthread=10```
+
+Set nthread to be the number of your real cpu (On Unix, this can be found using ```lscpu```)
+Some systems will have ```Thread(s) per core = 2```, for example, a 4 core cpu with 8 threads, in such case set ```nthread=4``` and not 8.
 
 #### Additional Notes
 * What are ```agaricus.txt.test.buffer``` and ```agaricus.txt.train.buffer``` generated during runexp.sh? 
-  - By default xgboost will automatically generate a binary format buffer of input data, with suffix ```buffer```. When next time you run xgboost, it detects i
+  - By default xgboost will automatically generate a binary format buffer of input data, with suffix ```buffer```. Next time when you run xgboost, it will detects these binary files.
-Demonstrating how to use XGBoost accomplish binary classification tasks  on UCI mushroom dataset  http://archive.ics.uci.edu/ml/datasets/Mushroom
+
 
 

demo/kaggle-otto/understandingXGBoostModel.Rmd

@@ -45,7 +45,7 @@ dim(train)
 train[1:6,1:5, with =F]
 
 # Test dataset dimensions
-dim(train)
+dim(test)
 
 # Test content
 test[1:6,1:5, with =F]
@@ -228,4 +228,4 @@ There are 4 documents you may also be interested in:
 * [xgboostPresentation.Rmd](https://github.com/dmlc/xgboost/blob/master/R-package/vignettes/xgboostPresentation.Rmd): general presentation
 * [discoverYourData.Rmd](https://github.com/dmlc/xgboost/blob/master/R-package/vignettes/discoverYourData.Rmd): explaining feature analysus
 * [Feature Importance Analysis with XGBoost in Tax audit](http://fr.slideshare.net/MichaelBENESTY/feature-importance-analysis-with-xgboost-in-tax-audit): use case
-* [The Elements of Statistical Learning](http://statweb.stanford.edu/~tibs/ElemStatLearn/): very good book to have a good understanding of the model
+* [The Elements of Statistical Learning](http://statweb.stanford.edu/~tibs/ElemStatLearn/): very good book to have a good understanding of the model

doc/README.md

@@ -20,7 +20,8 @@ How to get started
 Highlight Links
 ====
 This section is about blogposts, presentation and videos discussing how to use xgboost to solve your interesting problem. If you think something belongs to here, send a pull request.
-* [Kaggle Malware Prediction winning solution](https://github.com/xiaozhouwang/kaggle_Microsoft_Malware)
+* [Kaggle CrowdFlower winner's solution by Chenglong Chen](https://github.com/ChenglongChen/Kaggle_CrowdFlower)
+* [Kaggle Malware Prediction winner's solution](https://github.com/xiaozhouwang/kaggle_Microsoft_Malware)
 * [Kaggle Tradeshift winning solution by daxiongshu](https://github.com/daxiongshu/kaggle-tradeshift-winning-solution)
 * [Feature Importance Analysis with XGBoost in Tax audit](http://fr.slideshare.net/MichaelBENESTY/feature-importance-analysis-with-xgboost-in-tax-audit)
 * Video tutorial: [Better Optimization with Repeated Cross Validation and the XGBoost model](https://www.youtube.com/watch?v=Og7CGAfSr_Y)
@@ -29,3 +30,7 @@ This section is about blogposts, presentation and videos discussing how to use x
 Contribution
 ====
 Contribution of documents and use-cases are welcomed!
+* This package use Google C++ style
+* Check tool of codestyle
+  - clone https://github.com/dmlc/dmlc-core into root directory
+  - type ```make lint``` and fix possible errors.

doc/build.md

@@ -17,13 +17,15 @@ Here is the complete solution to use OpenMp-enabled compilers to install XGBoost
 
 1. Obtain gcc with openmp support by `brew install gcc --without-multilib` **or** clang with openmp by `brew install clang-omp`. The clang one is recommended because the first method requires us compiling gcc inside the machine (more than an hour in mine)! (BTW, `brew` is the de facto standard of `apt-get` on OS X. So installing [HPC](http://hpc.sourceforge.net/) separately is not recommended, but it should work.)
 
-2. **if plaing to use clang-omp** in step 3 and/or 4, change line 9 in `xgboost/src/utils/omp.h` to 
+2. **if you are planing to use clang-omp** - in step 3 and/or 4, change line 9 in `xgboost/src/utils/omp.h` to 
 
   ```C++
   #include <libiomp/omp.h> /* instead of #include <omp.h> */` 
   ```
 
-  to make it work, otherwise the following steps would show `src/tree/../utils/omp.h:9:10: error: 'omp.h' file not found...`
+  to make it work, otherwise you might get this error 
+  
+  `src/tree/../utils/omp.h:9:10: error: 'omp.h' file not found...`
 
 
 
@@ -41,13 +43,13 @@ Here is the complete solution to use OpenMp-enabled compilers to install XGBoost
   export CXX = clang-omp++
   ```
 
-  Remember to change `header` if using clang-omp. 
+  Remember to change `header` (mentioned in step 2) if using clang-omp. 
   
   Then `cd xgboost` then `bash build.sh` to compile XGBoost. And go to `wrapper` sub-folder to install python version.
 
 4. Set the `Makevars` file in highest piority for R. 
 
-  The point is, there are three `Makevars` inside the machine: `~/.R/Makevars`, `xgboost/R-package/src/Makevars`, and `/usr/local/Cellar/r/3.2.0/R.framework/Resources/etc/Makeconf` (the last one obtained by runing `file.path(R.home("etc"), "Makeconf")` in R), and `SHLIB_OPENMP_CXXFLAGS` is not set by default!! After trying, it seems that the first one has highest piority (surprise!).
+  The point is, there are three `Makevars` : `~/.R/Makevars`, `xgboost/R-package/src/Makevars`, and `/usr/local/Cellar/r/3.2.0/R.framework/Resources/etc/Makeconf` (the last one obtained by running `file.path(R.home("etc"), "Makeconf")` in R), and `SHLIB_OPENMP_CXXFLAGS` is not set by default!! After trying, it seems that the first one has highest piority (surprise!).
 
   So, **add** or **change** `~/.R/Makevars` to the following lines:
 

java/README.md

@@ -0,0 +1,28 @@
+# xgboost4j
+this is a java wrapper for xgboost 
+
+the structure of this wrapper is almost the same as the official python wrapper.
+
+core of this wrapper is two classes:
+
+* DMatrix: for handling data
+
+* Booster: for train and predict
+
+## usage:
+  please refer to [xgboost4j.md](doc/xgboost4j.md) for more information.
+
+  besides, simple examples could be found in [xgboost4j-demo](xgboost4j-demo/README.md)
+ 
+
+## build native library
+
+for windows: open the xgboost.sln in "../windows" folder, you will found the xgboostjavawrapper project, you should do the following steps to build wrapper library:
+ * Select x64/win32 and Release in build
+ * (if you have setted `JAVA_HOME` properly in windows environment variables, escape this step) right click on xgboostjavawrapper project -> choose "Properties" -> click on "C/C++" in the window -> change the "Additional Include Directories" to fit your jdk install path.
+ * rebuild all
+ * double click "create_wrap.bat" to set library to proper place
+
+for linux: 
+ * make sure you have installed jdk and `JAVA_HOME` has been setted properly
+ * run "create_wrap.sh"

java/create_wrap.bat

@@ -0,0 +1,20 @@
+echo "move native library"
+set libsource=..\windows\x64\Release\xgboostjavawrapper.dll
+
+if not exist %libsource% (
+goto end
+)
+
+set libfolder=xgboost4j\src\main\resources\lib
+set libpath=%libfolder%\xgboostjavawrapper.dll
+if not exist %libfolder% (mkdir %libfolder%)
+if exist %libpath% (del %libpath%)
+move %libsource% %libfolder%
+echo complete
+pause
+exit
+
+:end
+  echo "source library not found, please build it first from ..\windows\xgboost.sln"
+  pause
+  exit

java/create_wrap.sh

@@ -0,0 +1,15 @@
+echo "build java wrapper"
+cd ..
+make java
+cd java
+echo "move native lib"
+
+libPath="xgboost4j/src/main/resources/lib"
+if [ ! -d "$libPath" ]; then
+  mkdir -p "$libPath"
+fi
+
+rm -f xgboost4j/src/main/resources/lib/libxgboostjavawrapper.so
+mv libxgboostjavawrapper.so xgboost4j/src/main/resources/lib/
+
+echo "complete"

java/doc/xgboost4j.md

@@ -0,0 +1,156 @@
+xgboost4j : java wrapper for xgboost
+====
+
+This page will introduce xgboost4j, the java wrapper for xgboost, including:
+* [Building](#build-xgboost4j)
+* [Data Interface](#data-interface)
+* [Setting Parameters](#setting-parameters)
+* [Train Model](#training-model)
+* [Prediction](#prediction)
+
+=
+#### Build xgboost4j
+* Build native library  
+first make sure you have installed jdk and `JAVA_HOME` has been setted properly, then simply run `./create_wrap.sh`.
+
+* Package xgboost4j  
+to package xgboost4j, you can run `mvn package` in xgboost4j folder or just use IDE(eclipse/netbeans) to open this maven project and build.
+
+=
+#### Data Interface
+Like the xgboost python module, xgboost4j use ```DMatrix``` to handle data, libsvm txt format file, sparse matrix in CSR/CSC format, and dense matrix is supported.
+
+* To import ```DMatrix``` :
+```java
+import org.dmlc.xgboost4j.DMatrix;
+```
+
+* To load libsvm text format file, the usage is like :
+```java
+DMatrix dmat = new DMatrix("train.svm.txt");
+```
+
+* To load sparse matrix in CSR/CSC format is a little complicated, the usage is like :  
+suppose a sparse matrix :  
+1 0 2 0  
+4 0 0 3  
+3 1 2 0  
+
+  for CSR format
+```java
+long[] rowHeaders = new long[] {0,2,4,7};
+float[] data = new float[] {1f,2f,4f,3f,3f,1f,2f};
+int[] colIndex = new int[] {0,2,0,3,0,1,2};
+DMatrix dmat = new DMatrix(rowHeaders, colIndex, data, DMatrix.SparseType.CSR);
+```
+
+  for CSC format
+```java
+long[] colHeaders = new long[] {0,3,4,6,7};
+float[] data = new float[] {1f,4f,3f,1f,2f,2f,3f};
+int[] rowIndex = new int[] {0,1,2,2,0,2,1};
+DMatrix dmat = new DMatrix(colHeaders, rowIndex, data, DMatrix.SparseType.CSC);
+```
+
+* To load 3*2 dense matrix, the usage is like :  
+suppose a matrix :  
+1    2  
+3    4  
+5    6  
+ 
+```java
+float[] data = new float[] {1f,2f,3f,4f,5f,6f};
+int nrow = 3;
+int ncol = 2;
+float missing = 0.0f;
+DMatrix dmat = new Matrix(data, nrow, ncol, missing);
+```
+
+* To set weight :
+```java
+float[] weights = new float[] {1f,2f,1f};
+dmat.setWeight(weights);
+```
+
+#### Setting Parameters
+* in xgboost4j any ```Iterable<Entry<String, Object>>``` object could be used as parameters.
+
+* to set parameters, for non-multiple value params, you can simply use entrySet of an Map:
+```java
+Map<String, Object> paramMap = new HashMap<>() {
+  {
+    put("eta", 1.0);
+    put("max_depth", 2);
+    put("silent", 1);
+    put("objective", "binary:logistic");
+    put("eval_metric", "logloss");
+  }
+};
+Iterable<Entry<String, Object>> params = paramMap.entrySet();
+```
+* for the situation that multiple values with same param key, List<Entry<String, Object>> would be a good choice, e.g. :
+```java
+List<Entry<String, Object>> params = new ArrayList<Entry<String, Object>>() {
+    {
+        add(new SimpleEntry<String, Object>("eta", 1.0));
+        add(new SimpleEntry<String, Object>("max_depth", 2.0));
+        add(new SimpleEntry<String, Object>("silent", 1));
+        add(new SimpleEntry<String, Object>("objective", "binary:logistic"));
+    }
+};
+```
+
+#### Training Model
+With parameters and data, you are able to train a booster model.
+* Import ```Trainer``` and ```Booster``` :
+```java
+import org.dmlc.xgboost4j.Booster;
+import org.dmlc.xgboost4j.util.Trainer;
+```
+
+* Training
+```java
+DMatrix trainMat = new DMatrix("train.svm.txt");
+DMatrix validMat = new DMatrix("valid.svm.txt");
+//specifiy a watchList to see the performance
+//any Iterable<Entry<String, DMatrix>> object could be used as watchList
+List<Entry<String, DMatrix>> watchs =  new ArrayList<>();
+watchs.add(new SimpleEntry<>("train", trainMat));
+watchs.add(new SimpleEntry<>("test", testMat));
+int round = 2;
+Booster booster = Trainer.train(params, trainMat, round, watchs, null, null);
+```
+
+* Saving model
+After training, you can save model and dump it out.
+```java
+booster.saveModel("model.bin");
+```
+
+* Dump Model and Feature Map
+```java
+booster.dumpModel("modelInfo.txt", false)
+//dump with featureMap
+booster.dumpModel("modelInfo.txt", "featureMap.txt", false)
+```
+
+* Load a model
+```java
+Params param = new Params() {
+  {
+    put("silent", 1);
+    put("nthread", 6);
+  }
+};
+Booster booster = new Booster(param, "model.bin");
+```
+
+####Prediction
+after training and loading a model, you use it to predict other data, the predict results will be a two-dimension float array (nsample, nclass) ,for predict leaf, it would be (nsample, nclass*ntrees)
+```java
+DMatrix dtest = new DMatrix("test.svm.txt");
+//predict
+float[][] predicts = booster.predict(dtest);
+//predict leaf
+float[][] leafPredicts = booster.predict(dtest, 0, true);
+```

java/xgboost4j-demo/LICENSE

@@ -0,0 +1,15 @@
+/*
+Copyright (c) 2014 by Contributors 
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+    
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/

java/xgboost4j-demo/README.md

@@ -0,0 +1,10 @@
+xgboost4j examples
+====
+* [Basic walkthrough of wrappers](src/main/java/org/dmlc/xgboost4j/demo/BasicWalkThrough.java) 
+* [Cutomize loss function, and evaluation metric](src/main/java/org/dmlc/xgboost4j/demo/CustomObjective.java)
+* [Boosting from existing prediction](src/main/java/org/dmlc/xgboost4j/demo/BoostFromPrediction.java)
+* [Predicting using first n trees](src/main/java/org/dmlc/xgboost4j/demo/PredictFirstNtree.java)
+* [Generalized Linear Model](src/main/java/org/dmlc/xgboost4j/demo/GeneralizedLinearModel.java)
+* [Cross validation](src/main/java/org/dmlc/xgboost4j/demo/CrossValidation.java)
+* [Predicting leaf indices](src/main/java/org/dmlc/xgboost4j/demo/PredictLeafIndices.java)
+* [External Memory](src/main/java/org/dmlc/xgboost4j/demo/ExternalMemory.java)

java/xgboost4j-demo/pom.xml

@@ -0,0 +1,36 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+    <modelVersion>4.0.0</modelVersion>
+    <groupId>org.dmlc</groupId>
+    <artifactId>xgboost4j-demo</artifactId>
+    <version>1.0</version>
+    <packaging>jar</packaging>
+    <properties>
+        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
+        <maven.compiler.source>1.7</maven.compiler.source>
+        <maven.compiler.target>1.7</maven.compiler.target>
+    </properties>
+    <dependencies>
+        <dependency>
+            <groupId>org.dmlc</groupId>
+            <artifactId>xgboost4j</artifactId>
+            <version>1.1</version>
+        </dependency>
+        <dependency>
+            <groupId>commons-io</groupId>
+            <artifactId>commons-io</artifactId>
+            <version>2.4</version>
+        </dependency>
+        <dependency>
+            <groupId>org.apache.commons</groupId>
+            <artifactId>commons-lang3</artifactId>
+            <version>3.4</version>
+        </dependency>
+        <dependency>
+            <groupId>junit</groupId>
+            <artifactId>junit</artifactId>
+            <version>4.11</version>
+            <scope>test</scope>
+        </dependency>
+    </dependencies>
+</project>

java/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/BasicWalkThrough.java

@@ -0,0 +1,164 @@
+/*
+ Copyright (c) 2014 by Contributors 
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+    
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ */
+package org.dmlc.xgboost4j.demo;
+
+import java.io.File;
+import java.io.IOException;
+import java.io.UnsupportedEncodingException;
+import java.util.AbstractMap;
+import java.util.AbstractMap.SimpleEntry;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Map.Entry;
+import org.dmlc.xgboost4j.Booster;
+import org.dmlc.xgboost4j.DMatrix;
+import org.dmlc.xgboost4j.demo.util.DataLoader;
+import org.dmlc.xgboost4j.demo.util.Params;
+import org.dmlc.xgboost4j.util.Trainer;
+import org.dmlc.xgboost4j.util.XGBoostError;
+
+/**
+ * a simple example of java wrapper for xgboost
+ * @author hzx
+ */
+public class BasicWalkThrough {
+    public static boolean checkPredicts(float[][] fPredicts, float[][] sPredicts) {
+        if(fPredicts.length != sPredicts.length) {
+            return false;
+        }
+        
+        for(int i=0; i<fPredicts.length; i++) {
+            if(!Arrays.equals(fPredicts[i], sPredicts[i])) {
+                return false;
+            }
+        }
+        
+        return true;
+    }
+    
+    
+    public static void main(String[] args) throws UnsupportedEncodingException, IOException, XGBoostError {
+        // load file from text file, also binary buffer generated by xgboost4j
+        DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
+        DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
+        
+        
+        //specify parameters
+        //note: any Iterable<Entry<String, Object>> object would be used as paramters
+        //e.g.
+        //        Map<String, Object> paramMap = new HashMap<String, Object>() {
+        //            {
+        //                put("eta", 1.0);
+        //                put("max_depth", 2);
+        //                put("silent", 1);
+        //                put("objective", "binary:logistic");
+        //            }
+        //        };
+        //        Iterable<Entry<String, Object>> param = paramMap.entrySet();
+        
+        //or
+        //        List<Entry<String, Object>> param = new ArrayList<Entry<String, Object>>() {
+        //            {
+        //                add(new SimpleEntry<String, Object>("eta", 1.0));
+        //                add(new SimpleEntry<String, Object>("max_depth", 2.0));
+        //                add(new SimpleEntry<String, Object>("silent", 1));
+        //                add(new SimpleEntry<String, Object>("objective", "binary:logistic"));
+        //            }
+        //        };
+        
+        //we use a util class Params to handle parameters as example
+        Iterable<Entry<String, Object>> param = new Params() {
+            {
+                put("eta", 1.0);
+                put("max_depth", 2);
+                put("silent", 1);
+                put("objective", "binary:logistic");
+            }
+        };
+        
+        
+        
+        //specify watchList to set evaluation dmats
+        //note: any Iterable<Entry<String, DMatrix>> object would be used as watchList
+        //e.g.
+        //an entrySet of Map is good
+        //        Map<String, DMatrix> watchMap = new HashMap<>();
+        //        watchMap.put("train", trainMat);
+        //        watchMap.put("test", testMat);
+        //        Iterable<Entry<String, DMatrix>> watchs = watchMap.entrySet();
+       
+        //we use a List of Entry<String, DMatrix> WatchList as example
+        List<Entry<String, DMatrix>> watchs =  new ArrayList<>();
+        watchs.add(new SimpleEntry<>("train", trainMat));
+        watchs.add(new SimpleEntry<>("test", testMat));
+        
+        //set round
+        int round = 2;
+        
+        //train a boost model
+        Booster booster = Trainer.train(param, trainMat, round, watchs, null, null);
+        
+         //predict
+        float[][] predicts = booster.predict(testMat);
+        
+        //save model to modelPath
+        File file = new File("./model");
+        if(!file.exists()) {
+            file.mkdirs();
+        }
+        
+        String modelPath = "./model/xgb.model";
+        booster.saveModel(modelPath);
+        
+        //dump model
+        booster.dumpModel("./model/dump.raw.txt", false);
+        
+        //dump model with feature map
+        booster.dumpModel("./model/dump.nice.txt", "../../demo/data/featmap.txt", false);
+        
+        //save dmatrix into binary buffer
+        testMat.saveBinary("./model/dtest.buffer");
+        
+        //reload model and data
+        Booster booster2 = new Booster(param, "./model/xgb.model");
+        DMatrix testMat2 = new DMatrix("./model/dtest.buffer");
+        float[][] predicts2 = booster2.predict(testMat2);
+        
+        
+        //check the two predicts
+        System.out.println(checkPredicts(predicts, predicts2));
+        
+        System.out.println("start build dmatrix from csr sparse data ...");
+        //build dmatrix from CSR Sparse Matrix
+        DataLoader.CSRSparseData spData = DataLoader.loadSVMFile("../../demo/data/agaricus.txt.train");
+        
+        DMatrix trainMat2 = new DMatrix(spData.rowHeaders, spData.colIndex, spData.data, DMatrix.SparseType.CSR);
+        trainMat2.setLabel(spData.labels);
+        
+        //specify watchList
+        List<Entry<String, DMatrix>> watchs2 =  new ArrayList<>();
+        watchs2.add(new SimpleEntry<>("train", trainMat2));
+        watchs2.add(new SimpleEntry<>("test", testMat2));
+        Booster booster3 = Trainer.train(param, trainMat2, round, watchs2, null, null);
+        float[][] predicts3 = booster3.predict(testMat2);
+        
+        //check predicts
+        System.out.println(checkPredicts(predicts, predicts3));
+    }
+}

java/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/BoostFromPrediction.java

@@ -0,0 +1,67 @@
+/*
+ Copyright (c) 2014 by Contributors 
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+    
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ */
+package org.dmlc.xgboost4j.demo;
+
+import java.util.AbstractMap;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
+import org.dmlc.xgboost4j.Booster;
+import org.dmlc.xgboost4j.DMatrix;
+import org.dmlc.xgboost4j.demo.util.Params;
+import org.dmlc.xgboost4j.util.Trainer;
+import org.dmlc.xgboost4j.util.XGBoostError;
+
+/**
+ * example for start from a initial base prediction
+ * @author hzx
+ */
+public class BoostFromPrediction {
+    public static void main(String[] args) throws XGBoostError {
+        System.out.println("start running example to start from a initial prediction");
+        
+        // load file from text file, also binary buffer generated by xgboost4j
+        DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
+        DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
+        
+        //specify parameters
+        Params param = new Params() {
+            {
+                put("eta", 1.0);
+                put("max_depth", 2);
+                put("silent", 1);
+                put("objective", "binary:logistic");
+            }
+        };
+        
+        //specify watchList
+        List<Map.Entry<String, DMatrix>> watchs =  new ArrayList<>();
+        watchs.add(new AbstractMap.SimpleEntry<>("train", trainMat));
+        watchs.add(new AbstractMap.SimpleEntry<>("test", testMat));
+        
+        //train xgboost for 1 round
+        Booster booster = Trainer.train(param, trainMat, 1, watchs, null, null);
+        
+        float[][] trainPred = booster.predict(trainMat, true);
+        float[][] testPred = booster.predict(testMat, true);
+        
+        trainMat.setBaseMargin(trainPred);
+        testMat.setBaseMargin(testPred);
+        
+        System.out.println("result of running from initial prediction");
+        Booster booster2 = Trainer.train(param, trainMat, 1, watchs, null, null);
+    }
+}

java/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/CrossValidation.java

@@ -0,0 +1,54 @@
+/*
+ Copyright (c) 2014 by Contributors 
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+    
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ */
+package org.dmlc.xgboost4j.demo;
+
+import java.io.IOException;
+import org.dmlc.xgboost4j.DMatrix;
+import org.dmlc.xgboost4j.util.Trainer;
+import org.dmlc.xgboost4j.demo.util.Params;
+import org.dmlc.xgboost4j.util.XGBoostError;
+
+/**
+ * an example of cross validation
+ * @author hzx
+ */
+public class CrossValidation {
+    public static void main(String[] args) throws IOException, XGBoostError {
+         //load train mat
+        DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
+        
+        //set params
+        Params param = new Params() {
+            {
+                put("eta", 1.0);
+                put("max_depth", 3);
+                put("silent", 1);
+                put("nthread", 6);
+                put("objective", "binary:logistic");
+                put("gamma", 1.0);
+                put("eval_metric", "error");
+            }
+        };
+        
+        //do 5-fold cross validation
+        int round = 2;
+        int nfold = 5;
+        //set additional eval_metrics
+        String[] metrics = null;
+        
+        String[] evalHist = Trainer.crossValiation(param, trainMat, round, nfold, metrics, null, null);
+    }
+}

java/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/CustomObjective.java

@@ -0,0 +1,175 @@
+/*
+ Copyright (c) 2014 by Contributors 
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+    
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ */
+package org.dmlc.xgboost4j.demo;
+
+import java.util.AbstractMap;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
+import org.apache.commons.logging.Log;
+import org.apache.commons.logging.LogFactory;
+import org.dmlc.xgboost4j.Booster;
+import org.dmlc.xgboost4j.IEvaluation;
+import org.dmlc.xgboost4j.DMatrix;
+import org.dmlc.xgboost4j.IObjective;
+import org.dmlc.xgboost4j.demo.util.Params;
+import org.dmlc.xgboost4j.util.Trainer;
+import org.dmlc.xgboost4j.util.XGBoostError;
+
+/**
+ * an example user define objective and eval
+ * NOTE: when you do customized loss function, the default prediction value is margin
+ * this may make buildin evalution metric not function properly
+ * for example, we are doing logistic loss, the prediction is score before logistic transformation
+ * he buildin evaluation error assumes input is after logistic transformation
+ * Take this in mind when you use the customization, and maybe you need write customized evaluation function
+ * @author hzx
+ */
+public class CustomObjective {
+    /**
+     * loglikelihoode loss obj function
+     */
+    public static class LogRegObj implements IObjective {
+        private static final Log logger = LogFactory.getLog(LogRegObj.class);
+        
+        /**
+         * simple sigmoid func
+         * @param input
+         * @return 
+         * Note: this func is not concern about numerical stability, only used as example
+         */
+        public float sigmoid(float input) {
+            float val = (float) (1/(1+Math.exp(-input)));
+            return val;
+        }
+        
+        public float[][] transform(float[][] predicts) {
+            int nrow = predicts.length;
+            float[][] transPredicts = new float[nrow][1];
+            
+            for(int i=0; i<nrow; i++) {
+                transPredicts[i][0] = sigmoid(predicts[i][0]);
+            }
+            
+            return transPredicts;
+        }
+
+        @Override
+        public List<float[]> getGradient(float[][] predicts, DMatrix dtrain) {
+            int nrow = predicts.length;
+            List<float[]> gradients = new ArrayList<>();
+            float[] labels;
+            try {
+                labels = dtrain.getLabel();
+            } catch (XGBoostError ex) {
+                logger.error(ex);
+                return null;
+            }
+            float[] grad = new float[nrow];
+            float[] hess = new float[nrow];
+            
+            float[][] transPredicts = transform(predicts);
+            
+            for(int i=0; i<nrow; i++) {
+                float predict = transPredicts[i][0];
+                grad[i] = predict - labels[i];
+                hess[i] = predict * (1 - predict);
+            }
+            
+            gradients.add(grad);
+            gradients.add(hess);
+            return gradients;
+        }        
+    }
+    
+    /**
+     * user defined eval function.
+     * NOTE: when you do customized loss function, the default prediction value is margin
+     * this may make buildin evalution metric not function properly
+     * for example, we are doing logistic loss, the prediction is score before logistic transformation
+     * the buildin evaluation error assumes input is after logistic transformation
+     * Take this in mind when you use the customization, and maybe you need write customized evaluation function
+     */
+    public static class EvalError implements IEvaluation {
+        private static final Log logger = LogFactory.getLog(EvalError.class);
+        
+        String evalMetric = "custom_error";
+        
+        public EvalError() {
+        }
+        
+        @Override
+        public String getMetric() {
+            return evalMetric;
+        }
+
+        @Override
+        public float eval(float[][] predicts, DMatrix dmat) {
+            float error = 0f;
+            float[] labels;
+            try {
+                labels = dmat.getLabel();
+            } catch (XGBoostError ex) {
+                logger.error(ex);
+                return -1f;
+            }
+            int nrow = predicts.length;
+            for(int i=0; i<nrow; i++) {
+                if(labels[i]==0f && predicts[i][0]>0) {
+                    error++;
+                }
+                else if(labels[i]==1f && predicts[i][0]<=0) {
+                    error++;
+                }
+            }
+            
+            return error/labels.length;
+        }
+    }
+    
+    public static void main(String[] args) throws XGBoostError {
+        //load train mat (svmlight format)
+        DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
+        //load valid mat (svmlight format)
+        DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
+        
+        //set params
+        //set params
+        Params param = new Params() {
+            {
+                put("eta", 1.0);
+                put("max_depth", 2);
+                put("silent", 1);
+            }
+        };
+        
+        //set round
+        int round = 2;
+        
+        //specify watchList
+        List<Map.Entry<String, DMatrix>> watchs =  new ArrayList<>();
+        watchs.add(new AbstractMap.SimpleEntry<>("train", trainMat));
+        watchs.add(new AbstractMap.SimpleEntry<>("test", testMat));
+        
+        //user define obj and eval
+        IObjective obj = new LogRegObj();
+        IEvaluation eval = new EvalError();
+        
+        //train a booster
+        System.out.println("begin to train the booster model");        
+        Booster booster = Trainer.train(param, trainMat, round, watchs, obj, eval);
+    }
+}

java/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/ExternalMemory.java

@@ -0,0 +1,65 @@
+/*
+ Copyright (c) 2014 by Contributors 
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+    
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ */
+package org.dmlc.xgboost4j.demo;
+
+import java.util.AbstractMap;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
+import org.dmlc.xgboost4j.Booster;
+import org.dmlc.xgboost4j.DMatrix;
+import org.dmlc.xgboost4j.demo.util.Params;
+import org.dmlc.xgboost4j.util.Trainer;
+import org.dmlc.xgboost4j.util.XGBoostError;
+
+/**
+ * simple example for using external memory version
+ * @author hzx
+ */
+public class ExternalMemory {
+    public static void main(String[] args) throws XGBoostError {
+        //this is the only difference, add a # followed by a cache prefix name
+        //several cache file with the prefix will be generated
+        //currently only support convert from libsvm file
+        DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train#dtrain.cache");
+        DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test#dtest.cache");
+        
+        //specify parameters
+        Params param = new Params() {
+            {
+                put("eta", 1.0);
+                put("max_depth", 2);
+                put("silent", 1);
+                put("objective", "binary:logistic");
+            }
+        };
+        
+        //performance notice: set nthread to be the number of your real cpu
+        //some cpu offer two threads per core, for example, a 4 core cpu with 8 threads, in such case set nthread=4
+        //param.put("nthread", num_real_cpu);
+        
+        //specify watchList
+        List<Map.Entry<String, DMatrix>> watchs =  new ArrayList<>();
+        watchs.add(new AbstractMap.SimpleEntry<>("train", trainMat));
+        watchs.add(new AbstractMap.SimpleEntry<>("test", testMat));
+        
+        //set round
+        int round = 2;
+        
+        //train a boost model
+        Booster booster = Trainer.train(param, trainMat, round, watchs, null, null);
+    }
+}

java/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/GeneralizedLinearModel.java

@@ -0,0 +1,74 @@
+/*
+ Copyright (c) 2014 by Contributors 
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+    
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ */
+package org.dmlc.xgboost4j.demo;
+
+import java.util.AbstractMap;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
+import org.dmlc.xgboost4j.Booster;
+import org.dmlc.xgboost4j.DMatrix;
+import org.dmlc.xgboost4j.demo.util.CustomEval;
+import org.dmlc.xgboost4j.demo.util.Params;
+import org.dmlc.xgboost4j.util.Trainer;
+import org.dmlc.xgboost4j.util.XGBoostError;
+
+/**
+ * this is an example of fit generalized linear model in xgboost
+ * basically, we are using linear model, instead of tree for our boosters
+ * @author hzx
+ */
+public class GeneralizedLinearModel {
+    public static void main(String[] args) throws XGBoostError {
+        // load file from text file, also binary buffer generated by xgboost4j
+        DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
+        DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
+        
+        //specify parameters
+        //change booster to gblinear, so that we are fitting a linear model
+        // alpha is the L1 regularizer
+        //lambda is the L2 regularizer
+        //you can also set lambda_bias which is L2 regularizer on the bias term
+        Params param = new Params() {
+            {
+                put("alpha", 0.0001);
+                put("silent", 1);
+                put("objective", "binary:logistic");
+                put("booster", "gblinear");
+            }
+        };
+        //normally, you do not need to set eta (step_size)
+        //XGBoost uses a parallel coordinate descent algorithm (shotgun),
+        //there could be affection on convergence with parallelization on certain cases
+        //setting eta to be smaller value, e.g 0.5 can make the optimization more stable
+        //param.put("eta", "0.5");
+        
+        
+        //specify watchList
+        List<Map.Entry<String, DMatrix>> watchs =  new ArrayList<>();
+        watchs.add(new AbstractMap.SimpleEntry<>("train", trainMat));
+        watchs.add(new AbstractMap.SimpleEntry<>("test", testMat));
+        
+        //train a booster
+        int round = 4;
+        Booster booster = Trainer.train(param, trainMat, round, watchs, null, null);
+        
+        float[][] predicts = booster.predict(testMat);
+        
+        CustomEval eval = new CustomEval();
+        System.out.println("error=" + eval.eval(predicts, testMat));
+    }
+}

java/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/PredictFirstNtree.java

@@ -0,0 +1,69 @@
+/*
+ Copyright (c) 2014 by Contributors 
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+    
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ */
+package org.dmlc.xgboost4j.demo;
+
+import java.util.AbstractMap;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
+import org.dmlc.xgboost4j.Booster;
+import org.dmlc.xgboost4j.DMatrix;
+import org.dmlc.xgboost4j.util.Trainer;
+
+import org.dmlc.xgboost4j.demo.util.CustomEval;
+import org.dmlc.xgboost4j.demo.util.Params;
+import org.dmlc.xgboost4j.util.XGBoostError;
+
+/**
+ * predict first ntree
+ * @author hzx
+ */
+public class PredictFirstNtree {   
+    public static void main(String[] args) throws XGBoostError {
+        // load file from text file, also binary buffer generated by xgboost4j
+        DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
+        DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
+        
+        //specify parameters
+        Params param = new Params() {
+            {
+                put("eta", 1.0);
+                put("max_depth", 2);
+                put("silent", 1);
+                put("objective", "binary:logistic");
+            }
+        };
+        
+        //specify watchList
+        List<Map.Entry<String, DMatrix>> watchs =  new ArrayList<>();
+        watchs.add(new AbstractMap.SimpleEntry<>("train", trainMat));
+        watchs.add(new AbstractMap.SimpleEntry<>("test", testMat));
+        
+        //train a booster
+        int round = 3;
+        Booster booster = Trainer.train(param, trainMat, round, watchs, null, null);
+        
+        //predict use 1 tree
+        float[][] predicts1 = booster.predict(testMat, false, 1);
+        //by default all trees are used to do predict
+        float[][] predicts2 = booster.predict(testMat);
+        
+        //use a simple evaluation class to check error result
+        CustomEval eval = new CustomEval();
+        System.out.println("error of predicts1: " + eval.eval(predicts1, testMat));
+        System.out.println("error of predicts2: " + eval.eval(predicts2, testMat));
+    }
+}

java/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/PredictLeafIndices.java

@@ -0,0 +1,70 @@
+/*
+ Copyright (c) 2014 by Contributors 
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+    
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ */
+package org.dmlc.xgboost4j.demo;
+
+import java.util.AbstractMap;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+import java.util.Map;
+import org.dmlc.xgboost4j.Booster;
+import org.dmlc.xgboost4j.DMatrix;
+import org.dmlc.xgboost4j.util.Trainer;
+import org.dmlc.xgboost4j.demo.util.Params;
+import org.dmlc.xgboost4j.util.XGBoostError;
+
+/**
+ * predict leaf indices
+ * @author hzx
+ */
+public class PredictLeafIndices {
+    public static void main(String[] args) throws XGBoostError {
+        // load file from text file, also binary buffer generated by xgboost4j
+        DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
+        DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
+        
+        //specify parameters
+        Params param = new Params() {
+            {
+                put("eta", 1.0);
+                put("max_depth", 2);
+                put("silent", 1);
+                put("objective", "binary:logistic");
+            }
+        };
+        
+        //specify watchList
+        List<Map.Entry<String, DMatrix>> watchs =  new ArrayList<>();
+        watchs.add(new AbstractMap.SimpleEntry<>("train", trainMat));
+        watchs.add(new AbstractMap.SimpleEntry<>("test", testMat));
+        
+        //train a booster
+        int round = 3;
+        Booster booster = Trainer.train(param, trainMat, round, watchs, null, null);
+        
+        //predict using first 2 tree
+        float[][] leafindex = booster.predict(testMat, 2, true);
+        for(float[] leafs : leafindex) {
+            System.out.println(Arrays.toString(leafs));
+        }
+        
+        //predict all trees
+        leafindex = booster.predict(testMat, 0, true);
+        for(float[] leafs : leafindex) {
+            System.out.println(Arrays.toString(leafs));
+        }
+    }
+}

java/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/util/CustomEval.java

@@ -0,0 +1,60 @@
+/*
+ Copyright (c) 2014 by Contributors 
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+    
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ */
+package org.dmlc.xgboost4j.demo.util;
+
+import org.apache.commons.logging.Log;
+import org.apache.commons.logging.LogFactory;
+import org.dmlc.xgboost4j.DMatrix;
+import org.dmlc.xgboost4j.IEvaluation;
+import org.dmlc.xgboost4j.util.XGBoostError;
+
+/**
+ * a util evaluation class for examples
+ * @author hzx
+ */
+public class CustomEval implements IEvaluation {
+    private static final Log logger = LogFactory.getLog(CustomEval.class);
+
+    String evalMetric = "custom_error";
+        
+    @Override
+    public String getMetric() {
+        return evalMetric;
+    }
+
+    @Override
+    public float eval(float[][] predicts, DMatrix dmat) {
+        float error = 0f;
+        float[] labels;
+        try {
+            labels = dmat.getLabel();
+        } catch (XGBoostError ex) {
+            logger.error(ex);
+            return -1f;
+        }
+        int nrow = predicts.length;
+        for(int i=0; i<nrow; i++) {
+            if(labels[i]==0f && predicts[i][0]>0.5) {
+                error++;
+            }
+            else if(labels[i]==1f && predicts[i][0]<=0.5) {
+                error++;
+            }
+        }
+            
+        return error/labels.length;
+    }
+}

java/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/util/DataLoader.java

@@ -0,0 +1,127 @@
+/*
+ Copyright (c) 2014 by Contributors 
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+    
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ */
+package org.dmlc.xgboost4j.demo.util;
+
+import java.io.BufferedReader;
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.FileNotFoundException;
+import java.io.IOException;
+import java.io.InputStreamReader;
+import java.io.UnsupportedEncodingException;
+import java.util.ArrayList;
+import java.util.List;
+import org.apache.commons.lang3.ArrayUtils;
+
+/**
+ * util class for loading data
+ * @author hzx
+ */
+public class DataLoader {
+    public static class DenseData {
+        public float[] labels;
+        public float[] data;
+        public int nrow;
+        public int ncol;
+    }
+    
+    public static class CSRSparseData {
+        public float[] labels;
+        public float[] data;
+        public long[] rowHeaders;
+        public int[] colIndex;
+    }
+    
+    public static DenseData loadCSVFile(String filePath) throws FileNotFoundException, UnsupportedEncodingException, IOException {
+        DenseData denseData = new DenseData();
+        
+        File f = new File(filePath);
+        FileInputStream in = new FileInputStream(f);
+        BufferedReader reader = new BufferedReader(new InputStreamReader(in, "UTF-8"));
+        
+        denseData.nrow = 0;
+        denseData.ncol = -1;
+        String line;
+        List<Float> tlabels = new ArrayList<>();
+        List<Float> tdata = new ArrayList<>();
+        
+        while((line=reader.readLine()) != null) {
+            String[] items = line.trim().split(",");
+            if(items.length==0) {
+                continue;
+            }
+            denseData.nrow++;
+            if(denseData.ncol == -1) {
+                denseData.ncol = items.length - 1;
+            }
+            
+            tlabels.add(Float.valueOf(items[items.length-1]));
+            for(int i=0; i<items.length-1; i++) {
+                tdata.add(Float.valueOf(items[i]));
+            }
+        }
+        
+        reader.close();
+        in.close();
+        
+        denseData.labels = ArrayUtils.toPrimitive(tlabels.toArray(new Float[tlabels.size()]));
+        denseData.data = ArrayUtils.toPrimitive(tdata.toArray(new Float[tdata.size()]));
+        
+        return denseData;
+    }
+    
+    public static CSRSparseData loadSVMFile(String filePath) throws FileNotFoundException, UnsupportedEncodingException, IOException {
+        CSRSparseData spData = new CSRSparseData();
+        
+        List<Float> tlabels = new ArrayList<>();
+        List<Float> tdata = new ArrayList<>();
+        List<Long> theaders = new ArrayList<>();
+        List<Integer> tindex = new ArrayList<>();
+        
+        File f = new File(filePath);
+        FileInputStream in = new FileInputStream(f);
+        BufferedReader reader = new BufferedReader(new InputStreamReader(in, "UTF-8"));
+        
+        String line;
+        long rowheader = 0;
+        theaders.add(rowheader);
+        while((line=reader.readLine()) != null) {
+            String[] items = line.trim().split(" ");
+            if(items.length==0) {
+                continue;
+            }
+            
+            rowheader += items.length - 1;
+            theaders.add(rowheader);
+            tlabels.add(Float.valueOf(items[0]));
+            
+            for(int i=1; i<items.length; i++) {
+                String[] tup = items[i].split(":");
+                assert tup.length == 2;
+                
+                tdata.add(Float.valueOf(tup[1]));
+                tindex.add(Integer.valueOf(tup[0]));
+            }
+        }
+        
+        spData.labels = ArrayUtils.toPrimitive(tlabels.toArray(new Float[tlabels.size()]));
+        spData.data = ArrayUtils.toPrimitive(tdata.toArray(new Float[tdata.size()]));
+        spData.colIndex = ArrayUtils.toPrimitive(tindex.toArray(new Integer[tindex.size()]));
+        spData.rowHeaders = ArrayUtils.toPrimitive(theaders.toArray(new Long[theaders.size()]));
+        
+        return spData;
+    }
+}

java/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/util/Params.java

@@ -0,0 +1,54 @@
+/*
+ Copyright (c) 2014 by Contributors 
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+    
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ */
+package org.dmlc.xgboost4j.demo.util;
+
+import java.util.ArrayList;
+import java.util.Iterator;
+import java.util.List;
+import java.util.Map.Entry;
+import java.util.AbstractMap;
+
+
+/**
+ * a util class for handle params
+ * @author hzx
+ */
+public class Params implements Iterable<Entry<String, Object>>{
+    List<Entry<String, Object>> params = new ArrayList<>();
+    
+    /**
+     * put param key-value pair
+     * @param key
+     * @param value 
+     */
+    public void put(String key, Object value) {
+        params.add(new AbstractMap.SimpleEntry<>(key, value));
+    }
+    
+    @Override
+    public String toString(){ 
+        String paramsInfo = "";
+        for(Entry<String, Object> param : params) {
+            paramsInfo += param.getKey() + ":" + param.getValue() + "\n";
+        }
+        return paramsInfo;
+    }
+
+    @Override
+    public Iterator<Entry<String, Object>> iterator() {
+        return params.iterator();
+    }
+}

java/xgboost4j/LICENSE

@@ -0,0 +1,15 @@
+/*
+Copyright (c) 2014 by Contributors 
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+    
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/

java/xgboost4j/README.md

@@ -0,0 +1,23 @@
+# xgboost4j
+this is a java wrapper for xgboost (https://github.com/dmlc/xgboost)  
+the structure of this wrapper is almost the same as the official python wrapper.
+core of this wrapper is two classes:
+
+* DMatrix： for handling data
+
+* Booster: for train and predict
+
+## usage:
+  
+  simple examples could be found in test package:
+
+  * Simple Train Example: org.dmlc.xgboost4j.TrainExample.java
+  
+  * Simple Predict Example: org.dmlc.xgboost4j.PredictExample.java
+  
+  * Cross Validation Example: org.dmlc.xgboost4j.example.CVExample.java
+  
+## native library:
+  
+  only 64-bit linux/windows is supported now, if you want to build native wrapper library yourself, please refer to 
+  https://github.com/yanqingmen/xgboost-java, and put your native library to the "./src/main/resources/lib" folder and replace the originals. (either "libxgboostjavawrapper.so" for linux or "xgboostjavawrapper.dll" for windows) 

java/xgboost4j/pom.xml

@@ -0,0 +1,35 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+    <modelVersion>4.0.0</modelVersion>
+    <groupId>org.dmlc</groupId>
+    <artifactId>xgboost4j</artifactId>
+    <version>1.1</version>
+    <packaging>jar</packaging>
+    <properties>
+        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
+        <maven.compiler.source>1.7</maven.compiler.source>
+        <maven.compiler.target>1.7</maven.compiler.target>
+    </properties>
+    <reporting>
+        <plugins>
+            <plugin>          
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-javadoc-plugin</artifactId>
+                <version>2.10.3</version>
+            </plugin>
+        </plugins>
+    </reporting>
+    <dependencies>
+        <dependency>
+            <groupId>junit</groupId>
+            <artifactId>junit</artifactId>
+            <version>4.11</version>
+            <scope>test</scope>
+        </dependency>
+        <dependency>
+            <groupId>commons-logging</groupId>
+            <artifactId>commons-logging</artifactId>
+            <version>1.2</version>
+        </dependency>
+    </dependencies>
+</project>

java/xgboost4j/src/main/java/org/dmlc/xgboost4j/Booster.java

@@ -0,0 +1,484 @@
+/*
+ Copyright (c) 2014 by Contributors 
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+    
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ */
+package org.dmlc.xgboost4j;
+
+import java.io.BufferedWriter;
+import java.io.File;
+import java.io.FileNotFoundException;
+import java.io.FileOutputStream;
+import java.io.IOException;
+import java.io.OutputStreamWriter;
+import java.io.UnsupportedEncodingException;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Map.Entry;
+import org.apache.commons.logging.Log;
+import org.apache.commons.logging.LogFactory;
+
+import org.dmlc.xgboost4j.util.Initializer;
+import org.dmlc.xgboost4j.util.ErrorHandle;
+import org.dmlc.xgboost4j.util.XGBoostError;
+import org.dmlc.xgboost4j.wrapper.XgboostJNI;
+
+
+/**
+ * Booster for xgboost, similar to the python wrapper xgboost.py
+ * but custom obj function and eval function not supported at present.
+ * @author hzx
+ */
+public final class Booster {
+    private static final Log logger = LogFactory.getLog(Booster.class);
+    
+    long handle = 0;
+    
+    //load native library
+    static {
+        try {
+            Initializer.InitXgboost();
+        } catch (IOException ex) {
+            logger.error("load native library failed.");
+            logger.error(ex);
+        }
+    }
+    
+    /**
+     * init Booster from dMatrixs
+     * @param params parameters
+     * @param dMatrixs DMatrix array
+     * @throws org.dmlc.xgboost4j.util.XGBoostError
+     */
+    public Booster(Iterable<Entry<String, Object>> params, DMatrix[] dMatrixs) throws XGBoostError {
+        init(dMatrixs);
+        setParam("seed","0");
+        setParams(params);
+    }
+    
+
+    
+    /**
+     * load model from modelPath
+     * @param params parameters
+     * @param modelPath booster modelPath (model generated by booster.saveModel)
+     * @throws org.dmlc.xgboost4j.util.XGBoostError
+     */
+    public Booster(Iterable<Entry<String, Object>> params, String modelPath) throws XGBoostError {
+        init(null);
+        if(modelPath == null) {
+            throw new NullPointerException("modelPath : null");
+        }
+        loadModel(modelPath);
+        setParam("seed","0");
+        setParams(params);
+    }
+    
+ 
+    
+    
+    private void init(DMatrix[] dMatrixs) throws XGBoostError {
+        long[] handles = null;
+        if(dMatrixs != null) {
+            handles = dMatrixs2handles(dMatrixs);
+        }
+        long[] out = new long[1];
+        ErrorHandle.checkCall(XgboostJNI.XGBoosterCreate(handles, out));
+
+        handle = out[0];
+    }
+    
+    /**
+     * set parameter
+     * @param key param name
+     * @param value param value
+     * @throws org.dmlc.xgboost4j.util.XGBoostError
+     */
+    public final void setParam(String key, String value) throws XGBoostError {
+        ErrorHandle.checkCall(XgboostJNI.XGBoosterSetParam(handle, key, value));
+    }
+    
+    /**
+     * set parameters
+     * @param params parameters key-value map 
+     * @throws org.dmlc.xgboost4j.util.XGBoostError 
+     */
+    public void setParams(Iterable<Entry<String, Object>> params) throws XGBoostError {
+        if(params!=null) {
+            for(Map.Entry<String, Object> entry : params) {
+                setParam(entry.getKey(), entry.getValue().toString());
+            }
+        }
+    }
+    
+    
+    /**
+     * Update (one iteration)
+     * @param dtrain training data
+     * @param iter current iteration number
+     * @throws org.dmlc.xgboost4j.util.XGBoostError
+     */
+    public void update(DMatrix dtrain, int iter) throws XGBoostError {
+        ErrorHandle.checkCall(XgboostJNI.XGBoosterUpdateOneIter(handle, iter, dtrain.getHandle()));
+    }
+    
+    /**
+     * update with customize obj func
+     * @param dtrain training data
+     * @param iter current iteration number
+     * @param obj customized objective class
+     * @throws org.dmlc.xgboost4j.util.XGBoostError
+     */
+    public void update(DMatrix dtrain, int iter, IObjective obj) throws XGBoostError {
+        float[][] predicts = predict(dtrain, true);
+        List<float[]> gradients = obj.getGradient(predicts, dtrain);
+        boost(dtrain, gradients.get(0), gradients.get(1));
+    }
+    
+    /**
+     * update with give grad and hess
+     * @param dtrain training data
+     * @param grad first order of gradient
+     * @param hess seconde order of gradient
+     * @throws org.dmlc.xgboost4j.util.XGBoostError
+     */
+    public void boost(DMatrix dtrain, float[] grad, float[] hess) throws XGBoostError {
+        if(grad.length != hess.length) {
+            throw new AssertionError(String.format("grad/hess length mismatch %s / %s", grad.length, hess.length));
+        }
+        ErrorHandle.checkCall(XgboostJNI.XGBoosterBoostOneIter(handle, dtrain.getHandle(), grad, hess));
+    }
+    
+    /**
+     * evaluate with given dmatrixs.
+     * @param evalMatrixs dmatrixs for evaluation
+     * @param evalNames name for eval dmatrixs, used for check results
+     * @param iter current eval iteration
+     * @return eval information
+     * @throws org.dmlc.xgboost4j.util.XGBoostError
+     */
+    public String evalSet(DMatrix[] evalMatrixs, String[] evalNames,  int iter) throws XGBoostError {
+        long[] handles = dMatrixs2handles(evalMatrixs);
+        String[] evalInfo = new String[1];
+        ErrorHandle.checkCall(XgboostJNI.XGBoosterEvalOneIter(handle, iter, handles, evalNames, evalInfo));
+        return evalInfo[0];
+    }
+    
+    /**
+     * evaluate with given customized Evaluation class
+     * @param evalMatrixs
+     * @param evalNames
+     * @param iter
+     * @param eval
+     * @return eval information
+     * @throws org.dmlc.xgboost4j.util.XGBoostError
+     */
+    public String evalSet(DMatrix[] evalMatrixs, String[] evalNames,  int iter, IEvaluation eval) throws XGBoostError {
+        String evalInfo = "";
+        for(int i=0; i<evalNames.length; i++) {
+            String evalName = evalNames[i];
+            DMatrix evalMat = evalMatrixs[i];
+            float evalResult = eval.eval(predict(evalMat), evalMat);
+            String evalMetric = eval.getMetric();
+            evalInfo += String.format("\t%s-%s:%f", evalName,evalMetric, evalResult);
+        }
+        return evalInfo;
+    }
+    
+     /**
+     * evaluate with given dmatrix handles;
+     * @param dHandles evaluation data handles 
+     * @param evalNames name for eval dmatrixs, used for check results
+     * @param iter current eval iteration
+     * @return eval information
+     * @throws org.dmlc.xgboost4j.util.XGBoostError
+     */
+    public String evalSet(long[] dHandles, String[] evalNames,  int iter) throws XGBoostError {
+        String[] evalInfo = new String[1];
+        ErrorHandle.checkCall(XgboostJNI.XGBoosterEvalOneIter(handle, iter, dHandles, evalNames, evalInfo));
+        return evalInfo[0];
+    }
+    
+    
+    /**
+     * evaluate with given dmatrix, similar to evalSet
+     * @param evalMat
+     * @param evalName
+     * @param iter
+     * @return eval information
+     * @throws org.dmlc.xgboost4j.util.XGBoostError
+     */
+    public String eval(DMatrix evalMat, String evalName, int iter) throws XGBoostError {
+        DMatrix[] evalMats = new DMatrix[] {evalMat};
+        String[] evalNames = new String[] {evalName};
+        return  evalSet(evalMats, evalNames, iter);
+    }
+    
+    /**
+     * base function for Predict
+     * @param data
+     * @param outPutMargin
+     * @param treeLimit
+     * @param predLeaf
+     * @return predict results
+     */
+    private synchronized float[][] pred(DMatrix data, boolean outPutMargin, long treeLimit, boolean predLeaf) throws XGBoostError {
+        int optionMask = 0;
+        if(outPutMargin) {
+            optionMask = 1;
+        }
+        if(predLeaf) {
+            optionMask = 2;
+        }
+        float[][] rawPredicts = new float[1][];
+        ErrorHandle.checkCall(XgboostJNI.XGBoosterPredict(handle, data.getHandle(), optionMask, treeLimit, rawPredicts));
+        int row = (int) data.rowNum();
+        int col = (int) rawPredicts[0].length/row;
+        float[][] predicts = new float[row][col];
+        int r,c;
+        for(int i=0; i< rawPredicts[0].length; i++) {
+            r = i/col;
+            c = i%col;
+            predicts[r][c] = rawPredicts[0][i];
+        }
+        return predicts;
+    } 
+    
+    /**
+     * Predict with data
+     * @param data dmatrix storing the input
+     * @return predict result 
+     * @throws org.dmlc.xgboost4j.util.XGBoostError 
+     */
+    public float[][] predict(DMatrix data) throws XGBoostError {
+        return pred(data, false, 0, false);
+    }
+    
+    /**
+     * Predict with data
+     * @param data dmatrix storing the input
+     * @param outPutMargin Whether to output the raw untransformed margin value.
+     * @return predict result
+     * @throws org.dmlc.xgboost4j.util.XGBoostError
+     */
+    public float[][] predict(DMatrix data, boolean outPutMargin) throws XGBoostError {
+        return pred(data, outPutMargin, 0, false);
+    }
+    
+    /**
+     * Predict with data
+     * @param data dmatrix storing the input
+     * @param outPutMargin Whether to output the raw untransformed margin value.
+     * @param treeLimit Limit number of trees in the prediction; defaults to 0 (use all trees).
+     * @return predict result
+     * @throws org.dmlc.xgboost4j.util.XGBoostError
+     */
+    public float[][] predict(DMatrix data, boolean outPutMargin, long treeLimit) throws XGBoostError {
+        return pred(data, outPutMargin, treeLimit, false);
+    }
+    
+    /**
+     * Predict with data 
+     * @param data dmatrix storing the input
+     * @param treeLimit Limit number of trees in the prediction; defaults to 0 (use all trees).
+     * @param predLeaf When this option is on, the output will be a matrix of (nsample, ntrees), nsample = data.numRow
+            with each record indicating the predicted leaf index of each sample in each tree.
+            Note that the leaf index of a tree is unique per tree, so you may find leaf 1
+            in both tree 1 and tree 0.
+     * @return predict result
+     * @throws org.dmlc.xgboost4j.util.XGBoostError
+     */
+    public float[][] predict(DMatrix data , long treeLimit, boolean predLeaf) throws XGBoostError {
+        return pred(data, false, treeLimit, predLeaf);
+    }
+    
+    /**
+     * save model to modelPath
+     * @param modelPath 
+     */
+    public void saveModel(String modelPath) {
+        XgboostJNI.XGBoosterSaveModel(handle, modelPath);
+    }
+    
+    private void loadModel(String modelPath) {
+        XgboostJNI.XGBoosterLoadModel(handle, modelPath);
+    }
+    
+    /**
+     * get the dump of the model as a string array
+     * @param withStats Controls whether the split statistics are output.
+     * @return dumped model information
+     * @throws org.dmlc.xgboost4j.util.XGBoostError
+     */
+    public String[] getDumpInfo(boolean withStats) throws XGBoostError {
+        int statsFlag = 0;
+        if(withStats) {
+            statsFlag = 1;
+        }
+        String[][] modelInfos = new String[1][];
+        ErrorHandle.checkCall(XgboostJNI.XGBoosterDumpModel(handle, "", statsFlag, modelInfos));
+        return modelInfos[0];
+    }
+    
+    /**
+     * get the dump of the model as a string array
+     * @param featureMap featureMap file
+     * @param withStats Controls whether the split statistics are output.
+     * @return dumped model information
+     * @throws org.dmlc.xgboost4j.util.XGBoostError
+     */
+    public String[] getDumpInfo(String featureMap, boolean withStats) throws XGBoostError {
+        int statsFlag = 0;
+        if(withStats) {
+            statsFlag = 1;
+        }
+        String[][] modelInfos = new String[1][];
+        ErrorHandle.checkCall(XgboostJNI.XGBoosterDumpModel(handle, featureMap, statsFlag, modelInfos));
+        return modelInfos[0];
+    }
+    
+    /**
+     * Dump model into a text file.
+     * @param modelPath file to save dumped model info
+     * @param withStats bool
+            Controls whether the split statistics are output.
+     * @throws FileNotFoundException
+     * @throws UnsupportedEncodingException
+     * @throws IOException 
+     * @throws org.dmlc.xgboost4j.util.XGBoostError 
+     */
+    public void dumpModel(String modelPath, boolean withStats) throws FileNotFoundException, UnsupportedEncodingException, IOException, XGBoostError {
+        File tf = new File(modelPath);
+        FileOutputStream out = new FileOutputStream(tf);
+        BufferedWriter writer = new BufferedWriter(new OutputStreamWriter(out, "UTF-8"));
+        String[] modelInfos = getDumpInfo(withStats);
+        
+        for(int i=0; i<modelInfos.length; i++) {
+            writer.write("booster [" + i +"]:\n");
+            writer.write(modelInfos[i]);
+        }
+        
+        writer.close();
+        out.close();
+    }
+    
+    
+    /**
+     * Dump model into a text file.
+     * @param modelPath file to save dumped model info
+     * @param featureMap featureMap file
+     * @param withStats bool
+            Controls whether the split statistics are output.
+     * @throws FileNotFoundException
+     * @throws UnsupportedEncodingException
+     * @throws IOException 
+     * @throws org.dmlc.xgboost4j.util.XGBoostError 
+     */
+    public void dumpModel(String modelPath, String featureMap, boolean withStats) throws FileNotFoundException, UnsupportedEncodingException, IOException, XGBoostError {
+        File tf = new File(modelPath);
+        FileOutputStream out = new FileOutputStream(tf);
+        BufferedWriter writer = new BufferedWriter(new OutputStreamWriter(out, "UTF-8"));
+        String[] modelInfos = getDumpInfo(featureMap, withStats);
+        
+        for(int i=0; i<modelInfos.length; i++) {
+            writer.write("booster [" + i +"]:\n");
+            writer.write(modelInfos[i]);
+        }
+        
+        writer.close();
+        out.close();
+    }
+    
+    
+    /**
+     * get importance of each feature
+     * @return featureMap  key: feature index, value: feature importance score
+     * @throws org.dmlc.xgboost4j.util.XGBoostError
+     */
+    public Map<String, Integer> getFeatureScore() throws XGBoostError {
+        String[] modelInfos = getDumpInfo(false);
+        Map<String, Integer> featureScore = new HashMap<>();
+        for(String tree : modelInfos) {
+            for(String node : tree.split("\n")) {
+                String[] array = node.split("\\[");
+                if(array.length == 1) {
+                    continue;
+                }
+                String fid = array[1].split("\\]")[0];
+                fid = fid.split("<")[0];
+                if(featureScore.containsKey(fid)) {
+                    featureScore.put(fid, 1 + featureScore.get(fid));
+                }
+                else {
+                    featureScore.put(fid, 1);
+                }
+            }
+        }
+        return featureScore;
+    }
+    
+    
+    /**
+     * get importance of each feature
+     * @param featureMap file to save dumped model info
+     * @return featureMap  key: feature index, value: feature importance score
+     * @throws org.dmlc.xgboost4j.util.XGBoostError
+     */
+    public Map<String, Integer> getFeatureScore(String featureMap) throws XGBoostError {
+        String[] modelInfos = getDumpInfo(featureMap, false);
+        Map<String, Integer> featureScore = new HashMap<>();
+        for(String tree : modelInfos) {
+            for(String node : tree.split("\n")) {
+                String[] array = node.split("\\[");
+                if(array.length == 1) {
+                    continue;
+                }
+                String fid = array[1].split("\\]")[0];
+                fid = fid.split("<")[0];
+                if(featureScore.containsKey(fid)) {
+                    featureScore.put(fid, 1 + featureScore.get(fid));
+                }
+                else {
+                    featureScore.put(fid, 1);
+                }
+            }
+        }
+        return featureScore;
+    }
+    
+    /**
+     * transfer DMatrix array to handle array (used for native functions)
+     * @param dmatrixs
+     * @return handle array for input dmatrixs
+     */
+    private static long[] dMatrixs2handles(DMatrix[] dmatrixs) {
+        long[] handles = new long[dmatrixs.length];
+        for(int i=0; i<dmatrixs.length; i++) {
+            handles[i] = dmatrixs[i].getHandle();
+        }
+        return handles;
+    }
+    
+    @Override
+    protected void finalize() {
+        delete();
+    }
+    
+    public synchronized void delete() {
+        if(handle != 0l) {
+            XgboostJNI.XGBoosterFree(handle);
+            handle=0;
+        }
+    }
+}

java/xgboost4j/src/main/java/org/dmlc/xgboost4j/DMatrix.java

@@ -0,0 +1,263 @@
+/*
+ Copyright (c) 2014 by Contributors 
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+    
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ */
+package org.dmlc.xgboost4j;
+
+import java.io.IOException;
+import org.apache.commons.logging.Log;
+import org.apache.commons.logging.LogFactory;
+import org.dmlc.xgboost4j.util.ErrorHandle;
+import org.dmlc.xgboost4j.util.XGBoostError;
+import org.dmlc.xgboost4j.util.Initializer;
+import org.dmlc.xgboost4j.wrapper.XgboostJNI;
+
+/**
+ * DMatrix for xgboost, similar to the python wrapper xgboost.py
+ * @author hzx
+ */
+public class DMatrix {
+    private static final Log logger = LogFactory.getLog(DMatrix.class);    
+    long handle = 0;
+    
+    //load native library
+    static {
+        try {
+            Initializer.InitXgboost();
+        } catch (IOException ex) {
+            logger.error("load native library failed.");
+            logger.error(ex);
+        }
+    }
+    
+    /**
+     * sparse matrix type (CSR or CSC)
+     */
+    public static enum SparseType {
+        CSR,
+        CSC;
+    }
+    
+    /**
+     *  init DMatrix from file (svmlight format)
+     * @param dataPath 
+     * @throws org.dmlc.xgboost4j.util.XGBoostError 
+     */
+    public DMatrix(String dataPath) throws XGBoostError {
+        if(dataPath == null) {
+            throw new NullPointerException("dataPath: null");
+        }
+        long[] out = new long[1];
+        ErrorHandle.checkCall(XgboostJNI.XGDMatrixCreateFromFile(dataPath, 1, out));
+        handle = out[0];
+    }
+    
+    /**
+     * create DMatrix from sparse matrix
+     * @param headers index to headers (rowHeaders for CSR or colHeaders for CSC)
+     * @param indices Indices (colIndexs for CSR or rowIndexs for CSC)
+     * @param data non zero values (sequence by row for CSR or by col for CSC)
+     * @param st sparse matrix type (CSR or CSC)
+     * @throws org.dmlc.xgboost4j.util.XGBoostError
+     */
+    public DMatrix(long[] headers, int[] indices, float[] data, SparseType st) throws XGBoostError {
+        long[] out = new long[1];
+        if(st == SparseType.CSR) {
+            ErrorHandle.checkCall(XgboostJNI.XGDMatrixCreateFromCSR(headers, indices, data, out));
+        }
+        else if(st == SparseType.CSC) {
+            ErrorHandle.checkCall(XgboostJNI.XGDMatrixCreateFromCSC(headers, indices, data, out));
+        }
+        else {
+            throw new UnknownError("unknow sparsetype");
+        }
+        handle = out[0];
+    }
+    
+   /**
+     * create DMatrix from dense matrix
+     * @param data data values
+     * @param nrow number of rows
+     * @param ncol number of columns
+     * @throws org.dmlc.xgboost4j.util.XGBoostError
+     */
+    public DMatrix(float[] data, int nrow, int ncol) throws XGBoostError {
+        long[] out = new long[1];
+        ErrorHandle.checkCall(XgboostJNI.XGDMatrixCreateFromMat(data, nrow, ncol, 0.0f, out));
+        handle = out[0];
+    }
+    
+    /**
+     * used for DMatrix slice
+     * @param handle 
+     */
+    private DMatrix(long handle) {
+        this.handle = handle;
+    }
+    
+    
+    
+    /**
+     * set label of dmatrix
+     * @param labels 
+     */
+    public void setLabel(float[] labels) throws XGBoostError {
+        ErrorHandle.checkCall(XgboostJNI.XGDMatrixSetFloatInfo(handle, "label", labels));
+    }
+    
+    /**
+     * set weight of each instance
+     * @param weights 
+     * @throws org.dmlc.xgboost4j.util.XGBoostError 
+     */
+    public void setWeight(float[] weights) throws XGBoostError {
+        ErrorHandle.checkCall(XgboostJNI.XGDMatrixSetFloatInfo(handle, "weight", weights));
+    }
+    
+    /**
+     * if specified, xgboost will start from this init margin
+     * can be used to specify initial prediction to boost from
+     * @param baseMargin 
+     * @throws org.dmlc.xgboost4j.util.XGBoostError 
+     */
+    public void setBaseMargin(float[] baseMargin) throws XGBoostError {
+        ErrorHandle.checkCall(XgboostJNI.XGDMatrixSetFloatInfo(handle, "base_margin", baseMargin));
+    }
+    
+    /**
+     * if specified, xgboost will start from this init margin
+     * can be used to specify initial prediction to boost from
+     * @param baseMargin 
+     * @throws org.dmlc.xgboost4j.util.XGBoostError 
+     */
+    public void setBaseMargin(float[][] baseMargin) throws XGBoostError {
+        float[] flattenMargin = flatten(baseMargin);
+        setBaseMargin(flattenMargin);
+    }
+    
+    /**
+     * Set group sizes of DMatrix (used for ranking)
+     * @param group 
+     * @throws org.dmlc.xgboost4j.util.XGBoostError 
+     */
+    public void setGroup(int[] group) throws XGBoostError {
+        ErrorHandle.checkCall(XgboostJNI.XGDMatrixSetGroup(handle, group));
+    }
+    
+    private float[] getFloatInfo(String field) throws XGBoostError {
+        float[][] infos = new float[1][];
+        ErrorHandle.checkCall(XgboostJNI.XGDMatrixGetFloatInfo(handle, field, infos));
+        return infos[0];
+    }
+    
+    private int[] getIntInfo(String field) throws XGBoostError {
+        int[][] infos = new int[1][];
+        ErrorHandle.checkCall(XgboostJNI.XGDMatrixGetUIntInfo(handle, field, infos));
+        return infos[0];
+    }
+    
+    /**
+     * get label values
+     * @return label
+     * @throws org.dmlc.xgboost4j.util.XGBoostError
+     */
+    public float[] getLabel() throws XGBoostError {
+        return getFloatInfo("label");
+    }
+    
+    /**
+     * get weight of the DMatrix
+     * @return weights
+     * @throws org.dmlc.xgboost4j.util.XGBoostError
+     */
+    public float[] getWeight() throws XGBoostError {
+        return getFloatInfo("weight");
+    }
+    
+    /**
+     * get base margin of the DMatrix
+     * @return base margin
+     * @throws org.dmlc.xgboost4j.util.XGBoostError
+     */
+    public float[] getBaseMargin() throws XGBoostError {
+        return getFloatInfo("base_margin");
+    }
+    
+    /**
+     * Slice the DMatrix and return a new DMatrix that only contains `rowIndex`.
+     * @param rowIndex
+     * @return sliced new DMatrix
+     * @throws org.dmlc.xgboost4j.util.XGBoostError
+     */
+    public DMatrix slice(int[] rowIndex) throws XGBoostError {
+        long[] out = new long[1];
+        ErrorHandle.checkCall(XgboostJNI.XGDMatrixSliceDMatrix(handle, rowIndex, out));
+        long sHandle = out[0];
+        DMatrix sMatrix = new DMatrix(sHandle);
+        return sMatrix;
+    }
+    
+    /**
+     * get the row number of DMatrix
+     * @return number of rows
+     * @throws org.dmlc.xgboost4j.util.XGBoostError
+     */
+    public long rowNum() throws XGBoostError {
+        long[] rowNum = new long[1];
+        ErrorHandle.checkCall(XgboostJNI.XGDMatrixNumRow(handle,rowNum));
+        return rowNum[0];
+    }
+    
+    /**
+     * save DMatrix to filePath
+     * @param filePath 
+     */
+    public void saveBinary(String filePath) {
+        XgboostJNI.XGDMatrixSaveBinary(handle, filePath, 1);
+    }
+    
+    public long getHandle() {
+        return handle;
+    }
+    
+    /**
+     * flatten a mat to array
+     * @param mat
+     * @return 
+     */
+    private static float[] flatten(float[][] mat) {
+        int size = 0;
+        for (float[] array : mat) size += array.length;
+        float[] result = new float[size];
+        int pos = 0;
+        for (float[] ar : mat) {
+            System.arraycopy(ar, 0, result, pos, ar.length);
+            pos += ar.length;
+        }
+        
+        return result;
+    }
+    
+    @Override
+    protected void finalize() {
+        delete();
+    }
+    
+    public synchronized void delete() {
+        if(handle != 0) {
+            XgboostJNI.XGDMatrixFree(handle);
+            handle = 0;
+        }
+    }
+}

java/xgboost4j/src/main/java/org/dmlc/xgboost4j/IEvaluation.java

@@ -0,0 +1,36 @@
+/*
+ Copyright (c) 2014 by Contributors 
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+    
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ */
+package org.dmlc.xgboost4j;
+
+/**
+ *  interface for customized evaluation
+ * @author hzx
+ */
+public interface IEvaluation {    
+    /**
+     * get evaluate metric
+     * @return evalMetric
+     */
+    public abstract String getMetric();
+    
+    /**
+     * evaluate with predicts and data
+     * @param predicts
+     * @param dmat
+     * @return 
+     */
+    public abstract float eval(float[][] predicts, DMatrix dmat);
+}

java/xgboost4j/src/main/java/org/dmlc/xgboost4j/IObjective.java

@@ -0,0 +1,32 @@
+/*
+ Copyright (c) 2014 by Contributors 
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+    
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ */
+package org.dmlc.xgboost4j;
+
+import java.util.List;
+
+/**
+ * interface for customize Object function
+ * @author hzx
+ */
+public interface IObjective {
+    /**
+     * user define objective function, return gradient and second order gradient
+     * @param predicts untransformed margin predicts
+     * @param dtrain training data
+     * @return List with two float array, correspond to first order grad and second order grad 
+     */
+    public abstract List<float[]> getGradient(float[][] predicts, DMatrix dtrain);
+}

java/xgboost4j/src/main/java/org/dmlc/xgboost4j/util/CVPack.java

@@ -0,0 +1,89 @@
+/*
+ Copyright (c) 2014 by Contributors 
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+    
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ */
+package org.dmlc.xgboost4j.util;
+
+import java.util.Map;
+import org.dmlc.xgboost4j.IEvaluation;
+import org.dmlc.xgboost4j.Booster;
+import org.dmlc.xgboost4j.DMatrix;
+import org.dmlc.xgboost4j.IObjective;
+
+/**
+ * cross validation package for xgb
+ * @author hzx
+ */
+public class CVPack {
+    DMatrix dtrain;
+    DMatrix dtest;
+    DMatrix[] dmats;
+    String[] names;
+    Booster booster;
+    
+    /**
+     * create an cross validation package
+     * @param dtrain train data
+     * @param dtest test data
+     * @param params parameters
+     * @throws org.dmlc.xgboost4j.util.XGBoostError
+     */
+    public CVPack(DMatrix dtrain, DMatrix dtest, Iterable<Map.Entry<String, Object>> params) throws XGBoostError {
+        dmats = new DMatrix[] {dtrain, dtest};
+        booster = new Booster(params, dmats);
+        names = new String[] {"train", "test"};
+        this.dtrain = dtrain;
+        this.dtest = dtest;
+    }
+    
+    /**
+     * update one iteration
+     * @param iter iteration num
+     * @throws org.dmlc.xgboost4j.util.XGBoostError
+     */
+    public void update(int iter) throws XGBoostError {
+        booster.update(dtrain, iter);
+    }
+    
+    /**
+     * update one iteration
+     * @param iter iteration num
+     * @param obj customized objective
+     * @throws org.dmlc.xgboost4j.util.XGBoostError
+     */
+    public void update(int iter, IObjective obj) throws XGBoostError {
+        booster.update(dtrain, iter, obj);
+    }
+    
+    /**
+     * evaluation 
+     * @param iter iteration num
+     * @return 
+     * @throws org.dmlc.xgboost4j.util.XGBoostError 
+     */
+    public String eval(int iter) throws XGBoostError {
+        return booster.evalSet(dmats, names, iter);
+    }
+    
+    /**
+     * evaluation 
+     * @param iter iteration num
+     * @param eval customized eval
+     * @return 
+     * @throws org.dmlc.xgboost4j.util.XGBoostError 
+     */
+    public String eval(int iter, IEvaluation eval) throws XGBoostError {
+        return booster.evalSet(dmats, names, iter, eval);
+    }
+}

java/xgboost4j/src/main/java/org/dmlc/xgboost4j/util/ErrorHandle.java

@@ -0,0 +1,50 @@
+/*
+ Copyright (c) 2014 by Contributors 
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+    
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ */
+package org.dmlc.xgboost4j.util;
+
+import java.io.IOException;
+import org.apache.commons.logging.Log;
+import org.apache.commons.logging.LogFactory;
+import org.dmlc.xgboost4j.wrapper.XgboostJNI;
+
+/**
+ * error handle for Xgboost
+ * @author hzx
+ */
+public class ErrorHandle {
+    private static final Log logger = LogFactory.getLog(ErrorHandle.class);
+    
+    //load native library
+    static {
+        try {
+            Initializer.InitXgboost();
+        } catch (IOException ex) {
+            logger.error("load native library failed.");
+            logger.error(ex);
+        }
+    }
+    
+    /**
+     * check the return value of C API
+     * @param ret return valud of xgboostJNI C API call
+     * @throws org.dmlc.xgboost4j.util.XGBoostError
+     */
+    public static void checkCall(int ret) throws XGBoostError {
+        if(ret != 0) {
+            throw new XGBoostError(XgboostJNI.XGBGetLastError());
+        }
+    }
+}

java/xgboost4j/src/main/java/org/dmlc/xgboost4j/util/Initializer.java

@@ -0,0 +1,92 @@
+/*
+ Copyright (c) 2014 by Contributors 
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+    
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ */
+package org.dmlc.xgboost4j.util;
+
+import java.io.IOException;
+import java.lang.reflect.Field;
+
+import org.apache.commons.logging.Log;
+import org.apache.commons.logging.LogFactory;
+
+/**
+ * class to load native library
+ * @author hzx
+ */
+public class Initializer {
+    private static final Log logger = LogFactory.getLog(Initializer.class);
+    
+    static boolean initialized = false;
+    public static final String nativePath = "./lib";
+    public static final String nativeResourcePath = "/lib/";
+    public static final String[] libNames = new String[] {"xgboostjavawrapper"};
+    
+    public static synchronized void InitXgboost() throws IOException {
+        if(initialized == false) {
+            for(String libName: libNames) {
+                smartLoad(libName);
+            }
+            initialized = true;
+        }
+    }
+    
+    /**
+     * load native library, this method will first try to load library from java.library.path, then try to load library in jar package.
+     * @param libName
+     * @throws IOException 
+     */
+    private static void smartLoad(String libName) throws IOException {
+        addNativeDir(nativePath);
+        try {
+             System.loadLibrary(libName);
+         } 
+         catch (UnsatisfiedLinkError e) {
+             try {
+                 NativeUtils.loadLibraryFromJar(nativeResourcePath + System.mapLibraryName(libName));
+             }
+             catch (IOException e1) {
+                 throw e1;
+             }
+         }
+    }
+    
+    /**
+     * add libPath to java.library.path, then native library in libPath would be load properly
+     * @param libPath
+     * @throws IOException 
+     */
+    public static void addNativeDir(String libPath) throws IOException {
+        try {
+            Field field = ClassLoader.class.getDeclaredField("usr_paths");
+            field.setAccessible(true);
+            String[] paths = (String[]) field.get(null);
+            for (String path : paths) {
+                if (libPath.equals(path)) {
+                    return;
+                }
+            }
+            String[] tmp = new String[paths.length+1];
+            System.arraycopy(paths,0,tmp,0,paths.length);
+            tmp[paths.length] = libPath;
+            field.set(null, tmp);
+        } catch (IllegalAccessException  e) {
+            logger.error(e.getMessage());
+            throw new IOException("Failed to get permissions to set library path"); 
+        } catch (NoSuchFieldException e) {
+            logger.error(e.getMessage());
+            throw new IOException("Failed to get field handle to set library path"); 
+        }
+    }
+}

java/xgboost4j/src/main/java/org/dmlc/xgboost4j/util/NativeUtils.java

@@ -0,0 +1,109 @@
+/*
+ Copyright (c) 2014 by Contributors 
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+    
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ */
+package org.dmlc.xgboost4j.util;
+
+import java.io.File;
+import java.io.FileNotFoundException;
+import java.io.FileOutputStream;
+import java.io.IOException;
+import java.io.InputStream;
+import java.io.OutputStream;
+
+
+/**
+ * Simple library class for working with JNI (Java Native Interface)
+ * 
+ * @see http://adamheinrich.com/2012/how-to-load-native-jni-library-from-jar
+ *
+ * @author Adam Heirnich <adam@adamh.cz>, http://www.adamh.cz
+ */
+public class NativeUtils {
+ 
+    /**
+     * Private constructor - this class will never be instanced
+     */
+    private NativeUtils() {
+    }
+ 
+    /**
+     * Loads library from current JAR archive
+     * 
+     * The file from JAR is copied into system temporary directory and then loaded. The temporary file is deleted after exiting.
+     * Method uses String as filename because the pathname is "abstract", not system-dependent.
+     * 
+     * @param path The filename inside JAR as absolute path (beginning with '/'), e.g. /package/File.ext
+     * @throws IOException If temporary file creation or read/write operation fails
+     * @throws IllegalArgumentException If source file (param path) does not exist
+     * @throws IllegalArgumentException If the path is not absolute or if the filename is shorter than three characters (restriction of {@see File#createTempFile(java.lang.String, java.lang.String)}).
+     */
+    public static void loadLibraryFromJar(String path) throws IOException {
+ 
+        if (!path.startsWith("/")) {
+            throw new IllegalArgumentException("The path has to be absolute (start with '/').");
+        }
+ 
+        // Obtain filename from path
+        String[] parts = path.split("/");
+        String filename = (parts.length > 1) ? parts[parts.length - 1] : null;
+ 
+        // Split filename to prexif and suffix (extension)
+        String prefix = "";
+        String suffix = null;
+        if (filename != null) {
+            parts = filename.split("\\.", 2);
+            prefix = parts[0];
+            suffix = (parts.length > 1) ? "."+parts[parts.length - 1] : null; // Thanks, davs! :-)
+        }
+ 
+        // Check if the filename is okay
+        if (filename == null || prefix.length() < 3) {
+            throw new IllegalArgumentException("The filename has to be at least 3 characters long.");
+        }
+ 
+        // Prepare temporary file
+        File temp = File.createTempFile(prefix, suffix);
+        temp.deleteOnExit();
+ 
+        if (!temp.exists()) {
+            throw new FileNotFoundException("File " + temp.getAbsolutePath() + " does not exist.");
+        }
+ 
+        // Prepare buffer for data copying
+        byte[] buffer = new byte[1024];
+        int readBytes;
+ 
+        // Open and check input stream
+        InputStream is = NativeUtils.class.getResourceAsStream(path);
+        if (is == null) {
+            throw new FileNotFoundException("File " + path + " was not found inside JAR.");
+        }
+ 
+        // Open output stream and copy data between source file in JAR and the temporary file
+        OutputStream os = new FileOutputStream(temp);
+        try {
+            while ((readBytes = is.read(buffer)) != -1) {
+                os.write(buffer, 0, readBytes);
+            }
+        } finally {
+            // If read/write fails, close streams safely before throwing an exception
+            os.close();
+            is.close();
+        }
+ 
+        // Finally, load the library
+        System.load(temp.getAbsolutePath());
+    }
+}

java/xgboost4j/src/main/java/org/dmlc/xgboost4j/util/Trainer.java

@@ -0,0 +1,235 @@
+/*
+ Copyright (c) 2014 by Contributors 
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+    
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ */
+package org.dmlc.xgboost4j.util;
+
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Map.Entry;
+import org.apache.commons.logging.Log;
+import org.apache.commons.logging.LogFactory;
+import org.dmlc.xgboost4j.IEvaluation;
+import org.dmlc.xgboost4j.Booster;
+import org.dmlc.xgboost4j.DMatrix;
+import org.dmlc.xgboost4j.IObjective;
+
+
+/**
+ * trainer for xgboost
+ * @author hzx
+ */
+public class Trainer {
+    private static final Log logger = LogFactory.getLog(Trainer.class);
+    
+    /**
+     * Train a booster with given parameters.
+     * @param params Booster params.
+     * @param dtrain Data to be trained.
+     * @param round Number of boosting iterations.
+     * @param watchs a group of items to be evaluated during training, this allows user to watch performance on the validation set.
+     * @param obj customized objective (set to null if not used)
+     * @param eval customized evaluation (set to null if not used)
+     * @return trained booster
+     */
+    public static Booster train(Iterable<Entry<String, Object>> params, DMatrix dtrain, int round, 
+            Iterable<Entry<String, DMatrix>> watchs, IObjective obj, IEvaluation eval) throws XGBoostError {
+        
+        //collect eval matrixs
+        String[] evalNames;
+        DMatrix[] evalMats;
+        List<String> names = new ArrayList<>();
+        List<DMatrix> mats = new ArrayList<>();
+        
+        for(Entry<String, DMatrix> evalEntry : watchs) {
+            names.add(evalEntry.getKey());
+            mats.add(evalEntry.getValue());
+        }
+        
+        evalNames = names.toArray(new String[names.size()]);
+        evalMats = mats.toArray(new DMatrix[mats.size()]);
+        
+        //collect all data matrixs
+        DMatrix[] allMats;
+        if(evalMats!=null && evalMats.length>0) {
+            allMats = new DMatrix[evalMats.length+1];
+            allMats[0] = dtrain;
+            System.arraycopy(evalMats, 0, allMats, 1, evalMats.length);
+        }
+        else {
+            allMats = new DMatrix[1];
+            allMats[0] = dtrain;
+        }
+        
+        //initialize booster
+        Booster booster = new Booster(params, allMats);
+        
+        //begin to train
+        for(int iter=0; iter<round; iter++) {
+            if(obj != null) {
+                booster.update(dtrain, iter, obj);
+            } else {
+                booster.update(dtrain, iter);
+            }
+            
+            //evaluation
+            if(evalMats!=null && evalMats.length>0) {
+                String evalInfo;
+                if(eval != null) {
+                    evalInfo = booster.evalSet(evalMats, evalNames, iter, eval);
+                }
+                else {
+                    evalInfo = booster.evalSet(evalMats, evalNames, iter);
+                }
+                logger.info(evalInfo);
+            }
+        }
+        return booster;
+    }
+    
+    /**
+     * Cross-validation with given paramaters.
+     * @param params Booster params.
+     * @param data  Data to be trained.
+     * @param round Number of boosting iterations.
+     * @param nfold Number of folds in CV.
+     * @param metrics Evaluation metrics to be watched in CV.
+     * @param obj customized objective (set to null if not used)
+     * @param eval customized evaluation (set to null if not used)
+     * @return evaluation history
+     */
+    public static String[] crossValiation(Iterable<Entry<String, Object>> params, DMatrix data, int round, int nfold, String[] metrics, IObjective obj, IEvaluation eval) throws XGBoostError {
+        CVPack[] cvPacks = makeNFold(data, nfold, params, metrics);
+        String[] evalHist = new String[round];
+        String[] results = new String[cvPacks.length];
+        for(int i=0; i<round; i++) {
+            for(CVPack cvPack : cvPacks) {
+                if(obj != null) {
+                    cvPack.update(i, obj);
+                } 
+                else {
+                    cvPack.update(i);
+                }
+            }
+            
+            for(int j=0; j<cvPacks.length; j++) {
+                if(eval != null) {
+                    results[j] = cvPacks[j].eval(i, eval);
+                }
+                else {
+                    results[j] = cvPacks[j].eval(i);
+                }
+            }
+            
+            evalHist[i] = aggCVResults(results);
+            logger.info(evalHist[i]);
+        }
+        return evalHist;
+    }
+    
+    /**
+     * make an n-fold array of CVPack from random indices
+     * @param data original data
+     * @param nfold num of folds
+     * @param params booster parameters
+     * @param evalMetrics Evaluation metrics
+     * @return CV package array
+     */
+    public static CVPack[] makeNFold(DMatrix data, int nfold, Iterable<Entry<String, Object>> params, String[] evalMetrics) throws XGBoostError {
+        List<Integer> samples = genRandPermutationNums(0, (int) data.rowNum());
+        int step = samples.size()/nfold;
+        int[] testSlice = new int[step];
+        int[] trainSlice = new int[samples.size()-step];
+        int testid, trainid;
+        CVPack[] cvPacks = new CVPack[nfold];
+        for(int i=0; i<nfold; i++) {
+            testid = 0;
+            trainid = 0;
+            for(int j=0; j<samples.size(); j++) {
+                if(j>(i*step) && j<(i*step+step) && testid<step) {
+                    testSlice[testid] = samples.get(j);
+                    testid++;
+                }
+                else{
+                    if(trainid<samples.size()-step) {
+                        trainSlice[trainid] = samples.get(j);
+                        trainid++;
+                    }
+                    else {
+                        testSlice[testid] = samples.get(j);
+                        testid++;
+                    }
+                }
+            }
+            
+            DMatrix dtrain = data.slice(trainSlice);
+            DMatrix dtest = data.slice(testSlice);
+            CVPack cvPack = new CVPack(dtrain, dtest, params);
+            //set eval types
+            if(evalMetrics!=null) {
+                for(String type : evalMetrics) {
+                    cvPack.booster.setParam("eval_metric", type);
+                }
+            }
+            cvPacks[i] = cvPack;
+        }
+        
+        return cvPacks;
+    }
+    
+    private static List<Integer> genRandPermutationNums(int start, int end) {
+        List<Integer> samples = new ArrayList<>();
+        for(int i=start; i<end; i++) {
+            samples.add(i);
+        }
+        Collections.shuffle(samples);
+        return samples;
+    }
+    
+    /**
+     * Aggregate cross-validation results.
+     * @param results eval info from each data sample
+     * @return cross-validation eval info
+     */
+    public static String aggCVResults(String[] results) {
+        Map<String, List<Float> > cvMap = new HashMap<>();
+        String aggResult = results[0].split("\t")[0];
+        for(String result : results) {
+            String[] items = result.split("\t");
+            for(int i=1; i<items.length; i++) {
+                String[] tup = items[i].split(":");
+                String key = tup[0];
+                Float value = Float.valueOf(tup[1]);
+                if(!cvMap.containsKey(key)) {
+                    cvMap.put(key, new ArrayList<Float>());
+                }
+                cvMap.get(key).add(value);
+            }
+        }
+        
+        for(String key : cvMap.keySet()) {
+            float value = 0f;
+            for(Float tvalue : cvMap.get(key)) {
+                value += tvalue;
+            }
+            value /= cvMap.get(key).size();
+            aggResult += String.format("\tcv-%s:%f", key, value);
+        }
+        
+        return aggResult;
+    }
+}

java/xgboost4j/src/main/java/org/dmlc/xgboost4j/util/XGBoostError.java

@@ -0,0 +1,26 @@
+/*
+ Copyright (c) 2014 by Contributors 
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+    
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ */
+package org.dmlc.xgboost4j.util;
+
+/**
+ * custom error class for xgboost
+ * @author hzx
+ */
+public class XGBoostError extends Exception{
+    public XGBoostError(String message) {
+        super(message);
+    }
+}

java/xgboost4j/src/main/java/org/dmlc/xgboost4j/wrapper/XgboostJNI.java

@@ -0,0 +1,50 @@
+/*
+ Copyright (c) 2014 by Contributors 
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+    
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ */
+package org.dmlc.xgboost4j.wrapper;
+
+/**
+ * xgboost jni wrapper functions for xgboost_wrapper.h
+ * change 2015-7-6: *use a long[] (length=1) as container of handle to get the output DMatrix or Booster
+ * @author hzx
+ */
+public class XgboostJNI {
+  public final static native String XGBGetLastError();
+  public final static native int XGDMatrixCreateFromFile(String fname, int silent, long[] out);
+  public final static native int XGDMatrixCreateFromCSR(long[] indptr, int[] indices, float[] data, long[] out);
+  public final static native int XGDMatrixCreateFromCSC(long[] colptr, int[] indices, float[] data, long[] out);
+  public final static native int XGDMatrixCreateFromMat(float[] data, int nrow, int ncol, float missing, long[] out);
+  public final static native int XGDMatrixSliceDMatrix(long handle, int[] idxset, long[] out);
+  public final static native int XGDMatrixFree(long handle);
+  public final static native int XGDMatrixSaveBinary(long handle, String fname, int silent);
+  public final static native int XGDMatrixSetFloatInfo(long handle, String field, float[] array);
+  public final static native int XGDMatrixSetUIntInfo(long handle, String field, int[] array);
+  public final static native int XGDMatrixSetGroup(long handle, int[] group);
+  public final static native int XGDMatrixGetFloatInfo(long handle, String field, float[][] info);
+  public final static native int XGDMatrixGetUIntInfo(long handle, String filed, int[][] info);
+  public final static native int XGDMatrixNumRow(long handle, long[] row);
+  public final static native int XGBoosterCreate(long[] handles, long[] out);
+  public final static native int XGBoosterFree(long handle);
+  public final static native int XGBoosterSetParam(long handle, String name, String value);
+  public final static native int XGBoosterUpdateOneIter(long handle, int iter, long dtrain);
+  public final static native int XGBoosterBoostOneIter(long handle, long dtrain, float[] grad, float[] hess);
+  public final static native int XGBoosterEvalOneIter(long handle, int iter, long[] dmats, String[] evnames, String[] eval_info);
+  public final static native int XGBoosterPredict(long handle, long dmat, int option_mask, long ntree_limit, float[][] predicts);
+  public final static native int XGBoosterLoadModel(long handle, String fname);
+  public final static native int XGBoosterSaveModel(long handle, String fname);
+  public final static native int XGBoosterLoadModelFromBuffer(long handle, long buf, long len);
+  public final static native int XGBoosterGetModelRaw(long handle, String[] out_string);
+  public final static native int XGBoosterDumpModel(long handle, String fmap, int with_stats, String[][] out_strings);
+}

java/xgboost4j/src/test/java/org/dmlc/xgboost4j/BoosterTest.java

@@ -0,0 +1,108 @@
+/*
+ Copyright (c) 2014 by Contributors 
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+    
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ */
+package org.dmlc.xgboost4j;
+
+import java.util.AbstractMap;
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Map.Entry;
+import junit.framework.TestCase;
+import org.apache.commons.logging.Log;
+import org.apache.commons.logging.LogFactory;
+import org.dmlc.xgboost4j.util.Trainer;
+import org.dmlc.xgboost4j.util.XGBoostError;
+import org.junit.Test;
+
+/**
+ * test cases for Booster
+ * @author hzx
+ */
+public class BoosterTest {
+    public static class EvalError implements IEvaluation {
+        private static final Log logger = LogFactory.getLog(EvalError.class);
+        
+        String evalMetric = "custom_error";
+        
+        public EvalError() {
+        }
+        
+        @Override
+        public String getMetric() {
+            return evalMetric;
+        }
+
+        @Override
+        public float eval(float[][] predicts, DMatrix dmat) {
+            float error = 0f;
+            float[] labels;
+            try {
+                labels = dmat.getLabel();
+            } catch (XGBoostError ex) {
+                logger.error(ex);
+                return -1f;
+            }
+            int nrow = predicts.length;
+            for(int i=0; i<nrow; i++) {
+                if(labels[i]==0f && predicts[i][0]>0) {
+                    error++;
+                }
+                else if(labels[i]==1f && predicts[i][0]<=0) {
+                    error++;
+                }
+            }
+            
+            return error/labels.length;
+        }
+    }
+    
+    @Test
+    public void testBoosterBasic() throws XGBoostError {
+        DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
+        DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
+        
+        //set params
+        Map<String, Object> paramMap = new HashMap<String, Object>() {
+            {
+                put("eta", 1.0);
+                put("max_depth", 2);
+                put("silent", 1);
+                put("objective", "binary:logistic");
+            }
+        };
+        Iterable<Entry<String, Object>> param = paramMap.entrySet();
+        
+        //set watchList
+        List<Entry<String, DMatrix>> watchs =  new ArrayList<>();
+        watchs.add(new AbstractMap.SimpleEntry<>("train", trainMat));
+        watchs.add(new AbstractMap.SimpleEntry<>("test", testMat));
+
+         //set round
+        int round = 2;
+        
+        //train a boost model
+        Booster booster = Trainer.train(param, trainMat, round, watchs, null, null);
+        
+         //predict raw output
+        float[][] predicts = booster.predict(testMat, true);
+        
+        //eval
+        IEvaluation eval = new EvalError();
+        //error must be less than 0.1
+        TestCase.assertTrue(eval.eval(predicts, testMat)<0.1f);
+    }
+}

java/xgboost4j/src/test/java/org/dmlc/xgboost4j/DMatrixTest.java

@@ -0,0 +1,102 @@
+/*
+ Copyright (c) 2014 by Contributors 
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+    
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ */
+package org.dmlc.xgboost4j;
+
+import java.util.Arrays;
+import java.util.Random;
+import junit.framework.TestCase;
+import org.dmlc.xgboost4j.util.XGBoostError;
+import org.junit.Test;
+
+/**
+ * test cases for DMatrix
+ * @author hzx
+ */
+public class DMatrixTest {
+    
+    @Test
+    public void testCreateFromFile() throws XGBoostError {
+         //create DMatrix from file
+        DMatrix dmat = new DMatrix("../../demo/data/agaricus.txt.test");
+        //get label
+        float[] labels = dmat.getLabel();
+        //check length
+        TestCase.assertTrue(dmat.rowNum()==labels.length);
+        //set weights
+        float[] weights = Arrays.copyOf(labels, labels.length);
+        dmat.setWeight(weights);
+        float[] dweights = dmat.getWeight();
+        TestCase.assertTrue(Arrays.equals(weights, dweights));
+    }
+    
+    @Test
+    public void testCreateFromCSR() throws XGBoostError {
+        //create Matrix from csr format sparse Matrix and labels
+        /**
+         * sparse matrix
+         * 1 0 2 3 0
+         * 4 0 2 3 5
+         * 3 1 2 5 0
+         */
+        float[] data = new float[] {1, 2, 3, 4, 2, 3, 5, 3, 1, 2, 5};
+        int[] colIndex = new int[] {0, 2, 3, 0, 2, 3, 4, 0, 1, 2, 3};
+        long[] rowHeaders = new long[] {0, 3, 7, 11};
+        DMatrix dmat1 = new DMatrix(rowHeaders, colIndex, data, DMatrix.SparseType.CSR);
+        //check row num
+        System.out.println(dmat1.rowNum());
+        TestCase.assertTrue(dmat1.rowNum()==3);
+        //test set label
+        float[] label1 = new float[] {1, 0, 1};
+        dmat1.setLabel(label1);
+        float[] label2  = dmat1.getLabel();
+        TestCase.assertTrue(Arrays.equals(label1, label2));
+    }
+    
+    @Test
+    public void testCreateFromDenseMatrix() throws XGBoostError {
+         //create DMatrix from 10*5 dense matrix
+        int nrow = 10;
+        int ncol = 5;
+        float[] data0 = new float[nrow*ncol];
+        //put random nums
+        Random random = new Random();
+        for(int i=0; i<nrow*ncol; i++) {
+            data0[i] = random.nextFloat();
+        }
+        
+        //create label
+        float[] label0 = new float[nrow];
+        for(int i=0; i<nrow; i++) {
+            label0[i] = random.nextFloat();
+        }
+        
+        DMatrix dmat0 = new DMatrix(data0, nrow, ncol);
+        dmat0.setLabel(label0);
+        
+        //check
+        TestCase.assertTrue(dmat0.rowNum()==10);
+        TestCase.assertTrue(dmat0.getLabel().length==10);
+        
+        //set weights for each instance
+        float[] weights = new float[nrow];
+        for(int i=0; i<nrow; i++) {
+            weights[i] = random.nextFloat();
+        }
+        dmat0.setWeight(weights);
+        
+        TestCase.assertTrue(Arrays.equals(weights, dmat0.getWeight()));
+    }
+}

java/xgboost4j_wrapper.cpp

@@ -0,0 +1,680 @@
+/*
+ Copyright (c) 2014 by Contributors 
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+    
+ http://www.apache.org/licenses/LICENSE-2.0
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ */
+
+#include <jni.h>
+#include "../wrapper/xgboost_wrapper.h"
+#include "xgboost4j_wrapper.h"
+
+JNIEXPORT jstring JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGBGetLastError
+  (JNIEnv *jenv, jclass jcls) {
+    jstring jresult = 0 ;
+    char* result = 0;
+    result = (char *)XGBGetLastError();
+    if (result) jresult = jenv->NewStringUTF((const char *)result);
+    return jresult;
+}
+
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGDMatrixCreateFromFile
+  (JNIEnv *jenv, jclass jcls, jstring jfname, jint jsilent, jlongArray jout) {
+    jint jresult = 0 ;
+    char *fname = (char *) 0 ;
+    int silent;
+    void* result[1];
+    unsigned long out[1];
+    
+    fname = (char *)jenv->GetStringUTFChars(jfname, 0);
+    
+    silent = (int)jsilent; 
+    jresult = (jint) XGDMatrixCreateFromFile((char const *)fname, silent, result);
+    
+    
+    *(void **)&out[0] = *result;
+
+    if (fname) jenv->ReleaseStringUTFChars(jfname, (const char *)fname);
+    
+    jenv->SetLongArrayRegion(jout, 0, 1, (const jlong *) out);
+    return jresult;
+}
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGDMatrixCreateFromCSR
+ * Signature: ([J[J[F)J
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGDMatrixCreateFromCSR
+  (JNIEnv *jenv, jclass jcls, jlongArray jindptr, jintArray jindices, jfloatArray jdata, jlongArray jout) {
+    jint jresult = 0 ;
+    bst_ulong nindptr ;
+    bst_ulong nelem;
+    void *result[1];
+    unsigned long out[1];
+  
+    jlong* indptr = jenv->GetLongArrayElements(jindptr, 0);
+    jint* indices = jenv->GetIntArrayElements(jindices, 0);
+    jfloat* data = jenv->GetFloatArrayElements(jdata, 0); 
+    nindptr = (bst_ulong)jenv->GetArrayLength(jindptr); 
+    nelem = (bst_ulong)jenv->GetArrayLength(jdata); 
+
+    jresult = (jint) XGDMatrixCreateFromCSR((unsigned long const *)indptr, (unsigned int const *)indices, (float const *)data, nindptr, nelem, result);    
+    *(void **)&out[0] = *result;
+    jenv->SetLongArrayRegion(jout, 0, 1, (const jlong *) out);
+    
+    //release
+    jenv->ReleaseLongArrayElements(jindptr, indptr, 0);
+    jenv->ReleaseIntArrayElements(jindices, indices, 0);
+    jenv->ReleaseFloatArrayElements(jdata, data, 0);
+    
+    return jresult;
+}
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGDMatrixCreateFromCSC
+ * Signature: ([J[J[F)J
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGDMatrixCreateFromCSC
+  (JNIEnv *jenv, jclass jcls, jlongArray jindptr, jintArray jindices, jfloatArray jdata, jlongArray jout) {
+    jint jresult = 0;
+    bst_ulong nindptr ;
+    bst_ulong nelem;
+    void *result[1];
+    unsigned long out[1];
+  
+    jlong* indptr = jenv->GetLongArrayElements(jindptr, NULL);
+    jint* indices = jenv->GetIntArrayElements(jindices, 0);
+    jfloat* data = jenv->GetFloatArrayElements(jdata, NULL);
+    nindptr = (bst_ulong)jenv->GetArrayLength(jindptr); 
+    nelem = (bst_ulong)jenv->GetArrayLength(jdata); 
+
+    jresult = (jint) XGDMatrixCreateFromCSC((unsigned long const *)indptr, (unsigned int const *)indices, (float const *)data, nindptr, nelem, result);  
+    *(void **)&out[0] = *result;
+    jenv->SetLongArrayRegion(jout, 0, 1, (const jlong *) out);
+    
+    //release
+    jenv->ReleaseLongArrayElements(jindptr, indptr, 0);
+    jenv->ReleaseIntArrayElements(jindices, indices, 0);
+    jenv->ReleaseFloatArrayElements(jdata, data, 0);
+    
+    return jresult;
+}
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGDMatrixCreateFromMat
+ * Signature: ([FIIF)J
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGDMatrixCreateFromMat
+  (JNIEnv *jenv, jclass jcls, jfloatArray jdata, jint jnrow, jint jncol, jfloat jmiss, jlongArray jout) {
+    jint jresult = 0 ;
+    bst_ulong nrow ;
+    bst_ulong ncol ;
+    float miss ;
+    void *result[1];
+    unsigned long out[1];
+  
+
+    jfloat* data = jenv->GetFloatArrayElements(jdata, 0);
+    nrow = (bst_ulong)jnrow; 
+    ncol = (bst_ulong)jncol; 
+    miss = (float)jmiss; 
+    
+    jresult = (jint) XGDMatrixCreateFromMat((float const *)data, nrow, ncol, miss, result);
+    *(void **)&out[0] = *result;
+    jenv->SetLongArrayRegion(jout, 0, 1, (const jlong *) out);
+  
+    //release
+    jenv->ReleaseFloatArrayElements(jdata, data, 0);
+  
+    return jresult;
+}
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGDMatrixSliceDMatrix
+ * Signature: (J[I)J
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGDMatrixSliceDMatrix
+  (JNIEnv *jenv, jclass jcls, jlong jhandle, jintArray jindexset, jlongArray jout) {
+    jint jresult = 0 ;
+    void *handle = (void *) 0 ;
+    bst_ulong len;
+    void *result[1];
+    unsigned long out[1];
+
+    jint* indexset = jenv->GetIntArrayElements(jindexset, 0);
+    handle = *(void **)&jhandle; 
+    len = (bst_ulong)jenv->GetArrayLength(jindexset); 
+
+    jresult = (jint) XGDMatrixSliceDMatrix(handle, (int const *)indexset, len, result);
+    *(void **)&out[0] = *result;
+    jenv->SetLongArrayRegion(jout, 0, 1, (const jlong *) out);
+    
+    //release
+    jenv->ReleaseIntArrayElements(jindexset, indexset, 0);
+    
+    return jresult;
+}
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGDMatrixFree
+ * Signature: (J)V
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGDMatrixFree
+  (JNIEnv *jenv, jclass jcls, jlong jhandle) {
+    jint jresult = 0;
+    void *handle = (void *) 0 ;
+    handle = *(void **)&jhandle; 
+    jresult = (jint) XGDMatrixFree(handle);
+    return jresult;
+}
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGDMatrixSaveBinary
+ * Signature: (JLjava/lang/String;I)V
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGDMatrixSaveBinary
+  (JNIEnv *jenv, jclass jcls, jlong jhandle, jstring jfname, jint jsilent) {
+    jint jresult = 0;
+    void *handle = (void *) 0 ;
+    char *fname = (char *) 0 ;
+    int silent ;
+    handle = *(void **)&jhandle; 
+    fname = 0;
+    fname = (char *)jenv->GetStringUTFChars(jfname, 0);
+
+    silent = (int)jsilent; 
+    jresult = (jint) XGDMatrixSaveBinary(handle, (char const *)fname, silent);
+    if (fname) jenv->ReleaseStringUTFChars(jfname, (const char *)fname);
+    
+    return jresult;
+}
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGDMatrixSetFloatInfo
+ * Signature: (JLjava/lang/String;[F)V
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGDMatrixSetFloatInfo
+  (JNIEnv *jenv, jclass jcls, jlong jhandle, jstring jfield, jfloatArray jarray) {
+    jint jresult = 0;
+    void *handle = (void *) 0 ;
+    char *field = (char *) 0 ;
+    bst_ulong len;
+
+
+    handle = *(void **)&jhandle; 
+
+    field = (char *)jenv->GetStringUTFChars(jfield, 0);
+
+    
+    jfloat* array = jenv->GetFloatArrayElements(jarray, NULL);
+    len = (bst_ulong)jenv->GetArrayLength(jarray); 
+    jresult = (jint) XGDMatrixSetFloatInfo(handle, (char const *)field, (float const *)array, len);
+    
+    //release
+    if (field) jenv->ReleaseStringUTFChars(jfield, (const char *)field);
+    jenv->ReleaseFloatArrayElements(jarray, array, 0);
+    
+    return jresult;
+}
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGDMatrixSetUIntInfo
+ * Signature: (JLjava/lang/String;[I)V
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGDMatrixSetUIntInfo
+  (JNIEnv *jenv, jclass jcls, jlong jhandle, jstring jfield, jintArray jarray) {
+    jint jresult = 0;
+    void *handle = (void *) 0 ;
+    char *field = (char *) 0 ;
+    bst_ulong len ;
+    handle = *(void **)&jhandle; 
+    field = 0;
+    field = (char *)jenv->GetStringUTFChars(jfield, 0);
+
+    
+    jint* array = jenv->GetIntArrayElements(jarray, NULL);
+    len = (bst_ulong)jenv->GetArrayLength(jarray);
+
+    jresult = (jint) XGDMatrixSetUIntInfo(handle, (char const *)field, (unsigned int const *)array, len);
+    //release
+    if (field) jenv->ReleaseStringUTFChars(jfield, (const char *)field);
+    jenv->ReleaseIntArrayElements(jarray, array, 0);
+    
+    return jresult;
+}
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGDMatrixSetGroup
+ * Signature: (J[I)V
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGDMatrixSetGroup
+  (JNIEnv * jenv, jclass jcls, jlong jhandle, jintArray jarray) {
+    jint jresult = 0;
+    void *handle = (void *) 0 ;
+    bst_ulong len ;
+
+    handle = *(void **)&jhandle;
+    jint* array = jenv->GetIntArrayElements(jarray, NULL);
+    len = (bst_ulong)jenv->GetArrayLength(jarray); 
+
+    jresult = (jint) XGDMatrixSetGroup(handle, (unsigned int const *)array, len);
+    
+    //release
+    jenv->ReleaseIntArrayElements(jarray, array, 0);
+    
+    return jresult;
+}
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGDMatrixGetFloatInfo
+ * Signature: (JLjava/lang/String;)[F
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGDMatrixGetFloatInfo
+  (JNIEnv *jenv, jclass jcls, jlong jhandle, jstring jfield, jobjectArray jout) {
+    jint jresult = 0;
+    void *handle = (void *) 0 ;
+    char *field = (char *) 0 ;
+    bst_ulong len[1];
+    *len = 0;
+    float *result[1];
+
+    handle = *(void **)&jhandle; 
+    field = 0;
+    if (jfield) {
+        field = (char *)jenv->GetStringUTFChars(jfield, 0);
+        if (!field) return 0;
+    }
+    
+    jresult = (jint) XGDMatrixGetFloatInfo(handle, (char const *)field, len, (const float **) result);
+ 
+    if (field) jenv->ReleaseStringUTFChars(jfield, (const char *)field);
+  
+    jsize jlen = (jsize)*len;
+    jfloatArray jarray = jenv->NewFloatArray(jlen);
+    jenv->SetFloatArrayRegion(jarray, 0, jlen, (jfloat *) *result);
+    jenv->SetObjectArrayElement(jout, 0, (jobject) jarray);
+    
+    return jresult;
+}
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGDMatrixGetUIntInfo
+ * Signature: (JLjava/lang/String;)[I
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGDMatrixGetUIntInfo
+  (JNIEnv *jenv, jclass jcls, jlong jhandle, jstring jfield, jobjectArray jout) {
+    jint jresult = 0;
+    void *handle = (void *) 0 ;
+    char *field = (char *) 0 ;
+    bst_ulong len[1];
+    *len = 0;
+    unsigned int *result[1];
+
+    handle = *(void **)&jhandle; 
+    field = (char *)jenv->GetStringUTFChars(jfield, 0);
+    
+    jresult = (jint) XGDMatrixGetUIntInfo(handle, (char const *)field, len, (const unsigned int **) result);
+ 
+    if (field) jenv->ReleaseStringUTFChars(jfield, (const char *)field);
+  
+    jsize jlen = (jsize)*len;
+    jintArray jarray = jenv->NewIntArray(jlen);
+    jenv->SetIntArrayRegion(jarray, 0, jlen, (jint *) *result);
+    jenv->SetObjectArrayElement(jout, 0, jarray);
+    return jresult;
+}
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGDMatrixNumRow
+ * Signature: (J)J
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGDMatrixNumRow
+  (JNIEnv *jenv, jclass jcls, jlong jhandle, jlongArray jout) {
+    jint jresult = 0 ;
+    void *handle = (void *) 0 ;
+    bst_ulong result[1];
+    handle = *(void **)&jhandle; 
+    jresult = (jint) XGDMatrixNumRow(handle, result);
+    jenv->SetLongArrayRegion(jout, 0, 1, (const jlong *) result);
+    return jresult;
+}
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGBoosterCreate
+ * Signature: ([J)J
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGBoosterCreate
+  (JNIEnv *jenv, jclass jcls, jlongArray jhandles, jlongArray jout) {
+    jint jresult = 0;
+    void **handles = 0;
+    bst_ulong len = 0;
+    void *result[1];
+    jlong* cjhandles = 0;
+    unsigned long out[1];
+    
+    if(jhandles) {
+        len = (bst_ulong)jenv->GetArrayLength(jhandles);
+        handles = new void*[len];
+        //put handle from jhandles to chandles
+        cjhandles = jenv->GetLongArrayElements(jhandles, 0);
+        for(bst_ulong i=0; i<len; i++) {
+            handles[i] = *(void **)&cjhandles[i];
+        }
+    }
+    
+    jresult = (jint) XGBoosterCreate(handles, len, result);
+    
+    //release
+    if(jhandles) {
+        delete[] handles;
+        jenv->ReleaseLongArrayElements(jhandles, cjhandles, 0);
+    }
+    
+    *(void **)&out[0] = *result; 
+    jenv->SetLongArrayRegion(jout, 0, 1, (const jlong *) out);
+    
+    return jresult;
+}
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGBoosterFree
+ * Signature: (J)V
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGBoosterFree
+  (JNIEnv *jenv, jclass jcls, jlong jhandle) {
+    void *handle = (void *) 0 ;
+    handle = *(void **)&jhandle; 
+    return (jint) XGBoosterFree(handle);
+}
+
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGBoosterSetParam
+ * Signature: (JLjava/lang/String;Ljava/lang/String;)V
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGBoosterSetParam
+  (JNIEnv *jenv, jclass jcls, jlong jhandle, jstring jname, jstring jvalue) {
+    jint jresult = -1;
+    void *handle = (void *) 0 ;
+    char *name = (char *) 0 ;
+    char *value = (char *) 0 ;
+    handle = *(void **)&jhandle; 
+    
+    name = (char *)jenv->GetStringUTFChars(jname, 0);
+    value = (char *)jenv->GetStringUTFChars(jvalue, 0);
+
+    jresult = (jint) XGBoosterSetParam(handle, (char const *)name, (char const *)value);
+    if (name) jenv->ReleaseStringUTFChars(jname, (const char *)name);
+    if (value) jenv->ReleaseStringUTFChars(jvalue, (const char *)value);
+    
+    return jresult;
+}
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGBoosterUpdateOneIter
+ * Signature: (JIJ)V
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGBoosterUpdateOneIter
+  (JNIEnv *jenv, jclass jcls, jlong jhandle, jint jiter, jlong jdtrain) {
+    void *handle = (void *) 0 ;
+    int iter ;
+    void *dtrain = (void *) 0 ;
+    handle = *(void **)&jhandle; 
+    iter = (int)jiter; 
+    dtrain = *(void **)&jdtrain; 
+    return (jint) XGBoosterUpdateOneIter(handle, iter, dtrain);
+}
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGBoosterBoostOneIter
+ * Signature: (JJ[F[F)V
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGBoosterBoostOneIter
+  (JNIEnv *jenv, jclass jcls, jlong jhandle, jlong jdtrain, jfloatArray jgrad, jfloatArray jhess) {
+    jint jresult = 0;
+    void *handle = (void *) 0 ;
+    void *dtrain = (void *) 0 ;
+    bst_ulong len ;
+
+    handle = *(void **)&jhandle; 
+    dtrain = *(void **)&jdtrain;    
+    jfloat* grad = jenv->GetFloatArrayElements(jgrad, 0);
+    jfloat* hess = jenv->GetFloatArrayElements(jhess, 0);
+    len = (bst_ulong)jenv->GetArrayLength(jgrad);
+    jresult = (jint) XGBoosterBoostOneIter(handle, dtrain, grad, hess, len);
+    
+    //release
+    jenv->ReleaseFloatArrayElements(jgrad, grad, 0);
+    jenv->ReleaseFloatArrayElements(jhess, hess, 0);
+    
+    return jresult;
+}
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGBoosterEvalOneIter
+ * Signature: (JI[J[Ljava/lang/String;)Ljava/lang/String;
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGBoosterEvalOneIter
+  (JNIEnv *jenv, jclass jcls, jlong jhandle, jint jiter, jlongArray jdmats, jobjectArray jevnames, jobjectArray jout) {
+    jint jresult = 0 ;
+    void *handle = (void *) 0 ;
+    int iter ;
+    void **dmats = 0;
+    char **evnames = 0;
+    bst_ulong len ;
+    char *result[1];
+    
+    handle = *(void **)&jhandle; 
+    iter = (int)jiter; 
+    len = (bst_ulong)jenv->GetArrayLength(jdmats); 
+    
+    
+    if(len > 0) {
+        dmats = new void*[len];
+        evnames = new char*[len];
+    }
+    
+    //put handle from jhandles to chandles
+    jlong* cjdmats = jenv->GetLongArrayElements(jdmats, 0);
+    for(bst_ulong i=0; i<len; i++) {
+        dmats[i] = *(void **)&cjdmats[i];
+    }
+    
+    //transfer jObjectArray to char**
+    for(bst_ulong i=0; i<len; i++) {
+        jstring jevname = (jstring)jenv->GetObjectArrayElement(jevnames, i);
+        evnames[i] = (char *)jenv->GetStringUTFChars(jevname, 0);
+    }
+    
+    jresult = (jint) XGBoosterEvalOneIter(handle, iter, dmats, (char const *(*))evnames, len, (const char **) result);
+    
+    if(len > 0) {
+        delete[] dmats;
+        //release string chars
+        for(bst_ulong i=0; i<len; i++) {
+            jstring jevname = (jstring)jenv->GetObjectArrayElement(jevnames, i);
+            jenv->ReleaseStringUTFChars(jevname, (const char*)evnames[i]);
+        }        
+        delete[] evnames;
+        jenv->ReleaseLongArrayElements(jdmats, cjdmats, 0);
+    }
+    
+    jstring jinfo = 0;
+    if (*result) jinfo = jenv->NewStringUTF((const char *) *result);
+    jenv->SetObjectArrayElement(jout, 0, jinfo);
+  
+    return jresult;
+}
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGBoosterPredict
+ * Signature: (JJIJ)[F
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGBoosterPredict
+  (JNIEnv *jenv, jclass jcls, jlong jhandle, jlong jdmat, jint joption_mask, jlong jntree_limit, jobjectArray jout) {
+    jint jresult = 0;
+    void *handle = (void *) 0 ;
+    void *dmat = (void *) 0 ;
+    int option_mask ;
+    unsigned int ntree_limit ;
+    bst_ulong len[1];
+    *len = 0;
+    float *result[1];
+    
+    handle = *(void **)&jhandle; 
+    dmat = *(void **)&jdmat; 
+    option_mask = (int)joption_mask; 
+    ntree_limit = (unsigned int)jntree_limit; 
+
+    jresult = (jint) XGBoosterPredict(handle, dmat, option_mask, ntree_limit, len, (const float **) result);
+    
+    jsize jlen = (jsize)*len;
+    jfloatArray jarray = jenv->NewFloatArray(jlen);
+    jenv->SetFloatArrayRegion(jarray, 0, jlen, (jfloat *) *result);
+    jenv->SetObjectArrayElement(jout, 0, jarray);
+    
+    return jresult;
+}
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGBoosterLoadModel
+ * Signature: (JLjava/lang/String;)V
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGBoosterLoadModel
+  (JNIEnv *jenv, jclass jcls, jlong jhandle, jstring jfname) {
+    jint jresult = 0;
+    void *handle = (void *) 0 ;
+    char *fname = (char *) 0 ;
+    handle = *(void **)&jhandle; 
+    
+    fname = (char *)jenv->GetStringUTFChars(jfname, 0);
+
+    
+    jresult = (jint) XGBoosterLoadModel(handle,(char const *)fname);
+    if (fname) jenv->ReleaseStringUTFChars(jfname, (const char *)fname);
+    
+    return jresult;
+}
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGBoosterSaveModel
+ * Signature: (JLjava/lang/String;)V
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGBoosterSaveModel
+  (JNIEnv *jenv, jclass jcls, jlong jhandle, jstring jfname) {
+    jint jresult = 0;
+    void *handle = (void *) 0 ;
+    char *fname = (char *) 0 ;
+    handle = *(void **)&jhandle; 
+    fname = 0;
+    fname = (char *)jenv->GetStringUTFChars(jfname, 0);
+
+    jresult = (jint) XGBoosterSaveModel(handle, (char const *)fname);
+    if (fname) jenv->ReleaseStringUTFChars(jfname, (const char *)fname);
+    
+    return jresult;
+}
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGBoosterLoadModelFromBuffer
+ * Signature: (JJJ)V
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGBoosterLoadModelFromBuffer
+  (JNIEnv *jenv, jclass jcls, jlong jhandle, jlong jbuf, jlong jlen) {
+    void *handle = (void *) 0 ;
+    void *buf = (void *) 0 ;
+    bst_ulong len ;
+    handle = *(void **)&jhandle; 
+    buf = *(void **)&jbuf; 
+    len = (bst_ulong)jlen; 
+    return (jint) XGBoosterLoadModelFromBuffer(handle, (void const *)buf, len);
+}
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGBoosterGetModelRaw
+ * Signature: (J)Ljava/lang/String;
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGBoosterGetModelRaw
+  (JNIEnv * jenv, jclass jcls, jlong jhandle, jobjectArray jout) {
+    jint jresult = 0 ;
+    jstring jinfo = 0;
+    void *handle = (void *) 0 ;
+    bst_ulong len[1];
+    *len = 0;
+    char *result[1];
+    handle = *(void **)&jhandle; 
+
+    jresult = (jint)XGBoosterGetModelRaw(handle, len, (const char **) result);
+    if (*result){
+        jinfo = jenv->NewStringUTF((const char *) *result);
+        jenv->SetObjectArrayElement(jout, 0, jinfo);
+    }
+    return jresult;
+}
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGBoosterDumpModel
+ * Signature: (JLjava/lang/String;I)[Ljava/lang/String;
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGBoosterDumpModel
+  (JNIEnv *jenv, jclass jcls, jlong jhandle, jstring jfmap, jint jwith_stats, jobjectArray jout) {
+    jint jresult = 0;
+    void *handle = (void *) 0 ;
+    char *fmap = (char *) 0 ;
+    int with_stats ;
+    bst_ulong len[1];
+    *len = 0;
+    
+    char **result[1];
+    handle = *(void **)&jhandle; 
+    fmap = 0;
+    if (jfmap) {
+        fmap = (char *)jenv->GetStringUTFChars(jfmap, 0);
+        if (!fmap) return 0;
+    }
+    with_stats = (int)jwith_stats;
+
+    jresult = (jint) XGBoosterDumpModel(handle, (const char *)fmap, with_stats, len, (const char ***) result);
+    
+    jsize jlen = (jsize)*len;
+    jobjectArray jinfos = jenv->NewObjectArray(jlen, jenv->FindClass("java/lang/String"), jenv->NewStringUTF(""));
+    for(int i=0 ; i<jlen; i++) {
+        jenv->SetObjectArrayElement(jinfos, i, jenv->NewStringUTF((const char*) result[0][i]));
+    }
+    jenv->SetObjectArrayElement(jout, 0, jinfos);
+    
+    if (fmap) jenv->ReleaseStringUTFChars(jfmap, (const char *)fmap);
+    
+    return jresult;
+}

java/xgboost4j_wrapper.h

@@ -0,0 +1,221 @@
+/* DO NOT EDIT THIS FILE - it is machine generated */
+#include <jni.h>
+/* Header for class org_dmlc_xgboost4j_wrapper_XgboostJNI */
+
+#ifndef _Included_org_dmlc_xgboost4j_wrapper_XgboostJNI
+#define _Included_org_dmlc_xgboost4j_wrapper_XgboostJNI
+#ifdef __cplusplus
+extern "C" {
+#endif
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGBGetLastError
+ * Signature: ()Ljava/lang/String;
+ */
+JNIEXPORT jstring JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGBGetLastError
+  (JNIEnv *, jclass);
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGDMatrixCreateFromFile
+ * Signature: (Ljava/lang/String;I[J)I
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGDMatrixCreateFromFile
+  (JNIEnv *, jclass, jstring, jint, jlongArray);
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGDMatrixCreateFromCSR
+ * Signature: ([J[I[F[J)I
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGDMatrixCreateFromCSR
+  (JNIEnv *, jclass, jlongArray, jintArray, jfloatArray, jlongArray);
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGDMatrixCreateFromCSC
+ * Signature: ([J[I[F[J)I
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGDMatrixCreateFromCSC
+  (JNIEnv *, jclass, jlongArray, jintArray, jfloatArray, jlongArray);
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGDMatrixCreateFromMat
+ * Signature: ([FIIF[J)I
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGDMatrixCreateFromMat
+  (JNIEnv *, jclass, jfloatArray, jint, jint, jfloat, jlongArray);
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGDMatrixSliceDMatrix
+ * Signature: (J[I[J)I
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGDMatrixSliceDMatrix
+  (JNIEnv *, jclass, jlong, jintArray, jlongArray);
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGDMatrixFree
+ * Signature: (J)I
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGDMatrixFree
+  (JNIEnv *, jclass, jlong);
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGDMatrixSaveBinary
+ * Signature: (JLjava/lang/String;I)I
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGDMatrixSaveBinary
+  (JNIEnv *, jclass, jlong, jstring, jint);
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGDMatrixSetFloatInfo
+ * Signature: (JLjava/lang/String;[F)I
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGDMatrixSetFloatInfo
+  (JNIEnv *, jclass, jlong, jstring, jfloatArray);
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGDMatrixSetUIntInfo
+ * Signature: (JLjava/lang/String;[I)I
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGDMatrixSetUIntInfo
+  (JNIEnv *, jclass, jlong, jstring, jintArray);
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGDMatrixSetGroup
+ * Signature: (J[I)I
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGDMatrixSetGroup
+  (JNIEnv *, jclass, jlong, jintArray);
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGDMatrixGetFloatInfo
+ * Signature: (JLjava/lang/String;[[F)I
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGDMatrixGetFloatInfo
+  (JNIEnv *, jclass, jlong, jstring, jobjectArray);
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGDMatrixGetUIntInfo
+ * Signature: (JLjava/lang/String;[[I)I
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGDMatrixGetUIntInfo
+  (JNIEnv *, jclass, jlong, jstring, jobjectArray);
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGDMatrixNumRow
+ * Signature: (J[J)I
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGDMatrixNumRow
+  (JNIEnv *, jclass, jlong, jlongArray);
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGBoosterCreate
+ * Signature: ([J[J)I
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGBoosterCreate
+  (JNIEnv *, jclass, jlongArray, jlongArray);
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGBoosterFree
+ * Signature: (J)I
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGBoosterFree
+  (JNIEnv *, jclass, jlong);
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGBoosterSetParam
+ * Signature: (JLjava/lang/String;Ljava/lang/String;)I
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGBoosterSetParam
+  (JNIEnv *, jclass, jlong, jstring, jstring);
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGBoosterUpdateOneIter
+ * Signature: (JIJ)I
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGBoosterUpdateOneIter
+  (JNIEnv *, jclass, jlong, jint, jlong);
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGBoosterBoostOneIter
+ * Signature: (JJ[F[F)I
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGBoosterBoostOneIter
+  (JNIEnv *, jclass, jlong, jlong, jfloatArray, jfloatArray);
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGBoosterEvalOneIter
+ * Signature: (JI[J[Ljava/lang/String;[Ljava/lang/String;)I
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGBoosterEvalOneIter
+  (JNIEnv *, jclass, jlong, jint, jlongArray, jobjectArray, jobjectArray);
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGBoosterPredict
+ * Signature: (JJIJ[[F)I
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGBoosterPredict
+  (JNIEnv *, jclass, jlong, jlong, jint, jlong, jobjectArray);
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGBoosterLoadModel
+ * Signature: (JLjava/lang/String;)I
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGBoosterLoadModel
+  (JNIEnv *, jclass, jlong, jstring);
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGBoosterSaveModel
+ * Signature: (JLjava/lang/String;)I
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGBoosterSaveModel
+  (JNIEnv *, jclass, jlong, jstring);
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGBoosterLoadModelFromBuffer
+ * Signature: (JJJ)I
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGBoosterLoadModelFromBuffer
+  (JNIEnv *, jclass, jlong, jlong, jlong);
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGBoosterGetModelRaw
+ * Signature: (J[Ljava/lang/String;)I
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGBoosterGetModelRaw
+  (JNIEnv *, jclass, jlong, jobjectArray);
+
+/*
+ * Class:     org_dmlc_xgboost4j_wrapper_XgboostJNI
+ * Method:    XGBoosterDumpModel
+ * Signature: (JLjava/lang/String;I[[Ljava/lang/String;)I
+ */
+JNIEXPORT jint JNICALL Java_org_dmlc_xgboost4j_wrapper_XgboostJNI_XGBoosterDumpModel
+  (JNIEnv *, jclass, jlong, jstring, jint, jobjectArray);
+
+#ifdef __cplusplus
+}
+#endif
+#endif

scripts/travis_R_script.sh

@@ -0,0 +1,14 @@
+#!/bin/bash
+# Test R package of xgboost
+set -e
+export _R_CHECK_TIMINGS_=0
+export R_BUILD_ARGS="--no-build-vignettes --no-manual"
+export R_CHECK_ARGS="--no-vignettes --no-manual"
+
+curl -OL http://raw.github.com/craigcitro/r-travis/master/scripts/travis-tool.sh
+chmod 755 ./travis-tool.sh
+./travis-tool.sh bootstrap
+make Rpack
+cd ./xgboost
+../travis-tool.sh install_deps
+../travis-tool.sh run_tests

scripts/travis_after_failure.sh

@@ -0,0 +1,5 @@
+#!/bin/bash
+
+if [ ${TASK} == "R-package" ]; then
+    cat R-package/xgboost.Rcheck/*.log
+fi

scripts/travis_java_script.sh

@@ -0,0 +1,7 @@
+# Test java package of xgboost
+set -e
+cd java
+./create_wrap.sh
+cd xgboost4j
+mvn clean install -DskipTests=true
+mvn test

scripts/travis_script.sh

@@ -0,0 +1,33 @@
+#!/bin/bash
+
+# main script of travis
+if [ ${TASK} == "lint" ]; then
+    make lint  || exit -1
+fi
+
+if [ ${TASK} == "build" ]; then
+    make all CXX=${CXX} || exit -1
+fi
+
+if [ ${TASK} == "build-with-dmlc" ]; then
+    cd dmlc-core
+    cp make/config.mk .
+    echo "USE_S3=1" >> config.mk
+    make all CXX=${CXX}|| exit -1
+    cd ..
+    make dmlc=dmlc-core CXX=${CXX} || exit -1
+fi
+
+if [ ${TASK} == "R-package" ]; then
+    scripts/travis_R_script.sh || exit -1
+fi
+
+if [ ${TASK} == "python-package" ]; then
+    make all CXX=${CXX} || exit -1
+    nosetests tests/python || exit -1
+fi
+
+if [ ${TASK} == "java-package" ]; then
+    make java CXX=${CXX} || exit -1
+    scripts/travis_java_script.sh || exit -1
+fi

src/data.h

@@ -1,10 +1,12 @@
-#ifndef XGBOOST_DATA_H
-#define XGBOOST_DATA_H
 /*!
+ * Copyright (c) 2014 by Contributors
  * \file data.h
  * \brief the input data structure for gradient boosting
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_DATA_H_
+#define XGBOOST_DATA_H_
+
 #include <cstdio>
 #include <vector>
 #include "utils/utils.h"
@@ -32,7 +34,7 @@ struct bst_gpair {
   bst_gpair(bst_float grad, bst_float hess) : grad(grad), hess(hess) {}
 };
 
-/*! 
+/*!
  * \brief extra information that might needed by gbm and tree module
  * these information are not necessarily presented, and can be empty
  */
@@ -102,7 +104,7 @@ struct RowBatch : public SparseBatch {
     return Inst(data_ptr + ind_ptr[i], static_cast<bst_uint>(ind_ptr[i+1] - ind_ptr[i]));
   }
 };
-/*! 
+/*!
  * \brief read-only column batch, used to access columns,
  * the columns are not required to be continuous
  */
@@ -131,7 +133,7 @@ class IFMatrix {
   /*!\brief get column iterator */
   virtual utils::IIterator<ColBatch> *ColIterator(void) = 0;
   /*!
-   * \brief get the column iterator associated with FMatrix with subset of column features 
+   * \brief get the column iterator associated with FMatrix with subset of column features
    * \param fset is the list of column index set that must be contained in the returning Column iterator
    * \return the column iterator, initialized so that it reads the elements in fset
    */
@@ -154,11 +156,11 @@ class IFMatrix {
   /*! \brief get number of non-missing entries in column */
   virtual size_t GetColSize(size_t cidx) const = 0;
   /*! \brief get column density */
-  virtual float GetColDensity(size_t cidx) const = 0;  
+  virtual float GetColDensity(size_t cidx) const = 0;
   /*! \brief reference of buffered rowset */
   virtual const std::vector<bst_uint> &buffered_rowset(void) const = 0;
   // virtual destructor
   virtual ~IFMatrix(void){}
 };
 }  // namespace xgboost
-#endif  // XGBOOST_DATA_H
+#endif  // XGBOOST_DATA_H_

src/gbm/gblinear-inl.hpp

@@ -1,11 +1,13 @@
-#ifndef XGBOOST_GBM_GBLINEAR_INL_HPP_
-#define XGBOOST_GBM_GBLINEAR_INL_HPP_
 /*!
+ * Copyright by Contributors
  * \file gblinear-inl.hpp
  * \brief Implementation of Linear booster, with L1/L2 regularization: Elastic Net
  *        the update rule is parallel coordinate descent (shotgun)
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_GBM_GBLINEAR_INL_HPP_
+#define XGBOOST_GBM_GBLINEAR_INL_HPP_
+
 #include <vector>
 #include <string>
 #include <sstream>
@@ -33,10 +35,10 @@ class GBLinear : public IGradBooster {
       model.param.SetParam(name, val);
     }
   }
-  virtual void LoadModel(utils::IStream &fi, bool with_pbuffer) {
+  virtual void LoadModel(utils::IStream &fi, bool with_pbuffer) { // NOLINT(*)
     model.LoadModel(fi);
   }
-  virtual void SaveModel(utils::IStream &fo, bool with_pbuffer) const {
+  virtual void SaveModel(utils::IStream &fo, bool with_pbuffer) const { // NOLINT(*)
     model.SaveModel(fo);
   }
   virtual void InitModel(void) {
@@ -92,7 +94,8 @@ class GBLinear : public IGradBooster {
             sum_hess += p.hess * v * v;
           }
           float &w = model[fid][gid];
-          bst_float dw = static_cast<bst_float>(param.learning_rate * param.CalcDelta(sum_grad, sum_hess, w));
+          bst_float dw = static_cast<bst_float>(param.learning_rate *
+                                                param.CalcDelta(sum_grad, sum_hess, w));
           w += dw;
           // update grad value
           for (bst_uint j = 0; j < col.length; ++j) {
@@ -258,12 +261,12 @@ class GBLinear : public IGradBooster {
       std::fill(weight.begin(), weight.end(), 0.0f);
     }
     // save the model to file
-    inline void SaveModel(utils::IStream &fo) const {
+    inline void SaveModel(utils::IStream &fo) const { // NOLINT(*)
       fo.Write(&param, sizeof(Param));
       fo.Write(weight);
     }
     // load model from file
-    inline void LoadModel(utils::IStream &fi) {
+    inline void LoadModel(utils::IStream &fi) { // NOLINT(*)
       utils::Assert(fi.Read(&param, sizeof(Param)) != 0, "Load LinearBooster");
       fi.Read(&weight);
     }

src/gbm/gbm.cpp

@@ -1,3 +1,4 @@
+// Copyright by Contributors
 #define _CRT_SECURE_NO_WARNINGS
 #define _CRT_SECURE_NO_DEPRECATE
 #define NOMINMAX

src/gbm/gbm.h

@@ -1,11 +1,14 @@
-#ifndef XGBOOST_GBM_GBM_H_
-#define XGBOOST_GBM_GBM_H_
 /*!
+ * Copyright by Contributors
  * \file gbm.h
  * \brief interface of gradient booster, that learns through gradient statistics
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_GBM_GBM_H_
+#define XGBOOST_GBM_GBM_H_
+
 #include <vector>
+#include <string>
 #include "../data.h"
 #include "../utils/io.h"
 #include "../utils/fmap.h"
@@ -13,7 +16,7 @@
 namespace xgboost {
 /*! \brief namespace for gradient booster */
 namespace gbm {
-/*! 
+/*!
  * \brief interface of gradient boosting model
  */
 class IGradBooster {
@@ -29,26 +32,26 @@ class IGradBooster {
    * \param fi input stream
    * \param with_pbuffer whether the incoming data contains pbuffer
    */
-  virtual void LoadModel(utils::IStream &fi, bool with_pbuffer) = 0;
+  virtual void LoadModel(utils::IStream &fi, bool with_pbuffer) = 0; // NOLINT(*)
   /*!
    * \brief save model to stream
    * \param fo output stream
    * \param with_pbuffer whether save out pbuffer
    */
-  virtual void SaveModel(utils::IStream &fo, bool with_pbuffer) const = 0;
+  virtual void SaveModel(utils::IStream &fo, bool with_pbuffer) const = 0; // NOLINT(*)
   /*!
    * \brief initialize the model
    */
   virtual void InitModel(void) = 0;
-  /*! 
+  /*!
    * \brief reset the predict buffer
    * this will invalidate all the previous cached results
    * and recalculate from scratch
    */
   virtual void ResetPredBuffer(size_t num_pbuffer) {}
-  /*! 
+  /*!
    * \brief whether the model allow lazy checkpoint
-   * return true if model is only updated in DoBoost 
+   * return true if model is only updated in DoBoost
    * after all Allreduce calls
    */
   virtual bool AllowLazyCheckPoint(void) const {
@@ -76,20 +79,20 @@ class IGradBooster {
    *  the size of buffer is set by convention using IGradBooster.SetParam("num_pbuffer","size")
    * \param info extra side information that may be needed for prediction
    * \param out_preds output vector to hold the predictions
-   * \param ntree_limit limit the number of trees used in prediction, when it equals 0, this means 
+   * \param ntree_limit limit the number of trees used in prediction, when it equals 0, this means
    *    we do not limit number of trees, this parameter is only valid for gbtree, but not for gblinear
    */
   virtual void Predict(IFMatrix *p_fmat,
                        int64_t buffer_offset,
                        const BoosterInfo &info,
                        std::vector<float> *out_preds,
-                       unsigned ntree_limit = 0) = 0;  
+                       unsigned ntree_limit = 0) = 0;
   /*!
    * \brief online prediction funciton, predict score for one instance at a time
    *  NOTE: use the batch prediction interface if possible, batch prediction is usually
    *        more efficient than online prediction
    *        This function is NOT threadsafe, make sure you only call from one thread
-   *    
+   *
    * \param inst the instance you want to predict
    * \param out_preds output vector to hold the predictions
    * \param ntree_limit limit the number of trees used in prediction
@@ -106,7 +109,7 @@ class IGradBooster {
    * \param p_fmat feature matrix
    * \param info extra side information that may be needed for prediction
    * \param out_preds output vector to hold the predictions
-   * \param ntree_limit limit the number of trees used in prediction, when it equals 0, this means 
+   * \param ntree_limit limit the number of trees used in prediction, when it equals 0, this means
    *    we do not limit number of trees, this parameter is only valid for gbtree, but not for gblinear
    */
   virtual void PredictLeaf(IFMatrix *p_fmat,

src/gbm/gbtree-inl.hpp

@@ -1,13 +1,16 @@
-#ifndef XGBOOST_GBM_GBTREE_INL_HPP_
-#define XGBOOST_GBM_GBTREE_INL_HPP_
 /*!
+ * Copyright by Contributors
  * \file gbtree-inl.hpp
  * \brief gradient boosted tree implementation
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_GBM_GBTREE_INL_HPP_
+#define XGBOOST_GBM_GBTREE_INL_HPP_
+
 #include <vector>
 #include <utility>
 #include <string>
+#include <limits>
 #include "./gbm.h"
 #include "../utils/omp.h"
 #include "../tree/updater.h"
@@ -39,7 +42,7 @@ class GBTree : public IGradBooster {
     tparam.SetParam(name, val);
     if (trees.size() == 0) mparam.SetParam(name, val);
   }
-  virtual void LoadModel(utils::IStream &fi, bool with_pbuffer) {
+  virtual void LoadModel(utils::IStream &fi, bool with_pbuffer) { // NOLINT(*)
     this->Clear();
     utils::Check(fi.Read(&mparam, sizeof(ModelParam)) != 0,
                  "GBTree: invalid model file");
@@ -62,10 +65,10 @@ class GBTree : public IGradBooster {
                    "GBTree: invalid model file");
     }
   }
-  virtual void SaveModel(utils::IStream &fo, bool with_pbuffer) const {
+  virtual void SaveModel(utils::IStream &fo, bool with_pbuffer) const { // NOLINT(*)
     utils::Assert(mparam.num_trees == static_cast<int>(trees.size()), "GBTree");
     if (with_pbuffer) {
-      fo.Write(&mparam, sizeof(ModelParam));      
+      fo.Write(&mparam, sizeof(ModelParam));
     } else {
       ModelParam p = mparam;
       p.num_pbuffer = 0;
@@ -129,7 +132,7 @@ class GBTree : public IGradBooster {
                        int64_t buffer_offset,
                        const BoosterInfo &info,
                        std::vector<float> *out_preds,
-                       unsigned ntree_limit = 0) {    
+                       unsigned ntree_limit = 0) {
     int nthread;
     #pragma omp parallel
     {
@@ -160,12 +163,12 @@ class GBTree : public IGradBooster {
           this->Pred(batch[i],
                      buffer_offset < 0 ? -1 : buffer_offset + ridx,
                      gid, info.GetRoot(ridx), &feats,
-                     &preds[ridx * mparam.num_output_group + gid], stride, 
+                     &preds[ridx * mparam.num_output_group + gid], stride,
                      ntree_limit);
         }
       }
     }
-  }  
+  }
   virtual void Predict(const SparseBatch::Inst &inst,
                        std::vector<float> *out_preds,
                        unsigned ntree_limit,
@@ -178,10 +181,10 @@ class GBTree : public IGradBooster {
     // loop over output groups
     for (int gid = 0; gid < mparam.num_output_group; ++gid) {
       this->Pred(inst, -1, gid, root_index, &thread_temp[0],
-                 &(*out_preds)[gid], mparam.num_output_group, 
+                 &(*out_preds)[gid], mparam.num_output_group,
                  ntree_limit);
     }
-  }  
+  }
   virtual void PredictLeaf(IFMatrix *p_fmat,
                            const BoosterInfo &info,
                            std::vector<float> *out_preds,
@@ -196,7 +199,6 @@ class GBTree : public IGradBooster {
       thread_temp[i].Init(mparam.num_feature);
     }
     this->PredPath(p_fmat, info, out_preds, ntree_limit);
-    
   }
   virtual std::vector<std::string> DumpModel(const utils::FeatMap& fmap, int option) {
     std::vector<std::string> dump;
@@ -260,7 +262,7 @@ class GBTree : public IGradBooster {
     // update the trees
     for (size_t i = 0; i < updaters.size(); ++i) {
       updaters[i]->Update(gpair, p_fmat, info, new_trees);
-    }    
+    }
     // optimization, update buffer, if possible
     // this is only under distributed column mode
     // for safety check of lazy checkpoint
@@ -287,7 +289,7 @@ class GBTree : public IGradBooster {
   }
   // update buffer by pre-cached position
   inline void UpdateBufferByPosition(IFMatrix *p_fmat,
-                                     int64_t buffer_offset, 
+                                     int64_t buffer_offset,
                                      int bst_group,
                                      const tree::RegTree &new_tree,
                                      const int* leaf_position) {
@@ -313,11 +315,11 @@ class GBTree : public IGradBooster {
                    int bst_group,
                    unsigned root_index,
                    tree::RegTree::FVec *p_feats,
-                   float *out_pred, size_t stride, 
+                   float *out_pred, size_t stride,
                    unsigned ntree_limit) {
     size_t itop = 0;
     float  psum = 0.0f;
-    // sum of leaf vector 
+    // sum of leaf vector
     std::vector<float> vec_psum(mparam.size_leaf_vector, 0.0f);
     const int64_t bid = mparam.BufferOffset(buffer_index, bst_group);
     // number of valid trees
@@ -339,7 +341,7 @@ class GBTree : public IGradBooster {
           for (int j = 0; j < mparam.size_leaf_vector; ++j) {
             vec_psum[j] += trees[i]->leafvec(tid)[j];
           }
-          if(--treeleft == 0) break;
+          if (--treeleft == 0) break;
         }
       }
       p_feats->Drop(inst);
@@ -365,7 +367,7 @@ class GBTree : public IGradBooster {
     // number of valid trees
     if (ntree_limit == 0 || ntree_limit > trees.size()) {
       ntree_limit = static_cast<unsigned>(trees.size());
-    } 
+    }
     std::vector<float> &preds = *out_preds;
     preds.resize(info.num_row * ntree_limit);
     // start collecting the prediction
@@ -389,7 +391,7 @@ class GBTree : public IGradBooster {
       }
     }
   }
-                       
+
   // --- data structure ---
   /*! \brief training parameters */
   struct TrainParam {
@@ -442,10 +444,10 @@ class GBTree : public IGradBooster {
     int num_feature;
     /*! \brief size of predicton buffer allocated used for buffering */
     int64_t num_pbuffer;
-    /*! 
+    /*!
      * \brief how many output group a single instance can produce
      *  this affects the behavior of number of output we have:
-     *    suppose we have n instance and k group, output will be k*n 
+     *    suppose we have n instance and k group, output will be k*n
      */
     int num_output_group;
     /*! \brief size of leaf vector needed in tree */
@@ -478,8 +480,8 @@ class GBTree : public IGradBooster {
     inline size_t PredBufferSize(void) const {
       return num_output_group * num_pbuffer * (size_leaf_vector + 1);
     }
-    /*! 
+    /*!
-     * \brief get the buffer offset given a buffer index and group id  
+     * \brief get the buffer offset given a buffer index and group id
      * \return calculated buffer offset
      */
     inline int64_t BufferOffset(int64_t buffer_index, int bst_group) const {

src/io/dmlc_simple.cpp

@@ -1,6 +1,8 @@
+// Copyright by Contributors
 #define _CRT_SECURE_NO_WARNINGS
 #define _CRT_SECURE_NO_DEPRECATE
 #define NOMINMAX
+#include <string>
 #include "../utils/io.h"
 
 // implements a single no split version of DMLC
@@ -9,7 +11,7 @@
 namespace xgboost {
 namespace utils {
 /*!
- * \brief line split implementation from single FILE 
+ * \brief line split implementation from single FILE
  * simply returns lines of files, used for stdin
  */
 class SingleFileSplit : public dmlc::InputSplit {
@@ -32,7 +34,7 @@ class SingleFileSplit : public dmlc::InputSplit {
   }
   virtual size_t Read(void *ptr, size_t size) {
     return std::fread(ptr, 1, size, fp_);
-  }  
+  }
   virtual void Write(const void *ptr, size_t size) {
     utils::Error("cannot do write in inputsplit");
   }
@@ -47,13 +49,13 @@ class SingleFileSplit : public dmlc::InputSplit {
                                 chunk_end_);
     out_rec->dptr = chunk_begin_;
     out_rec->size = next - chunk_begin_;
-    chunk_begin_ = next;    
+    chunk_begin_ = next;
     return true;
   }
   virtual bool NextChunk(Blob *out_chunk) {
     if (chunk_begin_ == chunk_end_) {
       if (!LoadChunk()) return false;
-    }    
+    }
     out_chunk->dptr = chunk_begin_;
     out_chunk->size = chunk_end_ - chunk_begin_;
     chunk_begin_ = chunk_end_;
@@ -64,8 +66,8 @@ class SingleFileSplit : public dmlc::InputSplit {
     if (max_size <= overflow_.length()) {
       *size = 0; return true;
     }
-    if (overflow_.length() != 0) { 
+    if (overflow_.length() != 0) {
-      std::memcpy(buf, BeginPtr(overflow_), overflow_.length());  
+      std::memcpy(buf, BeginPtr(overflow_), overflow_.length());
     }
     size_t olen = overflow_.length();
     overflow_.resize(0);
@@ -88,13 +90,13 @@ class SingleFileSplit : public dmlc::InputSplit {
       return true;
     }
   }
-  
+
  protected:
   inline const char* FindLastRecordBegin(const char *begin,
                                          const char *end) {
     if (begin == end) return begin;
     for (const char *p = end - 1; p != begin; --p) {
-      if (*p == '\n' || *p == '\r') return p + 1; 
+      if (*p == '\n' || *p == '\r') return p + 1;
     }
     return begin;
   }
@@ -143,7 +145,7 @@ class StdFile : public dmlc::Stream {
  public:
   explicit StdFile(std::FILE *fp, bool use_stdio)
       : fp(fp), use_stdio(use_stdio) {
-  }  
+  }
   virtual ~StdFile(void) {
     this->Close();
   }
@@ -154,7 +156,7 @@ class StdFile : public dmlc::Stream {
     std::fwrite(ptr, size, 1, fp);
   }
   virtual void Seek(size_t pos) {
-    std::fseek(fp, static_cast<long>(pos), SEEK_SET);
+    std::fseek(fp, static_cast<long>(pos), SEEK_SET);  // NOLINT(*)
   }
   virtual size_t Tell(void) {
     return std::ftell(fp);
@@ -197,7 +199,7 @@ Stream *Stream::Create(const char *fname, const char * const mode, bool allow_nu
       "to use hdfs, s3 or distributed version, compile with make dmlc=1";
   utils::Check(strncmp(fname, "s3://", 5) != 0, msg);
   utils::Check(strncmp(fname, "hdfs://", 7) != 0, msg);
-  
+
   std::FILE *fp = NULL;
   bool use_stdio = false;
   using namespace std;

src/io/io.cpp

@@ -1,3 +1,4 @@
+// Copyright 2014 by Contributors
 #define _CRT_SECURE_NO_WARNINGS
 #define _CRT_SECURE_NO_DEPRECATE
 #define NOMINMAX
@@ -17,7 +18,7 @@ DataMatrix* LoadDataMatrix(const char *fname,
                            const char *cache_file) {
   using namespace std;
   std::string fname_ = fname;
-  
+
   const char *dlm = strchr(fname, '#');
   if (dlm != NULL) {
     utils::Check(strchr(dlm + 1, '#') == NULL,
@@ -29,7 +30,7 @@ DataMatrix* LoadDataMatrix(const char *fname,
     cache_file = dlm +1;
   }
 
-  if (cache_file == NULL) { 
+  if (cache_file == NULL) {
     if (!std::strcmp(fname, "stdin") ||
         !std::strncmp(fname, "s3://", 5) ||
         !std::strncmp(fname, "hdfs://", 7) ||
@@ -42,7 +43,7 @@ DataMatrix* LoadDataMatrix(const char *fname,
     utils::FileStream fs(utils::FopenCheck(fname, "rb"));
     utils::Check(fs.Read(&magic, sizeof(magic)) != 0, "invalid input file format");
     fs.Seek(0);
-    if (magic == DMatrixSimple::kMagic) { 
+    if (magic == DMatrixSimple::kMagic) {
       DMatrixSimple *dmat = new DMatrixSimple();
       dmat->LoadBinary(fs, silent, fname);
       fs.Close();
@@ -81,7 +82,7 @@ DataMatrix* LoadDataMatrix(const char *fname,
   }
 }
 
-void SaveDataMatrix(const DataMatrix &dmat, const char *fname, bool silent) {  
+void SaveDataMatrix(const DataMatrix &dmat, const char *fname, bool silent) {
   if (dmat.magic == DMatrixSimple::kMagic) {
     const DMatrixSimple *p_dmat = static_cast<const DMatrixSimple*>(&dmat);
     p_dmat->SaveBinary(fname, silent);

src/io/io.h

@@ -1,11 +1,13 @@
-#ifndef XGBOOST_IO_IO_H_
-#define XGBOOST_IO_IO_H_
 /*!
+ * Copyright 2014 by Contributors
  * \file io.h
  * \brief handles input data format of xgboost
  *    I/O module handles a specific DMatrix format
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_IO_IO_H_
+#define XGBOOST_IO_IO_H_
+
 #include "../data.h"
 #include "../learner/dmatrix.h"
 
@@ -32,7 +34,7 @@ DataMatrix* LoadDataMatrix(const char *fname,
                            bool loadsplit,
                            const char *cache_file = NULL);
 /*!
- * \brief save DataMatrix into stream, 
+ * \brief save DataMatrix into stream,
  *  note: the saved dmatrix format may not be in exactly same as input
  *  SaveDMatrix will choose the best way to materialize the dmatrix.
  * \param dmat the dmatrix to be saved
@@ -40,7 +42,6 @@ DataMatrix* LoadDataMatrix(const char *fname,
  * \param silent whether print message during saving
  */
 void SaveDataMatrix(const DataMatrix &dmat, const char *fname, bool silent = false);
-
 }  // namespace io
 }  // namespace xgboost
 #endif  // XGBOOST_IO_IO_H_

src/io/libsvm_parser.h

@@ -22,7 +22,7 @@ namespace io {
 /*! \brief page returned by libsvm parser */
 struct LibSVMPage : public SparsePage {
   std::vector<float> label;
-  // overload clear  
+  // overload clear
   inline void Clear() {
     SparsePage::Clear();
     label.clear();
@@ -35,7 +35,7 @@ struct LibSVMPage : public SparsePage {
  */
 class LibSVMPageFactory  {
  public:
-  explicit LibSVMPageFactory() 
+  LibSVMPageFactory()
       : bytes_read_(0), at_head_(true) {
   }
   inline bool Init(void) {
@@ -85,7 +85,7 @@ class LibSVMPageFactory  {
     data->resize(nthread);
     bytes_read_ += chunk.size;
     utils::Assert(chunk.size != 0, "LibSVMParser.FileData");
-    char *head = reinterpret_cast<char*>(chunk.dptr);        
+    char *head = reinterpret_cast<char*>(chunk.dptr);
     #pragma omp parallel num_threads(nthread_)
     {
       // threadid
@@ -150,7 +150,7 @@ class LibSVMPageFactory  {
     }
     return begin;
   }
-  
+
  private:
   // nthread
   int nthread_;
@@ -199,12 +199,13 @@ class LibSVMParser : public utils::IIterator<LibSVMPage> {
   inline size_t bytes_read(void) const {
     return itr.get_factory().bytes_read();
   }
+
  private:
   bool at_end_;
   size_t data_ptr_;
   std::vector<LibSVMPage> *data_;
   utils::ThreadBuffer<std::vector<LibSVMPage>*, LibSVMPageFactory> itr;
-}; 
+};
 
 }  // namespace io
 }  // namespace xgboost

src/io/page_dmatrix-inl.hpp

@@ -1,11 +1,15 @@
-#ifndef XGBOOST_IO_PAGE_DMATRIX_INL_HPP_
-#define XGBOOST_IO_PAGE_DMATRIX_INL_HPP_
 /*!
+ *  Copyright (c) 2014 by Contributors
  * \file page_dmatrix-inl.hpp
  *   row iterator based on sparse page
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_IO_PAGE_DMATRIX_INL_HPP_
+#define XGBOOST_IO_PAGE_DMATRIX_INL_HPP_
+
 #include <vector>
+#include <string>
+#include <algorithm>
 #include "../data.h"
 #include "../utils/iterator.h"
 #include "../utils/thread_buffer.h"
@@ -94,12 +98,12 @@ class DMatrixPageBase : public DataMatrix {
     fbin.Close();
     if (!silent) {
       utils::Printf("DMatrixPage: %lux%lu is saved to %s\n",
-                    static_cast<unsigned long>(mat.info.num_row()),
+                    static_cast<unsigned long>(mat.info.num_row()), // NOLINT(*)
-                    static_cast<unsigned long>(mat.info.num_col()), fname_);
+                    static_cast<unsigned long>(mat.info.num_col()), fname_); // NOLINT(*)
     }
   }
   /*! \brief load and initialize the iterator with fi */
-  inline void LoadBinary(utils::FileStream &fi,
+  inline void LoadBinary(utils::FileStream &fi,  // NOLINT(*)
                          bool silent,
                          const char *fname_) {
     this->set_cache_file(fname_);
@@ -114,8 +118,8 @@ class DMatrixPageBase : public DataMatrix {
     iter_->Load(fs);
     if (!silent) {
       utils::Printf("DMatrixPage: %lux%lu matrix is loaded",
-                    static_cast<unsigned long>(info.num_row()),
+                    static_cast<unsigned long>(info.num_row()),  // NOLINT(*)
-                    static_cast<unsigned long>(info.num_col()));
+                    static_cast<unsigned long>(info.num_col()));  // NOLINT(*)
       if (fname_ != NULL) {
         utils::Printf(" from %s\n", fname_);
       } else {
@@ -141,7 +145,7 @@ class DMatrixPageBase : public DataMatrix {
     }
     this->set_cache_file(cache_file);
     std::string fname_row = std::string(cache_file) + ".row.blob";
-    utils::FileStream fo(utils::FopenCheck(fname_row.c_str(), "wb"));    
+    utils::FileStream fo(utils::FopenCheck(fname_row.c_str(), "wb"));
     SparsePage page;
     size_t bytes_write = 0;
     double tstart = rabit::utils::GetTime();
@@ -178,8 +182,8 @@ class DMatrixPageBase : public DataMatrix {
     if (page.data.size() != 0) {
       page.Save(&fo);
     }
-    fo.Close();    
+    fo.Close();
-    iter_->Load(utils::FileStream(utils::FopenCheck(fname_row.c_str(), "rb")));    
+    iter_->Load(utils::FileStream(utils::FopenCheck(fname_row.c_str(), "rb")));
     // save data matrix
     utils::FileStream fs(utils::FopenCheck(cache_file, "wb"));
     int tmagic = kMagic;
@@ -188,8 +192,8 @@ class DMatrixPageBase : public DataMatrix {
     fs.Close();
     if (!silent) {
       utils::Printf("DMatrixPage: %lux%lu is parsed from %s\n",
-                    static_cast<unsigned long>(info.num_row()),
+                    static_cast<unsigned long>(info.num_row()),  // NOLINT(*)
-                    static_cast<unsigned long>(info.num_col()),
+                    static_cast<unsigned long>(info.num_col()),  // NOLINT(*)
                     uri);
     }
   }
@@ -241,12 +245,12 @@ class DMatrixHalfRAM : public DMatrixPageBase<0xffffab03> {
   virtual IFMatrix *fmat(void) const {
     return fmat_;
   }
-  virtual void set_cache_file(const std::string &cache_file) {    
+  virtual void set_cache_file(const std::string &cache_file) {
   }
   virtual void CheckMagic(int tmagic) {
     utils::Check(tmagic == DMatrixPageBase<0xffffab02>::kMagic ||
                  tmagic == DMatrixPageBase<0xffffab03>::kMagic,
-                 "invalid format,magic number mismatch");   
+                 "invalid format,magic number mismatch");
   }
   /*! \brief the real fmatrix */
   IFMatrix *fmat_;

src/io/page_fmatrix-inl.hpp

@@ -1,10 +1,16 @@
-#ifndef XGBOOST_IO_PAGE_FMATRIX_INL_HPP_
-#define XGBOOST_IO_PAGE_FMATRIX_INL_HPP_
 /*!
+ * Copyright (c) 2014 by Contributors
  * \file page_fmatrix-inl.hpp
  *   col iterator based on sparse page
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_IO_PAGE_FMATRIX_INL_HPP_
+#define XGBOOST_IO_PAGE_FMATRIX_INL_HPP_
+
+#include <vector>
+#include <string>
+#include <algorithm>
+
 namespace xgboost {
 namespace io {
 /*! \brief thread buffer iterator */
@@ -42,9 +48,9 @@ class ThreadColPageIterator: public utils::IIterator<ColBatch> {
   }
   // set index set
   inline void SetIndexSet(const std::vector<bst_uint> &fset, bool load_all) {
-    itr.get_factory().SetIndexSet(fset, load_all);    
+    itr.get_factory().SetIndexSet(fset, load_all);
   }
-  
+
  private:
   // output data
   ColBatch out_;
@@ -96,7 +102,7 @@ struct ColConvertFactory {
         return true;
       }
     }
-    if (tmp_.Size() != 0){
+    if (tmp_.Size() != 0) {
         this->MakeColPage(tmp_, BeginPtr(*buffered_rowset_) + btop,
                           *enabled_, val);
         return true;
@@ -104,7 +110,7 @@ struct ColConvertFactory {
       return false;
     }
   }
-  inline void Destroy(void) {}  
+  inline void Destroy(void) {}
   inline void BeforeFirst(void) {}
   inline void MakeColPage(const SparsePage &prow,
                           const bst_uint *ridx,
@@ -115,7 +121,7 @@ struct ColConvertFactory {
     #pragma omp parallel
     {
       nthread = omp_get_num_threads();
-      int max_nthread = std::max(omp_get_num_procs() / 2 - 4, 1); 
+      int max_nthread = std::max(omp_get_num_procs() / 2 - 4, 1);
       if (nthread > max_nthread) {
         nthread = max_nthread;
       }
@@ -130,10 +136,10 @@ struct ColConvertFactory {
       int tid = omp_get_thread_num();
       for (size_t j = prow.offset[i]; j < prow.offset[i+1]; ++j) {
         const SparseBatch::Entry &e = prow.data[j];
-        if (enabled[e.index]) { 
+        if (enabled[e.index]) {
           builder.AddBudget(e.index, tid);
         }
-      }    
+      }
     }
     builder.InitStorage();
     #pragma omp parallel for schedule(static) num_threads(nthread)
@@ -169,7 +175,7 @@ struct ColConvertFactory {
   // buffered rowset
   std::vector<bst_uint> *buffered_rowset_;
   // enabled marks
-  const std::vector<bool> *enabled_;  
+  const std::vector<bool> *enabled_;
   // internal temp cache
   SparsePage tmp_;
   /*! \brief page size 256 M */
@@ -191,7 +197,7 @@ class FMatrixPage : public IFMatrix {
     if (iter_ != NULL) delete iter_;
   }
   /*! \return whether column access is enabled */
-  virtual bool HaveColAccess(void) const {   
+  virtual bool HaveColAccess(void) const {
     return col_size_.size() != 0;
   }
   /*! \brief get number of colmuns */
@@ -212,7 +218,7 @@ class FMatrixPage : public IFMatrix {
     size_t nmiss = num_buffered_row_ - (col_size_[cidx]);
     return 1.0f - (static_cast<float>(nmiss)) / num_buffered_row_;
   }
-  virtual void InitColAccess(const std::vector<bool> &enabled, 
+  virtual void InitColAccess(const std::vector<bool> &enabled,
                              float pkeep, size_t max_row_perbatch) {
     if (this->HaveColAccess()) return;
     if (TryLoadColData()) return;
@@ -242,11 +248,11 @@ class FMatrixPage : public IFMatrix {
   /*!
    * \brief colmun based iterator
    */
-  virtual utils::IIterator<ColBatch> *ColIterator(const std::vector<bst_uint> &fset) {    
+  virtual utils::IIterator<ColBatch> *ColIterator(const std::vector<bst_uint> &fset) {
     size_t ncol = this->NumCol();
     col_index_.resize(0);
     for (size_t i = 0; i < fset.size(); ++i) {
-      if (fset[i] < ncol) col_index_.push_back(fset[i]); 
+      if (fset[i] < ncol) col_index_.push_back(fset[i]);
     }
     col_iter_.SetIndexSet(col_index_, false);
     col_iter_.BeforeFirst();
@@ -255,13 +261,13 @@ class FMatrixPage : public IFMatrix {
   // set the cache file name
   inline void set_cache_file(const std::string &cache_file) {
     col_data_name_ = std::string(cache_file) + ".col.blob";
-    col_meta_name_ = std::string(cache_file) + ".col.meta";    
+    col_meta_name_ = std::string(cache_file) + ".col.meta";
   }
 
  protected:
   inline bool TryLoadColData(void) {
     std::FILE *fi = fopen64(col_meta_name_.c_str(), "rb");
-    if (fi == NULL) return false;    
+    if (fi == NULL) return false;
     utils::FileStream fs(fi);
     LoadMeta(&fs);
     fs.Close();
@@ -306,12 +312,12 @@ class FMatrixPage : public IFMatrix {
     SparsePage *pcol;
     while (citer.Next(pcol)) {
       for (size_t i = 0; i < pcol->Size(); ++i) {
-        col_size_[i] += pcol->offset[i + 1] - pcol->offset[i];        
+        col_size_[i] += pcol->offset[i + 1] - pcol->offset[i];
       }
       pcol->Save(&fo);
       size_t spage = pcol->MemCostBytes();
       bytes_write += spage;
-      double tnow = rabit::utils::GetTime();      
+      double tnow = rabit::utils::GetTime();
       double tdiff = tnow - tstart;
       utils::Printf("Writting to %s in %g MB/s, %lu MB written current speed:%g MB/s\n",
                     col_data_name_.c_str(),

src/io/simple_dmatrix-inl.hpp

@@ -1,13 +1,15 @@
-#ifndef XGBOOST_IO_SIMPLE_DMATRIX_INL_HPP_
-#define XGBOOST_IO_SIMPLE_DMATRIX_INL_HPP_
 /*!
+ * Copyright 2014 by Contributors
  * \file simple_dmatrix-inl.hpp
- * \brief simple implementation of DMatrixS that can be used 
+ * \brief simple implementation of DMatrixS that can be used
  *  the data format of xgboost is templatized, which means it can accept
  *  any data structure that implements the function defined by FMatrix
  *  this file is a specific implementation of input data structure that can be used by BoostLearner
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_IO_SIMPLE_DMATRIX_INL_HPP_
+#define XGBOOST_IO_SIMPLE_DMATRIX_INL_HPP_
+
 #include <string>
 #include <cstring>
 #include <vector>
@@ -119,13 +121,13 @@ class DMatrixSimple : public DataMatrix {
       for (size_t i = 0; i < batch.data.size(); ++i) {
         info.info.num_col = std::max(info.info.num_col,
                                      static_cast<size_t>(batch.data[i].index+1));
-      }      
+      }
     }
     if (!silent) {
       utils::Printf("%lux%lu matrix with %lu entries is loaded from %s\n",
-                    static_cast<unsigned long>(info.num_row()),
+                    static_cast<unsigned long>(info.num_row()),  // NOLINT(*)
-                    static_cast<unsigned long>(info.num_col()),
+                    static_cast<unsigned long>(info.num_col()),  // NOLINT(*)
-                    static_cast<unsigned long>(row_data_.size()), uri);
+                    static_cast<unsigned long>(row_data_.size()), uri);  // NOLINT(*)
     }
     // try to load in additional file
     if (!loadsplit) {
@@ -141,7 +143,7 @@ class DMatrixSimple : public DataMatrix {
                      "DMatrix: weight data does not match the number of rows in features");
       }
       std::string mname = name + ".base_margin";
-      if (info.TryLoadFloatInfo("base_margin", mname.c_str(), silent)) {      
+      if (info.TryLoadFloatInfo("base_margin", mname.c_str(), silent)) {
       }
     }
   }
@@ -165,10 +167,11 @@ class DMatrixSimple : public DataMatrix {
    * \param silent whether print information during loading
    * \param fname file name, used to print message
    */
-  inline void LoadBinary(utils::IStream &fs, bool silent = false, const char *fname = NULL) {
+  inline void LoadBinary(utils::IStream &fs, bool silent = false, const char *fname = NULL) {  // NOLINT(*)
     int tmagic;
     utils::Check(fs.Read(&tmagic, sizeof(tmagic)) != 0, "invalid input file format");
-    utils::Check(tmagic == kMagic, "\"%s\" invalid format, magic number mismatch", fname == NULL ? "" : fname);
+    utils::Check(tmagic == kMagic, "\"%s\" invalid format, magic number mismatch",
+                 fname == NULL ? "" : fname);
 
     info.LoadBinary(fs);
     LoadBinary(fs, &row_ptr_, &row_data_);
@@ -176,9 +179,9 @@ class DMatrixSimple : public DataMatrix {
 
     if (!silent) {
       utils::Printf("%lux%lu matrix with %lu entries is loaded",
-                    static_cast<unsigned long>(info.num_row()),
+                    static_cast<unsigned long>(info.num_row()),  // NOLINT(*)
-                    static_cast<unsigned long>(info.num_col()),
+                    static_cast<unsigned long>(info.num_col()),  // NOLINT(*)
-                    static_cast<unsigned long>(row_data_.size()));
+                    static_cast<unsigned long>(row_data_.size()));  // NOLINT(*)
       if (fname != NULL) {
         utils::Printf(" from %s\n", fname);
       } else {
@@ -205,9 +208,9 @@ class DMatrixSimple : public DataMatrix {
 
     if (!silent) {
       utils::Printf("%lux%lu matrix with %lu entries is saved to %s\n",
-                    static_cast<unsigned long>(info.num_row()),
+                    static_cast<unsigned long>(info.num_row()),  // NOLINT(*)
-                    static_cast<unsigned long>(info.num_col()),
+                    static_cast<unsigned long>(info.num_col()),  // NOLINT(*)
-                    static_cast<unsigned long>(row_data_.size()), fname);
+                    static_cast<unsigned long>(row_data_.size()), fname);  // NOLINT(*)
       if (info.group_ptr.size() != 0) {
         utils::Printf("data contains %u groups\n",
                       static_cast<unsigned>(info.group_ptr.size()-1));
@@ -256,7 +259,7 @@ class DMatrixSimple : public DataMatrix {
    * \param ptr pointer data
    * \param data data content
    */
-  inline static void SaveBinary(utils::IStream &fo,
+  inline static void SaveBinary(utils::IStream &fo,  // NOLINT(*)
                                 const std::vector<size_t> &ptr,
                                 const std::vector<RowBatch::Entry> &data) {
     size_t nrow = ptr.size() - 1;
@@ -272,7 +275,7 @@ class DMatrixSimple : public DataMatrix {
    * \param out_ptr pointer data
    * \param out_data data content
    */
-  inline static void LoadBinary(utils::IStream &fi,
+  inline static void LoadBinary(utils::IStream &fi,  // NOLINT(*)
                                 std::vector<size_t> *out_ptr,
                                 std::vector<RowBatch::Entry> *out_data) {
     size_t nrow;
@@ -314,7 +317,7 @@ class DMatrixSimple : public DataMatrix {
     DMatrixSimple *parent_;
     // temporal space for batch
     RowBatch batch_;
-  }; 
+  };
 };
 }  // namespace io
 }  // namespace xgboost

src/io/simple_fmatrix-inl.hpp

@@ -1,11 +1,15 @@
-#ifndef XGBOOST_IO_SIMPLE_FMATRIX_INL_HPP_
-#define XGBOOST_IO_SIMPLE_FMATRIX_INL_HPP_
 /*!
+ * Copyright 2014 by Contributors
  * \file simple_fmatrix-inl.hpp
  * \brief the input data structure for gradient boosting
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_IO_SIMPLE_FMATRIX_INL_HPP_
+#define XGBOOST_IO_SIMPLE_FMATRIX_INL_HPP_
+
 #include <limits>
+#include <algorithm>
+#include <vector>
 #include "../data.h"
 #include "../utils/utils.h"
 #include "../utils/random.h"
@@ -30,7 +34,7 @@ class FMatrixS : public IFMatrix {
   }
   // destructor
   virtual ~FMatrixS(void) {
-    if (iter_ != NULL) delete iter_;    
+    if (iter_ != NULL) delete iter_;
   }
   /*! \return whether column access is enabled */
   virtual bool HaveColAccess(void) const {
@@ -39,7 +43,7 @@ class FMatrixS : public IFMatrix {
   /*! \brief get number of colmuns */
   virtual size_t NumCol(void) const {
     utils::Check(this->HaveColAccess(), "NumCol:need column access");
-    return col_size_.size() - 1;
+    return col_size_.size();
   }
   /*! \brief get number of buffered rows */
   virtual const std::vector<bst_uint> &buffered_rowset(void) const {
@@ -54,7 +58,7 @@ class FMatrixS : public IFMatrix {
     size_t nmiss = buffered_rowset_.size() - col_size_[cidx];
     return 1.0f - (static_cast<float>(nmiss)) / buffered_rowset_.size();
   }
-  virtual void InitColAccess(const std::vector<bool> &enabled, 
+  virtual void InitColAccess(const std::vector<bool> &enabled,
                              float pkeep, size_t max_row_perbatch) {
     if (this->HaveColAccess()) return;
     this->InitColData(enabled, pkeep, max_row_perbatch);
@@ -85,7 +89,7 @@ class FMatrixS : public IFMatrix {
     size_t ncol = this->NumCol();
     col_iter_.col_index_.resize(0);
     for (size_t i = 0; i < fset.size(); ++i) {
-      if (fset[i] < ncol) col_iter_.col_index_.push_back(fset[i]); 
+      if (fset[i] < ncol) col_iter_.col_index_.push_back(fset[i]);
     }
     col_iter_.BeforeFirst();
     return &col_iter_;
@@ -94,7 +98,7 @@ class FMatrixS : public IFMatrix {
    * \brief save column access data into stream
    * \param fo output stream to save to
    */
-  inline void SaveColAccess(utils::IStream &fo) const {
+  inline void SaveColAccess(utils::IStream &fo) const { // NOLINT(*)
     size_t n = 0;
     fo.Write(&n, sizeof(n));
   }
@@ -102,10 +106,10 @@ class FMatrixS : public IFMatrix {
    * \brief load column access data from stream
    * \param fo output stream to load from
    */
-  inline void LoadColAccess(utils::IStream &fi) {
+  inline void LoadColAccess(utils::IStream &fi) { // NOLINT(*)
     // do nothing in load col access
   }
-  
+
  protected:
   /*!
    * \brief intialize column data
@@ -129,7 +133,7 @@ class FMatrixS : public IFMatrix {
     for (size_t i = 0; i < col_iter_.cpages_.size(); ++i) {
       SparsePage *pcol = col_iter_.cpages_[i];
       for (size_t j = 0; j < pcol->Size(); ++j) {
-        col_size_[j] += pcol->offset[j + 1] - pcol->offset[j];        
+        col_size_[j] += pcol->offset[j + 1] - pcol->offset[j];
       }
     }
   }
@@ -139,7 +143,7 @@ class FMatrixS : public IFMatrix {
    * \param pcol the target column
    */
   inline void MakeOneBatch(const std::vector<bool> &enabled,
-                           float pkeep,                          
+                           float pkeep,
                            SparsePage *pcol) {
     // clear rowset
     buffered_rowset_.clear();
@@ -153,14 +157,14 @@ class FMatrixS : public IFMatrix {
     pcol->Clear();
     utils::ParallelGroupBuilder<SparseBatch::Entry>
         builder(&pcol->offset, &pcol->data);
-    builder.InitBudget(0, nthread);
+    builder.InitBudget(info_.num_col(), nthread);
     // start working
     iter_->BeforeFirst();
     while (iter_->Next()) {
       const RowBatch &batch = iter_->Value();
       bmap.resize(bmap.size() + batch.size, true);
-	  long batch_size = static_cast<long>(batch.size);
+      long batch_size = static_cast<long>(batch.size); // NOLINT(*)
-      for (long i = 0; i < batch_size; ++i) {
+      for (long i = 0; i < batch_size; ++i) { // NOLINT(*)
         bst_uint ridx = static_cast<bst_uint>(batch.base_rowid + i);
         if (pkeep == 1.0f || random::SampleBinary(pkeep)) {
           buffered_rowset_.push_back(ridx);
@@ -169,13 +173,13 @@ class FMatrixS : public IFMatrix {
         }
       }
       #pragma omp parallel for schedule(static)
-      for (long i = 0; i < batch_size; ++i) {
+      for (long i = 0; i < batch_size; ++i) { // NOLINT(*)
         int tid = omp_get_thread_num();
         bst_uint ridx = static_cast<bst_uint>(batch.base_rowid + i);
         if (bmap[ridx]) {
           RowBatch::Inst inst = batch[i];
           for (bst_uint j = 0; j < inst.length; ++j) {
-            if (enabled[inst[j].index]){ 
+            if (enabled[inst[j].index]) {
               builder.AddBudget(inst[j].index, tid);
             }
           }
@@ -183,18 +187,18 @@ class FMatrixS : public IFMatrix {
       }
     }
     builder.InitStorage();
-    
+
     iter_->BeforeFirst();
     while (iter_->Next()) {
       const RowBatch &batch = iter_->Value();
       #pragma omp parallel for schedule(static)
-      for (long i = 0; i < static_cast<long>(batch.size); ++i) {
+      for (long i = 0; i < static_cast<long>(batch.size); ++i) { // NOLINT(*)
         int tid = omp_get_thread_num();
         bst_uint ridx = static_cast<bst_uint>(batch.base_rowid + i);
         if (bmap[ridx]) {
           RowBatch::Inst inst = batch[i];
           for (bst_uint j = 0; j < inst.length; ++j) {
-            if (enabled[inst[j].index]) { 
+            if (enabled[inst[j].index]) {
               builder.Push(inst[j].index,
                            Entry((bst_uint)(batch.base_rowid+i),
                                  inst[j].fvalue), tid);
@@ -204,7 +208,8 @@ class FMatrixS : public IFMatrix {
       }
     }
 
-    utils::Assert(pcol->Size() == info_.num_col(), "inconsistent col data");
+    utils::Assert(pcol->Size() == info_.num_col(),
+                  "inconsistent col data");
     // sort columns
     bst_omp_uint ncol = static_cast<bst_omp_uint>(pcol->Size());
     #pragma omp parallel for schedule(dynamic, 1) num_threads(nthread)
@@ -260,7 +265,7 @@ class FMatrixS : public IFMatrix {
     #pragma omp parallel
     {
       nthread = omp_get_num_threads();
-      int max_nthread = std::max(omp_get_num_procs() / 2 - 2, 1); 
+      int max_nthread = std::max(omp_get_num_procs() / 2 - 2, 1);
       if (nthread > max_nthread) {
         nthread = max_nthread;
       }
@@ -276,7 +281,7 @@ class FMatrixS : public IFMatrix {
       RowBatch::Inst inst = batch[i];
       for (bst_uint j = 0; j < inst.length; ++j) {
         const SparseBatch::Entry &e = inst[j];
-        if (enabled[e.index]) { 
+        if (enabled[e.index]) {
           builder.AddBudget(e.index, tid);
         }
       }
@@ -329,10 +334,10 @@ class FMatrixS : public IFMatrix {
              static_cast<bst_uint>(pcol->offset[ridx + 1] - pcol->offset[ridx]));
       }
       batch_.col_index = BeginPtr(col_index_);
-      batch_.col_data = BeginPtr(col_data_);      
+      batch_.col_data = BeginPtr(col_data_);
       return true;
     }
-    virtual const ColBatch &Value(void) const {      
+    virtual const ColBatch &Value(void) const {
       return batch_;
     }
     inline void Clear(void) {
@@ -346,7 +351,7 @@ class FMatrixS : public IFMatrix {
     // column content
     std::vector<ColBatch::Inst> col_data_;
     // column sparse pages
-    std::vector<SparsePage*> cpages_;    
+    std::vector<SparsePage*> cpages_;
     // data pointer
     size_t data_ptr_;
     // temporal space for batch
@@ -356,7 +361,7 @@ class FMatrixS : public IFMatrix {
   // column iterator
   ColBatchIter col_iter_;
   // shared meta info with DMatrix
-  const learner::MetaInfo &info_;  
+  const learner::MetaInfo &info_;
   // row iterator
   utils::IIterator<RowBatch> *iter_;
   /*! \brief list of row index that are buffered */
@@ -366,4 +371,4 @@ class FMatrixS : public IFMatrix {
 };
 }  // namespace io
 }  // namespace xgboost
-#endif // XGBOOST_IO_SLICE_FMATRIX_INL_HPP
+#endif  // XGBOOST_IO_SLICE_FMATRIX_INL_HPP_

src/io/sparse_batch_page.h

@@ -1,18 +1,22 @@
-#ifndef XGBOOST_IO_SPARSE_BATCH_PAGE_H_
-#define XGBOOST_IO_SPARSE_BATCH_PAGE_H_
 /*!
+ * Copyright (c) 2014 by Contributors
  * \file sparse_batch_page.h
  *   content holder of sparse batch that can be saved to disk
  *   the representation can be effectively
  *   use in external memory computation
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_IO_SPARSE_BATCH_PAGE_H_
+#define XGBOOST_IO_SPARSE_BATCH_PAGE_H_
+
+#include <vector>
+#include <algorithm>
 #include "../data.h"
 
 namespace xgboost {
 namespace io {
 /*!
- * \brief storage unit of sparse batch  
+ * \brief storage unit of sparse batch
  */
 class SparsePage {
  public:
@@ -96,7 +100,7 @@ class SparsePage {
   }
   /*!
    * \brief save the data to fo, when a page was written
-   *    to disk it must contain all the elements in the 
+   *    to disk it must contain all the elements in the
    * \param fo output stream
    */
   inline void Save(utils::IStream *fo) const {
@@ -124,7 +128,7 @@ class SparsePage {
    */
   inline bool PushLoad(utils::IStream *fi) {
     if (!fi->Read(&disk_offset_)) return false;
-    data.resize(offset.back() + disk_offset_.back());    
+    data.resize(offset.back() + disk_offset_.back());
     if (disk_offset_.back() != 0) {
       utils::Check(fi->Read(BeginPtr(data) + offset.back(),
                             disk_offset_.back() * sizeof(SparseBatch::Entry)) != 0,
@@ -138,7 +142,7 @@ class SparsePage {
     }
     return true;
   }
-  /*! 
+  /*!
    * \brief Push row batch into the page
    * \param batch the row batch
    */
@@ -154,7 +158,7 @@ class SparsePage {
       offset[i + begin] = top + batch.ind_ptr[i + 1] - batch.ind_ptr[0];
     }
   }
-  /*! 
+  /*!
    * \brief Push a sparse page
    * \param batch the row page
    */
@@ -170,7 +174,7 @@ class SparsePage {
       offset[i + begin] = top + batch.offset[i + 1];
     }
   }
-  /*! 
+  /*!
    * \brief Push one instance into page
    *  \param row an instance row
    */
@@ -202,7 +206,7 @@ class SparsePage {
 };
 /*!
  * \brief factory class for SparsePage,
- *        used in threadbuffer template  
+ *        used in threadbuffer template
  */
 class SparsePageFactory {
  public:
@@ -217,7 +221,7 @@ class SparsePageFactory {
     return action_index_set_;
   }
   // set index set, will be used after next before first
-  inline void SetIndexSet(const std::vector<bst_uint> &index_set, 
+  inline void SetIndexSet(const std::vector<bst_uint> &index_set,
                           bool load_all) {
     set_load_all_ = load_all;
     if (!set_load_all_) {
@@ -229,7 +233,7 @@ class SparsePageFactory {
     return true;
   }
   inline void SetParam(const char *name, const char *val) {}
-  inline bool LoadNext(SparsePage *val) {    
+  inline bool LoadNext(SparsePage *val) {
     if (!action_load_all_) {
       if (action_index_set_.size() == 0) {
         return false;

src/learner/dmatrix.h

@@ -1,11 +1,13 @@
-#ifndef XGBOOST_LEARNER_DMATRIX_H_
-#define XGBOOST_LEARNER_DMATRIX_H_
 /*!
+ * Copyright 2014 by Contributors
  * \file dmatrix.h
- * \brief meta data and template data structure 
+ * \brief meta data and template data structure
  *        used for regression/classification/ranking
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_LEARNER_DMATRIX_H_
+#define XGBOOST_LEARNER_DMATRIX_H_
+
 #include <vector>
 #include <cstring>
 #include "../data.h"
@@ -16,8 +18,8 @@ namespace learner {
  * \brief meta information needed in training, including label, weight
  */
 struct MetaInfo {
-  /*! 
+  /*!
-   * \brief information needed by booster 
+   * \brief information needed by booster
    * BoosterInfo does not implement save and load,
    * all serialization is done in MetaInfo
    */
@@ -31,7 +33,7 @@ struct MetaInfo {
   std::vector<bst_uint> group_ptr;
   /*! \brief weights of each instance, optional */
   std::vector<float> weights;
-  /*! 
+  /*!
    * \brief initialized margins,
    * if specified, xgboost will start from this init margin
    * can be used to specify initial prediction to boost from
@@ -66,7 +68,7 @@ struct MetaInfo {
       return 1.0f;
     }
   }
-  inline void SaveBinary(utils::IStream &fo) const {
+  inline void SaveBinary(utils::IStream &fo) const { // NOLINT(*)
     int version = kVersion;
     fo.Write(&version, sizeof(version));
     fo.Write(&info.num_row, sizeof(info.num_row));
@@ -77,7 +79,7 @@ struct MetaInfo {
     fo.Write(info.root_index);
     fo.Write(base_margin);
   }
-  inline void LoadBinary(utils::IStream &fi) {
+  inline void LoadBinary(utils::IStream &fi) { // NOLINT(*)
     int version;
     utils::Check(fi.Read(&version, sizeof(version)) != 0, "MetaInfo: invalid format");
     utils::Check(fi.Read(&info.num_row, sizeof(info.num_row)) != 0, "MetaInfo: invalid format");
@@ -114,7 +116,7 @@ struct MetaInfo {
     return labels;
   }
   inline const std::vector<float>& GetFloatInfo(const char *field) const {
-    return ((MetaInfo*)this)->GetFloatInfo(field);
+    return ((MetaInfo*)this)->GetFloatInfo(field); // NOLINT(*)
   }
   inline std::vector<unsigned> &GetUIntInfo(const char *field) {
     using namespace std;
@@ -124,7 +126,7 @@ struct MetaInfo {
     return info.root_index;
   }
   inline const std::vector<unsigned> &GetUIntInfo(const char *field) const {
-    return ((MetaInfo*)this)->GetUIntInfo(field);
+    return ((MetaInfo*)this)->GetUIntInfo(field);  // NOLINT(*)
   }
   // try to load weight information from file, if exists
   inline bool TryLoadFloatInfo(const char *field, const char* fname, bool silent = false) {
@@ -149,14 +151,14 @@ struct MetaInfo {
  * \tparam FMatrix type of feature data source
  */
 struct DMatrix {
-  /*! 
+  /*!
-   * \brief magic number associated with this object 
+   * \brief magic number associated with this object
    *    used to check if it is specific instance
    */
   const int magic;
   /*! \brief meta information about the dataset */
   MetaInfo info;
-  /*! 
+  /*!
    * \brief cache pointer to verify if the data structure is cached in some learner
    *  used to verify if DMatrix is cached
    */

src/learner/evaluation-inl.hpp

@@ -1,10 +1,12 @@
+/*!
+ * Copyright 2014 by Contributors
+ * \file xgboost_evaluation-inl.hpp
+ * \brief evaluation metrics for regression and classification and rank
+ * \author Kailong Chen, Tianqi Chen
+ */
 #ifndef XGBOOST_LEARNER_EVALUATION_INL_HPP_
 #define XGBOOST_LEARNER_EVALUATION_INL_HPP_
-/*!
+
-* \file xgboost_evaluation-inl.hpp
-* \brief evaluation metrics for regression and classification and rank
-* \author Kailong Chen, Tianqi Chen
-*/
 #include <vector>
 #include <utility>
 #include <string>
@@ -18,8 +20,8 @@
 
 namespace xgboost {
 namespace learner {
-/*! 
+/*!
- * \brief base class of elementwise evaluation 
+ * \brief base class of elementwise evaluation
  * \tparam Derived the name of subclass
  */
 template<typename Derived>
@@ -47,15 +49,15 @@ struct EvalEWiseBase : public IEvaluator {
     }
     return Derived::GetFinal(dat[0], dat[1]);
   }
-  /*! 
+  /*!
-   * \brief to be implemented by subclass, 
+   * \brief to be implemented by subclass,
-   *   get evaluation result from one row 
+   *   get evaluation result from one row
    * \param label label of current instance
    * \param pred prediction value of current instance
    */
   inline static float EvalRow(float label, float pred);
-  /*! 
+  /*!
-   * \brief to be overide by subclas, final trasnformation 
+   * \brief to be overide by subclas, final trasnformation
    * \param esum the sum statistics returned by EvalRow
    * \param wsum sum of weight
    */
@@ -87,9 +89,9 @@ struct EvalLogLoss : public EvalEWiseBase<EvalLogLoss> {
     const float eps = 1e-16f;
     const float pneg = 1.0f - py;
     if (py < eps) {
-      return -y * std::log(eps) - (1.0f - y)  * std::log(1.0f - eps); 
+      return -y * std::log(eps) - (1.0f - y)  * std::log(1.0f - eps);
     } else if (pneg < eps) {
-      return -y * std::log(1.0f - eps) - (1.0f - y)  * std::log(eps); 
+      return -y * std::log(1.0f - eps) - (1.0f - y)  * std::log(eps);
     } else {
       return -y * std::log(py) - (1.0f - y) * std::log(pneg);
     }
@@ -119,7 +121,7 @@ struct EvalPoissionNegLogLik : public EvalEWiseBase<EvalPoissionNegLogLik> {
   }
 };
 
-/*! 
+/*!
  * \brief base class of multi-class evaluation
  * \tparam Derived the name of subclass
  */
@@ -139,7 +141,7 @@ struct EvalMClassBase : public IEvaluator {
     float sum = 0.0, wsum = 0.0;
     int label_error = 0;
     #pragma omp parallel for reduction(+: sum, wsum) schedule(static)
-    for (bst_omp_uint i = 0; i < ndata; ++i) {      
+    for (bst_omp_uint i = 0; i < ndata; ++i) {
       const float wt = info.GetWeight(i);
       int label =  static_cast<int>(info.labels[i]);
       if (label >= 0 && label < static_cast<int>(nclass)) {
@@ -161,18 +163,18 @@ struct EvalMClassBase : public IEvaluator {
     }
     return Derived::GetFinal(dat[0], dat[1]);
   }
-  /*! 
+  /*!
-   * \brief to be implemented by subclass, 
+   * \brief to be implemented by subclass,
-   *   get evaluation result from one row 
+   *   get evaluation result from one row
    * \param label label of current instance
-   * \param pred prediction value of current instance 
+   * \param pred prediction value of current instance
    * \param nclass number of class in the prediction
    */
   inline static float EvalRow(int label,
                               const float *pred,
                               size_t nclass);
-  /*! 
+  /*!
-   * \brief to be overide by subclas, final trasnformation 
+   * \brief to be overide by subclas, final trasnformation
    * \param esum the sum statistics returned by EvalRow
    * \param wsum sum of weight
    */
@@ -208,7 +210,7 @@ struct EvalMultiLogLoss : public EvalMClassBase<EvalMultiLogLoss> {
     } else {
       return -std::log(eps);
     }
-  } 
+  }
 };
 
 /*! \brief ctest */
@@ -240,7 +242,7 @@ struct EvalCTest: public IEvaluator {
           tpred.push_back(preds[i + (k + 1) * ndata]);
           tinfo.labels.push_back(info.labels[i]);
           tinfo.weights.push_back(info.GetWeight(i));
-        }        
+        }
       }
       wsum += base_->Eval(tpred, tinfo);
     }
@@ -328,7 +330,7 @@ struct EvalPrecisionRatio : public IEvaluator{
                      const MetaInfo &info,
                      bool distributed) const {
     utils::Check(!distributed, "metric %s do not support distributed evaluation", Name());
-    utils::Check(info.labels.size() != 0, "label set cannot be empty");    
+    utils::Check(info.labels.size() != 0, "label set cannot be empty");
     utils::Assert(preds.size() % info.labels.size() == 0,
                   "label size predict size not match");
     std::vector< std::pair<float, unsigned> > rec;
@@ -344,7 +346,8 @@ struct EvalPrecisionRatio : public IEvaluator{
   }
 
  protected:
-  inline double CalcPRatio(const std::vector< std::pair<float, unsigned> >& rec, const MetaInfo &info) const {
+  inline double CalcPRatio(const std::vector< std::pair<float, unsigned> >& rec,
+                           const MetaInfo &info) const {
     size_t cutoff = static_cast<size_t>(ratio_ * rec.size());
     double wt_hit = 0.0, wsum = 0.0, wt_sum = 0.0;
     for (size_t j = 0; j < cutoff; ++j) {
@@ -372,7 +375,7 @@ struct EvalAuc : public IEvaluator {
     utils::Check(info.labels.size() != 0, "label set cannot be empty");
     utils::Check(preds.size() % info.labels.size() == 0,
                  "label size predict size not match");
-    std::vector<unsigned> tgptr(2, 0); 
+    std::vector<unsigned> tgptr(2, 0);
     tgptr[1] = static_cast<unsigned>(info.labels.size());
 
     const std::vector<unsigned> &gptr = info.group_ptr.size() == 0 ? tgptr : info.group_ptr;
@@ -417,8 +420,8 @@ struct EvalAuc : public IEvaluator {
     }
     if (distributed) {
       float dat[2];
-	  dat[0] = static_cast<float>(sum_auc);
+      dat[0] = static_cast<float>(sum_auc);
-	  dat[1] = static_cast<float>(ngroup);      
+      dat[1] = static_cast<float>(ngroup);
       // approximately estimate auc using mean
       rabit::Allreduce<rabit::op::Sum>(dat, 2);
       return dat[0] / dat[1];
@@ -463,8 +466,8 @@ struct EvalRankList : public IEvaluator {
     }
     if (distributed) {
       float dat[2];
-	  dat[0] = static_cast<float>(sum_metric);
+      dat[0] = static_cast<float>(sum_metric);
-	  dat[1] = static_cast<float>(ngroup);      
+      dat[1] = static_cast<float>(ngroup);
       // approximately estimate auc using mean
       rabit::Allreduce<rabit::op::Sum>(dat, 2);
       return dat[0] / dat[1];
@@ -489,7 +492,7 @@ struct EvalRankList : public IEvaluator {
     }
   }
   /*! \return evaluation metric, given the pair_sort record, (pred,label) */
-  virtual float EvalMetric(std::vector< std::pair<float, unsigned> > &pair_sort) const = 0;
+  virtual float EvalMetric(std::vector< std::pair<float, unsigned> > &pair_sort) const = 0; // NOLINT(*)
 
  protected:
   unsigned topn_;
@@ -524,13 +527,13 @@ struct EvalNDCG : public EvalRankList{
     double sumdcg = 0.0;
     for (size_t i = 0; i < rec.size() && i < this->topn_; ++i) {
       const unsigned rel = rec[i].second;
-      if (rel != 0) { 
+      if (rel != 0) {
         sumdcg += ((1 << rel) - 1) / std::log(i + 2.0);
       }
     }
     return static_cast<float>(sumdcg);
   }
-  virtual float EvalMetric(std::vector< std::pair<float, unsigned> > &rec) const {
+  virtual float EvalMetric(std::vector< std::pair<float, unsigned> > &rec) const { // NOLINT(*)
     std::stable_sort(rec.begin(), rec.end(), CmpFirst);
     float dcg = this->CalcDCG(rec);
     std::stable_sort(rec.begin(), rec.end(), CmpSecond);

src/learner/evaluation.h

@@ -1,10 +1,12 @@
-#ifndef XGBOOST_LEARNER_EVALUATION_H_
-#define XGBOOST_LEARNER_EVALUATION_H_
 /*!
+ * Copyright 2014 by Contributors
  * \file evaluation.h
  * \brief interface of evaluation function supported in xgboost
  * \author Tianqi Chen, Kailong Chen
  */
+#ifndef XGBOOST_LEARNER_EVALUATION_H_
+#define XGBOOST_LEARNER_EVALUATION_H_
+
 #include <string>
 #include <vector>
 #include <cstdio>
@@ -19,7 +21,7 @@ struct IEvaluator{
    * \brief evaluate a specific metric
    * \param preds prediction
    * \param info information, including label etc.
-   * \param distributed whether a call to Allreduce is needed to gather 
+   * \param distributed whether a call to Allreduce is needed to gather
    *        the average statistics across all the node,
    *        this is only supported by some metrics
    */

src/learner/helper_utils.h

@@ -1,10 +1,12 @@
-#ifndef XGBOOST_LEARNER_HELPER_UTILS_H_
-#define XGBOOST_LEARNER_HELPER_UTILS_H_
 /*!
+ * Copyright 2014 by Contributors
  * \file helper_utils.h
  * \brief useful helper functions
  * \author Tianqi Chen, Kailong Chen
  */
+#ifndef XGBOOST_LEARNER_HELPER_UTILS_H_
+#define XGBOOST_LEARNER_HELPER_UTILS_H_
+
 #include <utility>
 #include <vector>
 #include <cmath>
@@ -61,7 +63,7 @@ inline float LogSum(const float *rec, size_t size) {
   for (size_t i = 0; i < size; ++i) {
     sum += std::exp(rec[i] - mx);
   }
-  return mx + std::log(sum);  
+  return mx + std::log(sum);
 }
 
 inline static bool CmpFirst(const std::pair<float, unsigned> &a,

src/learner/learner-inl.hpp

@@ -1,10 +1,12 @@
-#ifndef XGBOOST_LEARNER_LEARNER_INL_HPP_
-#define XGBOOST_LEARNER_LEARNER_INL_HPP_
 /*!
+ * Copyright 2014 by Contributors
  * \file learner-inl.hpp
- * \brief learning algorithm 
+ * \brief learning algorithm
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_LEARNER_LEARNER_INL_HPP_
+#define XGBOOST_LEARNER_LEARNER_INL_HPP_
+
 #include <algorithm>
 #include <vector>
 #include <utility>
@@ -19,7 +21,7 @@
 namespace xgboost {
 /*! \brief namespace for learning algorithm */
 namespace learner {
-/*! 
+/*!
  * \brief learner that takes do gradient boosting on specific objective functions
  *  and do training and prediction
  */
@@ -30,7 +32,7 @@ class BoostLearner : public rabit::Serializable {
     gbm_ = NULL;
     name_obj_ = "reg:linear";
     name_gbm_ = "gbtree";
-    silent= 0;
+    silent = 0;
     prob_buffer_row = 1.0f;
     distributed_mode = 0;
     updater_mode = 0;
@@ -47,10 +49,10 @@ class BoostLearner : public rabit::Serializable {
    * \brief add internal cache space for mat, this can speedup prediction for matrix,
    *        please cache prediction for training and eval data
    *    warning: if the model is loaded from file from some previous training history
-   *             set cache data must be called with exactly SAME 
+   *             set cache data must be called with exactly SAME
    *             data matrices to continue training otherwise it will cause error
    * \param mats array of pointers to matrix whose prediction result need to be cached
-   */          
+   */
   inline void SetCacheData(const std::vector<DMatrix*>& mats) {
     utils::Assert(cache_.size() == 0, "can only call cache data once");
     // assign buffer index
@@ -67,10 +69,10 @@ class BoostLearner : public rabit::Serializable {
       buffer_size += mats[i]->info.num_row();
     }
     char str_temp[25];
-    utils::SPrintf(str_temp, sizeof(str_temp), "%lu", 
+    utils::SPrintf(str_temp, sizeof(str_temp), "%lu",
-                   static_cast<unsigned long>(buffer_size));
+                   static_cast<unsigned long>(buffer_size)); // NOLINT(*)
     this->SetParam("num_pbuffer", str_temp);
-    this->pred_buffer_size = buffer_size;    
+    this->pred_buffer_size = buffer_size;
   }
   /*!
    * \brief set parameters from outside
@@ -79,7 +81,7 @@ class BoostLearner : public rabit::Serializable {
    */
   inline void SetParam(const char *name, const char *val) {
     using namespace std;
-    // in this version, bst: prefix is no longer required 
+    // in this version, bst: prefix is no longer required
     if (strncmp(name, "bst:", 4) != 0) {
       std::string n = "bst:"; n += name;
       this->SetParam(n.c_str(), val);
@@ -119,7 +121,7 @@ class BoostLearner : public rabit::Serializable {
       if (!strcmp(name, "objective")) name_obj_ = val;
       if (!strcmp(name, "booster")) name_gbm_ = val;
       mparam.SetParam(name, val);
-    }    
+    }
     if (gbm_ != NULL) gbm_->SetParam(name, val);
     if (obj_ != NULL) obj_->SetParam(name, val);
     if (gbm_ == NULL || obj_ == NULL) {
@@ -133,16 +135,16 @@ class BoostLearner : public rabit::Serializable {
       // estimate feature bound
       unsigned num_feature = 0;
       for (size_t i = 0; i < cache_.size(); ++i) {
-        num_feature = std::max(num_feature, 
+        num_feature = std::max(num_feature,
                                static_cast<unsigned>(cache_[i].mat_->info.num_col()));
       }
       // run allreduce on num_feature to find the maximum value
       rabit::Allreduce<rabit::op::Max>(&num_feature, 1);
       if (num_feature > mparam.num_feature) mparam.num_feature = num_feature;
-    } 
+    }
     char str_temp[25];
     utils::SPrintf(str_temp, sizeof(str_temp), "%d", mparam.num_feature);
-    this->SetParam("bst:num_feature", str_temp);   
+    this->SetParam("bst:num_feature", str_temp);
   }
   /*!
    * \brief initialize the model
@@ -161,13 +163,13 @@ class BoostLearner : public rabit::Serializable {
    * \param fi input stream
    * \param calc_num_feature whether call InitTrainer with calc_num_feature
    */
-  inline void LoadModel(utils::IStream &fi,
+  inline void LoadModel(utils::IStream &fi,  // NOLINT(*)
                         bool calc_num_feature = true) {
     utils::Check(fi.Read(&mparam, sizeof(ModelParam)) != 0,
                  "BoostLearner: wrong model format");
     {
       // backward compatibility code for compatible with old model type
-      // for new model, Read(&name_obj_) is suffice      
+      // for new model, Read(&name_obj_) is suffice
       uint64_t len;
       utils::Check(fi.Read(&len, sizeof(len)) != 0, "BoostLearner: wrong model format");
       if (len >= std::numeric_limits<unsigned>::max()) {
@@ -226,9 +228,9 @@ class BoostLearner : public rabit::Serializable {
       fi = utils::IStream::Create(fname, "r");
       this->LoadModel(*fi, true);
     }
-    delete fi;   
+    delete fi;
   }
-  inline void SaveModel(utils::IStream &fo, bool with_pbuffer) const {
+  inline void SaveModel(utils::IStream &fo, bool with_pbuffer) const { // NOLINT(*)
     ModelParam p = mparam;
     p.saved_with_pbuffer = static_cast<int>(with_pbuffer);
     fo.Write(&p, sizeof(ModelParam));
@@ -247,7 +249,7 @@ class BoostLearner : public rabit::Serializable {
       fo->Write("bs64\t", 5);
       utils::Base64OutStream bout(fo);
       this->SaveModel(bout, with_pbuffer);
-      bout.Finish('\n');    
+      bout.Finish('\n');
     } else {
       fo->Write("binf", 4);
       this->SaveModel(*fo, with_pbuffer);
@@ -260,7 +262,7 @@ class BoostLearner : public rabit::Serializable {
    * \param p_train pointer to the matrix used by training
    */
   inline void CheckInit(DMatrix *p_train) {
-    int ncol = static_cast<int>(p_train->info.info.num_col);    
+    int ncol = static_cast<int>(p_train->info.info.num_col);
     std::vector<bool> enabled(ncol, true);
     // set max row per batch to limited value
     // in distributed mode, use safe choice otherwise
@@ -345,10 +347,9 @@ class BoostLearner : public rabit::Serializable {
                       bool output_margin,
                       std::vector<float> *out_preds,
                       unsigned ntree_limit = 0,
-                      bool pred_leaf = false
+                      bool pred_leaf = false) const {
-                      ) const {
     if (pred_leaf) {
-      gbm_->PredictLeaf(data.fmat(), data.info.info, out_preds, ntree_limit);      
+      gbm_->PredictLeaf(data.fmat(), data.info.info, out_preds, ntree_limit);
     } else {
       this->PredictRaw(data, out_preds, ntree_limit);
       if (!output_margin) {
@@ -361,7 +362,7 @@ class BoostLearner : public rabit::Serializable {
    *  NOTE: use the batch prediction interface if possible, batch prediction is usually
    *        more efficient than online prediction
    *        This function is NOT threadsafe, make sure you only call from one thread
-   *    
+   *
    * \param inst the instance you want to predict
    * \param output_margin whether to only predict margin value instead of transformed prediction
    * \param out_preds output vector to hold the predictions
@@ -387,8 +388,8 @@ class BoostLearner : public rabit::Serializable {
   }
 
  protected:
-  /*! 
+  /*!
-   * \brief initialize the objective function and GBM, 
+   * \brief initialize the objective function and GBM,
    * if not yet done
    */
   inline void InitObjGBM(void) {
@@ -401,12 +402,12 @@ class BoostLearner : public rabit::Serializable {
     for (size_t i = 0; i < cfg_.size(); ++i) {
       obj_->SetParam(cfg_[i].first.c_str(), cfg_[i].second.c_str());
       gbm_->SetParam(cfg_[i].first.c_str(), cfg_[i].second.c_str());
-    }   
+    }
     if (evaluator_.Size() == 0) {
       evaluator_.AddEval(obj_->DefaultEvalMetric());
     }
   }
-  /*! 
+  /*!
    * \brief additional default value for specific objs
    */
   inline void InitAdditionDefaultParam(void) {
@@ -415,12 +416,12 @@ class BoostLearner : public rabit::Serializable {
       gbm_->SetParam("max_delta_step", "0.7");
     }
   }
-  /*! 
+  /*!
    * \brief get un-transformed prediction
    * \param data training data matrix
    * \param out_preds output vector that stores the prediction
    * \param ntree_limit limit number of trees used for boosted tree
-   *   predictor, when it equals 0, this means we are using all the trees   
+   *   predictor, when it equals 0, this means we are using all the trees
    */
   inline void PredictRaw(const DMatrix &data,
                          std::vector<float> *out_preds,
@@ -517,7 +518,7 @@ class BoostLearner : public rabit::Serializable {
 
  protected:
   // magic number to transform random seed
-  const static int kRandSeedMagic = 127;
+  static const int kRandSeedMagic = 127;
   // cache entry object that helps handle feature caching
   struct CacheEntry {
     const DMatrix *mat_;

src/learner/objective-inl.hpp

@@ -1,10 +1,12 @@
-#ifndef XGBOOST_LEARNER_OBJECTIVE_INL_HPP_
-#define XGBOOST_LEARNER_OBJECTIVE_INL_HPP_
 /*!
+ * Copyright 2014 by Contributors
  * \file objective-inl.hpp
  * \brief objective function implementations
  * \author Tianqi Chen, Kailong Chen
  */
+#ifndef XGBOOST_LEARNER_OBJECTIVE_INL_HPP_
+#define XGBOOST_LEARNER_OBJECTIVE_INL_HPP_
+
 #include <vector>
 #include <algorithm>
 #include <utility>
@@ -176,14 +178,14 @@ class RegLossObj : public IObjFunction {
 // poisson regression for count
 class PoissonRegression : public IObjFunction {
  public:
-  explicit PoissonRegression(void) {
+  PoissonRegression(void) {
     max_delta_step = 0.0f;
   }
   virtual ~PoissonRegression(void) {}
-  
+
   virtual void SetParam(const char *name, const char *val) {
     using namespace std;
-    if (!strcmp( "max_delta_step", name )) {
+    if (!strcmp("max_delta_step", name)) {
       max_delta_step = static_cast<float>(atof(val));
     }
   }
@@ -201,9 +203,9 @@ class PoissonRegression : public IObjFunction {
     // check if label in range
     bool label_correct = true;
     // start calculating gradient
-    const long ndata = static_cast<bst_omp_uint>(preds.size());
+    const long ndata = static_cast<bst_omp_uint>(preds.size()); // NOLINT(*)
     #pragma omp parallel for schedule(static)
-    for (long i = 0; i < ndata; ++i) {
+    for (long i = 0; i < ndata; ++i) { // NOLINT(*)
       float p = preds[i];
       float w = info.GetWeight(i);
       float y = info.labels[i];
@@ -219,9 +221,9 @@ class PoissonRegression : public IObjFunction {
   }
   virtual void PredTransform(std::vector<float> *io_preds) {
     std::vector<float> &preds = *io_preds;
-    const long ndata = static_cast<long>(preds.size());
+    const long ndata = static_cast<long>(preds.size()); // NOLINT(*)
     #pragma omp parallel for schedule(static)
-    for (long j = 0; j < ndata; ++j) {
+    for (long j = 0; j < ndata; ++j) {  // NOLINT(*)
       preds[j] = std::exp(preds[j]);
     }
   }
@@ -234,7 +236,7 @@ class PoissonRegression : public IObjFunction {
   virtual const char* DefaultEvalMetric(void) const {
     return "poisson-nloglik";
   }
-  
+
  private:
   float max_delta_step;
 };
@@ -467,7 +469,7 @@ class LambdaRankObj : public IObjFunction {
         : pos_index(pos_index), neg_index(neg_index), weight(1.0f) {}
   };
   /*!
-   * \brief get lambda weight for existing pairs 
+   * \brief get lambda weight for existing pairs
    * \param list a list that is sorted by pred score
    * \param io_pairs record of pairs, containing the pairs to fill in weights
    */
@@ -555,10 +557,10 @@ class LambdaRankObjMAP : public LambdaRankObj {
     float ap_acc;
     /*!
      * \brief the accumulated precision,
-     *   assuming a positive instance is missing 
+     *   assuming a positive instance is missing
      */
     float ap_acc_miss;
-    /*! 
+    /*!
      * \brief the accumulated precision,
      * assuming that one more positive instance is inserted ahead
      */

src/learner/objective.h

@@ -1,11 +1,14 @@
-#ifndef XGBOOST_LEARNER_OBJECTIVE_H_
-#define XGBOOST_LEARNER_OBJECTIVE_H_
 /*!
+ * Copyright 2014 by Contributors
  * \file objective.h
  * \brief interface of objective function used for gradient boosting
  * \author Tianqi Chen, Kailong Chen
  */
-#include "dmatrix.h"
+#ifndef XGBOOST_LEARNER_OBJECTIVE_H_
+#define XGBOOST_LEARNER_OBJECTIVE_H_
+
+#include <vector>
+#include "./dmatrix.h"
 
 namespace xgboost {
 namespace learner {
@@ -13,13 +16,13 @@ namespace learner {
 class IObjFunction{
  public:
   /*! \brief virtual destructor */
-  virtual ~IObjFunction(void){}
+  virtual ~IObjFunction(void) {}
   /*!
    * \brief set parameters from outside
    * \param name name of the parameter
    * \param val value of the parameter
    */
-  virtual void SetParam(const char *name, const char *val) = 0;  
+  virtual void SetParam(const char *name, const char *val) = 0;
   /*!
    * \brief get gradient over each of predictions, given existing information
    * \param preds prediction of current round
@@ -38,9 +41,9 @@ class IObjFunction{
    * \brief transform prediction values, this is only called when Prediction is called
    * \param io_preds prediction values, saves to this vector as well
    */
-  virtual void PredTransform(std::vector<float> *io_preds){}
+  virtual void PredTransform(std::vector<float> *io_preds) {}
   /*!
-   * \brief transform prediction values, this is only called when Eval is called, 
+   * \brief transform prediction values, this is only called when Eval is called,
    *  usually it redirect to PredTransform
    * \param io_preds prediction values, saves to this vector as well
    */
@@ -49,7 +52,7 @@ class IObjFunction{
   }
   /*!
    * \brief transform probability value back to margin
-   * this is used to transform user-set base_score back to margin 
+   * this is used to transform user-set base_score back to margin
    * used by gradient boosting
    * \return transformed value
    */
@@ -77,7 +80,7 @@ inline IObjFunction* CreateObjFunction(const char *name) {
   if (!strcmp("multi:softprob", name)) return new SoftmaxMultiClassObj(1);
   if (!strcmp("rank:pairwise", name )) return new PairwiseRankObj();
   if (!strcmp("rank:ndcg", name)) return new LambdaRankObjNDCG();
-  if (!strcmp("rank:map", name)) return new LambdaRankObjMAP();  
+  if (!strcmp("rank:map", name)) return new LambdaRankObjMAP();
   utils::Error("unknown objective function type: %s", name);
   return NULL;
 }

src/sync/sync.h

@@ -1,13 +1,13 @@
-#ifndef XGBOOST_SYNC_H_
-#define XGBOOST_SYNC_H_
 /*!
+ * Copyright 2014 by Contributors
  * \file sync.h
  * \brief the synchronization module of rabit
  *        redirects to subtree rabit header
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_SYNC_SYNC_H_
+#define XGBOOST_SYNC_SYNC_H_
+
 #include "../../subtree/rabit/include/rabit.h"
 #include "../../subtree/rabit/include/rabit/timer.h"
-#endif  // XGBOOST_SYNC_H_
+#endif  // XGBOOST_SYNC_SYNC_H_
-
-

src/tree/model.h

@@ -1,10 +1,12 @@
-#ifndef XGBOOST_TREE_MODEL_H_
-#define XGBOOST_TREE_MODEL_H_
 /*!
+ * Copyright 2014 by Contributors
  * \file model.h
  * \brief model structure for tree
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_TREE_MODEL_H_
+#define XGBOOST_TREE_MODEL_H_
+
 #include <string>
 #include <cstring>
 #include <sstream>
@@ -19,7 +21,7 @@
 namespace xgboost {
 namespace tree {
 /*!
- * \brief template class of TreeModel 
+ * \brief template class of TreeModel
  * \tparam TSplitCond data type to indicate split condition
  * \tparam TNodeStat auxiliary statistics of node to help tree building
  */
@@ -42,7 +44,7 @@ class TreeModel {
     int max_depth;
     /*! \brief  number of features used for tree construction */
     int num_feature;
-    /*! 
+    /*!
      * \brief leaf vector size, used for vector tree
      * used to store more than one dimensional information in tree
      */
@@ -55,8 +57,8 @@ class TreeModel {
       size_leaf_vector = 0;
       std::memset(reserved, 0, sizeof(reserved));
     }
-    /*! 
+    /*!
-     * \brief set parameters from outside 
+     * \brief set parameters from outside
      * \param name name of the parameter
      * \param val  value of the parameter
      */
@@ -70,7 +72,7 @@ class TreeModel {
   /*! \brief tree node */
   class Node {
    public:
-	Node(void) : sindex_(0) {}
+    Node(void) : sindex_(0) {}
     /*! \brief index of left child */
     inline int cleft(void) const {
       return this->cleft_;
@@ -119,15 +121,15 @@ class TreeModel {
     inline bool is_root(void) const {
       return parent_ == -1;
     }
-    /*! 
+    /*!
-     * \brief set the right child 
+     * \brief set the right child
      * \param nide node id to right child
      */
     inline void set_right_child(int nid) {
       this->cright_ = nid;
     }
-    /*! 
+    /*!
-     * \brief set split condition of current node 
+     * \brief set split condition of current node
      * \param split_index feature index to split
      * \param split_cond  split condition
      * \param default_left the default direction when feature is unknown
@@ -138,10 +140,10 @@ class TreeModel {
       this->sindex_ = split_index;
       (this->info_).split_cond = split_cond;
     }
-    /*! 
+    /*!
      * \brief set the leaf value of the node
      * \param value leaf value
-     * \param right right index, could be used to store 
+     * \param right right index, could be used to store
      *        additional information
      */
     inline void set_leaf(float value, int right = -1) {
@@ -153,12 +155,12 @@ class TreeModel {
     inline void mark_delete(void) {
       this->sindex_ = std::numeric_limits<unsigned>::max();
     }
-    
+
    private:
     friend class TreeModel<TSplitCond, TNodeStat>;
-    /*! 
+    /*!
-     * \brief in leaf node, we have weights, in non-leaf nodes, 
+     * \brief in leaf node, we have weights, in non-leaf nodes,
-     *        we have split condition 
+     *        we have split condition
      */
     union Info{
       float leaf_value;
@@ -203,7 +205,7 @@ class TreeModel {
                  "number of nodes in the tree exceed 2^31");
     nodes.resize(param.num_nodes);
     stats.resize(param.num_nodes);
-    leaf_vector.resize(param.num_nodes * param.size_leaf_vector); 
+    leaf_vector.resize(param.num_nodes * param.size_leaf_vector);
     return nd;
   }
   // delete a tree node, keep the parent field to allow trace back
@@ -215,7 +217,7 @@ class TreeModel {
   }
 
  public:
-  /*! 
+  /*!
    * \brief change a non leaf node to a leaf node, delete its children
    * \param rid node id of the node
    * \param new leaf value
@@ -229,7 +231,7 @@ class TreeModel {
     this->DeleteNode(nodes[rid].cright());
     nodes[rid].set_leaf(value);
   }
-  /*! 
+  /*!
    * \brief collapse a non leaf node to a leaf node, delete its children
    * \param rid node id of the node
    * \param new leaf value
@@ -273,7 +275,7 @@ class TreeModel {
     return &leaf_vector[nid * param.size_leaf_vector];
   }
   /*! \brief get leaf vector given nid */
-  inline const bst_float* leafvec(int nid) const{
+  inline const bst_float* leafvec(int nid) const {
     if (leaf_vector.size() == 0) return NULL;
     return &leaf_vector[nid * param.size_leaf_vector];
   }
@@ -288,15 +290,15 @@ class TreeModel {
       nodes[i].set_parent(-1);
     }
   }
-  /*! 
+  /*!
    * \brief load model from stream
    * \param fi input stream
    */
-  inline void LoadModel(utils::IStream &fi) {
+  inline void LoadModel(utils::IStream &fi) { // NOLINT(*)
     utils::Check(fi.Read(&param, sizeof(Param)) > 0,
                  "TreeModel: wrong format");
     nodes.resize(param.num_nodes); stats.resize(param.num_nodes);
-    utils::Assert(param.num_nodes != 0, "invalid model"); 
+    utils::Assert(param.num_nodes != 0, "invalid model");
     utils::Check(fi.Read(BeginPtr(nodes), sizeof(Node) * nodes.size()) > 0,
                  "TreeModel: wrong format");
     utils::Check(fi.Read(BeginPtr(stats), sizeof(NodeStat) * stats.size()) > 0,
@@ -313,22 +315,22 @@ class TreeModel {
                   "number of deleted nodes do not match, num_deleted=%d, dnsize=%lu, num_nodes=%d",
                   param.num_deleted, deleted_nodes.size(), param.num_nodes);
   }
-  /*! 
+  /*!
    * \brief save model to stream
    * \param fo output stream
    */
-  inline void SaveModel(utils::IStream &fo) const {
+  inline void SaveModel(utils::IStream &fo) const { // NOLINT(*)
     utils::Assert(param.num_nodes == static_cast<int>(nodes.size()),
                   "Tree::SaveModel");
     utils::Assert(param.num_nodes == static_cast<int>(stats.size()),
                   "Tree::SaveModel");
     fo.Write(&param, sizeof(Param));
-    utils::Assert(param.num_nodes != 0, "invalid model"); 
+    utils::Assert(param.num_nodes != 0, "invalid model");
     fo.Write(BeginPtr(nodes), sizeof(Node) * nodes.size());
     fo.Write(BeginPtr(stats), sizeof(NodeStat) * nodes.size());
     if (param.size_leaf_vector != 0) fo.Write(leaf_vector);
   }
-  /*! 
+  /*!
    * \brief add child nodes to node
    * \param nid node id to add childs
    */
@@ -340,8 +342,8 @@ class TreeModel {
     nodes[nodes[nid].cleft() ].set_parent(nid, true);
     nodes[nodes[nid].cright()].set_parent(nid, false);
   }
-  /*! 
+  /*!
-   * \brief only add a right child to a leaf node 
+   * \brief only add a right child to a leaf node
    * \param node id to add right child
    */
   inline void AddRightChild(int nid) {
@@ -385,7 +387,7 @@ class TreeModel {
   inline int num_extra_nodes(void) const {
     return param.num_nodes - param.num_roots - param.num_deleted;
   }
-  /*! 
+  /*!
    * \brief dump model to text string
    * \param fmap feature map of feature types
    * \param with_stats whether dump out statistics as well
@@ -400,7 +402,7 @@ class TreeModel {
   }
 
  private:
-  void Dump(int nid, std::stringstream &fo,
+  void Dump(int nid, std::stringstream &fo, // NOLINT(*)
             const utils::FeatMap& fmap, int depth, bool with_stats) {
     for (int i = 0;  i < depth; ++i) {
       fo << '\t';
@@ -469,7 +471,7 @@ struct RTreeNodeStat {
   /*! \brief number of child that is leaf node known up to now */
   int   leaf_child_cnt;
   /*! \brief print information of current stats to fo */
-  inline void Print(std::stringstream &fo, bool is_leaf) const {
+  inline void Print(std::stringstream &fo, bool is_leaf) const { // NOLINT(*)
     if (!is_leaf) {
       fo << ",gain=" << loss_chg << ",cover=" << sum_hess;
     } else {
@@ -481,13 +483,13 @@ struct RTreeNodeStat {
 /*! \brief define regression tree to be the most common tree model */
 class RegTree: public TreeModel<bst_float, RTreeNodeStat>{
  public:
-  /*! 
+  /*!
    * \brief dense feature vector that can be taken by RegTree
    * to do tranverse efficiently
    * and can be construct from sparse feature vector
    */
   struct FVec {
-    /*! 
+    /*!
      * \brief a union value of value and flag
      * when flag == -1, this indicate the value is missing
      */
@@ -510,7 +512,7 @@ class RegTree: public TreeModel<bst_float, RTreeNodeStat>{
       }
     }
     /*! \brief drop the trace after fill, must be called after fill */
-    inline void Drop(const RowBatch::Inst &inst) {      
+    inline void Drop(const RowBatch::Inst &inst) {
       for (bst_uint i = 0; i < inst.length; ++i) {
         if (inst[i].index >= data.size()) continue;
         data[inst[i].index].flag = -1;
@@ -526,10 +528,10 @@ class RegTree: public TreeModel<bst_float, RTreeNodeStat>{
     }
   };
   /*!
-   * \brief get the leaf index 
+   * \brief get the leaf index
    * \param feats dense feature vector, if the feature is missing the field is set to NaN
    * \param root_gid starting root index of the instance
-   * \return the leaf index of the given feature 
+   * \return the leaf index of the given feature
    */
   inline int GetLeafIndex(const FVec&feat, unsigned root_id = 0) const {
     // start from groups that belongs to current data
@@ -545,7 +547,7 @@ class RegTree: public TreeModel<bst_float, RTreeNodeStat>{
    * \brief get the prediction of regression tree, only accepts dense feature vector
    * \param feats dense feature vector, if the feature is missing the field is set to NaN
    * \param root_gid starting root index of the instance
-   * \return the leaf index of the given feature 
+   * \return the leaf index of the given feature
    */
   inline float Predict(const FVec &feat, unsigned root_id = 0) const {
     int pid = this->GetLeafIndex(feat, root_id);

src/tree/param.h

@@ -1,10 +1,13 @@
-#ifndef XGBOOST_TREE_PARAM_H_
-#define XGBOOST_TREE_PARAM_H_
 /*!
+ * Copyright 2014 by Contributors
  * \file param.h
  * \brief training parameters, statistics used to support tree construction
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_TREE_PARAM_H_
+#define XGBOOST_TREE_PARAM_H_
+
+#include <vector>
 #include <cstring>
 #include "../data.h"
 
@@ -27,7 +30,7 @@ struct TrainParam{
   // L1 regularization factor
   float reg_alpha;
   // default direction choice
-  int default_direction;  
+  int default_direction;
   // maximum delta update we can add in weight estimation
   // this parameter can be used to stablize update
   // default=0 means no constraint on weight delta
@@ -45,7 +48,7 @@ struct TrainParam{
   // accuracy of sketch
   float sketch_ratio;
   // leaf vector size
-  int size_leaf_vector;  
+  int size_leaf_vector;
   // option for parallelization
   int parallel_option;
   // option to open cacheline optimizaton
@@ -74,11 +77,11 @@ struct TrainParam{
     sketch_ratio = 2.0f;
     cache_opt = 1;
   }
-  /*! 
+  /*!
-   * \brief set parameters from outside 
+   * \brief set parameters from outside
    * \param name name of the parameter
    * \param val  value of the parameter
-   */            
+   */
   inline void SetParam(const char *name, const char *val) {
     using namespace std;
     // sync-names
@@ -116,7 +119,7 @@ struct TrainParam{
       if (reg_alpha == 0.0f) {
         return Sqr(sum_grad) / (sum_hess + reg_lambda);
       } else {
-        return Sqr(ThresholdL1(sum_grad, reg_alpha)) / (sum_hess + reg_lambda); 
+        return Sqr(ThresholdL1(sum_grad, reg_alpha)) / (sum_hess + reg_lambda);
       }
     } else {
       double w = CalcWeight(sum_grad, sum_hess);
@@ -213,7 +216,7 @@ struct GradStats {
   inline static void CheckInfo(const BoosterInfo &info) {
   }
   /*!
-   * \brief accumulate statistics 
+   * \brief accumulate statistics
    * \param p the gradient pair
    */
   inline void Add(bst_gpair p) {
@@ -222,7 +225,7 @@ struct GradStats {
   /*!
    * \brief accumulate statistics, more complicated version
    * \param gpair the vector storing the gradient statistics
-   * \param info the additional information 
+   * \param info the additional information
    * \param ridx instance index of this instance
    */
   inline void Add(const std::vector<bst_gpair> &gpair,
@@ -244,7 +247,7 @@ struct GradStats {
     this->Add(b.sum_grad, b.sum_hess);
   }
   /*! \brief same as add, reduce is used in All Reduce */
-  inline static void Reduce(GradStats &a, const GradStats &b) {
+  inline static void Reduce(GradStats &a, const GradStats &b) { // NOLINT(*)
     a.Add(b);
   }
   /*! \brief set current value to a - b */
@@ -257,8 +260,8 @@ struct GradStats {
     return sum_hess == 0.0;
   }
   /*! \brief set leaf vector value based on statistics */
-  inline void SetLeafVec(const TrainParam &param, bst_float *vec) const{
+  inline void SetLeafVec(const TrainParam &param, bst_float *vec) const {
-  }  
+  }
   // constructor to allow inheritance
   GradStats(void) {}
   /*! \brief add statistics to the data */
@@ -311,7 +314,7 @@ struct CVGradStats : public GradStats {
       ret += param.CalcGain(train[i].sum_grad,
                             train[i].sum_hess,
                             vsize * valid[i].sum_grad,
-                            vsize * valid[i].sum_hess);      
+                            vsize * valid[i].sum_hess);
     }
     return ret / vsize;
   }
@@ -324,7 +327,7 @@ struct CVGradStats : public GradStats {
     }
   }
   /*! \brief same as add, reduce is used in All Reduce */
-  inline static void Reduce(CVGradStats &a, const CVGradStats &b) {
+  inline static void Reduce(CVGradStats &a, const CVGradStats &b) { // NOLINT(*)
     a.Add(b);
   }
   /*! \brief set current value to a - b */
@@ -344,8 +347,8 @@ struct CVGradStats : public GradStats {
   }
 };
 
-/*! 
+/*!
- * \brief statistics that is helpful to store 
+ * \brief statistics that is helpful to store
  *   and represent a split solution for the tree
  */
 struct SplitEntry{
@@ -357,12 +360,12 @@ struct SplitEntry{
   float split_value;
   /*! \brief constructor */
   SplitEntry(void) : loss_chg(0.0f), sindex(0), split_value(0.0f) {}
-  /*! 
+  /*!
-   * \brief decides whether a we can replace current entry with the statistics given 
+   * \brief decides whether a we can replace current entry with the statistics given
    *   This function gives better priority to lower index when loss_chg equals
    *    not the best way, but helps to give consistent result during multi-thread execution
    * \param loss_chg the loss reduction get through the split
-   * \param split_index the feature index where the split is on 
+   * \param split_index the feature index where the split is on
    */
   inline bool NeedReplace(bst_float new_loss_chg, unsigned split_index) const {
     if (this->split_index() <= split_index) {
@@ -371,7 +374,7 @@ struct SplitEntry{
       return !(this->loss_chg > new_loss_chg);
     }
   }
-  /*! 
+  /*!
    * \brief update the split entry, replace it if e is better
    * \param e candidate split solution
    * \return whether the proposed split is better and can replace current split
@@ -386,7 +389,7 @@ struct SplitEntry{
       return false;
     }
   }
-  /*! 
+  /*!
    * \brief update the split entry, replace it if e is better
    * \param loss_chg loss reduction of new candidate
    * \param split_index feature index to split on
@@ -407,7 +410,7 @@ struct SplitEntry{
     }
   }
   /*! \brief same as update, used by AllReduce*/
-  inline static void Reduce(SplitEntry &dst, const SplitEntry &src) {
+  inline static void Reduce(SplitEntry &dst, const SplitEntry &src) { // NOLINT(*)
     dst.Update(src);
   }
   /*!\return feature index to split on */

src/tree/updater.cpp

@@ -1,3 +1,4 @@
+// Copyright 2014 by Contributors
 #define _CRT_SECURE_NO_WARNINGS
 #define _CRT_SECURE_NO_DEPRECATE
 #define NOMINMAX

src/tree/updater.h

@@ -1,10 +1,12 @@
-#ifndef XGBOOST_TREE_UPDATER_H_
-#define XGBOOST_TREE_UPDATER_H_
 /*!
+ * Copyright 2014 by Contributors
  * \file updater.h
  * \brief interface to update the tree
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_TREE_UPDATER_H_
+#define XGBOOST_TREE_UPDATER_H_
+
 #include <vector>
 
 #include "../data.h"
@@ -12,7 +14,7 @@
 
 namespace xgboost {
 namespace tree {
-/*! 
+/*!
  * \brief interface of tree update module, that performs update of a tree
  */
 class IUpdater {
@@ -21,7 +23,7 @@ class IUpdater {
    * \brief set parameters from outside
    * \param name name of the parameter
    * \param val  value of the parameter
-   */  
+   */
   virtual void SetParam(const char *name, const char *val) = 0;
   /*!
    * \brief peform update to the tree models
@@ -29,8 +31,8 @@ class IUpdater {
    * \param p_fmat feature matrix that provide access to features
    * \param info extra side information that may be need, such as root index
    * \param trees pointer to the trees to be updated, upater will change the content of the tree
-   *   note: all the trees in the vector are updated, with the same statistics, 
+   *   note: all the trees in the vector are updated, with the same statistics,
-   *         but maybe different random seeds, usually one tree is passed in at a time, 
+   *         but maybe different random seeds, usually one tree is passed in at a time,
    *         there can be multiple trees when we train random forest style model
    */
   virtual void Update(const std::vector<bst_gpair> &gpair,
@@ -38,7 +40,7 @@ class IUpdater {
                       const BoosterInfo &info,
                       const std::vector<RegTree*> &trees) = 0;
 
-  /*! 
+  /*!
    * \brief this is simply a function for optimizing performance
    * this function asks the updater to return the leaf position of each instance in the p_fmat,
    * if it is cached in the updater, if it is not available, return NULL
@@ -50,8 +52,8 @@ class IUpdater {
   // destructor
   virtual ~IUpdater(void) {}
 };
-/*! 
+/*!
- * \brief create a updater based on name 
+ * \brief create a updater based on name
  * \param name name of updater
  * \return return the updater instance
  */

src/tree/updater_basemaker-inl.hpp

@@ -1,12 +1,14 @@
-#ifndef XGBOOST_TREE_UPDATER_BASEMAKER_INL_HPP_
-#define XGBOOST_TREE_UPDATER_BASEMAKER_INL_HPP_
 /*!
+ * Copyright 2014 by Contributors
  * \file updater_basemaker-inl.hpp
  * \brief implement a common tree constructor
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_TREE_UPDATER_BASEMAKER_INL_HPP_
+#define XGBOOST_TREE_UPDATER_BASEMAKER_INL_HPP_
 #include <vector>
 #include <algorithm>
+#include <string>
 #include <limits>
 #include "../sync/sync.h"
 #include "../utils/random.h"
@@ -14,7 +16,7 @@
 
 namespace xgboost {
 namespace tree {
-/*! 
+/*!
  * \brief base tree maker class that defines common operation
  *  needed in tree making
  */
@@ -26,7 +28,7 @@ class BaseMaker: public IUpdater {
   virtual void SetParam(const char *name, const char *val) {
     param.SetParam(name, val);
   }
-   
+
  protected:
   // helper to collect and query feature meta information
   struct FMetaHelper {
@@ -60,8 +62,11 @@ class BaseMaker: public IUpdater {
       bst_float a = fminmax[fid * 2];
       bst_float b = fminmax[fid * 2 + 1];
       if (a == -std::numeric_limits<bst_float>::max()) return 0;
-      if (-a == b) return 1;
+      if (-a == b) {
-      else return 2;
+        return 1;
+      } else {
+        return 2;
+      }
     }
     inline bst_float MaxValue(bst_uint fid) const {
       return fminmax[fid *2 + 1];
@@ -70,7 +75,7 @@ class BaseMaker: public IUpdater {
       std::vector<bst_uint> &findex = *p_findex;
       findex.clear();
       for (size_t i = 0; i < fminmax.size(); i += 2) {
-		const bst_uint fid = static_cast<bst_uint>(i / 2);
+        const bst_uint fid = static_cast<bst_uint>(i / 2);
         if (this->Type(fid) != 0) findex.push_back(fid);
       }
       unsigned n = static_cast<unsigned>(p * findex.size());
@@ -86,7 +91,7 @@ class BaseMaker: public IUpdater {
       rabit::Broadcast(&s_cache, 0);
       fs.Read(&findex);
     }
-    
+
    private:
     std::vector<bst_float> fminmax;
   };
@@ -116,7 +121,7 @@ class BaseMaker: public IUpdater {
     }
     return nthread;
   }
-  // ------class member helpers---------
+  //  ------class member helpers---------
   /*! \brief initialize temp data structure */
   inline void InitData(const std::vector<bst_gpair> &gpair,
                        const IFMatrix &fmat,
@@ -124,7 +129,8 @@ class BaseMaker: public IUpdater {
                        const RegTree &tree) {
     utils::Assert(tree.param.num_nodes == tree.param.num_roots,
                   "TreeMaker: can only grow new tree");
-    {// setup position
+    {
+      // setup position
       position.resize(gpair.size());
       if (root_index.size() == 0) {
         std::fill(position.begin(), position.end(), 0);
@@ -147,7 +153,8 @@ class BaseMaker: public IUpdater {
         }
       }
     }
-    {// expand query
+    {
+      // expand query
       qexpand.reserve(256); qexpand.clear();
       for (int i = 0; i < tree.param.num_roots; ++i) {
         qexpand.push_back(i);
@@ -170,7 +177,7 @@ class BaseMaker: public IUpdater {
     this->UpdateNode2WorkIndex(tree);
   }
   // return decoded position
-  inline int DecodePosition(bst_uint ridx) const{
+  inline int DecodePosition(bst_uint ridx) const {
     const int pid = position[ridx];
     return pid < 0 ? ~pid : pid;
   }
@@ -182,23 +189,24 @@ class BaseMaker: public IUpdater {
       position[ridx] = nid;
     }
   }
-  /*! 
+  /*!
    * \brief this is helper function uses column based data structure,
    *        reset the positions to the lastest one
    * \param nodes the set of nodes that contains the split to be used
    * \param p_fmat feature matrix needed for tree construction
    * \param tree the regression tree structure
    */
-  inline void ResetPositionCol(const std::vector<int> &nodes, IFMatrix *p_fmat, const RegTree &tree) {
+  inline void ResetPositionCol(const std::vector<int> &nodes,
+                               IFMatrix *p_fmat, const RegTree &tree) {
     // set the positions in the nondefault
     this->SetNonDefaultPositionCol(nodes, p_fmat, tree);
     // set rest of instances to default position
     const std::vector<bst_uint> &rowset = p_fmat->buffered_rowset();
     // set default direct nodes to default
-    // for leaf nodes that are not fresh, mark then to ~nid, 
+    // for leaf nodes that are not fresh, mark then to ~nid,
     // so that they are ignored in future statistics collection
     const bst_omp_uint ndata = static_cast<bst_omp_uint>(rowset.size());
-    
+
     #pragma omp parallel for schedule(static)
     for (bst_omp_uint i = 0; i < ndata; ++i) {
       const bst_uint ridx = rowset[i];
@@ -237,7 +245,7 @@ class BaseMaker: public IUpdater {
     }
     std::sort(fsplits.begin(), fsplits.end());
     fsplits.resize(std::unique(fsplits.begin(), fsplits.end()) - fsplits.begin());
-    
+
     utils::IIterator<ColBatch> *iter = p_fmat->ColIterator(fsplits);
     while (iter->Next()) {
       const ColBatch &batch = iter->Value();
@@ -252,7 +260,7 @@ class BaseMaker: public IUpdater {
           const int nid = this->DecodePosition(ridx);
           // go back to parent, correct those who are not default
           if (!tree[nid].is_leaf() && tree[nid].split_index() == fid) {
-            if(fvalue < tree[nid].split_cond()) {
+            if (fvalue < tree[nid].split_cond()) {
               this->SetEncodePosition(ridx, tree[nid].cleft());
             } else {
               this->SetEncodePosition(ridx, tree[nid].cright());
@@ -324,7 +332,7 @@ class BaseMaker: public IUpdater {
       sketch->temp.size = 0;
     }
     /*!
-     * \brief push a new element to sketch 
+     * \brief push a new element to sketch
      * \param fvalue feature value, comes in sorted ascending order
      * \param w weight
      * \param max_size
@@ -337,31 +345,32 @@ class BaseMaker: public IUpdater {
         return;
       }
       if (last_fvalue != fvalue) {
-	double rmax = rmin + wmin;
+        double rmax = rmin + wmin;
         if (rmax >= next_goal && sketch->temp.size != max_size) {
-          if (sketch->temp.size == 0 || last_fvalue > sketch->temp.data[sketch->temp.size-1].value) {
+          if (sketch->temp.size == 0 ||
+              last_fvalue > sketch->temp.data[sketch->temp.size-1].value) {
             // push to sketch
             sketch->temp.data[sketch->temp.size] =
                 utils::WXQuantileSketch<bst_float, bst_float>::
                 Entry(static_cast<bst_float>(rmin),
-			          static_cast<bst_float>(rmax),
+                      static_cast<bst_float>(rmax),
-					  static_cast<bst_float>(wmin), last_fvalue);
+                      static_cast<bst_float>(wmin), last_fvalue);
             utils::Assert(sketch->temp.size < max_size,
                           "invalid maximum size max_size=%u, stemp.size=%lu\n",
                           max_size, sketch->temp.size);
             ++sketch->temp.size;
           }
           if (sketch->temp.size == max_size) {
-            next_goal = sum_total * 2.0f + 1e-5f;	    
+            next_goal = sum_total * 2.0f + 1e-5f;
-          } else{
+          } else {
             next_goal = static_cast<bst_float>(sketch->temp.size * sum_total / max_size);
           }
         } else {
-	  if (rmax >= next_goal) {
+          if (rmax >= next_goal) {
-	    rabit::TrackerPrintf("INFO: rmax=%g, sum_total=%g, next_goal=%g, size=%lu\n",
+            rabit::TrackerPrintf("INFO: rmax=%g, sum_total=%g, next_goal=%g, size=%lu\n",
-				 rmax, sum_total, next_goal, sketch->temp.size);
+                                 rmax, sum_total, next_goal, sketch->temp.size);
-	  }
+          }
-	}
+        }
         rmin = rmax;
         wmin = w;
         last_fvalue = fvalue;
@@ -375,13 +384,13 @@ class BaseMaker: public IUpdater {
       if (sketch->temp.size == 0 || last_fvalue > sketch->temp.data[sketch->temp.size-1].value) {
         utils::Assert(sketch->temp.size <= max_size,
                       "Finalize: invalid maximum size, max_size=%u, stemp.size=%lu",
-                      sketch->temp.size, max_size );
+                      sketch->temp.size, max_size);
         // push to sketch
         sketch->temp.data[sketch->temp.size] =
             utils::WXQuantileSketch<bst_float, bst_float>::
             Entry(static_cast<bst_float>(rmin),
-			      static_cast<bst_float>(rmax),
+                  static_cast<bst_float>(rmax),
-				  static_cast<bst_float>(wmin), last_fvalue);
+                  static_cast<bst_float>(wmin), last_fvalue);
         ++sketch->temp.size;
       }
       sketch->PushTemp();
@@ -415,4 +424,4 @@ class BaseMaker: public IUpdater {
 };
 }  // namespace tree
 }  // namespace xgboost
-#endif // XGBOOST_TREE_UPDATER_BASEMAKER_INL_HPP_
+#endif  // XGBOOST_TREE_UPDATER_BASEMAKER_INL_HPP_

src/tree/updater_colmaker-inl.hpp

@@ -1,10 +1,12 @@
-#ifndef XGBOOST_TREE_UPDATER_COLMAKER_INL_HPP_
-#define XGBOOST_TREE_UPDATER_COLMAKER_INL_HPP_
 /*!
+ * Copyright 2014 by Contributors
  * \file updater_colmaker-inl.hpp
  * \brief use columnwise update to construct a tree
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_TREE_UPDATER_COLMAKER_INL_HPP_
+#define XGBOOST_TREE_UPDATER_COLMAKER_INL_HPP_
+
 #include <vector>
 #include <cmath>
 #include <algorithm>
@@ -114,10 +116,13 @@ class ColMaker: public IUpdater {
     // initialize temp data structure
     inline void InitData(const std::vector<bst_gpair> &gpair,
                          const IFMatrix &fmat,
-                         const std::vector<unsigned> &root_index, const RegTree &tree) {
+                         const std::vector<unsigned> &root_index,
-      utils::Assert(tree.param.num_nodes == tree.param.num_roots, "ColMaker: can only grow new tree");
+                         const RegTree &tree) {
+      utils::Assert(tree.param.num_nodes == tree.param.num_roots,
+                    "ColMaker: can only grow new tree");
       const std::vector<bst_uint> &rowset = fmat.buffered_rowset();
-      {// setup position
+      {
+        // setup position
         position.resize(gpair.size());
         if (root_index.size() == 0) {
           for (size_t i = 0; i < rowset.size(); ++i) {
@@ -127,7 +132,8 @@ class ColMaker: public IUpdater {
           for (size_t i = 0; i < rowset.size(); ++i) {
             const bst_uint ridx = rowset[i];
             position[ridx] = root_index[ridx];
-            utils::Assert(root_index[ridx] < (unsigned)tree.param.num_roots, "root index exceed setting");
+            utils::Assert(root_index[ridx] < (unsigned)tree.param.num_roots,
+                          "root index exceed setting");
           }
         }
         // mark delete for the deleted datas
@@ -154,11 +160,12 @@ class ColMaker: public IUpdater {
         }
         unsigned n = static_cast<unsigned>(param.colsample_bytree * feat_index.size());
         random::Shuffle(feat_index);
-        //utils::Check(n > 0, "colsample_bytree is too small that no feature can be included");
+        utils::Check(n > 0, "colsample_bytree=%g is too small that no feature can be included",
-        utils::Check(n > 0, "colsample_bytree=%g is too small that no feature can be included", param.colsample_bytree);
+                     param.colsample_bytree);
         feat_index.resize(n);
       }
-      {// setup temp space for each thread
+      {
+        // setup temp space for each thread
         #pragma omp parallel
         {
           this->nthread = omp_get_num_threads();
@@ -171,20 +178,25 @@ class ColMaker: public IUpdater {
         }
         snode.reserve(256);
       }
-      {// expand query
+      {
+        // expand query
         qexpand_.reserve(256); qexpand_.clear();
         for (int i = 0; i < tree.param.num_roots; ++i) {
           qexpand_.push_back(i);
         }
       }
     }
-    /*! \brief initialize the base_weight, root_gain, and NodeEntry for all the new nodes in qexpand */
+    /*!
+     * \brief initialize the base_weight, root_gain,
+     *  and NodeEntry for all the new nodes in qexpand
+     */
     inline void InitNewNode(const std::vector<int> &qexpand,
                             const std::vector<bst_gpair> &gpair,
                             const IFMatrix &fmat,
                             const BoosterInfo &info,
                             const RegTree &tree) {
-      {// setup statistics space for each tree node
+      {
+        // setup statistics space for each tree node
         for (size_t i = 0; i < stemp.size(); ++i) {
           stemp[i].resize(tree.param.num_nodes, ThreadEntry(param));
         }
@@ -226,7 +238,7 @@ class ColMaker: public IUpdater {
       }
       // use new nodes for qexpand
       qexpand = newnodes;
-    }    
+    }
     // parallel find the best split of current fid
     // this function does not support nested functions
     inline void ParallelFindSplit(const ColBatch::Inst &col,
@@ -280,26 +292,30 @@ class ColMaker: public IUpdater {
           ThreadEntry &e = stemp[tid][nid];
           float fsplit;
           if (tid != 0) {
-            if(std::abs(stemp[tid - 1][nid].last_fvalue - e.first_fvalue) > rt_2eps) {
+            if (std::abs(stemp[tid - 1][nid].last_fvalue - e.first_fvalue) > rt_2eps) {
               fsplit = (stemp[tid - 1][nid].last_fvalue - e.first_fvalue) * 0.5f;
             } else {
               continue;
             }
           } else {
             fsplit = e.first_fvalue - rt_eps;
-          }                        
+          }
           if (need_forward && tid != 0) {
             c.SetSubstract(snode[nid].stats, e.stats);
-            if (c.sum_hess >= param.min_child_weight && e.stats.sum_hess >= param.min_child_weight) {
+            if (c.sum_hess >= param.min_child_weight &&
-              bst_float loss_chg = static_cast<bst_float>(e.stats.CalcGain(param) + c.CalcGain(param) - snode[nid].root_gain);
+                e.stats.sum_hess >= param.min_child_weight) {
+              bst_float loss_chg = static_cast<bst_float>(e.stats.CalcGain(param) +
+                                                          c.CalcGain(param) - snode[nid].root_gain);
               e.best.Update(loss_chg, fid, fsplit, false);
             }
           }
           if (need_backward) {
             tmp.SetSubstract(sum, e.stats);
             c.SetSubstract(snode[nid].stats, tmp);
-            if (c.sum_hess >= param.min_child_weight && tmp.sum_hess >= param.min_child_weight) {
+            if (c.sum_hess >= param.min_child_weight &&
-              bst_float loss_chg = static_cast<bst_float>(tmp.CalcGain(param) + c.CalcGain(param) - snode[nid].root_gain);
+                tmp.sum_hess >= param.min_child_weight) {
+              bst_float loss_chg = static_cast<bst_float>(tmp.CalcGain(param) +
+                                                          c.CalcGain(param) - snode[nid].root_gain);
               e.best.Update(loss_chg, fid, fsplit, true);
             }
           }
@@ -308,8 +324,10 @@ class ColMaker: public IUpdater {
           tmp = sum;
           ThreadEntry &e = stemp[nthread-1][nid];
           c.SetSubstract(snode[nid].stats, tmp);
-          if (c.sum_hess >= param.min_child_weight && tmp.sum_hess >= param.min_child_weight) {
+          if (c.sum_hess >= param.min_child_weight &&
-            bst_float loss_chg = static_cast<bst_float>(tmp.CalcGain(param) + c.CalcGain(param) - snode[nid].root_gain);
+              tmp.sum_hess >= param.min_child_weight) {
+            bst_float loss_chg = static_cast<bst_float>(tmp.CalcGain(param) +
+                                                        c.CalcGain(param) - snode[nid].root_gain);
             e.best.Update(loss_chg, fid, e.last_fvalue + rt_eps, true);
           }
         }
@@ -335,25 +353,31 @@ class ColMaker: public IUpdater {
             e.first_fvalue = fvalue;
           } else {
             // forward default right
-            if (std::abs(fvalue - e.first_fvalue) > rt_2eps){
+            if (std::abs(fvalue - e.first_fvalue) > rt_2eps) {
-              if (need_forward) { 
+              if (need_forward) {
                 c.SetSubstract(snode[nid].stats, e.stats);
-                if (c.sum_hess >= param.min_child_weight && e.stats.sum_hess >= param.min_child_weight) {
+                if (c.sum_hess >= param.min_child_weight &&
-                  bst_float loss_chg = static_cast<bst_float>(e.stats.CalcGain(param) + c.CalcGain(param) - snode[nid].root_gain);
+                    e.stats.sum_hess >= param.min_child_weight) {
+                  bst_float loss_chg = static_cast<bst_float>(e.stats.CalcGain(param) +
+                                                              c.CalcGain(param) -
+                                                              snode[nid].root_gain);
                   e.best.Update(loss_chg, fid, (fvalue + e.first_fvalue) * 0.5f, false);
                 }
               }
               if (need_backward) {
                 cright.SetSubstract(e.stats_extra, e.stats);
                 c.SetSubstract(snode[nid].stats, cright);
-                if (c.sum_hess >= param.min_child_weight && cright.sum_hess >= param.min_child_weight) {
+                if (c.sum_hess >= param.min_child_weight &&
-                  bst_float loss_chg = static_cast<bst_float>(cright.CalcGain(param) + c.CalcGain(param) - snode[nid].root_gain);
+                    cright.sum_hess >= param.min_child_weight) {
+                  bst_float loss_chg = static_cast<bst_float>(cright.CalcGain(param) +
+                                                              c.CalcGain(param) -
+                                                              snode[nid].root_gain);
                   e.best.Update(loss_chg, fid, (fvalue + e.first_fvalue) * 0.5f, true);
                 }
               }
-            }          
+            }
             e.stats.Add(gpair, info, ridx);
-            e.first_fvalue = fvalue;            
+            e.first_fvalue = fvalue;
           }
         }
       }
@@ -361,7 +385,7 @@ class ColMaker: public IUpdater {
     // update enumeration solution
     inline void UpdateEnumeration(int nid, bst_gpair gstats,
                                   float fvalue, int d_step, bst_uint fid,
-                                  TStats &c, std::vector<ThreadEntry> &temp) {
+                                  TStats &c, std::vector<ThreadEntry> &temp) { // NOLINT(*)
       // get the statistics of nid
       ThreadEntry &e = temp[nid];
       // test if first hit, this is fine, because we set 0 during init
@@ -370,10 +394,12 @@ class ColMaker: public IUpdater {
         e.last_fvalue = fvalue;
       } else {
         // try to find a split
-        if (std::abs(fvalue - e.last_fvalue) > rt_2eps && e.stats.sum_hess >= param.min_child_weight) {
+        if (std::abs(fvalue - e.last_fvalue) > rt_2eps &&
+            e.stats.sum_hess >= param.min_child_weight) {
           c.SetSubstract(snode[nid].stats, e.stats);
           if (c.sum_hess >= param.min_child_weight) {
-            bst_float loss_chg = static_cast<bst_float>(e.stats.CalcGain(param) + c.CalcGain(param) - snode[nid].root_gain);
+            bst_float loss_chg = static_cast<bst_float>(e.stats.CalcGain(param) +
+                                                        c.CalcGain(param) - snode[nid].root_gain);
             e.best.Update(loss_chg, fid, (fvalue + e.last_fvalue) * 0.5f, d_step == -1);
           }
         }
@@ -388,7 +414,7 @@ class ColMaker: public IUpdater {
                                        int d_step,
                                        bst_uint fid,
                                        const std::vector<bst_gpair> &gpair,
-                                       std::vector<ThreadEntry> &temp) {
+                                       std::vector<ThreadEntry> &temp) { // NOLINT(*)
       const std::vector<int> &qexpand = qexpand_;
       // clear all the temp statistics
       for (size_t j = 0; j < qexpand.size(); ++j) {
@@ -423,7 +449,7 @@ class ColMaker: public IUpdater {
           this->UpdateEnumeration(nid, buf_gpair[i],
                                   p->fvalue, d_step,
                                   fid, c, temp);
-        }        
+        }
       }
       // finish up the ending piece
       for (it = align_end, i = 0; it != end; ++i, it += d_step) {
@@ -436,14 +462,15 @@ class ColMaker: public IUpdater {
         this->UpdateEnumeration(nid, buf_gpair[i],
                                 it->fvalue, d_step,
                                 fid, c, temp);
-      }            
+      }
       // finish updating all statistics, check if it is possible to include all sum statistics
       for (size_t i = 0; i < qexpand.size(); ++i) {
         const int nid = qexpand[i];
         ThreadEntry &e = temp[nid];
         c.SetSubstract(snode[nid].stats, e.stats);
         if (e.stats.sum_hess >= param.min_child_weight && c.sum_hess >= param.min_child_weight) {
-          bst_float loss_chg = static_cast<bst_float>(e.stats.CalcGain(param) + c.CalcGain(param) - snode[nid].root_gain);
+          bst_float loss_chg = static_cast<bst_float>(e.stats.CalcGain(param) +
+                                                      c.CalcGain(param) - snode[nid].root_gain);
           const float gap = std::abs(e.last_fvalue) + rt_eps;
           const float delta = d_step == +1 ? gap: -gap;
           e.best.Update(loss_chg, fid, e.last_fvalue + delta, d_step == -1);
@@ -458,7 +485,7 @@ class ColMaker: public IUpdater {
                                bst_uint fid,
                                const std::vector<bst_gpair> &gpair,
                                const BoosterInfo &info,
-                               std::vector<ThreadEntry> &temp) {
+                               std::vector<ThreadEntry> &temp) { // NOLINT(*)
       // use cacheline aware optimization
       if (TStats::kSimpleStats != 0 && param.cache_opt != 0) {
         EnumerateSplitCacheOpt(begin, end, d_step, fid, gpair, temp);
@@ -471,7 +498,7 @@ class ColMaker: public IUpdater {
       }
       // left statistics
       TStats c(param);
-      for(const ColBatch::Entry *it = begin; it != end; it += d_step) {
+      for (const ColBatch::Entry *it = begin; it != end; it += d_step) {
         const bst_uint ridx = it->index;
         const int nid = position[ridx];
         if (nid < 0) continue;
@@ -485,10 +512,12 @@ class ColMaker: public IUpdater {
           e.last_fvalue = fvalue;
         } else {
           // try to find a split
-          if (std::abs(fvalue - e.last_fvalue) > rt_2eps && e.stats.sum_hess >= param.min_child_weight) {
+          if (std::abs(fvalue - e.last_fvalue) > rt_2eps &&
+              e.stats.sum_hess >= param.min_child_weight) {
             c.SetSubstract(snode[nid].stats, e.stats);
             if (c.sum_hess >= param.min_child_weight) {
-              bst_float loss_chg = static_cast<bst_float>(e.stats.CalcGain(param) + c.CalcGain(param) - snode[nid].root_gain);
+              bst_float loss_chg = static_cast<bst_float>(e.stats.CalcGain(param) +
+                                                          c.CalcGain(param) - snode[nid].root_gain);
               e.best.Update(loss_chg, fid, (fvalue + e.last_fvalue) * 0.5f, d_step == -1);
             }
           }
@@ -503,7 +532,8 @@ class ColMaker: public IUpdater {
         ThreadEntry &e = temp[nid];
         c.SetSubstract(snode[nid].stats, e.stats);
         if (e.stats.sum_hess >= param.min_child_weight && c.sum_hess >= param.min_child_weight) {
-          bst_float loss_chg = static_cast<bst_float>(e.stats.CalcGain(param) + c.CalcGain(param) - snode[nid].root_gain);
+          bst_float loss_chg = static_cast<bst_float>(e.stats.CalcGain(param) +
+                                                      c.CalcGain(param) - snode[nid].root_gain);
           const float gap = std::abs(e.last_fvalue) + rt_eps;
           const float delta = d_step == +1 ? gap: -gap;
           e.best.Update(loss_chg, fid, e.last_fvalue + delta, d_step == -1);
@@ -511,14 +541,14 @@ class ColMaker: public IUpdater {
       }
     }
 
-    // update the solution candidate 
+    // update the solution candidate
     virtual void UpdateSolution(const ColBatch &batch,
                                 const std::vector<bst_gpair> &gpair,
                                 const IFMatrix &fmat,
                                 const BoosterInfo &info) {
       // start enumeration
       const bst_omp_uint nsize = static_cast<bst_omp_uint>(batch.size);
-      #if defined(_OPENMP)                                                                
+      #if defined(_OPENMP)
       const int batch_size = std::max(static_cast<int>(nsize / this->nthread / 32), 1);
       #endif
       int poption = param.parallel_option;
@@ -533,11 +563,11 @@ class ColMaker: public IUpdater {
           const ColBatch::Inst c = batch[i];
           const bool ind = c.length != 0 && c.data[0].fvalue == c.data[c.length - 1].fvalue;
           if (param.need_forward_search(fmat.GetColDensity(fid), ind)) {
-            this->EnumerateSplit(c.data, c.data + c.length, +1, 
+            this->EnumerateSplit(c.data, c.data + c.length, +1,
                                  fid, gpair, info, stemp[tid]);
           }
           if (param.need_backward_search(fmat.GetColDensity(fid), ind)) {
-            this->EnumerateSplit(c.data + c.length - 1, c.data - 1, -1, 
+            this->EnumerateSplit(c.data + c.length - 1, c.data - 1, -1,
                                  fid, gpair, info, stemp[tid]);
           }
         }
@@ -546,7 +576,7 @@ class ColMaker: public IUpdater {
           this->ParallelFindSplit(batch[i], batch.col_index[i],
                                   fmat, gpair, info);
         }
-      }      
+      }
     }
     // find splits at current level, do split per level
     inline void FindSplit(int depth,
@@ -571,7 +601,7 @@ class ColMaker: public IUpdater {
       // get the best result, we can synchronize the solution
       for (size_t i = 0; i < qexpand.size(); ++i) {
         const int nid = qexpand[i];
-        NodeEntry &e = snode[nid];        
+        NodeEntry &e = snode[nid];
         // now we know the solution in snode[nid], set split
         if (e.best.loss_chg > rt_eps) {
           p_tree->AddChilds(nid);
@@ -582,19 +612,20 @@ class ColMaker: public IUpdater {
         } else {
           (*p_tree)[nid].set_leaf(e.weight * param.learning_rate);
         }
-      } 
+      }
     }
     // reset position of each data points after split is created in the tree
-    inline void ResetPosition(const std::vector<int> &qexpand, IFMatrix *p_fmat, const RegTree &tree) {
+    inline void ResetPosition(const std::vector<int> &qexpand,
+                              IFMatrix *p_fmat, const RegTree &tree) {
       // set the positions in the nondefault
-      this->SetNonDefaultPosition(qexpand, p_fmat, tree);      
+      this->SetNonDefaultPosition(qexpand, p_fmat, tree);
       // set rest of instances to default position
       const std::vector<bst_uint> &rowset = p_fmat->buffered_rowset();
       // set default direct nodes to default
-      // for leaf nodes that are not fresh, mark then to ~nid, 
+      // for leaf nodes that are not fresh, mark then to ~nid,
       // so that they are ignored in future statistics collection
       const bst_omp_uint ndata = static_cast<bst_omp_uint>(rowset.size());
-      
+
       #pragma omp parallel for schedule(static)
       for (bst_omp_uint i = 0; i < ndata; ++i) {
         const bst_uint ridx = rowset[i];
@@ -655,7 +686,7 @@ class ColMaker: public IUpdater {
             const float fvalue = col[j].fvalue;
             // go back to parent, correct those who are not default
             if (!tree[nid].is_leaf() && tree[nid].split_index() == fid) {
-              if(fvalue < tree[nid].split_cond()) {
+              if (fvalue < tree[nid].split_cond()) {
                 this->SetEncodePosition(ridx, tree[nid].cleft());
               } else {
                 this->SetEncodePosition(ridx, tree[nid].cright());
@@ -667,7 +698,7 @@ class ColMaker: public IUpdater {
     }
     // utils to get/set position, with encoded format
     // return decoded position
-    inline int DecodePosition(bst_uint ridx) const{
+    inline int DecodePosition(bst_uint ridx) const {
       const int pid = position[ridx];
       return pid < 0 ? ~pid : pid;
     }
@@ -679,7 +710,7 @@ class ColMaker: public IUpdater {
         position[ridx] = nid;
       }
     }
-    //--data fields--
+    //  --data fields--
     const TrainParam &param;
     // number of omp thread used during training
     int nthread;

src/tree/updater_distcol-inl.hpp

@@ -1,11 +1,15 @@
-#ifndef XGBOOST_TREE_UPDATER_DISTCOL_INL_HPP_
-#define XGBOOST_TREE_UPDATER_DISTCOL_INL_HPP_
 /*!
+ * Copyright 2014 by Contributors
  * \file updater_distcol-inl.hpp
- * \brief beta distributed version that takes a sub-column 
+ * \brief beta distributed version that takes a sub-column
  *        and construct a tree
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_TREE_UPDATER_DISTCOL_INL_HPP_
+#define XGBOOST_TREE_UPDATER_DISTCOL_INL_HPP_
+
+#include <vector>
+#include <algorithm>
 #include "../sync/sync.h"
 #include "../utils/bitmap.h"
 #include "../utils/io.h"
@@ -27,7 +31,7 @@ class DistColMaker : public ColMaker<TStats> {
   virtual void Update(const std::vector<bst_gpair> &gpair,
                       IFMatrix *p_fmat,
                       const BoosterInfo &info,
-                      const std::vector<RegTree*> &trees) {    
+                      const std::vector<RegTree*> &trees) {
     TStats::CheckInfo(info);
     utils::Check(trees.size() == 1, "DistColMaker: only support one tree at a time");
     // build the tree
@@ -39,11 +43,12 @@ class DistColMaker : public ColMaker<TStats> {
   }
   virtual const int* GetLeafPosition(void) const {
     return builder.GetLeafPosition();
-  }  
+  }
+
  private:
   struct Builder : public ColMaker<TStats>::Builder {
    public:
-    Builder(const TrainParam &param) 
+    explicit Builder(const TrainParam &param)
         : ColMaker<TStats>::Builder(param) {
     }
     inline void UpdatePosition(IFMatrix *p_fmat, const RegTree &tree) {
@@ -63,7 +68,8 @@ class DistColMaker : public ColMaker<TStats> {
     virtual const int* GetLeafPosition(void) const {
       return BeginPtr(this->position);
     }
-   protected:    
+
+   protected:
     virtual void SetNonDefaultPosition(const std::vector<int> &qexpand,
                                        IFMatrix *p_fmat, const RegTree &tree) {
       // step 2, classify the non-default data into right places
@@ -87,7 +93,7 @@ class DistColMaker : public ColMaker<TStats> {
         #pragma omp parallel for schedule(static)
         for (bst_omp_uint j = 0; j < ndata; ++j) {
             boolmap[j] = 0;
-        }        
+        }
       }
       utils::IIterator<ColBatch> *iter = p_fmat->ColIterator(fsplits);
       while (iter->Next()) {
@@ -111,7 +117,7 @@ class DistColMaker : public ColMaker<TStats> {
           }
         }
       }
-      
+
       bitmap.InitFromBool(boolmap);
       // communicate bitmap
       rabit::Allreduce<rabit::op::BitOR>(BeginPtr(bitmap.data), bitmap.data.size());
@@ -142,7 +148,7 @@ class DistColMaker : public ColMaker<TStats> {
         }
         vec.push_back(this->snode[nid].best);
       }
-      // TODO, lazy version
+      // TODO(tqchen) lazy version
       // communicate best solution
       reducer.Allreduce(BeginPtr(vec), vec.size());
       // assign solution back
@@ -151,7 +157,7 @@ class DistColMaker : public ColMaker<TStats> {
         this->snode[nid].best = vec[i];
       }
     }
-    
+
    private:
     utils::BitMap bitmap;
     std::vector<int> boolmap;
@@ -162,8 +168,8 @@ class DistColMaker : public ColMaker<TStats> {
   // training parameter
   TrainParam param;
   // pointer to the builder
-  Builder builder; 
+  Builder builder;
 };
 }  // namespace tree
 }  // namespace xgboost
-#endif
+#endif  // XGBOOST_TREE_UPDATER_DISTCOL_INL_HPP_

src/tree/updater_histmaker-inl.hpp

@@ -1,10 +1,12 @@
-#ifndef XGBOOST_TREE_UPDATER_HISTMAKER_INL_HPP_
-#define XGBOOST_TREE_UPDATER_HISTMAKER_INL_HPP_
 /*!
+ * Copyright 2014 by Contributors
  * \file updater_histmaker-inl.hpp
  * \brief use histogram counting to construct a tree
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_TREE_UPDATER_HISTMAKER_INL_HPP_
+#define XGBOOST_TREE_UPDATER_HISTMAKER_INL_HPP_
+
 #include <vector>
 #include <algorithm>
 #include "../sync/sync.h"
@@ -38,7 +40,7 @@ class HistMaker: public BaseMaker {
   struct HistUnit {
     /*! \brief cutting point of histogram, contains maximum point */
     const bst_float *cut;
-    /*! \brief content of statistics data */    
+    /*! \brief content of statistics data */
     TStats *data;
     /*! \brief size of histogram */
     unsigned size;
@@ -48,13 +50,13 @@ class HistMaker: public BaseMaker {
     HistUnit(const bst_float *cut, TStats *data, unsigned size)
         : cut(cut), data(data), size(size) {}
     /*! \brief add a histogram to data */
-    inline void Add(bst_float fv, 
+    inline void Add(bst_float fv,
                     const std::vector<bst_gpair> &gpair,
                     const BoosterInfo &info,
                     const bst_uint ridx) {
       unsigned i = std::upper_bound(cut, cut + size, fv) - cut;
       utils::Assert(size != 0, "try insert into size=0");
-      utils::Assert(i < size, 
+      utils::Assert(i < size,
                     "maximum value must be in cut, fv = %g, cutmax=%g", fv, cut[size-1]);
       data[i].Add(gpair, info, ridx);
     }
@@ -74,7 +76,7 @@ class HistMaker: public BaseMaker {
                       rptr[fid+1] - rptr[fid]);
     }
   };
-  // thread workspace 
+  // thread workspace
   struct ThreadWSpace {
     /*! \brief actual unit pointer */
     std::vector<unsigned> rptr;
@@ -92,7 +94,7 @@ class HistMaker: public BaseMaker {
         }
         hset[tid].rptr = BeginPtr(rptr);
         hset[tid].cut = BeginPtr(cut);
-        hset[tid].data.resize(cut.size(), TStats(param));        
+        hset[tid].data.resize(cut.size(), TStats(param));
       }
     }
     // aggregate all statistics to hset[0]
@@ -147,7 +149,7 @@ class HistMaker: public BaseMaker {
   }
   // this function does two jobs
   // (1) reset the position in array position, to be the latest leaf id
-  // (2) propose a set of candidate cuts and set wspace.rptr wspace.cut correctly 
+  // (2) propose a set of candidate cuts and set wspace.rptr wspace.cut correctly
   virtual void ResetPosAndPropose(const std::vector<bst_gpair> &gpair,
                                   IFMatrix *p_fmat,
                                   const BoosterInfo &info,
@@ -171,8 +173,9 @@ class HistMaker: public BaseMaker {
                           const BoosterInfo &info,
                           const std::vector <bst_uint> &fset,
                           const RegTree &tree)  = 0;
+
  private:
-  inline void EnumerateSplit(const HistUnit &hist, 
+  inline void EnumerateSplit(const HistUnit &hist,
                              const TStats &node_sum,
                              bst_uint fid,
                              SplitEntry *best,
@@ -187,7 +190,7 @@ class HistMaker: public BaseMaker {
         c.SetSubstract(node_sum, s);
         if (c.sum_hess >= param.min_child_weight) {
           double loss_chg = s.CalcGain(param) + c.CalcGain(param) - root_gain;
-          if (best->Update((float)loss_chg, fid, hist.cut[i], false)) {
+          if (best->Update(static_cast<float>(loss_chg), fid, hist.cut[i], false)) {
             *left_sum = s;
           }
         }
@@ -200,7 +203,7 @@ class HistMaker: public BaseMaker {
         c.SetSubstract(node_sum, s);
         if (c.sum_hess >= param.min_child_weight) {
           double loss_chg = s.CalcGain(param) + c.CalcGain(param) - root_gain;
-          if (best->Update((float)loss_chg, fid, hist.cut[i-1], true)) {
+          if (best->Update(static_cast<float>(loss_chg), fid, hist.cut[i-1], true)) {
             *left_sum = c;
           }
         }
@@ -216,22 +219,22 @@ class HistMaker: public BaseMaker {
     const size_t num_feature = fset.size();
     // get the best split condition for each node
     std::vector<SplitEntry> sol(qexpand.size());
-    std::vector<TStats> left_sum(qexpand.size());    
+    std::vector<TStats> left_sum(qexpand.size());
     bst_omp_uint nexpand = static_cast<bst_omp_uint>(qexpand.size());
     #pragma omp parallel for schedule(dynamic, 1)
-    for (bst_omp_uint wid = 0; wid < nexpand; ++ wid) {
+    for (bst_omp_uint wid = 0; wid < nexpand; ++wid) {
       const int nid = qexpand[wid];
       utils::Assert(node2workindex[nid] == static_cast<int>(wid),
                     "node2workindex inconsistent");
       SplitEntry &best = sol[wid];
       TStats &node_sum = wspace.hset[0][num_feature + wid * (num_feature + 1)].data[0];
-      for (size_t i = 0; i < fset.size(); ++ i) {
+      for (size_t i = 0; i < fset.size(); ++i) {
         EnumerateSplit(this->wspace.hset[0][i + wid * (num_feature+1)],
                        node_sum, fset[i], &best, &left_sum[wid]);
       }
     }
     // get the best result, we can synchronize the solution
-    for (bst_omp_uint wid = 0; wid < nexpand; ++ wid) {
+    for (bst_omp_uint wid = 0; wid < nexpand; ++wid) {
       const int nid = qexpand[wid];
       const SplitEntry &best = sol[wid];
       const TStats &node_sum = wspace.hset[0][num_feature + wid * (num_feature + 1)].data[0];
@@ -244,7 +247,7 @@ class HistMaker: public BaseMaker {
         (*p_tree)[nid].set_split(best.split_index(),
                                  best.split_value, best.default_left());
         // mark right child as 0, to indicate fresh leaf
-        (*p_tree)[(*p_tree)[nid].cleft()].set_leaf(0.0f, 0);        
+        (*p_tree)[(*p_tree)[nid].cleft()].set_leaf(0.0f, 0);
         (*p_tree)[(*p_tree)[nid].cright()].set_leaf(0.0f, 0);
         // right side sum
         TStats right_sum;
@@ -256,11 +259,11 @@ class HistMaker: public BaseMaker {
       }
     }
   }
-  
+
   inline void SetStats(RegTree *p_tree, int nid, const TStats &node_sum) {
     p_tree->stat(nid).base_weight = static_cast<float>(node_sum.CalcWeight(param));
     p_tree->stat(nid).sum_hess = static_cast<float>(node_sum.sum_hess);
-    node_sum.SetLeafVec(param, p_tree->leafvec(nid));    
+    node_sum.SetLeafVec(param, p_tree->leafvec(nid));
   }
 };
 
@@ -270,7 +273,7 @@ class CQHistMaker: public HistMaker<TStats> {
   struct HistEntry {
     typename HistMaker<TStats>::HistUnit hist;
     unsigned istart;
-    /*! 
+    /*!
      * \brief add a histogram to data,
      * do linear scan, start from istart
      */
@@ -282,7 +285,7 @@ class CQHistMaker: public HistMaker<TStats> {
       utils::Assert(istart != hist.size, "the bound variable must be max");
       hist.data[istart].Add(gpair, info, ridx);
     }
-    /*! 
+    /*!
      * \brief add a histogram to data,
      * do linear scan, start from istart
      */
@@ -302,7 +305,7 @@ class CQHistMaker: public HistMaker<TStats> {
     feat_helper.InitByCol(p_fmat, tree);
     feat_helper.SampleCol(this->param.colsample_bytree, p_fset);
   }
-  // code to create histogram  
+  // code to create histogram
   virtual void CreateHist(const std::vector<bst_gpair> &gpair,
                           IFMatrix *p_fmat,
                           const BoosterInfo &info,
@@ -313,7 +316,7 @@ class CQHistMaker: public HistMaker<TStats> {
     std::fill(feat2workindex.begin(), feat2workindex.end(), -1);
     for (size_t i = 0; i < fset.size(); ++i) {
       feat2workindex[fset[i]] = static_cast<int>(i);
-    } 
+    }
     // start to work
     this->wspace.Init(this->param, 1);
     // if it is C++11, use lazy evaluation for Allreduce,
@@ -350,11 +353,11 @@ class CQHistMaker: public HistMaker<TStats> {
     // sync the histogram
     // if it is C++11, use lazy evaluation for Allreduce
 #if __cplusplus >= 201103L
-    this->histred.Allreduce(BeginPtr(this->wspace.hset[0].data), 
+    this->histred.Allreduce(BeginPtr(this->wspace.hset[0].data),
                             this->wspace.hset[0].data.size(), lazy_get_hist);
 #else
-    this->histred.Allreduce(BeginPtr(this->wspace.hset[0].data), this->wspace.hset[0].data.size());   
+    this->histred.Allreduce(BeginPtr(this->wspace.hset[0].data), this->wspace.hset[0].data.size());
-#endif    
+#endif
   }
   virtual void ResetPositionAfterSplit(IFMatrix *p_fmat,
                                        const RegTree &tree) {
@@ -374,11 +377,11 @@ class CQHistMaker: public HistMaker<TStats> {
         feat2workindex[fset[i]] = static_cast<int>(freal_set.size());
         freal_set.push_back(fset[i]);
       } else {
-        feat2workindex[fset[i]] = -2;  
+        feat2workindex[fset[i]] = -2;
       }
     }
     this->GetNodeStats(gpair, *p_fmat, tree, info,
-                       &thread_stats, &node_stats);       
+                       &thread_stats, &node_stats);
     sketchs.resize(this->qexpand.size() * freal_set.size());
     for (size_t i = 0; i < sketchs.size(); ++i) {
       sketchs[i].Init(info.num_row, this->param.sketch_eps);
@@ -394,7 +397,8 @@ class CQHistMaker: public HistMaker<TStats> {
 #if __cplusplus >= 201103L
     auto lazy_get_summary = [&]()
 #endif
-    {// get smmary
+        {
+      // get smmary
       thread_sketch.resize(this->get_nthread());
       // number of rows in
       const size_t nrows = p_fmat->buffered_rowset().size();
@@ -457,9 +461,9 @@ class CQHistMaker: public HistMaker<TStats> {
           this->wspace.rptr.push_back(static_cast<unsigned>(this->wspace.cut.size()));
         } else {
           utils::Assert(offset == -2, "BUG in mark");
-          bst_float cpt = feat_helper.MaxValue(fset[i]);        
+          bst_float cpt = feat_helper.MaxValue(fset[i]);
           this->wspace.cut.push_back(cpt + fabs(cpt) + rt_eps);
-          this->wspace.rptr.push_back(static_cast<unsigned>(this->wspace.cut.size()));        
+          this->wspace.rptr.push_back(static_cast<unsigned>(this->wspace.cut.size()));
         }
       }
       // reserve last value for global statistics
@@ -470,7 +474,7 @@ class CQHistMaker: public HistMaker<TStats> {
                   (fset.size() + 1) * this->qexpand.size() + 1,
                   "cut space inconsistent");
   }
-  
+
  private:
   inline void UpdateHistCol(const std::vector<bst_gpair> &gpair,
                             const ColBatch::Inst &c,
@@ -554,9 +558,9 @@ class CQHistMaker: public HistMaker<TStats> {
       }
     } else {
       for (size_t i = 0; i < this->qexpand.size(); ++i) {
-        const unsigned nid = this->qexpand[i];        
+        const unsigned nid = this->qexpand[i];
         sbuilder[nid].sum_total = static_cast<bst_float>(nstats[nid].sum_hess);
-      } 
+      }
     }
     // if only one value, no need to do second pass
     if (c[0].fvalue  == c[c.length-1].fvalue) {
@@ -589,7 +593,7 @@ class CQHistMaker: public HistMaker<TStats> {
           if (nid >= 0) {
             sbuilder[nid].Push(c[j + i].fvalue, buf_hess[i], max_size);
           }
-        }        
+        }
       }
       for (bst_uint j = align_length; j < c.length; ++j) {
         const bst_uint ridx = c[j].index;
@@ -617,7 +621,7 @@ class CQHistMaker: public HistMaker<TStats> {
   // temp space to map feature id to working index
   std::vector<int> feat2workindex;
   // set of index from fset that are real
-  std::vector<bst_uint> freal_set; 
+  std::vector<bst_uint> freal_set;
   // thread temp data
   std::vector< std::vector<BaseMaker::SketchEntry> > thread_sketch;
   // used to hold statistics
@@ -631,18 +635,18 @@ class CQHistMaker: public HistMaker<TStats> {
   // reducer for summary
   rabit::SerializeReducer<WXQSketch::SummaryContainer> sreducer;
   // per node, per feature sketch
-  std::vector< utils::WXQuantileSketch<bst_float, bst_float> > sketchs;  
+  std::vector< utils::WXQuantileSketch<bst_float, bst_float> > sketchs;
 };
 
 template<typename TStats>
-class QuantileHistMaker: public HistMaker<TStats> {  
+class QuantileHistMaker: public HistMaker<TStats> {
  protected:
   typedef utils::WXQuantileSketch<bst_float, bst_float> WXQSketch;
   virtual void ResetPosAndPropose(const std::vector<bst_gpair> &gpair,
                                   IFMatrix *p_fmat,
                                   const BoosterInfo &info,
                                   const std::vector <bst_uint> &fset,
-                                  const RegTree &tree) {    
+                                  const RegTree &tree) {
     // initialize the data structure
     int nthread = BaseMaker::get_nthread();
     sketchs.resize(this->qexpand.size() * tree.param.num_feature);
@@ -658,7 +662,7 @@ class QuantileHistMaker: public HistMaker<TStats> {
       utils::ParallelGroupBuilder<SparseBatch::Entry> builder(&col_ptr, &col_data, &thread_col_ptr);
       builder.InitBudget(tree.param.num_feature, nthread);
 
-      const bst_omp_uint nbatch = static_cast<bst_omp_uint>(batch.size);      
+      const bst_omp_uint nbatch = static_cast<bst_omp_uint>(batch.size);
       #pragma omp parallel for schedule(static)
       for (bst_omp_uint i = 0; i < nbatch; ++i) {
         RowBatch::Inst inst = batch[i];
@@ -667,11 +671,11 @@ class QuantileHistMaker: public HistMaker<TStats> {
         if (nid >= 0) {
           if (!tree[nid].is_leaf()) {
             this->position[ridx] = nid = HistMaker<TStats>::NextLevel(inst, tree, nid);
-          } 
+          }
           if (this->node2workindex[nid] < 0) {
             this->position[ridx] = ~nid;
-          } else{
+          } else {
-            for (bst_uint j = 0; j < inst.length; ++j) { 
+            for (bst_uint j = 0; j < inst.length; ++j) {
               builder.AddBudget(inst[j].index, omp_get_thread_num());
             }
           }
@@ -712,8 +716,8 @@ class QuantileHistMaker: public HistMaker<TStats> {
       summary_array[i].Reserve(max_size);
       summary_array[i].SetPrune(out, max_size);
     }
-    
+
-    size_t nbytes = WXQSketch::SummaryContainer::CalcMemCost(max_size);    
+    size_t nbytes = WXQSketch::SummaryContainer::CalcMemCost(max_size);
     sreducer.Allreduce(BeginPtr(summary_array), nbytes, summary_array.size());
     // now we get the final result of sketch, setup the cut
     this->wspace.cut.clear();

src/tree/updater_prune-inl.hpp

@@ -1,10 +1,12 @@
-#ifndef XGBOOST_TREE_UPDATER_PRUNE_INL_HPP_
-#define XGBOOST_TREE_UPDATER_PRUNE_INL_HPP_
 /*!
+ * Copyright 2014 by Contributors
  * \file updater_prune-inl.hpp
- * \brief prune a tree given the statistics 
+ * \brief prune a tree given the statistics
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_TREE_UPDATER_PRUNE_INL_HPP_
+#define XGBOOST_TREE_UPDATER_PRUNE_INL_HPP_
+
 #include <vector>
 #include "./param.h"
 #include "./updater.h"
@@ -37,9 +39,10 @@ class TreePruner: public IUpdater {
     param.learning_rate = lr;
     syncher.Update(gpair, p_fmat, info, trees);
   }
+
  private:
   // try to prune off current leaf
-  inline int TryPruneLeaf(RegTree &tree, int nid, int depth, int npruned) {
+  inline int TryPruneLeaf(RegTree &tree, int nid, int depth, int npruned) { // NOLINT(*)
     if (tree[nid].is_root()) return npruned;
     int pid = tree[nid].parent();
     RegTree::NodeStat &s = tree.stat(pid);
@@ -51,10 +54,10 @@ class TreePruner: public IUpdater {
       return this->TryPruneLeaf(tree, pid, depth - 1, npruned+2);
     } else {
       return npruned;
-    }    
+    }
   }
   /*! \brief do prunning of a tree */
-  inline void DoPrune(RegTree &tree) {
+  inline void DoPrune(RegTree &tree) { // NOLINT(*)
     int npruned = 0;
     // initialize auxiliary statistics
     for (int nid = 0; nid < tree.param.num_nodes; ++nid) {

src/tree/updater_refresh-inl.hpp

@@ -1,10 +1,12 @@
-#ifndef XGBOOST_TREE_UPDATER_REFRESH_INL_HPP_
-#define XGBOOST_TREE_UPDATER_REFRESH_INL_HPP_
 /*!
+ * Copyright 2014 by Contributors
  * \file updater_refresh-inl.hpp
  * \brief refresh the statistics and leaf value on the tree on the dataset
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_TREE_UPDATER_REFRESH_INL_HPP_
+#define XGBOOST_TREE_UPDATER_REFRESH_INL_HPP_
+
 #include <vector>
 #include <limits>
 #include "../sync/sync.h"
@@ -27,7 +29,7 @@ class TreeRefresher: public IUpdater {
   virtual void Update(const std::vector<bst_gpair> &gpair,
                       IFMatrix *p_fmat,
                       const BoosterInfo &info,
-                      const std::vector<RegTree*> &trees) {        
+                      const std::vector<RegTree*> &trees) {
     if (trees.size() == 0) return;
     // number of threads
     // thread temporal space
@@ -100,7 +102,7 @@ class TreeRefresher: public IUpdater {
     float lr = param.learning_rate;
     param.learning_rate = lr / trees.size();
     int offset = 0;
-    for (size_t i = 0; i < trees.size(); ++i) {      
+    for (size_t i = 0; i < trees.size(); ++i) {
       for (int rid = 0; rid < trees[i]->param.num_roots; ++rid) {
         this->Refresh(BeginPtr(stemp[0]) + offset, rid, trees[i]);
       }
@@ -147,7 +149,7 @@ class TreeRefresher: public IUpdater {
   // training parameter
   TrainParam param;
   // reducer
-  rabit::Reducer<TStats, TStats::Reduce> reducer;  
+  rabit::Reducer<TStats, TStats::Reduce> reducer;
 };
 
 }  // namespace tree

src/tree/updater_skmaker-inl.hpp

@@ -1,11 +1,13 @@
-#ifndef XGBOOST_TREE_UPDATER_SKMAKER_INL_HPP_
-#define XGBOOST_TREE_UPDATER_SKMAKER_INL_HPP_
 /*!
+ * Copyright 2014 by Contributors
  * \file updater_skmaker-inl.hpp
  * \brief use approximation sketch to construct a tree,
           a refresh is needed to make the statistics exactly correct
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_TREE_UPDATER_SKMAKER_INL_HPP_
+#define XGBOOST_TREE_UPDATER_SKMAKER_INL_HPP_
+
 #include <vector>
 #include <algorithm>
 #include "../sync/sync.h"
@@ -30,7 +32,7 @@ class SketchMaker: public BaseMaker {
     }
     param.learning_rate = lr;
   }
- 
+
  protected:
   inline void Update(const std::vector<bst_gpair> &gpair,
                       IFMatrix *p_fmat,
@@ -79,9 +81,9 @@ class SketchMaker: public BaseMaker {
     double pos_grad;
     /*! \brief sum of all negative gradient */
     double neg_grad;
-    /*! \brief sum of hessian statistics */    
+    /*! \brief sum of hessian statistics */
     double sum_hess;
-    explicit SKStats(void) {}
+    SKStats(void) {}
     // constructor
     explicit SKStats(const TrainParam &param) {
       this->Clear();
@@ -123,7 +125,7 @@ class SketchMaker: public BaseMaker {
       sum_hess += b.sum_hess;
     }
     /*! \brief same as add, reduce is used in All Reduce */
-    inline static void Reduce(SKStats &a, const SKStats &b) {
+    inline static void Reduce(SKStats &a, const SKStats &b) { // NOLINT(*)
       a.Add(b);
     }
     /*! \brief set leaf vector value based on statistics */
@@ -139,7 +141,7 @@ class SketchMaker: public BaseMaker {
       sketchs[i].Init(info.num_row, this->param.sketch_eps);
     }
     thread_sketch.resize(this->get_nthread());
-    // number of rows in 
+    // number of rows in
     const size_t nrows = p_fmat->buffered_rowset().size();
     // start accumulating statistics
     utils::IIterator<ColBatch> *iter = p_fmat->ColIterator();
@@ -156,7 +158,7 @@ class SketchMaker: public BaseMaker {
                               batch[i].length == nrows,
                               &thread_sketch[omp_get_thread_num()]);
       }
-    }       
+    }
     // setup maximum size
     unsigned max_size = param.max_sketch_size();
     // synchronize sketch
@@ -167,8 +169,8 @@ class SketchMaker: public BaseMaker {
       summary_array[i].Reserve(max_size);
       summary_array[i].SetPrune(out, max_size);
     }
-    size_t nbytes = WXQSketch::SummaryContainer::CalcMemCost(max_size);    
+    size_t nbytes = WXQSketch::SummaryContainer::CalcMemCost(max_size);
-    sketch_reducer.Allreduce(BeginPtr(summary_array), nbytes, summary_array.size());    
+    sketch_reducer.Allreduce(BeginPtr(summary_array), nbytes, summary_array.size());
   }
   // update sketch information in column fid
   inline void UpdateSketchCol(const std::vector<bst_gpair> &gpair,
@@ -209,7 +211,7 @@ class SketchMaker: public BaseMaker {
         const unsigned nid = this->qexpand[i];
         sbuilder[3 * nid + 0].sum_total = static_cast<bst_float>(nstats[nid].pos_grad);
         sbuilder[3 * nid + 1].sum_total = static_cast<bst_float>(nstats[nid].neg_grad);
-        sbuilder[3 * nid + 2].sum_total = static_cast<bst_float>(nstats[nid].sum_hess);        
+        sbuilder[3 * nid + 2].sum_total = static_cast<bst_float>(nstats[nid].sum_hess);
       }
     }
     // if only one value, no need to do second pass
@@ -217,7 +219,9 @@ class SketchMaker: public BaseMaker {
       for (size_t i = 0; i < this->qexpand.size(); ++i) {
         const int nid = this->qexpand[i];
         for (int k = 0; k < 3; ++k) {
-          sbuilder[3 * nid + k].sketch->Push(c[0].fvalue, static_cast<bst_float>(sbuilder[3 * nid + k].sum_total));
+          sbuilder[3 * nid + k].sketch->Push(c[0].fvalue,
+                                             static_cast<bst_float>(
+                                                 sbuilder[3 * nid + k].sum_total));
         }
       }
       return;
@@ -250,7 +254,7 @@ class SketchMaker: public BaseMaker {
         sbuilder[3 * nid + k].Finalize(max_size);
       }
     }
-  }  
+  }
   inline void SyncNodeStats(void) {
     utils::Assert(qexpand.size() != 0, "qexpand must not be empty");
     std::vector<SKStats> tmp(qexpand.size());
@@ -272,12 +276,12 @@ class SketchMaker: public BaseMaker {
     std::vector<SplitEntry> sol(qexpand.size());
     bst_omp_uint nexpand = static_cast<bst_omp_uint>(qexpand.size());
     #pragma omp parallel for schedule(dynamic, 1)
-    for (bst_omp_uint wid = 0; wid < nexpand; ++ wid) {
+    for (bst_omp_uint wid = 0; wid < nexpand; ++wid) {
       const int nid = qexpand[wid];
       utils::Assert(node2workindex[nid] == static_cast<int>(wid),
                     "node2workindex inconsistent");
       SplitEntry &best = sol[wid];
-      for (bst_uint fid = 0; fid < num_feature; ++ fid) {
+      for (bst_uint fid = 0; fid < num_feature; ++fid) {
         unsigned base = (wid * p_tree->param.num_feature + fid) * 3;
         EnumerateSplit(summary_array[base + 0],
                        summary_array[base + 1],
@@ -286,7 +290,7 @@ class SketchMaker: public BaseMaker {
       }
     }
     // get the best result, we can synchronize the solution
-    for (bst_omp_uint wid = 0; wid < nexpand; ++ wid) {
+    for (bst_omp_uint wid = 0; wid < nexpand; ++wid) {
       const int nid = qexpand[wid];
       const SplitEntry &best = sol[wid];
       // set up the values
@@ -337,7 +341,7 @@ class SketchMaker: public BaseMaker {
     feat_sum.neg_grad = neg_grad.data[neg_grad.size - 1].rmax;
     feat_sum.sum_hess = sum_hess.data[sum_hess.size - 1].rmax;
     size_t ipos = 0, ineg = 0, ihess = 0;
-    for (size_t i = 1; i < fsplits.size(); ++i) {      
+    for (size_t i = 1; i < fsplits.size(); ++i) {
       WXQSketch::Entry pos = pos_grad.Query(fsplits[i], ipos);
       WXQSketch::Entry neg = neg_grad.Query(fsplits[i], ineg);
       WXQSketch::Entry hess = sum_hess.Query(fsplits[i], ihess);
@@ -345,11 +349,11 @@ class SketchMaker: public BaseMaker {
       s.pos_grad = 0.5f * (pos.rmin + pos.rmax - pos.wmin);
       s.neg_grad = 0.5f * (neg.rmin + neg.rmax - neg.wmin);
       s.sum_hess = 0.5f * (hess.rmin + hess.rmax - hess.wmin);
-      c.SetSubstract(node_sum, s);      
+      c.SetSubstract(node_sum, s);
       // forward
       if (s.sum_hess >= param.min_child_weight &&
           c.sum_hess >= param.min_child_weight) {
-        double loss_chg = s.CalcGain(param) + c.CalcGain(param) - root_gain;        
+        double loss_chg = s.CalcGain(param) + c.CalcGain(param) - root_gain;
         best->Update(static_cast<bst_float>(loss_chg), fid, fsplits[i], false);
       }
       // backward
@@ -357,22 +361,23 @@ class SketchMaker: public BaseMaker {
       s.SetSubstract(node_sum, c);
       if (s.sum_hess >= param.min_child_weight &&
           c.sum_hess >= param.min_child_weight) {
-        double loss_chg = s.CalcGain(param) + c.CalcGain(param) - root_gain;        
+        double loss_chg = s.CalcGain(param) + c.CalcGain(param) - root_gain;
         best->Update(static_cast<bst_float>(loss_chg), fid, fsplits[i], true);
-      }      
+      }
     }
-    {// all including
+    {
+      // all including
       SKStats s = feat_sum, c;
       c.SetSubstract(node_sum, s);
       if (s.sum_hess >= param.min_child_weight &&
           c.sum_hess >= param.min_child_weight) {
         bst_float cpt = fsplits.back();
-        double loss_chg = s.CalcGain(param) + c.CalcGain(param) - root_gain;        
+        double loss_chg = s.CalcGain(param) + c.CalcGain(param) - root_gain;
         best->Update(static_cast<bst_float>(loss_chg), fid, cpt + fabsf(cpt) + 1.0f, false);
       }
     }
   }
-   
+
   // thread temp data
   // used to hold temporal sketch
   std::vector< std::vector<SketchEntry> > thread_sketch;
@@ -389,6 +394,6 @@ class SketchMaker: public BaseMaker {
   // per node, per feature sketch
   std::vector< utils::WXQuantileSketch<bst_float, bst_float> > sketchs;
 };
-}  // tree
+}  // namespace tree
-}  // xgboost
+}  // namespace xgboost
-#endif
+#endif  // XGBOOST_TREE_UPDATER_SKMAKER_INL_HPP_

src/tree/updater_sync-inl.hpp

@@ -1,18 +1,21 @@
-#ifndef XGBOOST_TREE_UPDATER_SYNC_INL_HPP_
-#define XGBOOST_TREE_UPDATER_SYNC_INL_HPP_
 /*!
+ * Copyright 2014 by Contributors
  * \file updater_sync-inl.hpp
  * \brief synchronize the tree in all distributed nodes
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_TREE_UPDATER_SYNC_INL_HPP_
+#define XGBOOST_TREE_UPDATER_SYNC_INL_HPP_
+
 #include <vector>
+#include <string>
 #include <limits>
 #include "../sync/sync.h"
 #include "./updater.h"
 
 namespace xgboost {
 namespace tree {
-/*! 
+/*!
  * \brief syncher that synchronize the tree in all distributed nodes
  * can implement various strategies, so far it is always set to node 0's tree
  */
@@ -28,7 +31,7 @@ class TreeSyncher: public IUpdater {
                       const std::vector<RegTree*> &trees) {
     this->SyncTrees(trees);
   }
-  
+
  private:
   // synchronize the trees in different nodes, take tree from rank 0
   inline void SyncTrees(const std::vector<RegTree *> &trees) {
@@ -43,7 +46,7 @@ class TreeSyncher: public IUpdater {
     }
     fs.Seek(0);
     rabit::Broadcast(&s_model, 0);
-    for (size_t i = 0; i < trees.size(); ++i) {      
+    for (size_t i = 0; i < trees.size(); ++i) {
       trees[i]->LoadModel(fs);
     }
   }

src/utils/base64-inl.h

@@ -1,13 +1,16 @@
-#ifndef XGBOOST_UTILS_BASE64_INL_H_
-#define XGBOOST_UTILS_BASE64_INL_H_
 /*!
+ * Copyright 2014 by Contributors
  * \file base64.h
  * \brief data stream support to input and output from/to base64 stream
  * base64 is easier to store and pass as text format in mapreduce
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_UTILS_BASE64_INL_H_
+#define XGBOOST_UTILS_BASE64_INL_H_
+
 #include <cctype>
 #include <cstdio>
+#include <string>
 #include "./io.h"
 
 namespace xgboost {
@@ -15,7 +18,7 @@ namespace utils {
 /*! \brief buffer reader of the stream that allows you to get */
 class StreamBufferReader {
  public:
-  StreamBufferReader(size_t buffer_size)
+  explicit StreamBufferReader(size_t buffer_size)
       :stream_(NULL),
        read_len_(1), read_ptr_(1) {
     buffer_.resize(buffer_size);
@@ -45,7 +48,7 @@ class StreamBufferReader {
   inline bool AtEnd(void) const {
     return read_len_ == 0;
   }
-  
+
  private:
   /*! \brief the underlying stream */
   IStream *stream_;
@@ -75,7 +78,7 @@ const char DecodeTable[] = {
 };
 static const char EncodeTable[] =
     "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/";
-} // namespace base64
+}  // namespace base64
 /*! \brief the stream that reads from base64, note we take from file pointers */
 class Base64InStream: public IStream {
  public:
@@ -83,8 +86,8 @@ class Base64InStream: public IStream {
     reader_.set_stream(fs);
     num_prev = 0; tmp_ch = 0;
   }
-  /*! 
+  /*!
-   * \brief initialize the stream position to beginning of next base64 stream 
+   * \brief initialize the stream position to beginning of next base64 stream
    * call this function before actually start read
    */
   inline void InitPosition(void) {
@@ -132,19 +135,19 @@ class Base64InStream: public IStream {
       {
         // second byte
         utils::Check((tmp_ch = reader_.GetChar(), tmp_ch != EOF && !isspace(tmp_ch)),
-              "invalid base64 format");
+                     "invalid base64 format");
         nvalue |= DecodeTable[tmp_ch] << 12;
         *cptr++ = (nvalue >> 16) & 0xFF; --tlen;
       }
       {
         // third byte
         utils::Check((tmp_ch = reader_.GetChar(), tmp_ch != EOF && !isspace(tmp_ch)),
-              "invalid base64 format");
+                     "invalid base64 format");
         // handle termination
         if (tmp_ch == '=') {
           utils::Check((tmp_ch = reader_.GetChar(), tmp_ch == '='), "invalid base64 format");
           utils::Check((tmp_ch = reader_.GetChar(), tmp_ch == EOF || isspace(tmp_ch)),
-                "invalid base64 format");
+                       "invalid base64 format");
           break;
         }
         nvalue |= DecodeTable[tmp_ch] << 6;
@@ -157,10 +160,10 @@ class Base64InStream: public IStream {
       {
         // fourth byte
         utils::Check((tmp_ch = reader_.GetChar(), tmp_ch != EOF && !isspace(tmp_ch)),
-              "invalid base64 format");
+                     "invalid base64 format");
         if (tmp_ch == '=') {
           utils::Check((tmp_ch = reader_.GetChar(), tmp_ch == EOF || isspace(tmp_ch)),
-                "invalid base64 format");
+                       "invalid base64 format");
           break;
         }
         nvalue |= DecodeTable[tmp_ch];
@@ -240,13 +243,13 @@ class Base64OutStream: public IStream {
     if (endch != EOF) PutChar(endch);
     this->Flush();
   }
-    
+
- private:  
+ private:
   IStream *fp;
   int buf_top;
   unsigned char buf[4];
   std::string out_buf;
-  const static size_t kBufferSize = 256;
+  static const size_t kBufferSize = 256;
 
   inline void PutChar(char ch) {
     out_buf += ch;
@@ -260,5 +263,5 @@ class Base64OutStream: public IStream {
   }
 };
 }  // namespace utils
-}  // namespace rabit
+}  // namespace xgboost
-#endif  // RABIT_LEARN_UTILS_BASE64_INL_H_
+#endif  // XGBOOST_UTILS_BASE64_INL_H_

src/utils/bitmap.h

@@ -1,11 +1,13 @@
-#ifndef XGBOOST_UTILS_BITMAP_H_
-#define XGBOOST_UTILS_BITMAP_H_
 /*!
+ * Copyright 2014 by Contributors
  * \file bitmap.h
  * \brief a simple implement of bitmap
  *  NOTE: bitmap is only threadsafe per word access, remember this when using bitmap
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_UTILS_BITMAP_H_
+#define XGBOOST_UTILS_BITMAP_H_
+
 #include <vector>
 #include "./utils.h"
 #include "./omp.h"
@@ -16,22 +18,22 @@ namespace utils {
 struct BitMap {
   /*! \brief internal data structure */
   std::vector<uint32_t> data;
-  /*! 
+  /*!
-   * \brief resize the bitmap to be certain size 
+   * \brief resize the bitmap to be certain size
    * \param size the size of bitmap
    */
   inline void Resize(size_t size) {
     data.resize((size + 31U) >> 5, 0);
   }
-  /*! 
+  /*!
-   * \brief query the i-th position of bitmap 
+   * \brief query the i-th position of bitmap
-   * \param i the position in 
+   * \param i the position in
    */
   inline bool Get(size_t i) const {
     return (data[i >> 5] >> (i & 31U)) & 1U;
   }
-  /*! 
+  /*!
-   * \brief set i-th position to true 
+   * \brief set i-th position to true
    * \param i position index
    */
   inline void SetTrue(size_t i) {
@@ -63,4 +65,4 @@ struct BitMap {
 };
 }  // namespace utils
 }  // namespace xgboost
-#endif
+#endif  // XGBOOST_UTILS_BITMAP_H_

src/utils/config.h

@@ -1,10 +1,12 @@
-#ifndef XGBOOST_UTILS_CONFIG_H_
-#define XGBOOST_UTILS_CONFIG_H_
 /*!
+ * Copyright 2014 by Contributors
  * \file config.h
  * \brief helper class to load in configures from file
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_UTILS_CONFIG_H_
+#define XGBOOST_UTILS_CONFIG_H_
+
 #include <cstdio>
 #include <cstring>
 #include <string>
@@ -14,26 +16,26 @@
 
 namespace xgboost {
 namespace utils {
-/*! 
+/*!
  * \brief base implementation of config reader
  */
 class ConfigReaderBase {
  public:
-  /*! 
+  /*!
    * \brief get current name, called after Next returns true
-   * \return current parameter name 
+   * \return current parameter name
    */
   inline const char *name(void) const {
     return s_name.c_str();
   }
-  /*! 
+  /*!
    * \brief get current value, called after Next returns true
-   * \return current parameter value 
+   * \return current parameter value
    */
   inline const char *val(void) const {
     return s_val.c_str();
   }
-  /*! 
+  /*!
    * \brief move iterator to next position
    * \return true if there is value in next position
    */
@@ -55,7 +57,7 @@ class ConfigReaderBase {
  protected:
   /*!
    * \brief to be implemented by subclass,
-   * get next token, return EOF if end of file 
+   * get next token, return EOF if end of file
    */
   virtual char GetChar(void) = 0;
   /*! \brief to be implemented by child, check if end of stream */
@@ -144,9 +146,9 @@ class ConfigReaderBase {
  */
 class ConfigStreamReader: public ConfigReaderBase {
  public:
-  /*! 
+  /*!
-   * \brief constructor 
+   * \brief constructor
-   * \param istream input stream 
+   * \param istream input stream
    */
   explicit ConfigStreamReader(std::istream &fin) : fin(fin) {}
 
@@ -163,13 +165,13 @@ class ConfigStreamReader: public ConfigReaderBase {
   std::istream &fin;
 };
 
-/*! 
+/*!
  * \brief an iterator that iterates over a configure file and gets the configures
  */
 class ConfigIterator: public ConfigStreamReader {
  public:
-  /*! 
+  /*!
-   * \brief constructor 
+   * \brief constructor
    * \param fname name of configure file
    */
   explicit ConfigIterator(const char *fname) : ConfigStreamReader(fi) {

src/utils/fmap.h

@@ -1,10 +1,12 @@
-#ifndef XGBOOST_UTILS_FMAP_H_
-#define XGBOOST_UTILS_FMAP_H_
 /*!
+ * Copyright 2014 by Contributors
  * \file fmap.h
  * \brief helper class that holds the feature names and interpretations
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_UTILS_FMAP_H_
+#define XGBOOST_UTILS_FMAP_H_
+
 #include <vector>
 #include <string>
 #include <cstring>
@@ -78,4 +80,4 @@ class FeatMap {
 
 }  // namespace utils
 }  // namespace xgboost
-#endif  // XGBOOST_FMAP_H_
+#endif  // XGBOOST_UTILS_FMAP_H_

src/utils/group_data.h

@@ -1,6 +1,5 @@
-#ifndef XGBOOST_UTILS_GROUP_DATA_H_
-#define XGBOOST_UTILS_GROUP_DATA_H_
 /*!
+ * Copyright 2014 by Contributors
  * \file group_data.h
  * \brief this file defines utils to group data by integer keys
  *     Input: given input sequence (key,value), (k1,v1), (k2,v2)
@@ -12,6 +11,11 @@
  * The major algorithm is a two pass linear scan algorithm that requires two pass scan over the data
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_UTILS_GROUP_DATA_H_
+#define XGBOOST_UTILS_GROUP_DATA_H_
+
+#include <vector>
+
 namespace xgboost {
 namespace utils {
 /*!
@@ -32,10 +36,10 @@ struct ParallelGroupBuilder {
                        std::vector< std::vector<SizeType> > *p_thread_rptr)
       : rptr(*p_rptr), data(*p_data), thread_rptr(*p_thread_rptr) {
   }
-  
+
  public:
   /*!
-   * \brief step 1: initialize the helper, with hint of number keys 
+   * \brief step 1: initialize the helper, with hint of number keys
    *                and thread used in the construction
    * \param nkeys number of keys in the matrix, can be smaller than expected
    * \param nthread number of thread that will be used in construction
@@ -56,7 +60,7 @@ struct ParallelGroupBuilder {
   inline void AddBudget(size_t key, int threadid, SizeType nelem = 1) {
     std::vector<SizeType> &trptr = thread_rptr[threadid];
     if (trptr.size() < key + 1) {
-      trptr.resize(key + 1, 0);      
+      trptr.resize(key + 1, 0);
     }
     trptr[key] += nelem;
   }
@@ -84,13 +88,13 @@ struct ParallelGroupBuilder {
     data.resize(start);
   }
   /*!
-   * \brief step 4: add data to the allocated space, 
+   * \brief step 4: add data to the allocated space,
    *   the calls to this function should be exactly match previous call to AddBudget
    *
-   * \param key the key of 
+   * \param key the key of
    * \param threadid the id of thread that calls this function
    */
-  inline void Push(size_t key, ValueType value, int threadid) {    
+  inline void Push(size_t key, ValueType value, int threadid) {
     SizeType &rp = thread_rptr[threadid][key];
     data[rp++] = value;
   }
@@ -107,5 +111,4 @@ struct ParallelGroupBuilder {
 };
 }  // namespace utils
 }  // namespace xgboost
-#endif
+#endif  // XGBOOST_UTILS_GROUP_DATA_H_
-

src/utils/io.h

@@ -1,16 +1,19 @@
-#ifndef XGBOOST_UTILS_IO_H
+/*!
-#define XGBOOST_UTILS_IO_H
+ * Copyright 2014 by Contributors
+ * \file io.h
+ * \brief general stream interface for serialization, I/O
+ * \author Tianqi Chen
+ */
+
+#ifndef XGBOOST_UTILS_IO_H_
+#define XGBOOST_UTILS_IO_H_
 #include <cstdio>
 #include <vector>
 #include <string>
 #include <cstring>
 #include "./utils.h"
 #include "../sync/sync.h"
-/*!
+
- * \file io.h
- * \brief general stream interface for serialization, I/O
- * \author Tianqi Chen
- */
 namespace xgboost {
 namespace utils {
 // reuse the definitions of streams
@@ -23,7 +26,7 @@ typedef rabit::utils::MemoryBufferStream MemoryBufferStream;
 class FileStream : public ISeekStream {
  public:
   explicit FileStream(std::FILE *fp) : fp(fp) {}
-  explicit FileStream(void) {
+  FileStream(void) {
     this->fp = NULL;
   }
   virtual size_t Read(void *ptr, size_t size) {
@@ -33,7 +36,7 @@ class FileStream : public ISeekStream {
     std::fwrite(ptr, size, 1, fp);
   }
   virtual void Seek(size_t pos) {
-    std::fseek(fp, static_cast<long>(pos), SEEK_SET);
+    std::fseek(fp, static_cast<long>(pos), SEEK_SET); // NOLINT(*)
   }
   virtual size_t Tell(void) {
     return std::ftell(fp);
@@ -42,7 +45,7 @@ class FileStream : public ISeekStream {
     return std::feof(fp) != 0;
   }
   inline void Close(void) {
-    if (fp != NULL){
+    if (fp != NULL) {
       std::fclose(fp); fp = NULL;
     }
   }
@@ -52,6 +55,5 @@ class FileStream : public ISeekStream {
 };
 }  // namespace utils
 }  // namespace xgboost
-
 #include "./base64-inl.h"
-#endif
+#endif  // XGBOOST_UTILS_IO_H_

src/utils/iterator.h

@@ -1,11 +1,13 @@
-#ifndef XGBOOST_UTILS_ITERATOR_H
-#define XGBOOST_UTILS_ITERATOR_H
-#include <cstdio>
 /*!
+ * Copyright 2014 by Contributors
  * \file iterator.h
  * \brief itertator interface
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_UTILS_ITERATOR_H_
+#define XGBOOST_UTILS_ITERATOR_H_
+#include <cstdio>
+
 namespace xgboost {
 namespace utils {
 /*!
@@ -16,7 +18,7 @@ template<typename DType>
 class IIterator {
  public:
   /*!
-   * \brief set the parameter 
+   * \brief set the parameter
    * \param name name of parameter
    * \param val value of parameter
    */
@@ -36,5 +38,5 @@ class IIterator {
 
 }  // namespace utils
 }  // namespace xgboost
-#endif
+#endif  // XGBOOST_UTILS_ITERATOR_H_
 

src/utils/math.h

@@ -1,10 +1,12 @@
-#ifndef XGBOOST_UTILS_MATH_H_
-#define XGBOOST_UTILS_MATH_H_
 /*!
+ * Copyright 2014 by Contributors
  * \file math.h
  * \brief support additional math
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_UTILS_MATH_H_
+#define XGBOOST_UTILS_MATH_H_
+
 #include <cmath>
 
 namespace xgboost {
@@ -28,7 +30,8 @@ inline T LogGamma(T v) {
 #if _MSC_VER >= 1800
   return lgamma(v);
 #else
-#pragma message ("Warning: lgamma function was not available until VS2013, poisson regression will be disabled")
+#pragma message("Warning: lgamma function was not available until VS2013"\
+                ", poisson regression will be disabled")
   utils::Error("lgamma function was not available until VS2013");
   return static_cast<T>(1.0);
 #endif

src/utils/omp.h

@@ -1,16 +1,20 @@
-#ifndef XGBOOST_UTILS_OMP_H_
-#define XGBOOST_UTILS_OMP_H_
 /*!
+ * Copyright 2014 by Contributors
  * \file omp.h
  * \brief header to handle OpenMP compatibility issues
  * \author Tianqi Chen
  */
+#ifndef XGBOOST_UTILS_OMP_H_
+#define XGBOOST_UTILS_OMP_H_
+
 #if defined(_OPENMP)
 #include <omp.h>
 #else
 #ifndef DISABLE_OPENMP
 // use pragma message instead of warning
-#pragma message ("Warning: OpenMP is not available, xgboost will be compiled into single-thread code. Use OpenMP-enabled compiler to get benefit of multi-threading")
+#pragma message("Warning: OpenMP is not available,"\
+                "xgboost will be compiled into single-thread code."\
+                "Use OpenMP-enabled compiler to get benefit of multi-threading")
 #endif
 inline int omp_get_thread_num() { return 0; }
 inline int omp_get_num_threads() { return 1; }
@@ -25,6 +29,6 @@ typedef int bst_omp_uint;
 #else
 typedef unsigned bst_omp_uint;
 #endif
-} // namespace xgboost
+}  // namespace xgboost
 
 #endif  // XGBOOST_UTILS_OMP_H_