make some fix

java/README.md

@@ -17,11 +17,11 @@ core of this wrapper is two classes:
 
 ## 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:
+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
- * move the dll "xgboostjavawrapper.dll" to "xgboost4j/src/main/resources/lib/"(you may need to create this folder if necessary.) 
+ * 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

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

@@ -6,7 +6,7 @@ echo "move native lib"
 
 libPath="xgboost4j/src/main/resources/lib"
 if [ ! -d "$libPath" ]; then
-  mkdir "$libPath"
+  mkdir -p "$libPath"
 fi
 
 rm -f xgboost4j/src/main/resources/lib/libxgboostjavawrapper.so

java/doc/xgboost4j.md

@@ -82,9 +82,9 @@ import org.dmlc.xgboost4j.util.Params;
 ```java
 Params params = new Params() {
   {
-    put("eta", "1.0");
+    put("eta", 1.0);
-    put("max_depth", "2");
+    put("max_depth", 2);
-    put("silent", "1");
+    put("silent", 1);
     put("objective", "binary:logistic");
     put("eval_metric", "logloss");
   }
@@ -94,9 +94,9 @@ Params params = new Params() {
 ```java
 Params params = new Params() {
   {
-    put("eta", "1.0");
+    put("eta", 1.0);
-    put("max_depth", "2");
+    put("max_depth", 2);
-    put("silent", "1");
+    put("silent", 1);
     put("objective", "binary:logistic");
     put("eval_metric", "logloss");
     put("eval_metric", "error");
@@ -110,16 +110,19 @@ With parameters and data, you are able to train a booster model.
 ```java
 import org.dmlc.xgboost4j.Booster;
 import org.dmlc.xgboost4j.util.Trainer;
+import org.dmlc.xgboost4j.util.WatchList;
 ```
 
 * Training
 ```java
 DMatrix trainMat = new DMatrix("train.svm.txt");
 DMatrix validMat = new DMatrix("valid.svm.txt");
-DMatrix[] evalMats = new DMatrix[] {trainMat, validMat};
+//specifiy a watchList to see the performance
-String[] evalNames = new String[] {"train", "valid"};
+WatchList watchs = new WatchList();
+watchs.put("train", trainMat);
+watchs.put("test", testMat);
 int round = 2;
-Booster booster = Trainer.train(params, trainMat, round, evalMats, evalNames, null, null);
+Booster booster = Trainer.train(params, trainMat, round, watchs, null, null);
 ```
 
 * Saving model
@@ -139,8 +142,8 @@ booster.dumpModel("modelInfo.txt", "featureMap.txt", false)
 ```java
 Params param = new Params() {
   {
-    put("silent", "1");
+    put("silent", 1);
-    put("nthread", "6");
+    put("nthread", 6);
   }
 };
 Booster booster = new Booster(param, "model.bin");

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

@@ -24,6 +24,7 @@ import org.dmlc.xgboost4j.DMatrix;
 import org.dmlc.xgboost4j.demo.util.DataLoader;
 import org.dmlc.xgboost4j.util.Params;
 import org.dmlc.xgboost4j.util.Trainer;
+import org.dmlc.xgboost4j.util.WatchList;
 
 /**
  * a simple example of java wrapper for xgboost
@@ -53,22 +54,23 @@ public class BasicWalkThrough {
         //specify parameters
         Params param = new Params() {
             {
-                put("eta", "1.0");
+                put("eta", 1.0);
-                put("max_depth", "2");
+                put("max_depth", 2);
-                put("silent", "1");
+                put("silent", 1);
                 put("objective", "binary:logistic");
             }
         };
         
-        //specify evaluate datasets and evaluate names
+        //specify watchList
-        DMatrix[] dmats = new DMatrix[] {trainMat, testMat};
+        WatchList watchs = new WatchList();
-        String[] evalNames = new String[] {"train", "test"};
+        watchs.put("train", trainMat);
+        watchs.put("test", testMat);
         
         //set round
         int round = 2;
         
         //train a boost model
-        Booster booster = Trainer.train(param, trainMat, round, dmats, evalNames, null, null);
+        Booster booster = Trainer.train(param, trainMat, round, watchs, null, null);
         
          //predict
         float[][] predicts = booster.predict(testMat);
@@ -107,8 +109,11 @@ public class BasicWalkThrough {
         DMatrix trainMat2 = new DMatrix(spData.rowHeaders, spData.colIndex, spData.data, DMatrix.SparseType.CSR);
         trainMat2.setLabel(spData.labels);
         
-        dmats = new DMatrix[] {trainMat2, testMat};
+        //specify watchList
-        Booster booster3 = Trainer.train(param, trainMat2, round, dmats, evalNames, null, null);
+        WatchList watchs2 = new WatchList();
+        watchs2.put("train", trainMat2);
+        watchs2.put("test", testMat);
+        Booster booster3 = Trainer.train(param, trainMat2, round, watchs2, null, null);
         float[][] predicts3 = booster3.predict(testMat2);
         
         //check predicts

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

@@ -19,6 +19,7 @@ import org.dmlc.xgboost4j.Booster;
 import org.dmlc.xgboost4j.DMatrix;
 import org.dmlc.xgboost4j.util.Params;
 import org.dmlc.xgboost4j.util.Trainer;
+import org.dmlc.xgboost4j.util.WatchList;
 
 /**
  * example for start from a initial base prediction
@@ -35,19 +36,20 @@ public class BoostFromPrediction {
         //specify parameters
         Params param = new Params() {
             {
-                put("eta", "1.0");
+                put("eta", 1.0);
-                put("max_depth", "2");
+                put("max_depth", 2);
-                put("silent", "1");
+                put("silent", 1);
                 put("objective", "binary:logistic");
             }
         };
         
-        //specify evaluate datasets and evaluate names
+         //specify watchList
-        DMatrix[] dmats = new DMatrix[] {trainMat, testMat};
+        WatchList watchs = new WatchList();
-        String[] evalNames = new String[] {"train", "test"};
+        watchs.put("train", trainMat);
+        watchs.put("test", testMat);
         
         //train xgboost for 1 round
-        Booster booster = Trainer.train(param, trainMat, 1, dmats, evalNames, null, null);
+        Booster booster = Trainer.train(param, trainMat, 1, watchs, null, null);
         
         float[][] trainPred = booster.predict(trainMat, true);
         float[][] testPred = booster.predict(testMat, true);
@@ -56,6 +58,6 @@ public class BoostFromPrediction {
         testMat.setBaseMargin(testPred);
         
         System.out.println("result of running from initial prediction");
-        Booster booster2 = Trainer.train(param, trainMat, 1, dmats, evalNames, null, null);
+        Booster booster2 = Trainer.train(param, trainMat, 1, watchs, null, null);
     }
 }

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

@@ -32,12 +32,12 @@ public class CrossValidation {
         //set params
         Params param = new Params() {
             {
-                put("eta", "1.0");
+                put("eta", 1.0);
-                put("max_depth", "3");
+                put("max_depth", 3);
-                put("silent", "1");
+                put("silent", 1);
-                put("nthread", "6");
+                put("nthread", 6);
                 put("objective", "binary:logistic");
-                put("gamma", "1.0");
+                put("gamma", 1.0);
                 put("eval_metric", "error");
             }
         };

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

@@ -16,7 +16,6 @@
 package org.dmlc.xgboost4j.demo;
 
 import java.util.ArrayList;
-import java.util.Arrays;
 import java.util.List;
 import org.dmlc.xgboost4j.Booster;
 import org.dmlc.xgboost4j.IEvaluation;
@@ -24,6 +23,7 @@ import org.dmlc.xgboost4j.DMatrix;
 import org.dmlc.xgboost4j.IObjective;
 import org.dmlc.xgboost4j.util.Params;
 import org.dmlc.xgboost4j.util.Trainer;
+import org.dmlc.xgboost4j.util.WatchList;
 
 /**
  * an example user define objective and eval
@@ -130,18 +130,19 @@ public class CustomObjective {
         //set params
         Params param = new Params() {
             {
-                put("eta", "1.0");
+                put("eta", 1.0);
-                put("max_depth", "2");
+                put("max_depth", 2);
-                put("silent", "1");
+                put("silent", 1);
             }
         };
         
         //set round
         int round = 2;
         
-        //set evaluation data
+        //specify watchList
-        DMatrix[] dmats = new DMatrix[] {trainMat, testMat};
+        WatchList watchs = new WatchList();
-        String[] evalNames = new String[] {"train", "eval"};
+        watchs.put("train", trainMat);
+        watchs.put("test", testMat);
         
         //user define obj and eval
         IObjective obj = new LogRegObj();
@@ -149,6 +150,6 @@ public class CustomObjective {
         
         //train a booster
         System.out.println("begin to train the booster model");        
-        Booster booster = Trainer.train(param, trainMat, round, dmats, evalNames, obj, eval);
+        Booster booster = Trainer.train(param, trainMat, round, watchs, obj, eval);
     }
 }

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

@@ -19,6 +19,7 @@ import org.dmlc.xgboost4j.Booster;
 import org.dmlc.xgboost4j.DMatrix;
 import org.dmlc.xgboost4j.util.Params;
 import org.dmlc.xgboost4j.util.Trainer;
+import org.dmlc.xgboost4j.util.WatchList;
 
 /**
  * simple example for using external memory version
@@ -35,25 +36,26 @@ public class ExternalMemory {
         //specify parameters
         Params param = new Params() {
             {
-                put("eta", "1.0");
+                put("eta", 1.0);
-                put("max_depth", "2");
+                put("max_depth", 2);
-                put("silent", "1");
+                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");
+        //param.put("nthread", num_real_cpu);
         
-        //specify evaluate datasets and evaluate names
+        //specify watchList
-        DMatrix[] dmats = new DMatrix[] {trainMat, testMat};
+        WatchList watchs = new WatchList();
-        String[] evalNames = new String[] {"train", "test"};
+        watchs.put("train", trainMat);
+        watchs.put("test", testMat);
         
         //set round
         int round = 2;
         
         //train a boost model
-        Booster booster = Trainer.train(param, trainMat, round, dmats, evalNames, null, null);
+        Booster booster = Trainer.train(param, trainMat, round, watchs, null, null);
     }
 }

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

@@ -20,6 +20,7 @@ import org.dmlc.xgboost4j.DMatrix;
 import org.dmlc.xgboost4j.demo.util.CustomEval;
 import org.dmlc.xgboost4j.util.Params;
 import org.dmlc.xgboost4j.util.Trainer;
+import org.dmlc.xgboost4j.util.WatchList;
 
 /**
  * this is an example of fit generalized linear model in xgboost
@@ -39,8 +40,8 @@ public class GeneralizedLinearModel {
         //you can also set lambda_bias which is L2 regularizer on the bias term
         Params param = new Params() {
             {
-                put("alpha", "0.0001");
+                put("alpha", 0.0001);
-                put("silent", "1");
+                put("silent", 1);
                 put("objective", "binary:logistic");
                 put("booster", "gblinear");
             }
@@ -52,13 +53,14 @@ public class GeneralizedLinearModel {
         //param.put("eta", "0.5");
         
         
-        //specify evaluate datasets and evaluate names
+        //specify watchList
-        DMatrix[] dmats = new DMatrix[] {trainMat, testMat};
+        WatchList watchs = new WatchList();
-        String[] evalNames = new String[] {"train", "test"};
+        watchs.put("train", trainMat);
+        watchs.put("test", testMat);
         
         //train a booster
         int round = 4;
-        Booster booster = Trainer.train(param, trainMat, round, dmats, evalNames, null, null);
+        Booster booster = Trainer.train(param, trainMat, round, watchs, null, null);
         
         float[][] predicts = booster.predict(testMat);
         

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

@@ -21,6 +21,7 @@ import org.dmlc.xgboost4j.util.Params;
 import org.dmlc.xgboost4j.util.Trainer;
 
 import org.dmlc.xgboost4j.demo.util.CustomEval;
+import org.dmlc.xgboost4j.util.WatchList;
 
 /**
  * predict first ntree
@@ -35,20 +36,21 @@ public class PredictFirstNtree {
         //specify parameters
         Params param = new Params() {
             {
-                put("eta", "1.0");
+                put("eta", 1.0);
-                put("max_depth", "2");
+                put("max_depth", 2);
-                put("silent", "1");
+                put("silent", 1);
                 put("objective", "binary:logistic");
             }
         };
         
-        //specify evaluate datasets and evaluate names
+        //specify watchList
-        DMatrix[] dmats = new DMatrix[] {trainMat, testMat};
+        WatchList watchs = new WatchList();
-        String[] evalNames = new String[] {"train", "test"};
+        watchs.put("train", trainMat);
+        watchs.put("test", testMat);
         
         //train a booster
         int round = 3;
-        Booster booster = Trainer.train(param, trainMat, round, dmats, evalNames, null, null);
+        Booster booster = Trainer.train(param, trainMat, round, watchs, null, null);
         
         //predict use 1 tree
         float[][] predicts1 = booster.predict(testMat, false, 1);

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

@@ -20,6 +20,7 @@ import org.dmlc.xgboost4j.Booster;
 import org.dmlc.xgboost4j.DMatrix;
 import org.dmlc.xgboost4j.util.Params;
 import org.dmlc.xgboost4j.util.Trainer;
+import org.dmlc.xgboost4j.util.WatchList;
 
 /**
  * predict leaf indices
@@ -34,20 +35,21 @@ public class PredictLeafIndices {
         //specify parameters
         Params param = new Params() {
             {
-                put("eta", "1.0");
+                put("eta", 1.0);
-                put("max_depth", "2");
+                put("max_depth", 2);
-                put("silent", "1");
+                put("silent", 1);
                 put("objective", "binary:logistic");
             }
         };
         
-        //specify evaluate datasets and evaluate names
+        //specify watchList
-        DMatrix[] dmats = new DMatrix[] {trainMat, testMat};
+        WatchList watchs = new WatchList();
-        String[] evalNames = new String[] {"train", "test"};
+        watchs.put("train", trainMat);
+        watchs.put("test", testMat);
         
         //train a booster
         int round = 3;
-        Booster booster = Trainer.train(param, trainMat, round, dmats, evalNames, null, null);
+        Booster booster = Trainer.train(param, trainMat, round, watchs, null, null);
         
         //predict using first 2 tree
         float[][] leafindex = booster.predict(testMat, 2, true);

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

@@ -30,7 +30,6 @@ import org.apache.commons.logging.LogFactory;
 
 import org.dmlc.xgboost4j.util.Initializer;
 import org.dmlc.xgboost4j.util.Params;
-import org.dmlc.xgboost4j.util.TransferUtil;
 import org.dmlc.xgboost4j.wrapper.XgboostJNI;
 
 
@@ -85,7 +84,7 @@ public final class Booster {
     private void init(DMatrix[] dMatrixs) {
         long[] handles = null;
         if(dMatrixs != null) {
-            handles = TransferUtil.dMatrixs2handles(dMatrixs);
+            handles = dMatrixs2handles(dMatrixs);
         }
         handle = XgboostJNI.XGBoosterCreate(handles);
     }
@@ -105,8 +104,8 @@ public final class Booster {
      */
     public void setParams(Params params) {
         if(params!=null) {
-            for(Map.Entry<String, String> entry : params) {
+            for(Map.Entry<String, Object> entry : params) {
-                setParam(entry.getKey(), entry.getValue());
+                setParam(entry.getKey(), entry.getValue().toString());
             }
         }
     }
@@ -154,7 +153,7 @@ public final class Booster {
      * @return eval information
      */
     public String evalSet(DMatrix[] evalMatrixs, String[] evalNames,  int iter) {
-        long[] handles = TransferUtil.dMatrixs2handles(evalMatrixs);
+        long[] handles = dMatrixs2handles(evalMatrixs);
         String evalInfo = XgboostJNI.XGBoosterEvalOneIter(handle, iter, handles, evalNames);
         return evalInfo;
     }
@@ -424,6 +423,19 @@ public final class Booster {
         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();

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

@@ -19,7 +19,6 @@ import java.io.IOException;
 import org.apache.commons.logging.Log;
 import org.apache.commons.logging.LogFactory;
 import org.dmlc.xgboost4j.util.Initializer;
-import org.dmlc.xgboost4j.util.TransferUtil;
 import org.dmlc.xgboost4j.wrapper.XgboostJNI;
 
 /**
@@ -126,7 +125,7 @@ public class DMatrix {
      * @param baseMargin 
      */
     public void setBaseMargin(float[][] baseMargin) {
-        float[] flattenMargin = TransferUtil.flatten(baseMargin);
+        float[] flattenMargin = flatten(baseMargin);
         setBaseMargin(flattenMargin);
     }
     
@@ -203,6 +202,24 @@ public class DMatrix {
         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();

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

@@ -28,7 +28,6 @@ public class CVPack {
     DMatrix dtrain;
     DMatrix dtest;
     DMatrix[] dmats;
-    long[] dataArray;
     String[] names;
     Booster booster;
     
@@ -41,7 +40,6 @@ public class CVPack {
     public CVPack(DMatrix dtrain, DMatrix dtest, Params params) {
         dmats = new DMatrix[] {dtrain, dtest};
         booster = new Booster(params, dmats);
-        dataArray = TransferUtil.dMatrixs2handles(dmats);
         names = new String[] {"train", "test"};
         this.dtrain = dtrain;
         this.dtest = dtest;
@@ -70,7 +68,7 @@ public class CVPack {
      * @return 
      */
     public String eval(int iter) {
-        return booster.evalSet(dataArray, names, iter);
+        return booster.evalSet(dmats, names, iter);
     }
     
     /**

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

@@ -43,7 +43,7 @@ public class Initializer {
     }
     
     /**
-     * load native library, this method will first try to load library from java.library.path, then try to load from library in jar package.
+     * 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 
      */

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

@@ -1,7 +1,17 @@
 /*
- * To change this license header, choose License Headers in Project Properties.
+ Copyright (c) 2014 by Contributors 
- * To change this template file, choose Tools | Templates
+
- * and open the template in the editor.
+ 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;
 

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

@@ -26,29 +26,29 @@ import java.util.AbstractMap;
  * a util class for handle params
  * @author hzx
  */
-public class Params implements Iterable<Entry<String, String>>{
+public class Params implements Iterable<Entry<String, Object>>{
-    List<Entry<String, String>> params = new ArrayList<>();
+    List<Entry<String, Object>> params = new ArrayList<>();
     
     /**
      * put param key-value pair
      * @param key
      * @param value 
      */
-    public void put(String key, String value) {
+    public void put(String key, Object value) {
         params.add(new AbstractMap.SimpleEntry<>(key, value));
     }
     
     @Override
     public String toString(){ 
         String paramsInfo = "";
-        for(Entry<String, String> param : params) {
+        for(Entry<String, Object> param : params) {
             paramsInfo += param.getKey() + ":" + param.getValue() + "\n";
         }
         return paramsInfo;
     }
 
     @Override
-    public Iterator<Entry<String, String>> iterator() {
+    public Iterator<Entry<String, Object>> iterator() {
         return params.iterator();
     }
 }

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

@@ -20,6 +20,7 @@ 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;
@@ -40,14 +41,26 @@ public class Trainer {
      * @param params Booster params.
      * @param dtrain Data to be trained.
      * @param round Number of boosting iterations.
-     * @param evalMats Data to be evaluated (may include dtrain)
+     * @param watchs a group of items to be evaluated during training, this allows user to watch performance on the validation set.
-     * @param evalNames name of data (used for evaluation info)
      * @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(Params params, DMatrix dtrain, int round, 
-            DMatrix[] evalMats, String[] evalNames, IObjective obj, IEvaluation eval) {
+            WatchList watchs, IObjective obj, IEvaluation eval) {
+        
+        //collect eval matrixs
+        int len = watchs.size();
+        int i = 0;
+        String[] evalNames = new String[len];
+        DMatrix[] evalMats = new DMatrix[len];
+        
+        for(Entry<String, DMatrix> evalEntry : watchs) {
+            evalNames[i] = evalEntry.getKey();
+            evalMats[i] = evalEntry.getValue();
+            i++;
+        }
+        
         //collect all data matrixs
         DMatrix[] allMats;
         if(evalMats!=null && evalMats.length>0) {
@@ -63,16 +76,6 @@ public class Trainer {
         //initialize booster
         Booster booster = new Booster(params, allMats);
         
-        //used for evaluation
-        long[] dataArray = null;
-        String[] names = null;
-        
-        if(dataArray==null || names==null) {
-            //prepare data for evaluation
-            dataArray = TransferUtil.dMatrixs2handles(evalMats);
-            names = evalNames;
-        }
-        
         //begin to train
         for(int iter=0; iter<round; iter++) {
             if(obj != null) {
@@ -88,7 +91,7 @@ public class Trainer {
                     evalInfo = booster.evalSet(evalMats, evalNames, iter, eval);
                 }
                 else {
-                    evalInfo = booster.evalSet(dataArray, names, iter);
+                    evalInfo = booster.evalSet(evalMats, evalNames, iter);
                 }
                 logger.info(evalInfo);
             }

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

@@ -1,55 +0,0 @@
-/*
- 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 org.dmlc.xgboost4j.DMatrix;
-
-/**
- *
- * @author hzx
- */
-public class TransferUtil {
-    /**
-     * transfer DMatrix array to handle array (used for native functions)
-     * @param dmatrixs
-     * @return handle array for input dmatrixs
-     */
-    public 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;
-    }
-    
-    /**
-     * flatten a mat to array
-     * @param mat
-     * @return 
-     */
-    public 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;
-    }
-}

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

@@ -0,0 +1,49 @@
+/*
+ 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.AbstractMap;
+import java.util.ArrayList;
+import java.util.Iterator;
+import java.util.List;
+import java.util.Map.Entry;
+import org.dmlc.xgboost4j.DMatrix;
+
+/**
+ * class to handle evaluation dmatrix
+ * @author hzx
+ */
+public class WatchList implements Iterable<Entry<String, DMatrix> >{
+    List<Entry<String, DMatrix>> watchList = new ArrayList<>();
+    
+    /**
+     * put eval dmatrix and it's name 
+     * @param name
+     * @param dmat 
+     */
+    public void put(String name, DMatrix dmat) {
+        watchList.add(new AbstractMap.SimpleEntry<>(name, dmat));
+    }
+    
+    public int size() {
+        return watchList.size();
+    }
+
+    @Override
+    public Iterator<Entry<String, DMatrix>> iterator() {
+        return watchList.iterator();
+    }    
+}

java/xgboost4j/src/main/resources/lib/README.md

@@ -1 +0,0 @@
-please put native library in this package.