PolyFeatures.cpp

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#include <shogun/features/PolyFeatures.h>

using namespace shogun;

CPolyFeatures::CPolyFeatures() :CDotFeatures()
{
    m_feat=NULL;
    m_degree=0;
    m_normalize=false;
    m_input_dimensions=0;
    m_multi_index=NULL;
    m_multinomial_coefficients=NULL;
    m_normalization_values=NULL;
    m_output_dimensions=0;

    register_parameters();
}

CPolyFeatures::CPolyFeatures(CDenseFeatures<float64_t>* feat, int32_t degree, bool normalize)
    : CDotFeatures(), m_multi_index(NULL), m_multinomial_coefficients(NULL),
        m_normalization_values(NULL)
{
    ASSERT(feat)

    m_feat = feat;
    SG_REF(m_feat);
    m_degree=degree;
    m_normalize=normalize;
    m_input_dimensions=feat->get_num_features();
    m_output_dimensions=calc_feature_space_dimensions(m_input_dimensions, m_degree);

    store_multi_index();
    store_multinomial_coefficients();
    if (m_normalize)
        store_normalization_values();

    register_parameters();
}


CPolyFeatures::~CPolyFeatures()
{
    SG_FREE(m_multi_index);
    SG_FREE(m_multinomial_coefficients);
    SG_FREE(m_normalization_values);
    SG_UNREF(m_feat);
}

CPolyFeatures::CPolyFeatures(const CPolyFeatures & orig)
{
    SG_PRINT("CPolyFeatures:\n")
    SG_NOTIMPLEMENTED
};

int32_t CPolyFeatures::get_dim_feature_space() const
{
    return m_output_dimensions;
}

int32_t CPolyFeatures::get_nnz_features_for_vector(int32_t num)
{
    return m_output_dimensions;
}

EFeatureType CPolyFeatures::get_feature_type() const
{
    return F_UNKNOWN;
}

EFeatureClass CPolyFeatures::get_feature_class() const
{
    return C_POLY;
}

int32_t CPolyFeatures::get_num_vectors() const
{
    if (m_feat)
        return m_feat->get_num_vectors();
    else
        return 0;

}

void* CPolyFeatures::get_feature_iterator(int32_t vector_index)
{
    SG_NOTIMPLEMENTED
    return NULL;
}

bool CPolyFeatures::get_next_feature(int32_t& index, float64_t& value, void* iterator)
{
    SG_NOTIMPLEMENTED
    return false;
}

void CPolyFeatures::free_feature_iterator(void* iterator)
{
    SG_NOTIMPLEMENTED
}



float64_t CPolyFeatures::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())

    CPolyFeatures* pf=(CPolyFeatures*) df;

    int32_t len1;
    bool do_free1;
    float64_t* vec1 = m_feat->get_feature_vector(vec_idx1, len1, do_free1);

    int32_t len2;
    bool do_free2;
    float64_t* vec2 = pf->m_feat->get_feature_vector(vec_idx2, len2, do_free2);

    float64_t sum=0;
    int cnt=0;
    for (int j=0; j<m_output_dimensions; j++)
    {
        float64_t out1=m_multinomial_coefficients[j];
        float64_t out2=m_multinomial_coefficients[j];
        for (int k=0; k<m_degree; k++)
        {
            out1*=vec1[m_multi_index[cnt]];
            out2*=vec2[m_multi_index[cnt]];
            cnt++;
        }
        sum+=out1*out2;
    }
    m_feat->free_feature_vector(vec1, len1, do_free1);
    pf->m_feat->free_feature_vector(vec2, len2, do_free2);

    return sum;
}

float64_t CPolyFeatures::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)

    int32_t len;
    bool do_free;
    float64_t* vec = m_feat->get_feature_vector(vec_idx1, len, do_free);


    int cnt=0;
    float64_t sum=0;
    for (int j=0; j<vec2_len; j++)
    {
        float64_t output=m_multinomial_coefficients[j];
        for (int k=0; k<m_degree; k++)
        {
            output*=vec[m_multi_index[cnt]];
            cnt++;
        }
        sum+=output*vec2[j];
    }
    if (m_normalize)
        sum = sum/m_normalization_values[vec_idx1];

    m_feat->free_feature_vector(vec, len, do_free);
    return sum;
}
void CPolyFeatures::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)

    int32_t len;
    bool do_free;
    float64_t* vec = m_feat->get_feature_vector(vec_idx1, len, do_free);


    int cnt=0;
    float32_t norm_val=1;
    if (m_normalize)
        norm_val = m_normalization_values[vec_idx1];
    alpha/=norm_val;
    for (int j=0; j<vec2_len; j++)
    {
        float64_t output=m_multinomial_coefficients[j];
        for (int k=0; k<m_degree; k++)
        {
            output*=vec[m_multi_index[cnt]];
            cnt++;
        }
        if (abs_val)
            output=CMath::abs(output);

        vec2[j]+=alpha*output;
    }
    m_feat->free_feature_vector(vec, len, do_free);
}
void CPolyFeatures::store_normalization_values()
{
    SG_FREE(m_normalization_values);

    int32_t num_vec = this->get_num_vectors();

    m_normalization_values=SG_MALLOC(float32_t, num_vec);
    for (int i=0; i<num_vec; i++)
    {
        float64_t tmp = CMath::sqrt(dot(i, this,i));
        if (tmp==0)
            // trap division by zero
            m_normalization_values[i]=1;
        else
            m_normalization_values[i]=tmp;
    }

}

void CPolyFeatures::store_multi_index()
{
    SG_FREE(m_multi_index);

        m_multi_index=SG_MALLOC(uint16_t, m_output_dimensions*m_degree);

        uint16_t* exponents = SG_MALLOC(uint16_t, m_input_dimensions);
        if (!exponents)
        SG_ERROR("Error allocating mem \n")
    /*copy adress: otherwise it will be overwritten in recursion*/
        uint16_t* index = m_multi_index;
        enumerate_multi_index(0, &index, exponents, m_degree);

    SG_FREE(exponents);
}

void CPolyFeatures::enumerate_multi_index(const int32_t feat_idx, uint16_t** index, uint16_t* exponents, const int32_t degree)
{
    if (feat_idx==m_input_dimensions-1 || degree==0)
    {
        if (feat_idx==m_input_dimensions-1)
            exponents[feat_idx] = degree;
        if (degree==0)
            exponents[feat_idx] = 0;
        int32_t i, j;
        for (j=0; j<feat_idx+1; j++)
            for (i=0; i<exponents[j]; i++)
            {
                **index = j;
                (*index)++;
            }
        exponents[feat_idx] = 0;
        return;
    }
    int32_t k;
    for (k=0; k<=degree; k++)
    {
        exponents[feat_idx] =  k;
        enumerate_multi_index(feat_idx+1, index,  exponents, degree-k);
    }
    return;

}

void CPolyFeatures::store_multinomial_coefficients()
{
    SG_FREE(m_multinomial_coefficients);

    m_multinomial_coefficients = SG_MALLOC(float64_t, m_output_dimensions);
    int32_t* exponents = SG_MALLOC(int32_t, m_input_dimensions);
    if (!exponents)
        SG_ERROR("Error allocating mem \n")
    int32_t j=0;
    for (j=0; j<m_input_dimensions; j++)
        exponents[j] = 0;
    int32_t k, cnt=0;
    for (j=0; j<m_output_dimensions; j++)
    {
        for (k=0; k<m_degree; k++)
        {
            exponents[m_multi_index[cnt]] ++;
            cnt++;
        }
        m_multinomial_coefficients[j] =  sqrt((double) multinomialcoef(exponents, m_input_dimensions));
        for (k=0; k<m_input_dimensions; k++)
        {
            exponents[k]=0;
        }
    }
    SG_FREE(exponents);
}

int32_t CPolyFeatures::bico2(int32_t n, int32_t k)
{

    /* for this problem k is usually small (<=degree),
     * thus it is efficient to
     * to use recursion and prune end recursions*/
    if (n<k)
        return 0;
    if (k>n/2)
        k = n-k;
    if (k<0)
        return 0;
    if (k==0)
        return 1;
    if (k==1)
        return n;
    if (k<4)
        return bico2(n-1, k-1)+bico2(n-1, k);

    /* call function as implemented in numerical recipies:
     * much more efficient for large binomial coefficients*/
    return bico(n, k);

}

int32_t CPolyFeatures::calc_feature_space_dimensions(int32_t N, int32_t D)
{
    if (N==1)
        return 1;
    if (D==0)
        return 1;
    int32_t d;
    int32_t ret = 0;
    for (d=0; d<=D; d++)
        ret += calc_feature_space_dimensions(N-1, d);

    return ret;
}

int32_t CPolyFeatures::multinomialcoef(int32_t* exps, int32_t len)
{
    int32_t ret = 1, i;
    int32_t n = 0;
    for (i=0; i<len; i++)
    {
        n += exps[i];
        ret *= bico2(n, exps[i]);
    }
    return ret;
}

/* gammln as implemented in the
 * second edition of Numerical Recipes in C */
float64_t CPolyFeatures::gammln(float64_t xx)
{
    float64_t x,y,tmp,ser;
    static float64_t cof[6]={76.18009172947146,    -86.50532032941677,
                          24.01409824083091,    -1.231739572450155,
                          0.1208650973866179e-2,-0.5395239384953e-5};
    int32_t j;

    y=x=xx;
    tmp=x+5.5;
    tmp -= (x+0.5)*log(tmp);
    ser=1.000000000190015;
    for (j=0;j<=5;j++) ser += cof[j]/++y;
    return -tmp+log(2.5066282746310005*ser/x);
}

float64_t CPolyFeatures::factln(int32_t n)
{
    static float64_t a[101];

    if (n < 0) SG_ERROR("Negative factorial in routine factln\n")
    if (n <= 1) return 0.0;
    if (n <= 100) return a[n] ? a[n] : (a[n]=gammln(n+1.0));
    else return gammln(n+1.0);
}

int32_t CPolyFeatures::bico(int32_t n, int32_t k)
{
    /* use floor to clean roundoff errors*/
    return (int32_t) floor(0.5+exp(factln(n)-factln(k)-factln(n-k)));
}
CFeatures* CPolyFeatures::duplicate() const
{
    return new CPolyFeatures(*this);
}

void CPolyFeatures::register_parameters()
{
    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.");

    multi_index_length=m_output_dimensions*m_degree;
    m_parameters->add_vector(
            &m_multi_index,
            &multi_index_length,
            "multi_index",
            "Flattened matrix of all multi indices that sum do the"
            " degree of the polynomial kernel.");

    multinomial_coefficients_length=m_output_dimensions;
    m_parameters->add_vector(&m_multinomial_coefficients,
            &multinomial_coefficients_length, "multinomial_coefficients",
            "Multinomial coefficients for all multi-indices.");

    normalization_values_length=get_num_vectors();
    m_parameters->add_vector(&m_normalization_values,
            &normalization_values_length, "normalization_values",
            "Norm of each training example.");
}