SparsePolyFeatures.cpp

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/*
 * 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.
 *
 * Written (W) 2010 Soeren Sonnenburg
 * Copyright (C) 2010 Berlin Institute of Technology
 */
#include <shogun/features/SparsePolyFeatures.h>
#include <shogun/lib/Hash.h>

using namespace shogun;

CSparsePolyFeatures::CSparsePolyFeatures()
{
    SG_UNSTABLE("CSparsePolyFeatures::CSparsePolyFeatures(void)",
                "\n");

    m_feat = NULL;
    m_degree = 0;
    m_normalize = false;
    m_input_dimensions = 0;
    m_output_dimensions = 0;
    m_normalization_values = NULL;
    mask = 0;
    m_hash_bits = 0;
}

CSparsePolyFeatures::CSparsePolyFeatures(CSparseFeatures<float64_t>* feat, int32_t degree, bool normalize, int32_t hash_bits)
    : CDotFeatures(), m_normalization_values(NULL)
{
    ASSERT(feat);

    m_feat = feat;
    SG_REF(m_feat);
    m_degree=degree;
    m_normalize=normalize;
    m_hash_bits=hash_bits;
    mask=(uint32_t) (((uint64_t) 1)<<m_hash_bits)-1;
    m_output_dimensions=1<<m_hash_bits;
    m_input_dimensions=feat->get_num_features();

    if (m_normalize)
        store_normalization_values();
}

CSparsePolyFeatures::~CSparsePolyFeatures()
{
    SG_FREE(m_normalization_values);
    SG_UNREF(m_feat);
}

float64_t CSparsePolyFeatures::dot(int32_t vec_idx1, CDotFeatures* df, int32_t vec_idx2)
{
    ASSERT(df);
    ASSERT(df->get_feature_type() == get_feature_type());
    ASSERT(df->get_feature_class() == get_feature_class());

    CSparsePolyFeatures* pf=(CSparsePolyFeatures*) df;

    SGSparseVector<float64_t> vec1=m_feat->get_sparse_feature_vector(vec_idx1);
    SGSparseVector<float64_t> vec2=pf->m_feat->get_sparse_feature_vector(
            vec_idx2);

    float64_t result=CSparseFeatures<float64_t>::sparse_dot(1, vec1.features,
            vec1.num_feat_entries, vec2.features, vec2.num_feat_entries);
    result=CMath::pow(result, m_degree);

    m_feat->free_feature_vector(vec1, vec_idx1);
    pf->m_feat->free_feature_vector(vec2, vec_idx2);

    return result;
}

float64_t CSparsePolyFeatures::dense_dot(int32_t vec_idx1, const float64_t* vec2, int32_t vec2_len)
{
    if (vec2_len != m_output_dimensions)
        SG_ERROR("Dimensions don't match, vec2_dim=%d, m_output_dimensions=%d\n", vec2_len, m_output_dimensions);

    SGSparseVector<float64_t> vec=m_feat->get_sparse_feature_vector(vec_idx1);

    float64_t result=0;

    if (vec.features)
    {
        if (m_degree==2)
        {
            /* (a+b)^2 = a^2 + 2ab +b^2 */
            for (int32_t i=0; i<vec.num_feat_entries; i++)
            {
                float64_t v1=vec.features[i].entry;
                uint32_t seed=CHash::MurmurHash2(
                        (uint8_t*)&(vec.features[i].feat_index),
                        sizeof(int32_t), 0xDEADBEAF);

                for (int32_t j=i; j<vec.num_feat_entries; j++)
                {
                    float64_t v2=vec.features[j].entry;
                    uint32_t h=CHash::MurmurHash2(
                            (uint8_t*)&(vec.features[j].feat_index),
                            sizeof(int32_t), seed)&mask;
                    float64_t v;

                    if (i==j)
                        v=v1*v1;
                    else
                        v=CMath::sqrt(2.0)*v1*v2;

                    result+=v*vec2[h];
                }
            }
        }
        else if (m_degree==3)
            SG_NOTIMPLEMENTED;
    }
    
    if (m_normalize)
        result/=m_normalization_values[vec_idx1];

    m_feat->free_feature_vector(vec, vec_idx1);
    return result;
}

void CSparsePolyFeatures::add_to_dense_vec(float64_t alpha, int32_t vec_idx1, float64_t* vec2, int32_t vec2_len, bool abs_val)
{
    if (vec2_len!=m_output_dimensions)
        SG_ERROR("Dimensions don't match, vec2_dim=%d, m_output_dimensions=%d\n", vec2_len, m_output_dimensions);

    SGSparseVector<float64_t> vec=m_feat->get_sparse_feature_vector(vec_idx1);

    float64_t norm_val=1.0;
    if (m_normalize)
        norm_val = m_normalization_values[vec_idx1];
    alpha/=norm_val;

    if (m_degree==2)
    {
        /* (a+b)^2 = a^2 + 2ab +b^2 */
        for (int32_t i=0; i<vec.num_feat_entries; i++)
        {
            float64_t v1=vec.features[i].entry;
            uint32_t seed=CHash::MurmurHash2(
                    (uint8_t*)&(vec.features[i].feat_index), sizeof(int32_t),
                    0xDEADBEAF);

            for (int32_t j=i; j<vec.num_feat_entries; j++)
            {
                float64_t v2=vec.features[j].entry;
                uint32_t h=CHash::MurmurHash2(
                        (uint8_t*)&(vec.features[j].feat_index),
                        sizeof(int32_t), seed)&mask;
                float64_t v;

                if (i==j)
                    v=alpha*v1*v1;
                else
                    v=alpha*CMath::sqrt(2.0)*v1*v2;

                if (abs_val)
                    vec2[h]+=CMath::abs(v); 
                else
                    vec2[h]+=v; 
            }
        }
    }
    else if (m_degree==3)
        SG_NOTIMPLEMENTED;

    m_feat->free_feature_vector(vec, vec_idx1);
}

void CSparsePolyFeatures::store_normalization_values()
{
    SG_FREE(m_normalization_values);

    m_normalization_values_len = this->get_num_vectors();

    m_normalization_values=SG_MALLOC(float64_t, m_normalization_values_len);
    for (int i=0; i<m_normalization_values_len; i++)
    {
        float64_t val = CMath::sqrt(dot(i, this,i)); 
        if (val==0)
            // trap division by zero
            m_normalization_values[i]=1.0;
        else 
            m_normalization_values[i]=val;
    }
        
}

CFeatures* CSparsePolyFeatures::duplicate() const
{
    return new CSparsePolyFeatures(*this);
}

void CSparsePolyFeatures::init()
{
    m_parameters->add((CSGObject**) &m_feat, "features",
            "Features in original space.");
    m_parameters->add(&m_degree, "degree", "Degree of the polynomial kernel.");
    m_parameters->add(&m_normalize, "normalize", "Normalize");
    m_parameters->add(&m_input_dimensions, "input_dimensions",
            "Dimensions of the input space.");
    m_parameters->add(&m_output_dimensions, "output_dimensions",
            "Dimensions of the feature space of the polynomial kernel.");
    m_normalization_values_len = get_num_vectors();
    m_parameters->add_vector(&m_normalization_values, &m_normalization_values_len,
            "m_normalization_values", "Norm of each training example");
    m_parameters->add(&mask, "mask", "Mask.");
    m_parameters->add(&m_hash_bits, "m_hash_bits", "Number of bits in hash");
}