Added Gradient checking in the GradientModelSelection model. Added

ARD versions of Linear and Gaussian Kernels
shogun-toolbox · Jul 12, 2012 · 8246e94 · 8246e94
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Showing 8 changed files with 614 additions and 3 deletions.
diff --git a/src/shogun/kernel/GaussianARDKernel.cpp b/src/shogun/kernel/GaussianARDKernel.cpp
@@ -0,0 +1,132 @@
+/*
+ * This program is free software; you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation; either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * (W) 2012 Jacob Walker
+ *
+ * Adapted from WeightedDegreeRBFKernel.cpp
+ *
+ */
+
+#include <shogun/lib/common.h>
+#include <shogun/kernel/GaussianARDKernel.h>
+#include <shogun/features/Features.h>
+#include <shogun/io/SGIO.h>
+
+using namespace shogun;
+
+CGaussianARDKernel::CGaussianARDKernel()
+: CDotKernel(), m_dimension(1), m_weights(0)
+{
+	init();
+}
+
+
+CGaussianARDKernel::CGaussianARDKernel(int32_t size, float64_t width)
+: CDotKernel(size), m_dimension(0), m_weights(0), m_width(width)
+{
+	init();
+}
+
+CGaussianARDKernel::CGaussianARDKernel(CDenseFeatures<float64_t>* l,
+		CDenseFeatures<float64_t>* r,
+		int32_t size, float64_t width)
+: CDotKernel(size), m_weights(0), m_width(width)
+{
+	init();
+	init(l,r);
+}
+
+void CGaussianARDKernel::init()
+{
+	m_weights = NULL;
+	m_dimension = 0;
+	m_width = 2.0;
+
+	m_parameters->add_vector(&m_weights, &m_dimension, "weights");
+	SG_ADD(&m_width, "width", "Kernel Width", MS_AVAILABLE);
+}
+
+CGaussianARDKernel::~CGaussianARDKernel()
+{
+	SG_FREE(m_weights);
+	m_weights=NULL;
+	CKernel::cleanup();
+}
+
+bool CGaussianARDKernel::init(CFeatures* l, CFeatures* r)
+{
+	CDotKernel::init(l, r);
+
+	int32_t alen, blen;
+
+	alen = ((CDenseFeatures<float64_t>*) lhs)->get_num_features();
+	blen = ((CDenseFeatures<float64_t>*) rhs)->get_num_features();
+
+	ASSERT(alen==blen);
+
+	m_dimension = alen;
+
+	SG_DEBUG("Initialized GaussianARDKernel (%p).\n", this);
+
+	return (init_normalizer() && init_ft_weights());
+}
+
+bool CGaussianARDKernel::init_ft_weights()
+{
+	ASSERT(m_dimension>0);
+
+	if (m_weights!=0)
+		SG_FREE(m_weights);
+
+	m_weights=SG_MALLOC(float64_t, m_dimension);
+
+	if (m_weights)
+	{
+		for (index_t i=0; i<m_dimension; i++)
+			m_weights[i]=1.0;
+
+		SG_DEBUG("Initialized weights for GaussianARDKernel (%p).\n", this);
+		return true;
+	}
+
+	else
+		return false;
+}
+
+void CGaussianARDKernel::set_weight(float64_t w, index_t i)
+{
+	if (i > m_dimension-1)
+		SG_ERROR("Index %i out of range for GaussianARDKernel."\
+				 "Number of features is %i.\n", i, m_dimension);
+	m_weights[i]=w;
+}
+
+float64_t CGaussianARDKernel::get_weight(index_t i)
+{
+	if (i > m_dimension-1)
+		SG_ERROR("Index %i out of range for GaussianARDKernel."\
+				 "Number of features is %i.\n", i, m_dimension);
+	return m_weights[i];
+}
+
+float64_t CGaussianARDKernel::compute(int32_t idx_a, int32_t idx_b)
+{
+	int32_t alen, blen;
+	bool afree, bfree;
+
+	float64_t* avec=((CDenseFeatures<float64_t>*) lhs)->
+			get_feature_vector(idx_a, alen, afree);
+	float64_t* bvec=((CDenseFeatures<float64_t>*) rhs)->
+			get_feature_vector(idx_b, blen, bfree);
+	ASSERT(alen==blen);
+
+	float64_t result=0;
+
+	for (index_t i = 0; i < m_dimension; i++)
+		result += CMath::pow((avec[i]-bvec[i])*m_weights[i], 2);
+
+	return CMath::exp(-result/m_width);
+}
diff --git a/src/shogun/kernel/GaussianARDKernel.h b/src/shogun/kernel/GaussianARDKernel.h
@@ -0,0 +1,148 @@
+/*
+ * This program is free software; you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation; either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * (W) 2012 Jacob Walker
+ *
+ * Adapted from WeightedDegreeRBFKernel.h
+ *
+ */
+
+#ifndef GAUSSIANARDKERNEL_H_
+#define GAUSSIANARDKERNEL_H_
+
+#include <shogun/lib/common.h>
+#include <shogun/kernel/DotKernel.h>
+#include <shogun/features/DenseFeatures.h>
+
+namespace shogun {
+
+class CGaussianARDKernel: public CDotKernel {
+
+public:
+	/** default constructor
+	 *
+	 */
+	CGaussianARDKernel();
+
+	/** constructor
+	 *
+	 * @param size cache size
+	 */
+	CGaussianARDKernel(int32_t size, float64_t width);
+
+	/** constructor
+	 *
+	 * @param l features of left-hand side
+	 * @param r features of right-hand side
+	 * @param size cache size
+	 */
+	CGaussianARDKernel(CDenseFeatures<float64_t>* l, CDenseFeatures<float64_t>* r,
+		int32_t size=10, float64_t width = 2.0);
+
+	virtual ~CGaussianARDKernel();
+
+	/** initialize kernel
+	 *
+	 * @param l features of left-hand side
+	 * @param r features of right-hand side
+	 * @return if initializing was successful
+	 */
+	virtual bool init(CFeatures* l, CFeatures* r);
+
+	/** return what type of kernel we are
+	 *
+	 * @return kernel type LINEARARD
+	 */
+	virtual EKernelType get_kernel_type() { return K_GAUSSIANARD; }
+
+	/** return the kernel's name
+	 *
+	 * @return name LinearARDKernel
+	 */
+	inline virtual const char* get_name() const { return "GaussianARDKernel"; }
+
+
+	/** return feature class the kernel can deal with
+	 *
+	 * @return feature class SIMPLE
+	 */
+	inline virtual EFeatureClass get_feature_class() { return C_DENSE; }
+
+	/** return feature type the kernel can deal with
+	 *
+	 * @return float64_t feature type
+	 */
+	virtual EFeatureType get_feature_type() { return F_DREAL; }
+
+	/*Set weight of particular feature
+	 *
+	 * @param w weight to set
+	 * @param i index of feature
+	 */
+	virtual void set_weight(float64_t w, index_t i);
+
+	/*Get weight of particular feature
+	 *
+	 * @param i index of feature
+	 *
+	 * @return weight of feature
+	 */
+	virtual float64_t get_weight(index_t i);
+
+	/** set the kernel's width
+	 *
+	 * @param w kernel width
+	 */
+	inline virtual void set_width(float64_t w)
+	{
+		m_width = w;
+	}
+
+	/** return the kernel's width
+	 *
+	 * @return kernel width
+	 */
+	inline virtual float64_t get_width() const
+	{
+		return m_width;
+	}
+
+	protected:
+
+	/** compute kernel function for features a and b
+	 * idx_{a,b} denote the index of the feature vectors
+	 * in the corresponding feature object
+	 *
+	 * @param idx_a index a
+	 * @param idx_b index b
+	 * @return computed kernel function at indices a,b
+	 */
+	virtual float64_t compute(int32_t idx_a, int32_t idx_b);
+
+	/** init feature weights
+	 *
+	 * @return if initialization was successful
+	 */
+	bool init_ft_weights();
+
+private:
+
+	void init();
+
+protected:
+
+	/** dimension */
+	index_t m_dimension;
+
+	/** weights */
+	float64_t* m_weights;
+
+	/* kernel width */
+	float64_t m_width;
+};
+
+} /* namespace shogun */
+#endif /* GAUSSIANARDKERNEL_H_ */
diff --git a/src/shogun/kernel/Kernel.cpp b/src/shogun/kernel/Kernel.cpp
@@ -753,6 +753,8 @@ void CKernel::list_kernel()
 		ENUM_CASE(K_JENSENSHANNON)
 		ENUM_CASE(K_DIRECTOR)
 		ENUM_CASE(K_PRODUCT)
+		ENUM_CASE(K_LINEARARD)
+		ENUM_CASE(K_GAUSSIANARD)
 	}
 
 	switch (get_feature_class())

diff --git a/src/shogun/kernel/Kernel.h b/src/shogun/kernel/Kernel.h
@@ -108,7 +108,9 @@ enum EKernelType
 	K_BESSEL = 460,
 	K_JENSENSHANNON = 470,
 	K_DIRECTOR = 480,
-	K_PRODUCT = 490
+	K_PRODUCT = 490,
+	K_LINEARARD = 500,
+	K_GAUSSIANARD = 510
 };
 
 /** kernel property */