WIP Write Generalized Linear Machine class

src/shogun/classifier/AveragedPerceptron.cpp

@@ -40,7 +40,7 @@ void AveragedPerceptron::init()
 	    ParameterProperties::MODEL)
 }
 
-void AveragedPerceptron::init_model(std::shared_ptr<Features> data)
+void AveragedPerceptron::init_model(const std::shared_ptr<Features> data)
 {
 	ASSERT(m_labels)
 	if (data)

src/shogun/classifier/AveragedPerceptron.h

@@ -69,8 +69,8 @@ namespace shogun
 		/** registers and initializes parameters */
 		void init();
 
-		virtual void init_model(std::shared_ptr<Features> data);
-		virtual void iteration();
+		void init_model(std::shared_ptr<Features> data) override;
+		void iteration() override;
 
 	protected:
 		/** learning rate */

src/shogun/classifier/Perceptron.cpp

@@ -34,7 +34,7 @@ Perceptron::~Perceptron()
 {
 }
 
-void Perceptron::init_model(std::shared_ptr<Features> data)
+void Perceptron::init_model(const std::shared_ptr<Features> data)
 {
 	if (data)
 	{

src/shogun/classifier/Perceptron.h

@@ -59,8 +59,8 @@ class Perceptron : public IterativeMachine<LinearMachine>
 		virtual const char* get_name() const { return "Perceptron"; }
 
 	protected:
-		virtual void init_model(std::shared_ptr<Features> data);
-		virtual void iteration();
+		void init_model(std::shared_ptr<Features> data) override;
+		void iteration() override;
 
 	protected:
 		/** learning rate */

src/shogun/classifier/svm/NewtonSVM.cpp

@@ -48,7 +48,7 @@ NewtonSVM::~NewtonSVM()
 {
 }
 
-void NewtonSVM::init_model(std::shared_ptr<Features> data)
+void NewtonSVM::init_model(const std::shared_ptr<Features> data)
 {
 	if (data)
 	{

src/shogun/classifier/svm/NewtonSVM.h

@@ -93,8 +93,8 @@ class NewtonSVM : public IterativeMachine<LinearMachine>
 		virtual const char* get_name() const { return "NewtonSVM"; }
 
 	protected:
-		virtual void init_model(std::shared_ptr<Features> data);
-		virtual void iteration();
+		void init_model(std::shared_ptr<Features> data) override;
+		void iteration() override;
 
 	private:
 		void obj_fun_linear();

src/shogun/machine/GLM.cpp

@@ -0,0 +1,170 @@
+/*
+ * This software is distributed under BSD 3-clause license (see LICENSE file).
+ *
+ * Author: Tej Sukhatme
+ */
+
+#include <shogun/features/DotFeatures.h>
+#include <shogun/labels/Labels.h>
+#include <shogun/labels/RegressionLabels.h>
+#include <shogun/lib/observers/ObservedValueTemplated.h>
+#include <shogun/machine/GLM.h>
+#include <shogun/machine/LinearMachine.h>
+#include <shogun/mathematics/NormalDistribution.h>
+#include <shogun/mathematics/RandomNamespace.h>
+#include <shogun/mathematics/linalg/LinalgNamespace.h>
+#include <shogun/optimization/ConstLearningRate.h>
+#include <shogun/optimization/ElasticNetPenalty.h>
+#include <shogun/optimization/GradientDescendUpdater.h>
+#include <shogun/optimization/SGDMinimizer.h>
+
+#include <cmath>
+
+using namespace shogun;
+
+GLM::GLM()
+{
+	SG_ADD_OPTIONS(
+	    (machine_int_t*)&distribution, "distribution_type",
+	    "variable to store name of distribution type",
+	    ParameterProperties::HYPER, SG_OPTIONS(POISSON));
+	SG_ADD(
+	    &m_eta, "eta",
+	    "threshold parameter that linearizes the exp() function above eta",
+	    ParameterProperties::HYPER);
+	SG_ADD(
+	    &m_lambda, "lambda", "regularization parameter of penalty term",
+	    ParameterProperties::HYPER);
+	SG_ADD(
+	    &m_alpha, "alpha",
+	    "weighting between L1 penalty and L2 penalty term of the loss function",
+	    ParameterProperties::HYPER);
+	SG_ADD(
+	    &m_tolerance, "tolerance", "convergence threshold or stopping criteria",
+	    ParameterProperties::HYPER);
+	SG_ADD(
+	    &m_learning_rate, "learning_rate", "learning rate for gradient descent",
+	    ParameterProperties::HYPER);
+
+	m_gradient_updater = std::make_shared<GradientDescendUpdater>();
+	m_penalty = std::make_shared<ElasticNetPenalty>();
+	m_cost_function = std::make_shared<GLMCostFunction>();
+}
+
+GLM::GLM(
+    GLM_DISTRIBUTION distr, float64_t alpha, float64_t lambda,
+    float64_t learning_rate, int32_t max_iterations, float64_t tolerance,
+    float64_t eta)
+    : GLM()
+{
+	distribution = distr;
+	m_alpha = alpha;
+	m_lambda = lambda;
+	m_learning_rate = learning_rate;
+	m_max_iterations = max_iterations;
+	m_tolerance = tolerance;
+	m_eta = eta;
+
+	m_penalty->set_l1_ratio(m_alpha);
+}
+
+std::shared_ptr<RegressionLabels>
+GLM::apply_regression(std::shared_ptr<Features> data)
+{
+	if (data)
+	{
+		if (!data->has_property(FP_DOT))
+			error("Specified features are not of type CDotFeatures");
+		set_features(std::static_pointer_cast<DotFeatures>(data));
+	}
+
+	require(features, "Features are not provided");
+
+	auto num = features->get_num_vectors();
+	ASSERT(num > 0)
+	ASSERT(m_w.vlen == features->get_dim_feature_space())
+	SGVector<float64_t> out(num);
+	features->dense_dot_range(
+	    out.vector, 0, num, NULL, m_w.vector, m_w.vlen, bias);
+	auto result = m_cost_function->non_linearity(
+	    out, m_compute_bias, m_eta, distribution);
+
+	return std::make_shared<RegressionLabels>(result);
+}
+
+void GLM::init_model(const std::shared_ptr<Features> data)
+{
+	ASSERT(m_labels)
+	if (data)
+	{
+		if (!data->has_property(FP_DOT))
+			error("Specified features are not of type CDotFeatures");
+		set_features(std::static_pointer_cast<DotFeatures>(data));
+	}
+	ASSERT(features)
+
+	NormalDistribution<float64_t> normal_dist;
+	const auto& n_features = features->get_dim_feature_space();
+
+	if (m_w.vlen == 0)
+	{
+		if (m_compute_bias && bias == 0)
+			bias = 1.0 / (n_features + 1) * normal_dist(m_prng);
+
+		if (n_features > 0)
+		{
+			m_w = SGVector<float64_t>(n_features);
+
+			std::generate(m_w.begin(), m_w.end(), [&]() {
+				auto rand = normal_dist(m_prng);
+				return 1.0 / (n_features + 1) * rand;
+			});
+		}
+	}
+}
+
+void GLM::iteration()
+{
+	SGVector<float64_t> w_old = m_w.clone();
+
+	auto X = get_features()->get_computed_dot_feature_matrix();
+	auto y = regression_labels(get_labels())->get_labels();
+
+	auto gradient_w = m_cost_function->get_gradient_weights(
+	    X, y, m_w, bias, m_lambda, m_alpha, m_compute_bias, m_eta,
+	    distribution);
+	auto gradient_bias = m_cost_function->get_gradient_bias(
+	    X, y, m_w, bias, m_compute_bias, m_eta, distribution);
+
+	// Update
+	// TODO: Use gradient updater
+	// m_gradient_updater->update_variable(m_w, gradient_w, learning_rate);
+	m_w = linalg::add(m_w, gradient_w, 1.0, -1 * m_learning_rate);
+
+	if (m_compute_bias)
+		bias -= m_learning_rate * gradient_bias;
+
+	// Apply proximal operator
+	// TODO: Use proximity updater.
+	// m_penalty->update_variable_for_proximity(m_w, m_lambda * m_alpha);
+	for (auto i : range(m_w.vlen))
+	{
+		if (std::abs(m_w[i]) < (m_lambda * m_alpha))
+			m_w[i] = 0;
+		else
+		{
+			if (m_w[i] > 0)
+				m_w[i] -= (m_lambda * m_alpha);
+			else
+				m_w[i] += (m_lambda * m_alpha);
+		}
+	}
+
+	// Convergence by relative parameter change tolerance
+	auto norm_update = linalg::norm(linalg::add(m_w, w_old, 1.0, -1.0));
+	float32_t checker = linalg::norm(m_w) == 0
+	                        ? norm_update
+	                        : std::abs(norm_update / linalg::norm(m_w));
+	if (m_current_iteration > 0 && checker < m_tolerance)
+		m_complete = true;
+}