PLearn 0.1
Public Member Functions | Static Public Member Functions | Public Attributes | Static Public Attributes | Protected Member Functions | Static Protected Member Functions | Private Types | Private Member Functions | Private Attributes
PLearn::PLearnerDiagonalKernel Class Reference

Diagonal kernel from the output of a PLearner. More...

#include <PLearnerDiagonalKernel.h>

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List of all members.

Public Member Functions

 PLearnerDiagonalKernel ()
 Default constructor.
virtual real evaluate (const Vec &x1, const Vec &x2) const
 Compute K(x1,x2).
virtual void computeGramMatrix (Mat K) const
 Compute the Gram Matrix.
virtual void computeGramMatrixDerivative (Mat &KD, const string &kernel_param, real epsilon=1e-6) const
 Directly compute the derivative with respect to hyperparameters (Faster than finite differences...)
virtual void evaluate_all_i_x (const Vec &x, const Vec &k_xi_x, real squared_norm_of_x=-1, int istart=0) const
 Fill k_xi_x with K(x_i, x), for all i from istart to istart + k_xi_x.length() - 1.
virtual string classname () const
virtual OptionListgetOptionList () const
virtual OptionMapgetOptionMap () const
virtual RemoteMethodMapgetRemoteMethodMap () const
virtual PLearnerDiagonalKerneldeepCopy (CopiesMap &copies) const
virtual void build ()
 Post-constructor.
virtual void makeDeepCopyFromShallowCopy (CopiesMap &copies)
 Transforms a shallow copy into a deep copy.

Static Public Member Functions

static string _classname_ ()
static OptionList_getOptionList_ ()
static RemoteMethodMap_getRemoteMethodMap_ ()
static Object_new_instance_for_typemap_ ()
static bool _isa_ (const Object *o)
static void _static_initialize_ ()
static const PPathdeclaringFile ()

Public Attributes

PP< PLearnerm_learner
 Learner we are taking output from.
real m_isp_signal_sigma
 Inverse softplus of the global noise variance. Default value = 0.0.

Static Public Attributes

static StaticInitializer _static_initializer_

Protected Member Functions

void computeGramMatrixDerivIspSignalSigma (Mat &KD) const
 Compute derivative w.r.t. isp_signal_sigma for WHOLE MATRIX.

Static Protected Member Functions

static void declareOptions (OptionList &ol)
 Declares the class options.

Private Types

typedef KroneckerBaseKernel inherited

Private Member Functions

void build_ ()
 This does the actual building.

Private Attributes

Vec m_output_buffer
 Buffer for evaluation of computeOutput.

Detailed Description

Diagonal kernel from the output of a PLearner.

The output of this kernel is given by:

k(x,x) = isp_signal_sigma * exp(learner->computeOutput(x))

and is 0 for x != y.

This is useful for representing heteroscedastic noise in Gaussian Processes, where the log-noise process is the output of another learner (e.g. another Gaussian Process).

Definition at line 61 of file PLearnerDiagonalKernel.h.


Member Typedef Documentation

Reimplemented from PLearn::KroneckerBaseKernel.

Definition at line 63 of file PLearnerDiagonalKernel.h.


Constructor & Destructor Documentation

PLearn::PLearnerDiagonalKernel::PLearnerDiagonalKernel ( )

Default constructor.

Definition at line 61 of file PLearnerDiagonalKernel.cc.

    : m_isp_signal_sigma(0.)
{ }

Member Function Documentation

string PLearn::PLearnerDiagonalKernel::_classname_ ( ) [static]

Reimplemented from PLearn::KroneckerBaseKernel.

Definition at line 58 of file PLearnerDiagonalKernel.cc.

OptionList & PLearn::PLearnerDiagonalKernel::_getOptionList_ ( ) [static]

Reimplemented from PLearn::KroneckerBaseKernel.

Definition at line 58 of file PLearnerDiagonalKernel.cc.

RemoteMethodMap & PLearn::PLearnerDiagonalKernel::_getRemoteMethodMap_ ( ) [static]

Reimplemented from PLearn::KroneckerBaseKernel.

Definition at line 58 of file PLearnerDiagonalKernel.cc.

bool PLearn::PLearnerDiagonalKernel::_isa_ ( const Object o) [static]

Reimplemented from PLearn::KroneckerBaseKernel.

Definition at line 58 of file PLearnerDiagonalKernel.cc.

Object * PLearn::PLearnerDiagonalKernel::_new_instance_for_typemap_ ( ) [static]

Reimplemented from PLearn::KroneckerBaseKernel.

Definition at line 58 of file PLearnerDiagonalKernel.cc.

StaticInitializer PLearnerDiagonalKernel::_static_initializer_ & PLearn::PLearnerDiagonalKernel::_static_initialize_ ( ) [static]

Reimplemented from PLearn::KroneckerBaseKernel.

Definition at line 58 of file PLearnerDiagonalKernel.cc.

void PLearn::PLearnerDiagonalKernel::build ( ) [virtual]

Post-constructor.

The normal implementation should call simply inherited::build(), then this class's build_(). This method should be callable again at later times, after modifying some option fields to change the "architecture" of the object.

Reimplemented from PLearn::KroneckerBaseKernel.

Definition at line 89 of file PLearnerDiagonalKernel.cc.

References PLearn::KroneckerBaseKernel::build(), and build_().

{
    // ### Nothing to add here, simply calls build_
    inherited::build();
    build_();
}

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void PLearn::PLearnerDiagonalKernel::build_ ( ) [private]

This does the actual building.

Reimplemented from PLearn::KroneckerBaseKernel.

Definition at line 99 of file PLearnerDiagonalKernel.cc.

References PLearn::PP< T >::isNull(), PLearn::KroneckerBaseKernel::m_default_value, m_learner, and PLERROR.

Referenced by build().

{
    if (m_learner.isNull())
        PLERROR("PLearnerDiagonalKernel::build: the option 'learner' must be specified");

    // At build-time, we don't yet know the learner outputsize
    // if (m_learner->outputsize() != 1)
    //     PLERROR("PLearnerDiagonalKernel::build: the learner must have an outputsize of 1; "
    //             "current outputsize is %d", m_learner->outputsize());
    
    // Ensure that we multiply in Kronecker terms
    inherited::m_default_value = 1.0;
}

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string PLearn::PLearnerDiagonalKernel::classname ( ) const [virtual]

Reimplemented from PLearn::KroneckerBaseKernel.

Definition at line 58 of file PLearnerDiagonalKernel.cc.

void PLearn::PLearnerDiagonalKernel::computeGramMatrix ( Mat  K) const [virtual]

Compute the Gram Matrix.

Reimplemented from PLearn::KroneckerBaseKernel.

Definition at line 137 of file PLearnerDiagonalKernel.cc.

References PLearn::Kernel::cache_gram_matrix, PLearn::Kernel::evaluate_i_j(), PLearn::exp(), PLearn::TMat< T >::fill(), PLearn::Kernel::gram_matrix, PLearn::Kernel::gram_matrix_is_cached, i, PLearn::PP< T >::isNull(), PLearn::TMat< T >::length(), PLearn::MemoryCachedKernel::m_data_cache, m_isp_signal_sigma, m_learner, m_output_buffer, n, PLASSERT, PLearn::TMat< T >::resize(), PLearn::TVec< T >::resize(), PLearn::TMat< T >::size(), PLearn::softplus(), and PLearn::TMat< T >::width().

{
    PLASSERT( K.size() == 0 || m_data_cache.size() > 0 );  // Ensure data cached OK
    PLASSERT( ! m_learner.isNull() && m_learner->outputsize() == 1);

    m_output_buffer.resize(m_learner->outputsize());
    
    // Most elements are zero, except for the diagonal
    K.fill(0.0);

    real sigma = softplus(m_isp_signal_sigma);
    int  n = m_data_cache.length();
    
    PLASSERT( K.length() == n && K.width() == n );
    
    for (int i=0 ; i<n ; ++i) {
        real gating_term = inherited::evaluate_i_j(i, i);
        Vec input_i = m_data_cache(i);
        m_learner->computeOutput(input_i, m_output_buffer);
        real diag_term = exp(m_output_buffer[0]);
        K(i,i) = sigma * gating_term * diag_term;
    }

    if (cache_gram_matrix) {
        gram_matrix.resize(n,n);
        gram_matrix << K;
        gram_matrix_is_cached = true;
    }
}

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void PLearn::PLearnerDiagonalKernel::computeGramMatrixDerivative ( Mat KD,
const string &  kernel_param,
real  epsilon = 1e-6 
) const [virtual]

Directly compute the derivative with respect to hyperparameters (Faster than finite differences...)

Reimplemented from PLearn::Kernel.

Definition at line 170 of file PLearnerDiagonalKernel.cc.

References PLearn::Kernel::computeGramMatrixDerivative(), and computeGramMatrixDerivIspSignalSigma().

{
    static const string ISS("isp_signal_sigma");

    if (kernel_param == ISS) {
        computeGramMatrixDerivIspSignalSigma(KD);
        
        // computeGramMatrixDerivNV<
        //     PLearnerDiagonalKernel,
        //     &PLearnerDiagonalKernel::derivIspSignalSigma>(KD, this, -1);
    }
    else
        inherited::computeGramMatrixDerivative(KD, kernel_param, epsilon);
}

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void PLearn::PLearnerDiagonalKernel::computeGramMatrixDerivIspSignalSigma ( Mat KD) const [protected]

Compute derivative w.r.t. isp_signal_sigma for WHOLE MATRIX.

Definition at line 198 of file PLearnerDiagonalKernel.cc.

References PLearn::Kernel::data, PLearn::Kernel::gram_matrix, PLearn::TMat< T >::length(), PLearn::VMat::length(), m_isp_signal_sigma, PLASSERT_MSG, PLearn::TMat< T >::resize(), PLearn::sigmoid(), PLearn::softplus(), and PLearn::TMat< T >::width().

Referenced by computeGramMatrixDerivative().

{
    int l = data->length();
    KD.resize(l,l);
    PLASSERT_MSG(
        gram_matrix.width() == l && gram_matrix.length() == l,
        "To compute the derivative with respect to 'isp_signal_sigma', the\n"
        "Gram matrix must be precomputed and cached in PLearnerDiagonalKernel.");
    
    KD << gram_matrix;
    KD *= sigmoid(m_isp_signal_sigma)/softplus(m_isp_signal_sigma);
}

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void PLearn::PLearnerDiagonalKernel::declareOptions ( OptionList ol) [static, protected]

Declares the class options.

Reimplemented from PLearn::KroneckerBaseKernel.

Definition at line 68 of file PLearnerDiagonalKernel.cc.

References PLearn::OptionBase::buildoption, PLearn::declareOption(), PLearn::KroneckerBaseKernel::declareOptions(), m_isp_signal_sigma, and m_learner.

{
    declareOption(
        ol, "learner",
        &PLearnerDiagonalKernel::m_learner,
        OptionBase::buildoption,
        "Learner we are taking output from.");
    
    declareOption(
        ol, "isp_signal_sigma",
        &PLearnerDiagonalKernel::m_isp_signal_sigma,
        OptionBase::buildoption,
        "Inverse softplus of the global noise variance.  Default value = 0.0.");
        
    // Now call the parent class' declareOptions
    inherited::declareOptions(ol);
}

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static const PPath& PLearn::PLearnerDiagonalKernel::declaringFile ( ) [inline, static]

Reimplemented from PLearn::KroneckerBaseKernel.

Definition at line 102 of file PLearnerDiagonalKernel.h.

:
PLearnerDiagonalKernel * PLearn::PLearnerDiagonalKernel::deepCopy ( CopiesMap copies) const [virtual]

Reimplemented from PLearn::KroneckerBaseKernel.

Definition at line 58 of file PLearnerDiagonalKernel.cc.

real PLearn::PLearnerDiagonalKernel::evaluate ( const Vec x1,
const Vec x2 
) const [virtual]

Compute K(x1,x2).

Reimplemented from PLearn::KroneckerBaseKernel.

Definition at line 116 of file PLearnerDiagonalKernel.cc.

References PLearn::KroneckerBaseKernel::evaluate(), PLearn::exp(), PLearn::PP< T >::isNull(), m_isp_signal_sigma, m_learner, m_output_buffer, PLASSERT, PLearn::TVec< T >::resize(), PLearn::TVec< T >::size(), and PLearn::softplus().

{
    PLASSERT( x1.size() == x2.size() );
    PLASSERT( ! m_learner.isNull() && m_learner->outputsize() == 1);

    m_output_buffer.resize(m_learner->outputsize());
    
    if (x1 == x2) {
        real gating_term = inherited::evaluate(x1,x2);
        real sigma = softplus(m_isp_signal_sigma);
        m_learner->computeOutput(x1, m_output_buffer);
        real diag_term = exp(m_output_buffer[0]);
        return sigma * gating_term * diag_term;
    }
    else
        return 0.0;
}

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void PLearn::PLearnerDiagonalKernel::evaluate_all_i_x ( const Vec x,
const Vec k_xi_x,
real  squared_norm_of_x = -1,
int  istart = 0 
) const [virtual]

Fill k_xi_x with K(x_i, x), for all i from istart to istart + k_xi_x.length() - 1.

Reimplemented from PLearn::Kernel.

Definition at line 189 of file PLearnerDiagonalKernel.cc.

References PLearn::Kernel::k_xi_x, and x.

{
    evaluateAllIXNV<PLearnerDiagonalKernel>(x, k_xi_x, istart);
}
OptionList & PLearn::PLearnerDiagonalKernel::getOptionList ( ) const [virtual]

Reimplemented from PLearn::KroneckerBaseKernel.

Definition at line 58 of file PLearnerDiagonalKernel.cc.

OptionMap & PLearn::PLearnerDiagonalKernel::getOptionMap ( ) const [virtual]

Reimplemented from PLearn::KroneckerBaseKernel.

Definition at line 58 of file PLearnerDiagonalKernel.cc.

RemoteMethodMap & PLearn::PLearnerDiagonalKernel::getRemoteMethodMap ( ) const [virtual]

Reimplemented from PLearn::KroneckerBaseKernel.

Definition at line 58 of file PLearnerDiagonalKernel.cc.

void PLearn::PLearnerDiagonalKernel::makeDeepCopyFromShallowCopy ( CopiesMap copies) [virtual]

Transforms a shallow copy into a deep copy.

Reimplemented from PLearn::KroneckerBaseKernel.

Definition at line 213 of file PLearnerDiagonalKernel.cc.

References PLearn::deepCopyField(), m_learner, m_output_buffer, and PLearn::KroneckerBaseKernel::makeDeepCopyFromShallowCopy().

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Member Data Documentation

Reimplemented from PLearn::KroneckerBaseKernel.

Definition at line 102 of file PLearnerDiagonalKernel.h.

Inverse softplus of the global noise variance. Default value = 0.0.

Definition at line 72 of file PLearnerDiagonalKernel.h.

Referenced by computeGramMatrix(), computeGramMatrixDerivIspSignalSigma(), declareOptions(), and evaluate().

Learner we are taking output from.

Definition at line 69 of file PLearnerDiagonalKernel.h.

Referenced by build_(), computeGramMatrix(), declareOptions(), evaluate(), and makeDeepCopyFromShallowCopy().

Buffer for evaluation of computeOutput.

Definition at line 123 of file PLearnerDiagonalKernel.h.

Referenced by computeGramMatrix(), evaluate(), and makeDeepCopyFromShallowCopy().


The documentation for this class was generated from the following files:
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