Add constructor to Perceptron for weighted data for KFoldCV.

src/mlpack/methods/perceptron/perceptron.hpp

@@ -68,6 +68,29 @@ class Perceptron
              const size_t numClasses,
              const size_t maxIterations = 1000);
 
+  /**
+   * Constructor: construct the perceptron by building the weights matrix, which
+   * is later used in classification.  The number of classes should be specified
+   * separately, and the labels vector should contain values in the range [0,
+   * numClasses - 1].  The data::NormalizeLabels() function can be used if the
+   * labels vector does not contain values in the required range.
+   *
+   * This constructor supports weights for each data point.
+   *
+   * @param data Input, training data.
+   * @param labels Labels of dataset.
+   * @param numClasses Number of classes in the dataset.
+   * @param instanceWeights Weight vector to use for each training point while
+   *     training.
+   * @param maxIterations Maximum number of iterations for the perceptron
+   *     learning algorithm.
+   */
+  Perceptron(const MatType& data,
+             const arma::Row<size_t>& labels,
+             const size_t numClasses,
+             const arma::rowvec& instanceWeights,
+             const size_t maxIterations = 1000);
+
   /**
    * Alternate constructor which copies parameters from an already initiated
    * perceptron.

src/mlpack/methods/perceptron/perceptron_impl.hpp

@@ -62,6 +62,32 @@ Perceptron<LearnPolicy, WeightInitializationPolicy, MatType>::Perceptron(
   Train(data, labels, numClasses);
 }
 
+/**
+ * Constructor: construct the perceptron by building the weights matrix, which
+ * is later used in classification.  The number of classes should be specified
+ * separately, and the labels vector should contain values in the range [0,
+ * numClasses - 1].  The data::NormalizeLabels() function can be used if the
+ * labels vector does not contain values in the required range.
+ *
+ * This constructor supports weights for each data point.
+ */
+template<
+    typename LearnPolicy,
+    typename WeightInitializationPolicy,
+    typename MatType
+>
+Perceptron<LearnPolicy, WeightInitializationPolicy, MatType>::Perceptron(
+    const MatType& data,
+    const arma::Row<size_t>& labels,
+    const size_t numClasses,
+    const arma::rowvec& instanceWeights,
+    const size_t maxIterations) :
+    maxIterations(maxIterations)
+{
+  // Start training.
+  Train(data, labels, numClasses, instanceWeights);
+}
+
 /**
  * Alternate constructor which copies parameters from an already initiated
  * perceptron.

src/mlpack/tests/perceptron_test.cpp

@@ -244,4 +244,28 @@ TEST_CASE("SecondaryConstructor", "[PerceptronTest]")
   Perceptron<> p1(trainData, labels.row(0), 2, 1000);
 
   Perceptron<> p2(p1);
+
+  REQUIRE(p1.Weights().n_elem > 0);
+  REQUIRE(p2.Weights().n_elem > 0);
+}
+
+/**
+ * This tests that we can build the Perceptron when specifying instance weights.
+ */
+TEST_CASE("InstanceWeightsConstructor", "[PerceptronTest]")
+{
+  mat trainData;
+  trainData = { { 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8 },
+                { 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2 } };
+
+  Mat<size_t> labels;
+  labels = { 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1 };
+
+  rowvec instanceWeights;
+  instanceWeights = { 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 0.9,
+      0.8, 0.7, 0.6, 0.5, 0.4 };
+
+  Perceptron<> p(trainData, labels.row(0), 2, instanceWeights, 1000);
+
+  REQUIRE(p.Weights().n_elem > 0);
 }