Minor change to reduce GC overhead

JSAT/src/jsat/classifiers/svm/extended/CPM.java

@@ -405,7 +405,7 @@ else if(i == old_owner)
      * @param sign_mul Either positive or negative 1. Controls whether or not
      * the positive or negative class is to be enveloped by the polytype
      */
-    private void sgdTrain(ClassificationDataSet D, MatrixOfVecs W, Vec b, int sign_mul)
+    private void sgdTrain(ClassificationDataSet D, MatrixOfVecs W, Vec b, int sign_mul, ExecutorService ex)
     {
         IntList order = new IntList(D.getSampleSize());
         ListUtils.addRange(order, 0, D.getSampleSize(), 1);
@@ -417,6 +417,8 @@ private void sgdTrain(ClassificationDataSet D, MatrixOfVecs W, Vec b, int sign_m
         int[] assignments = new int[D.getSampleSize()];//who owns each data point
         Arrays.fill(assignments, -1);//Starts out that no one is assigned! 
 
+        Vec dots = new DenseVector(W.rows());
+
         long t = 0;
         for(int epoch = 0; epoch < epochs; epoch++)
         {
@@ -428,8 +430,10 @@ private void sgdTrain(ClassificationDataSet D, MatrixOfVecs W, Vec b, int sign_m
                 Vec x_i = D.getDataPoint(i).getNumericalValues();
                 int y_i = (D.getDataPointCategory(i)*2-1)*sign_mul;
 
-                Vec dots = W.multiply(x_i);
-                dots.mutableAdd(b);
+                //this sets dots = bias, which we then add to with matrix-vector product
+                //result is the same as dots =  W x_i + b
+                b.copyTo(dots);
+                W.multiply(x_i, 1.0, dots);
 
                 if(y_i == -1)
                 {
@@ -490,8 +494,8 @@ public void trainC(ClassificationDataSet dataSet, ExecutorService threadPool)
         MatrixOfVecs W_p = new MatrixOfVecs(Wv_p);
         MatrixOfVecs W_n = new MatrixOfVecs(Wv_n);
 
-        sgdTrain(dataSet, W_p, bp, +1);
-        sgdTrain(dataSet, W_n, bn, -1);
+        sgdTrain(dataSet, W_p, bp, +1, threadPool);
+        sgdTrain(dataSet, W_n, bn, -1, threadPool);
 
         this.Wp = new DenseMatrix(W_p);
         this.Wn = new DenseMatrix(W_n);