refactor

src/mlpack/methods/linear_svm/linear_svm.hpp

@@ -107,13 +107,17 @@ class LinearSVM
    * value of lambda is 0.0001.  Be sure to use Train() before calling
    * Classify() or ComputeAccuracy(), otherwise the results may be meaningless.
    *
+   * @param inputSize Size of the input feature vector.
    * @param numClasses Number of classes for classification.
    * @param lambda L2-regularization constant.
    * @paran delta Margin of difference between correct class and other classes.
+   * @param fitIntercept add intercept term or not.
    */
-  LinearSVM(const size_t numClasses = 0,
+  LinearSVM(const size_t inputSize,
+            const size_t numClasses = 0,
             const double lambda = 0.0001,
-            const double delta = 1.0);
+            const double delta = 1.0,
+            const bool fitIntercept = false);
 
   /**
    * Classify the given points, returning the predicted labels for each point.

src/mlpack/methods/linear_svm/linear_svm_impl.hpp

@@ -38,14 +38,18 @@ LinearSVM<MatType>::LinearSVM(
 
 template <typename MatType>
 LinearSVM<MatType>::LinearSVM(
+    const size_t inputSize,
     const size_t numClasses,
     const double lambda,
-    const double delta) :
+    const double delta,
+    const bool fitIntercept) :
     numClasses(numClasses),
     lambda(lambda),
-    delta(delta)
+    delta(delta),
+    fitIntercept(fitIntercept)
 {
-  // No training to do here.
+  LinearSVMFunction<MatType>::InitializeWeights(
+      parameters, inputSize, numClasses, fitIntercept);
 }
 
 template <typename MatType>

src/mlpack/tests/linear_svm_test.cpp

@@ -454,7 +454,6 @@ BOOST_AUTO_TEST_CASE(LinearSVMLGFGSSimpleTest)
 {
   const size_t numClasses = 2;
   const double lambda = 0.0001;
-  const double delta = 1.0;
 
   // A very simple fake dataset
   arma::mat dataset = "2 0 0;"
@@ -467,8 +466,7 @@ BOOST_AUTO_TEST_CASE(LinearSVMLGFGSSimpleTest)
   arma::Row<size_t> labels = "1 0 1";
 
   // Create a linear svm object using L-BFGS optimizer.
-  LinearSVM<arma::mat> lsvm(dataset, labels, numClasses, lambda,
-      delta, false, ens::L_BFGS());
+  LinearSVM<arma::mat> lsvm(dataset, labels, numClasses, lambda);
 
   // Compare training accuracy to 1.
   const double acc = lsvm.ComputeAccuracy(dataset, labels);
@@ -518,7 +516,6 @@ BOOST_AUTO_TEST_CASE(LinearSVMLBFGSTwoClasses)
   const size_t inputSize = 3;
   const size_t numClasses = 2;
   const double lambda = 0.5;
-  const double delta = 1.0;
 
   // Generate two-Gaussian dataset.
   GaussianDistribution g1(arma::vec("1.0 9.0 1.0"), arma::eye<arma::mat>(3, 3));
@@ -539,8 +536,7 @@ BOOST_AUTO_TEST_CASE(LinearSVMLBFGSTwoClasses)
   }
 
   // Create a linear svm object using L-BFGS optimizer.
-  LinearSVM<arma::mat> lsvm(data, labels, numClasses, lambda,
-      delta, false, ens::L_BFGS());
+  LinearSVM<arma::mat> lsvm(data, labels, numClasses, lambda);
 
   // Compare training accuracy to 1.
   const double acc = lsvm.ComputeAccuracy(data, labels);
@@ -652,7 +648,7 @@ BOOST_AUTO_TEST_CASE(LinearSVMDeltaLBFGSTwoClasses)
 
   // Create a linear svm object using L-BFGS optimizer.
   LinearSVM<arma::mat> lsvm(data, labels, numClasses, lambda,
-      delta, false, ens::L_BFGS());
+      delta);
 
   // Compare training accuracy to 1.
   const double acc = lsvm.ComputeAccuracy(data, labels);
@@ -815,7 +811,6 @@ BOOST_AUTO_TEST_CASE(LinearSVMLBFGSMultipleClasses)
   const size_t inputSize = 5;
   const size_t numClasses = 5;
   const double lambda = 0.5;
-  const double delta = 1.0;
 
   // Generate five-Gaussian dataset.
   arma::mat identity = arma::eye<arma::mat>(5, 5);
@@ -855,8 +850,7 @@ BOOST_AUTO_TEST_CASE(LinearSVMLBFGSMultipleClasses)
   }
 
   // Train linear svm object using L-BFGS optimizer.
-  LinearSVM<arma::mat> lsvm(data, labels, numClasses, lambda,
-      delta, false, ens::L_BFGS());
+  LinearSVM<arma::mat> lsvm(data, labels, numClasses, lambda);
 
   // Compare training accuracy to 1.
   const double acc = lsvm.ComputeAccuracy(data, labels);
@@ -935,7 +929,6 @@ BOOST_AUTO_TEST_CASE(LinearSVMClassifySinglePointTest)
   const size_t inputSize = 5;
   const size_t numClasses = 5;
   const double lambda = 0.5;
-  const double delta = 1.0;
 
   // Generate five-Gaussian dataset.
   arma::mat identity = arma::eye<arma::mat>(5, 5);
@@ -975,8 +968,7 @@ BOOST_AUTO_TEST_CASE(LinearSVMClassifySinglePointTest)
   }
 
   // Train linear svm object.
-  LinearSVM<arma::mat> lsvm(data, labels, numClasses, lambda,
-     delta, false, ens::L_BFGS());
+  LinearSVM<arma::mat> lsvm(data, labels, numClasses, lambda);
 
   // Create test dataset.
   for (size_t i = 0; i < points / 5; i++)
@@ -1023,7 +1015,6 @@ BOOST_AUTO_TEST_CASE(SinglePointClassifyTest)
   const size_t inputSize = 5;
   const size_t numClasses = 5;
   const double lambda = 0.5;
-  const double delta = 1.0;
 
   // Generate five-Gaussian dataset.
   arma::mat identity = arma::eye<arma::mat>(5, 5);
@@ -1063,8 +1054,7 @@ BOOST_AUTO_TEST_CASE(SinglePointClassifyTest)
   }
 
   // Train linear svm object.
-  LinearSVM<arma::mat> lsvm(data, labels, numClasses, lambda,
-      delta, false, ens::L_BFGS());
+  LinearSVM<arma::mat> lsvm(data, labels, numClasses, lambda);
 
   // Create test dataset.
   for (size_t i = 0; i < points / 5; i++)