added AdaDelta method

src/shogun/optimization/AdaDeltaUpdater.cpp

@@ -0,0 +1,172 @@
+/*
+ * Copyright (c) The Shogun Machine Learning Toolbox
+ * Written (w) 2015 Wu Lin
+ * All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright notice, this
+ *    list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright notice,
+ *    this list of conditions and the following disclaimer in the documentation
+ *    and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+ * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ * The views and conclusions contained in the software and documentation are those
+ * of the authors and should not be interpreted as representing official policies,
+ * either expressed or implied, of the Shogun Development Team.
+ *
+ */
+
+#include <shogun/optimization/AdaDeltaUpdater.h>
+#include <shogun/lib/config.h>
+using namespace shogun;
+
+AdaDeltaUpdater::AdaDeltaUpdater()
+	:DescendUpdaterWithCorrection()
+{
+	init();
+}
+
+void AdaDeltaUpdater::set_learning_rate(float64_t learning_rate)
+{
+	REQUIRE(learning_rate>0,"Learning_rate (%f) must be positive\n",
+		learning_rate);
+	m_learning_rate=learning_rate;
+}
+
+void AdaDeltaUpdater::set_epsilon(float64_t epsilon)
+{
+	REQUIRE(epsilon>0,"Epsilon (%f) must be non-negative\n",
+		epsilon);
+	m_epsilon=epsilon;
+}
+
+void AdaDeltaUpdater::set_decay_factor(float64_t decay_factor)
+{
+	REQUIRE(decay_factor>=0.0 && decay_factor<1.0,
+		"Decay factor (%f) must in [0,1)\n",
+		decay_factor);
+	m_decay_factor=decay_factor;
+}
+
+AdaDeltaUpdater::~AdaDeltaUpdater()
+{
+}
+
+void AdaDeltaUpdater::init()
+{
+	m_decay_factor=0.9;
+	m_epsilon=1e-6;
+	m_learning_rate=1.0;
+	m_gradient_accuracy=SGVector<float64_t>();
+	m_gradient_delta_accuracy=SGVector<float64_t>();
+}
+
+void AdaDeltaUpdater::update_context(CMinimizerContext* context)
+{
+	DescendUpdaterWithCorrection::update_context(context);
+	REQUIRE(context, "Context must set\n");
+
+	SGVector<float64_t> value(m_gradient_accuracy.vlen);
+	std::copy(m_gradient_accuracy.vector,
+		m_gradient_accuracy.vector+m_gradient_accuracy.vlen,
+		value.vector);
+	std::string key="AdaDeltaUpdater::m_gradient_accuracy";
+	context->save_data(key, value);
+
+	value=SGVector<float64_t>(m_gradient_delta_accuracy.vlen);
+	std::copy(m_gradient_delta_accuracy.vector,
+		m_gradient_delta_accuracy.vector+m_gradient_delta_accuracy.vlen,
+		value.vector);
+	key="AdaDeltaUpdater::m_gradient_delta_accuracy";
+	context->save_data(key, value);
+}
+
+void AdaDeltaUpdater::load_from_context(CMinimizerContext* context)
+{
+	DescendUpdaterWithCorrection::load_from_context(context);
+	REQUIRE(context, "context must set\n");
+
+	std::string key="AdaDeltaUpdater::m_gradient_accuracy";
+	SGVector<float64_t> value=context->get_data_sgvector_float64(key);
+	m_gradient_accuracy=SGVector<float64_t>(value.vlen);
+	std::copy(value.vector, value.vector+value.vlen,
+		m_gradient_accuracy.vector);
+
+	key="AdaDeltaUpdater::m_gradient_delta_accuracy";
+	value=context->get_data_sgvector_float64(key);
+	m_gradient_delta_accuracy=SGVector<float64_t>(value.vlen);
+	std::copy(value.vector, value.vector+value.vlen,
+		m_gradient_delta_accuracy.vector);
+}
+
+float64_t AdaDeltaUpdater::get_negative_descend_direction(float64_t variable,
+	float64_t gradient, index_t idx)
+{
+	REQUIRE(idx>=0 && idx<m_gradient_accuracy.vlen,
+		"Index (%d) is invalid\n", idx);
+	REQUIRE(idx>=0 && idx<m_gradient_delta_accuracy.vlen,
+		"Index (%d) is invalid\n", idx);
+	float64_t scale=m_decay_factor*m_gradient_accuracy[idx]+
+		(1.0-m_decay_factor)*gradient*gradient;
+	m_gradient_accuracy[idx]=scale;
+	float64_t res=m_learning_rate*gradient*CMath::sqrt(m_gradient_delta_accuracy[idx]+m_epsilon)/CMath::sqrt(scale+m_epsilon);
+	m_gradient_delta_accuracy[idx]=m_decay_factor*m_gradient_delta_accuracy[idx]+(1.0-m_decay_factor)*res*res;
+	return res;
+
+}
+
+void AdaDeltaUpdater::update_variable(SGVector<float64_t> variable_reference,
+	SGVector<float64_t> raw_negative_descend_direction)
+{
+	REQUIRE(variable_reference.vlen>0,"variable_reference must set\n");
+	REQUIRE(variable_reference.vlen==raw_negative_descend_direction.vlen,
+		"The length of variable_reference (%d) and the length of gradient (%d) do not match\n",
+		variable_reference.vlen,raw_negative_descend_direction.vlen);
+	if(m_gradient_accuracy.vlen==0)
+	{
+		m_gradient_accuracy=SGVector<float64_t>(variable_reference.vlen);
+		m_gradient_accuracy.set_const(0.0);
+
+		m_gradient_delta_accuracy=SGVector<float64_t>(m_gradient_accuracy.vlen);
+		m_gradient_delta_accuracy.set_const(0.0);
+	}
+	if(m_correction)
+	{
+		MomentumCorrection* momentum_correction=dynamic_cast<MomentumCorrection *>(m_correction);
+		if(momentum_correction)
+		{
+			if(!momentum_correction->is_initialized())
+				momentum_correction->initialize_previous_direction(variable_reference.vlen);
+		}
+
+		for(index_t idx=0; idx<variable_reference.vlen; idx++)
+		{
+			float64_t neg_des_dir=get_negative_descend_direction(
+				variable_reference[idx], raw_negative_descend_direction[idx], idx);
+
+			DescendPair pair=m_correction->get_corrected_descend_direction(
+				neg_des_dir, idx);
+			float64_t delta=pair.delta;
+			variable_reference[idx]+=pair.descend_direction;
+			m_gradient_delta_accuracy[idx]+=
+				(1.0-m_decay_factor)*(delta*delta-neg_des_dir*neg_des_dir);
+		}
+	}
+	else
+	{
+		DescendUpdaterWithCorrection::update_variable(variable_reference, raw_negative_descend_direction);
+	}
+}

src/shogun/optimization/AdaDeltaUpdater.h

@@ -0,0 +1,127 @@
+/*
+ * Copyright (c) The Shogun Machine Learning Toolbox
+ * Written (w) 2015 Wu Lin
+ * All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright notice, this
+ *    list of conditions and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above copyright notice,
+ *    this list of conditions and the following disclaimer in the documentation
+ *    and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+ * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ * The views and conclusions contained in the software and documentation are those
+ * of the authors and should not be interpreted as representing official policies,
+ * either expressed or implied, of the Shogun Development Team.
+ *
+ */
+
+#ifndef ADADELTAUPDATER_H
+#define ADADELTAUPDATER_H
+#include <shogun/optimization/DescendUpdaterWithCorrection.h>
+#include <shogun/optimization/LearningRate.h>
+namespace shogun
+{
+/** @brief The class implements the AdaDelta method.
+ */
+#define IGNORE_IN_CLASSLIST
+IGNORE_IN_CLASSLIST class AdaDeltaUpdater: public DescendUpdaterWithCorrection
+{
+public:
+	/* Constructor */
+	AdaDeltaUpdater();
+
+	/* Destructor */
+	virtual ~AdaDeltaUpdater();
+
+	/** Set learning rate
+	 *
+	 * @param learning_rate learning rate
+	 */
+	virtual void set_learning_rate(float64_t learning_rate);
+
+	/** Set epsilon
+	 *
+	 * @param epsilon epsilon
+	 */
+	virtual void set_epsilon(float64_t epsilon);
+
+	/** Set decay_factor
+	 *
+	 * @param decay_factor decay factor
+	 */
+	virtual void set_decay_factor(float64_t decay_factor);
+
+	/** Update a context object to store mutable variables
+	 *
+	 * This method will be called by
+	 * FirstOrderMinimizer::save_to_context()
+	 *
+	 * @param context, a context object
+	 */
+	virtual void update_context(CMinimizerContext* context);
+
+	/** Return a context object which stores mutable variables
+	 * Usually it is used in serialization.
+	 *
+	 * This method will be called by
+	 * FirstOrderMinimizer::load_from_context(CMinimizerContext* context)
+	 *
+	 * @return a context object
+	 */
+	virtual void load_from_context(CMinimizerContext* context);
+
+	/** Update the target variable based on the given negative descend direction
+	 *
+	 * Note that this method will update the target variable in place.
+	 * This method will be called by FirstOrderMinimizer::minimize()
+	 * 
+	 * @param variable_reference a reference of the target variable
+	 * @param raw_negative_descend_direction the negative descend direction given the current value
+	 */
+	virtual void update_variable(SGVector<float64_t> variable_reference,
+		SGVector<float64_t> raw_negative_descend_direction);
+
+protected:
+	/** Get the negative descend direction given current variable and gradient
+	 *
+	 * It will be called at update_variable()
+	 *
+	 * @param variable current variable
+	 * @param gradient current gradient
+	 * @param idx the index of the variable
+	 * 
+	 * @return negative descend direction (that is, the given gradient in the class)
+	 */
+	virtual float64_t get_negative_descend_direction(float64_t variable,
+		float64_t gradient, index_t idx);
+
+	/* learning_rate at iteration */
+	float64_t m_learning_rate;
+
+	float64_t m_epsilon;
+
+	float64_t m_decay_factor;
+
+	SGVector<float64_t> m_gradient_accuracy;
+	SGVector<float64_t> m_gradient_delta_accuracy;
+private:
+	/*  Init */
+	void init();
+};
+
+}
+#endif