Fix HFT

Team.md

@@ -15,4 +15,4 @@
 
 ## Former Team Members
 * **陈旭** (Core Developer)，清华大学软件学院博士，研究方向主要是推荐系统，机器学习，情感分析等。目前主要研究如何把评论信息应用于推荐系统中，以对用户或者商品进行更加准确的建模，进而缓解传统的推荐系统中普遍存在的数据稀疏问题，冷启动问题等。
-* **汤嘉熹** (Core Developer)，加拿大Simon Fraser University博士，研究方向为推荐系统与用户行为建模，个人主页 [http://www.sfu.ca/~jiaxit](http://www.sfu.ca/~jiaxit)。
+* **汤嘉熹** (Core Developer)，加拿大Simon Fraser University博士，研究方向为推荐系统与用户行为建模，个人主页 [http://www.sfu.ca/~jiaxit](http://www.sfu.ca/~jiaxit)。

core/src/main/java/net/librec/data/model/ArffDataModel.java

@@ -32,6 +32,7 @@
 import net.librec.util.ReflectionUtil;
 
 import java.io.IOException;
+import java.util.ArrayList;
 import java.util.List;
 
 /**
@@ -147,4 +148,13 @@ public BiMap<String, Integer> getItemMappingData() {
     public DataSet getDatetimeDataSet() {
         return null;
     }
+
+    /**
+     * Get all features mapping data.
+     *
+     * @return  the item {raw id, inner id} map of data model.
+     */
+    public ArrayList<BiMap<String, Integer>> getAllFeaturesMappingData() {
+        return ((ArffDataConvertor)dataConvertor).getAllFeatureIds();
+    }
 }

core/src/main/java/net/librec/recommender/TensorRecommender.java

@@ -21,6 +21,7 @@
 import net.librec.common.LibrecException;
 import net.librec.conf.Configuration;
 import net.librec.data.DataModel;
+import net.librec.data.model.ArffDataModel;
 import net.librec.eval.Measure.MeasureValue;
 import net.librec.eval.RecommenderEvaluator;
 import net.librec.math.structure.SparseTensor;
@@ -31,6 +32,7 @@
 import org.apache.commons.logging.Log;
 import org.apache.commons.logging.LogFactory;
 
+import java.util.ArrayList;
 import java.util.List;
 import java.util.Map;
 
@@ -95,6 +97,7 @@ public abstract class TensorRecommender implements Recommender {
      */
     public BiMap<String, Integer> itemMappingData;
 
+    public ArrayList<BiMap<String, Integer>> allFeaturesMappingData;
     /**
      * early-stop criteria
      */
@@ -222,6 +225,9 @@ protected void setup() throws LibrecException {
 
         userMappingData = getDataModel().getUserMappingData();
         itemMappingData = getDataModel().getItemMappingData();
+        numUsers = userMappingData.size();
+        numItems = itemMappingData.size();
+        allFeaturesMappingData = ((ArffDataModel)getDataModel()).getAllFeaturesMappingData();
 
         userDimension = trainTensor.getUserDimension();
         itemDimension = trainTensor.getItemDimension();
@@ -383,7 +389,7 @@ protected boolean isConverged(int iter) throws LibrecException {
      * <li>Leon Bottou, Stochastic Gradient Descent Tricks</li>
      * <li>more ways to adapt learning rate can refer to: http://www.willamette.edu/~gorr/classes/cs449/momrate.html</li>
      * </ol>
-     * 
+     *
      * @param iter the current iteration
      */
     protected void updateLRate(int iter) {