PLearn 0.1
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#include <DivisiveNormalizationKernel.h>
Public Member Functions | |
DivisiveNormalizationKernel () | |
Default constructor. | |
DivisiveNormalizationKernel (Ker the_source, bool the_remove_bias=false) | |
Created from an existing kernel. | |
virtual void | build () |
Simply calls inherited::build() then build_(). | |
virtual void | makeDeepCopyFromShallowCopy (CopiesMap &copies) |
Transforms a shallow copy into a deep copy. | |
virtual string | classname () const |
virtual OptionList & | getOptionList () const |
virtual OptionMap & | getOptionMap () const |
virtual RemoteMethodMap & | getRemoteMethodMap () const |
virtual DivisiveNormalizationKernel * | deepCopy (CopiesMap &copies) const |
virtual real | evaluate (const Vec &x1, const Vec &x2) const |
Overridden. | |
virtual real | evaluate_i_j (int i, int j) const |
returns evaluate(data(i),data(j)) | |
virtual real | evaluate_i_x (int i, const Vec &x, real squared_norm_of_x=-1) const |
Return evaluate(data(i),x). | |
virtual real | evaluate_x_i (const Vec &x, int i, real squared_norm_of_x=-1) const |
returns evaluate(x,data(i)) [default version calls evaluate_i_x if kernel is_symmetric] | |
virtual real | evaluate_i_x_again (int i, const Vec &x, real squared_norm_of_x=-1, bool first_time=false) const |
Return evaluate(data(i),x), where x is the same as in the precedent call to this same function (except if 'first_time' is true). | |
virtual real | evaluate_x_i_again (const Vec &x, int i, real squared_norm_of_x=-1, bool first_time=false) const |
virtual void | computeGramMatrix (Mat K) const |
Call evaluate_i_j to fill each of the entries (i,j) of symmetric matrix K. | |
virtual void | setDataForKernelMatrix (VMat the_data) |
** Subclasses may override these methods to provide efficient kernel matrix access ** | |
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 PPath & | declaringFile () |
Public Attributes | |
bool | data_will_change |
bool | remove_bias |
Static Public Attributes | |
static StaticInitializer | _static_initializer_ |
Protected Member Functions | |
real | computeAverage (const Vec &x, bool on_row, real squared_norm_of_x=-1) const |
Return the average of K(x,x_i) or K(x_i,x), depending on the value of 'on_row' (true or false, respectively). | |
Static Protected Member Functions | |
static void | declareOptions (OptionList &ol) |
Declares this class' options. | |
Protected Attributes | |
Vec | average_col |
Vec | average_row |
real | avg_evaluate_i_x_again |
The last average computed in evaluate_i_x_again(). | |
real | avg_evaluate_x_i_again |
The last average computed in evaluate_x_i_again(). | |
Private Types | |
typedef SourceKernel | inherited |
Private Member Functions | |
void | build_ () |
This does the actual building. | |
Private Attributes | |
Vec | all_k_x |
Used to store the values of the source kernel. |
Definition at line 52 of file DivisiveNormalizationKernel.h.
typedef SourceKernel PLearn::DivisiveNormalizationKernel::inherited [private] |
Reimplemented from PLearn::SourceKernel.
Definition at line 57 of file DivisiveNormalizationKernel.h.
PLearn::DivisiveNormalizationKernel::DivisiveNormalizationKernel | ( | ) |
Default constructor.
Definition at line 52 of file DivisiveNormalizationKernel.cc.
: data_will_change(false), remove_bias(false) {}
PLearn::DivisiveNormalizationKernel::DivisiveNormalizationKernel | ( | Ker | the_source, |
bool | the_remove_bias = false |
||
) |
Created from an existing kernel.
Definition at line 57 of file DivisiveNormalizationKernel.cc.
References build(), and PLearn::SourceKernel::source_kernel.
: data_will_change(false), remove_bias(the_remove_bias) { source_kernel = the_source; build(); }
string PLearn::DivisiveNormalizationKernel::_classname_ | ( | ) | [static] |
Reimplemented from PLearn::SourceKernel.
Definition at line 72 of file DivisiveNormalizationKernel.cc.
OptionList & PLearn::DivisiveNormalizationKernel::_getOptionList_ | ( | ) | [static] |
Reimplemented from PLearn::SourceKernel.
Definition at line 72 of file DivisiveNormalizationKernel.cc.
RemoteMethodMap & PLearn::DivisiveNormalizationKernel::_getRemoteMethodMap_ | ( | ) | [static] |
Reimplemented from PLearn::SourceKernel.
Definition at line 72 of file DivisiveNormalizationKernel.cc.
Reimplemented from PLearn::SourceKernel.
Definition at line 72 of file DivisiveNormalizationKernel.cc.
Object * PLearn::DivisiveNormalizationKernel::_new_instance_for_typemap_ | ( | ) | [static] |
Reimplemented from PLearn::SourceKernel.
Definition at line 72 of file DivisiveNormalizationKernel.cc.
StaticInitializer DivisiveNormalizationKernel::_static_initializer_ & PLearn::DivisiveNormalizationKernel::_static_initialize_ | ( | ) | [static] |
Reimplemented from PLearn::SourceKernel.
Definition at line 72 of file DivisiveNormalizationKernel.cc.
void PLearn::DivisiveNormalizationKernel::build | ( | ) | [virtual] |
Simply calls inherited::build() then build_().
Reimplemented from PLearn::SourceKernel.
Definition at line 103 of file DivisiveNormalizationKernel.cc.
References PLearn::SourceKernel::build(), and build_().
Referenced by DivisiveNormalizationKernel().
{ inherited::build(); build_(); }
void PLearn::DivisiveNormalizationKernel::build_ | ( | ) | [private] |
This does the actual building.
Reimplemented from PLearn::SourceKernel.
Definition at line 112 of file DivisiveNormalizationKernel.cc.
Referenced by build().
{ // ### This method should do the real building of the object, // ### according to set 'options', in *any* situation. // ### Typical situations include: // ### - Initial building of an object from a few user-specified options // ### - Building of a "reloaded" object: i.e. from the complete set of all serialised options. // ### - Updating or "re-building" of an object after a few "tuning" options have been modified. // ### You should assume that the parent class' build_() has already been called. }
string PLearn::DivisiveNormalizationKernel::classname | ( | ) | const [virtual] |
Reimplemented from PLearn::SourceKernel.
Definition at line 72 of file DivisiveNormalizationKernel.cc.
real PLearn::DivisiveNormalizationKernel::computeAverage | ( | const Vec & | x, |
bool | on_row, | ||
real | squared_norm_of_x = -1 |
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) | const [inline, protected] |
Return the average of K(x,x_i) or K(x_i,x), depending on the value of 'on_row' (true or false, respectively).
Definition at line 126 of file DivisiveNormalizationKernel.cc.
References all_k_x, PLearn::Kernel::is_symmetric, PLearn::Kernel::n_examples, PLearn::TVec< T >::resize(), PLearn::SourceKernel::source_kernel, and PLearn::sum().
Referenced by evaluate(), evaluate_i_x(), evaluate_i_x_again(), evaluate_x_i(), and evaluate_x_i_again().
{ all_k_x.resize(n_examples); if (is_symmetric || !on_row) { source_kernel->evaluate_all_i_x(x, all_k_x, squared_norm_of_x); } else { source_kernel->evaluate_all_x_i(x, all_k_x, squared_norm_of_x); } return sum(all_k_x) / real(n_examples); }
void PLearn::DivisiveNormalizationKernel::computeGramMatrix | ( | Mat | K | ) | const [virtual] |
Call evaluate_i_j to fill each of the entries (i,j) of symmetric matrix K.
Reimplemented from PLearn::SourceKernel.
Definition at line 139 of file DivisiveNormalizationKernel.cc.
{ // Uses default Kernel implementation. Kernel::computeGramMatrix(K); }
void PLearn::DivisiveNormalizationKernel::declareOptions | ( | OptionList & | ol | ) | [static, protected] |
Declares this class' options.
Reimplemented from PLearn::SourceKernel.
Definition at line 77 of file DivisiveNormalizationKernel.cc.
References average_col, average_row, PLearn::OptionBase::buildoption, data_will_change, PLearn::declareOption(), PLearn::SourceKernel::declareOptions(), PLearn::OptionBase::learntoption, and remove_bias.
{ // Build options. declareOption(ol, "data_will_change", &DivisiveNormalizationKernel::data_will_change, OptionBase::buildoption, "If set to 1, then the Gram matrix will be always recomputed, even if\n" "it's not completely sure the data has changed."); declareOption(ol, "remove_bias", &DivisiveNormalizationKernel::remove_bias, OptionBase::buildoption, "If set to 1, then the bias induced by the K(x_i,x_i) will be removed.\n"); // Learnt options. declareOption(ol, "average_col", &DivisiveNormalizationKernel::average_col, OptionBase::learntoption, "The average of the underlying kernel over each column of the Gram matrix."); declareOption(ol, "average_row", &DivisiveNormalizationKernel::average_row, OptionBase::learntoption, "The average of the underlying kernel over each row of the Gram matrix."); // Now call the parent class' declareOptions inherited::declareOptions(ol); }
static const PPath& PLearn::DivisiveNormalizationKernel::declaringFile | ( | ) | [inline, static] |
Reimplemented from PLearn::SourceKernel.
Definition at line 129 of file DivisiveNormalizationKernel.h.
DivisiveNormalizationKernel * PLearn::DivisiveNormalizationKernel::deepCopy | ( | CopiesMap & | copies | ) | const [virtual] |
Reimplemented from PLearn::SourceKernel.
Definition at line 72 of file DivisiveNormalizationKernel.cc.
real PLearn::DivisiveNormalizationKernel::evaluate | ( | const Vec & | x1, |
const Vec & | x2 | ||
) | const [virtual] |
Overridden.
Reimplemented from PLearn::SourceKernel.
Definition at line 147 of file DivisiveNormalizationKernel.cc.
References computeAverage(), PLearn::SourceKernel::source_kernel, and PLearn::sqrt().
{ real avg_1 = computeAverage(x1, true); real avg_2 = computeAverage(x2, false); return source_kernel->evaluate(x1, x2) / sqrt(avg_1 * avg_2); }
returns evaluate(data(i),data(j))
Reimplemented from PLearn::SourceKernel.
Definition at line 156 of file DivisiveNormalizationKernel.cc.
References average_col, average_row, PLearn::SourceKernel::source_kernel, and PLearn::sqrt().
{ return source_kernel->evaluate_i_j(i,j) / sqrt(average_row[i] * average_col[j]); }
real PLearn::DivisiveNormalizationKernel::evaluate_i_x | ( | int | i, |
const Vec & | x, | ||
real | squared_norm_of_x = -1 |
||
) | const [virtual] |
Return evaluate(data(i),x).
[squared_norm_of_x is just a hint that may allow to speed up computation if it is already known, but it's optional]
Reimplemented from PLearn::SourceKernel.
Definition at line 163 of file DivisiveNormalizationKernel.cc.
References average_row, computeAverage(), PLearn::SourceKernel::source_kernel, and PLearn::sqrt().
{ return source_kernel->evaluate_i_x(i, x, squared_norm_of_x) / sqrt(average_row[i] * computeAverage(x, false, squared_norm_of_x)); }
real PLearn::DivisiveNormalizationKernel::evaluate_i_x_again | ( | int | i, |
const Vec & | x, | ||
real | squared_norm_of_x = -1 , |
||
bool | first_time = false |
||
) | const [virtual] |
Return evaluate(data(i),x), where x is the same as in the precedent call to this same function (except if 'first_time' is true).
This can be used to speed up successive computations of K(x_i, x) (default version just calls evaluate_i_x).
Reimplemented from PLearn::Kernel.
Definition at line 171 of file DivisiveNormalizationKernel.cc.
References average_row, avg_evaluate_i_x_again, computeAverage(), PLearn::SourceKernel::source_kernel, and PLearn::sqrt().
{ if (first_time) { avg_evaluate_i_x_again = computeAverage(x, false, squared_norm_of_x); } return source_kernel->evaluate_i_x_again(i, x, squared_norm_of_x, first_time) / sqrt(average_row[i] * avg_evaluate_i_x_again); }
real PLearn::DivisiveNormalizationKernel::evaluate_x_i | ( | const Vec & | x, |
int | i, | ||
real | squared_norm_of_x = -1 |
||
) | const [virtual] |
returns evaluate(x,data(i)) [default version calls evaluate_i_x if kernel is_symmetric]
Reimplemented from PLearn::SourceKernel.
Definition at line 182 of file DivisiveNormalizationKernel.cc.
References average_col, computeAverage(), PLearn::SourceKernel::source_kernel, and PLearn::sqrt().
{ return source_kernel->evaluate_x_i(x, i, squared_norm_of_x) / sqrt(average_col[i] * computeAverage(x, true, squared_norm_of_x)); }
real PLearn::DivisiveNormalizationKernel::evaluate_x_i_again | ( | const Vec & | x, |
int | i, | ||
real | squared_norm_of_x = -1 , |
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bool | first_time = false |
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) | const [virtual] |
Reimplemented from PLearn::Kernel.
Definition at line 190 of file DivisiveNormalizationKernel.cc.
References average_col, avg_evaluate_x_i_again, computeAverage(), PLearn::SourceKernel::source_kernel, and PLearn::sqrt().
{ if (first_time) { avg_evaluate_x_i_again = computeAverage(x, true, squared_norm_of_x); } return source_kernel->evaluate_x_i_again(x, i, squared_norm_of_x, first_time) / sqrt(average_col[i] * avg_evaluate_x_i_again); }
OptionList & PLearn::DivisiveNormalizationKernel::getOptionList | ( | ) | const [virtual] |
Reimplemented from PLearn::SourceKernel.
Definition at line 72 of file DivisiveNormalizationKernel.cc.
OptionMap & PLearn::DivisiveNormalizationKernel::getOptionMap | ( | ) | const [virtual] |
Reimplemented from PLearn::SourceKernel.
Definition at line 72 of file DivisiveNormalizationKernel.cc.
RemoteMethodMap & PLearn::DivisiveNormalizationKernel::getRemoteMethodMap | ( | ) | const [virtual] |
Reimplemented from PLearn::SourceKernel.
Definition at line 72 of file DivisiveNormalizationKernel.cc.
void PLearn::DivisiveNormalizationKernel::makeDeepCopyFromShallowCopy | ( | CopiesMap & | copies | ) | [virtual] |
Transforms a shallow copy into a deep copy.
Reimplemented from PLearn::SourceKernel.
Definition at line 201 of file DivisiveNormalizationKernel.cc.
References PLearn::SourceKernel::makeDeepCopyFromShallowCopy(), and PLERROR.
{ inherited::makeDeepCopyFromShallowCopy(copies); // ### Call deepCopyField on all "pointer-like" fields // ### that you wish to be deepCopied rather than // ### shallow-copied. // ### ex: // deepCopyField(trainvec, copies); // ### Remove this line when you have fully implemented this method. PLERROR("DivisiveNormalizationKernel::makeDeepCopyFromShallowCopy not fully (correctly) implemented yet!"); }
void PLearn::DivisiveNormalizationKernel::setDataForKernelMatrix | ( | VMat | the_data | ) | [virtual] |
** Subclasses may override these methods to provide efficient kernel matrix access **
This method sets the data VMat that will be used to define the kernel matrix. It may precompute values from this that may later accelerate the evaluation of a kernel matrix element
Reimplemented from PLearn::SourceKernel.
Definition at line 218 of file DivisiveNormalizationKernel.cc.
References average_col, average_row, PLearn::Kernel::data, data_will_change, PLearn::TVec< T >::fill(), i, PLearn::Kernel::is_symmetric, j, PLearn::TVec< T >::length(), PLearn::VMat::length(), n, remove_bias, PLearn::TVec< T >::resize(), PLearn::SourceKernel::setDataForKernelMatrix(), and PLearn::SourceKernel::source_kernel.
{ bool there_was_data_and_it_changed = data && !(data->looksTheSameAs(the_data)); // Set the data for this kernel as well as for the underlying kernel. inherited::setDataForKernelMatrix(the_data); // Check whether we need to recompute the Gram matrix and its average. int n = the_data->length(); if ( data_will_change || average_row.length() != n || there_was_data_and_it_changed) { // Compute the underlying Gram matrix. Mat gram(n, n); source_kernel->computeGramMatrix(gram); // Compute the row (and column) average. average_row.resize(n); average_row.fill(0); if (is_symmetric) { average_col = average_row; } else { average_col.resize(n); average_col.fill(0); } real k_x_x; for (int i = 0; i < n; i++) { if (is_symmetric) { real v; k_x_x = gram(i,i); if (!remove_bias) { average_row[i] += k_x_x; } for (int j = i + 1; j < n; j++) { v = gram(i,j); average_row[i] += v; average_row[j] += v; } } else { for (int j = 0; j < n; j++) { if (!remove_bias || j != i) { average_row[i] += gram(i,j); average_col[i] += gram(j,i); if (j == i) { } } } } } real n_terms_in_sum; // The number of terms summed in average_row. if (remove_bias) { // The diagonal terms were not added. n_terms_in_sum = real(n - 1); } else { n_terms_in_sum = real(n); } average_row /= n_terms_in_sum; if (!is_symmetric) { average_col /= n_terms_in_sum; } } }
Reimplemented from PLearn::SourceKernel.
Definition at line 129 of file DivisiveNormalizationKernel.h.
Vec PLearn::DivisiveNormalizationKernel::all_k_x [mutable, private] |
Used to store the values of the source kernel.
Definition at line 60 of file DivisiveNormalizationKernel.h.
Referenced by computeAverage().
Vec PLearn::DivisiveNormalizationKernel::average_col [protected] |
Definition at line 68 of file DivisiveNormalizationKernel.h.
Referenced by declareOptions(), evaluate_i_j(), evaluate_x_i(), evaluate_x_i_again(), and setDataForKernelMatrix().
Vec PLearn::DivisiveNormalizationKernel::average_row [protected] |
Definition at line 69 of file DivisiveNormalizationKernel.h.
Referenced by declareOptions(), evaluate_i_j(), evaluate_i_x(), evaluate_i_x_again(), and setDataForKernelMatrix().
real PLearn::DivisiveNormalizationKernel::avg_evaluate_i_x_again [mutable, protected] |
The last average computed in evaluate_i_x_again().
Definition at line 74 of file DivisiveNormalizationKernel.h.
Referenced by evaluate_i_x_again().
real PLearn::DivisiveNormalizationKernel::avg_evaluate_x_i_again [mutable, protected] |
The last average computed in evaluate_x_i_again().
Definition at line 77 of file DivisiveNormalizationKernel.h.
Referenced by evaluate_x_i_again().
Definition at line 85 of file DivisiveNormalizationKernel.h.
Referenced by declareOptions(), and setDataForKernelMatrix().
Definition at line 86 of file DivisiveNormalizationKernel.h.
Referenced by declareOptions(), and setDataForKernelMatrix().