[DOC-JVM] Refactor JVM docs

doc/jvm/java_intro.md

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+XGBoost4J Java API
+==================
+This tutorial introduces
+
+## 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);
+```