PLearn 0.1
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#include <ThresholdedKernel.h>
Public Member Functions | |
ThresholdedKernel () | |
Default constructor. | |
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 ThresholdedKernel * | 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 |
Overridden for a more efficient implementation when the underlying kernel has cached its Gram matrix. | |
virtual void | setDataForKernelMatrix (VMat the_data) |
Overridden to precompute nearest neighbors in the dataset. | |
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 | |
int | knn |
real | knn_approximation |
int | max_size_for_full_gram |
string | method |
real | threshold |
Static Public Attributes | |
static StaticInitializer | _static_initializer_ |
Protected Member Functions | |
virtual void | thresholdGramMatrix (const Mat &K) const |
Replace all elements in the Gram matrix K by 'threshold' when they meet the thresholding condition defined by the thresholding method. | |
void | evaluate_random_k_x_i (const Vec &x, const Vec &k_x_xi) const |
Fill 'k_x_xi' with values of the source kernel evaluated at points xi sampled randomly from the dataset. | |
Static Protected Member Functions | |
static void | declareOptions (OptionList &ol) |
Declares this class' options. | |
Protected Attributes | |
bool | knn_approx |
Set to 'true' at build time iff we use the knn approximation, i.e. | |
int | knn_sub |
Number of actual nearest neighbors to consider (will be less than 'knn' when using the knn approximation, otherwise will be equal to 'knn'). | |
int | n_approx |
Set to the number of points to sample when using the knn approximation. | |
Vec | knn_kernel_values |
Element i is equal to K(x_i, n_knn(x_i)) with K the source kernel. | |
Vec | k_x_xi |
Used to store kernel values. | |
Mat | k_x_xi_mat |
Points to the same data as k_x_xi;. | |
real | k_x_threshold |
The value K(x, n_knn(x)). | |
Private Types | |
typedef SourceKernel | inherited |
Private Member Functions | |
void | build_ () |
This does the actual building. |
Definition at line 51 of file ThresholdedKernel.h.
typedef SourceKernel PLearn::ThresholdedKernel::inherited [private] |
Reimplemented from PLearn::SourceKernel.
Definition at line 56 of file ThresholdedKernel.h.
PLearn::ThresholdedKernel::ThresholdedKernel | ( | ) |
Default constructor.
Definition at line 53 of file ThresholdedKernel.cc.
: knn(2), knn_approximation(0), max_size_for_full_gram(5000), method("knn"), threshold(0) { }
string PLearn::ThresholdedKernel::_classname_ | ( | ) | [static] |
Reimplemented from PLearn::SourceKernel.
Definition at line 65 of file ThresholdedKernel.cc.
OptionList & PLearn::ThresholdedKernel::_getOptionList_ | ( | ) | [static] |
Reimplemented from PLearn::SourceKernel.
Definition at line 65 of file ThresholdedKernel.cc.
RemoteMethodMap & PLearn::ThresholdedKernel::_getRemoteMethodMap_ | ( | ) | [static] |
Reimplemented from PLearn::SourceKernel.
Definition at line 65 of file ThresholdedKernel.cc.
Reimplemented from PLearn::SourceKernel.
Definition at line 65 of file ThresholdedKernel.cc.
Object * PLearn::ThresholdedKernel::_new_instance_for_typemap_ | ( | ) | [static] |
Reimplemented from PLearn::SourceKernel.
Definition at line 65 of file ThresholdedKernel.cc.
StaticInitializer ThresholdedKernel::_static_initializer_ & PLearn::ThresholdedKernel::_static_initialize_ | ( | ) | [static] |
Reimplemented from PLearn::SourceKernel.
Definition at line 65 of file ThresholdedKernel.cc.
void PLearn::ThresholdedKernel::build | ( | ) | [virtual] |
Simply calls inherited::build() then build_().
Reimplemented from PLearn::SourceKernel.
Definition at line 104 of file ThresholdedKernel.cc.
References PLearn::SourceKernel::build(), and build_().
{ inherited::build(); build_(); }
void PLearn::ThresholdedKernel::build_ | ( | ) | [private] |
This does the actual building.
Reimplemented from PLearn::SourceKernel.
Definition at line 113 of file ThresholdedKernel.cc.
References PLearn::fast_exact_is_equal(), knn_approx, knn_approximation, n_approx, PLASSERT, PLERROR, and PLearn::SourceKernel::source_kernel.
Referenced by build().
{ if (source_kernel && !source_kernel->is_symmetric) PLERROR("In ThresholdedKernel::build_ - The source kernel must currently " "be symmetric"); PLASSERT( knn_approximation >= 0); knn_approx = !fast_exact_is_equal(knn_approximation, 0); if (knn_approx) { if (knn_approximation > 1) n_approx = int(round(knn_approximation)); // Otherwise, n_approx is set in setDataForKernelMatrix. } else n_approx = -1; }
string PLearn::ThresholdedKernel::classname | ( | ) | const [virtual] |
Reimplemented from PLearn::SourceKernel.
Definition at line 65 of file ThresholdedKernel.cc.
void PLearn::ThresholdedKernel::computeGramMatrix | ( | Mat | K | ) | const [virtual] |
Overridden for a more efficient implementation when the underlying kernel has cached its Gram matrix.
Reimplemented from PLearn::SourceKernel.
Definition at line 131 of file ThresholdedKernel.cc.
References PLearn::Kernel::cache_gram_matrix, PLearn::Kernel::gram_matrix, PLearn::Kernel::gram_matrix_is_cached, PLearn::TMat< T >::length(), PLearn::TMat< T >::resize(), PLearn::SourceKernel::source_kernel, and thresholdGramMatrix().
{ if (cache_gram_matrix && gram_matrix_is_cached) { K << gram_matrix; return; } source_kernel->computeGramMatrix(K); thresholdGramMatrix(K); if (cache_gram_matrix) { int l = K.length(); gram_matrix.resize(l,l); gram_matrix << K; gram_matrix_is_cached = true; } }
void PLearn::ThresholdedKernel::declareOptions | ( | OptionList & | ol | ) | [static, protected] |
Declares this class' options.
Reimplemented from PLearn::SourceKernel.
Definition at line 70 of file ThresholdedKernel.cc.
References PLearn::OptionBase::buildoption, PLearn::declareOption(), PLearn::SourceKernel::declareOptions(), knn, knn_approximation, max_size_for_full_gram, method, and threshold.
{ declareOption(ol, "method", &ThresholdedKernel::method, OptionBase::buildoption, "Which method is used to threshold the underlying kernel:\n" " - 'knn' : if y is such that K(x,y) is strictly less than K(x,n_k(x)) where n_k(x)\n" " is the k-th neighbor of x as given by K, and K(x,y) < K(n_k(y), y), then\n" " K(x,y) is thresholded\n"); declareOption(ol, "threshold", &ThresholdedKernel::threshold, OptionBase::buildoption, "The value returned when K(x,y) is thresholded."); declareOption(ol, "knn", &ThresholdedKernel::knn, OptionBase::buildoption, "When 'method' is 'knn', this is 'k' in n_k(x) (x will be counted if in data matrix)."); declareOption(ol, "knn_approximation", &ThresholdedKernel::knn_approximation, OptionBase::buildoption, "When 'method' is 'knn', this option can take several values:\n" " - 0 : it is ignored, the 'knn' nearest neighbors are computed normally\n" " - p > 1 : the value of K(x,n_k(x)) is estimated by computing K(x,y) for p\n" " values of y taken randomly in the dataset (the same value of y\n" " may be taken more than once)\n" " - 0 < f <= 1 : same as above, with p = f * n (n = dataset size)"); declareOption(ol, "max_size_for_full_gram", &ThresholdedKernel::max_size_for_full_gram, OptionBase::buildoption, "When the dataset has more than 'max_size_for_full_gram' samples, the full Gram\n" "matrix will not be computed in memory (less efficient, but scales better)."); // Now call the parent class' declareOptions inherited::declareOptions(ol); }
static const PPath& PLearn::ThresholdedKernel::declaringFile | ( | ) | [inline, static] |
Reimplemented from PLearn::SourceKernel.
Definition at line 144 of file ThresholdedKernel.h.
ThresholdedKernel * PLearn::ThresholdedKernel::deepCopy | ( | CopiesMap & | copies | ) | const [virtual] |
Reimplemented from PLearn::SourceKernel.
Definition at line 65 of file ThresholdedKernel.cc.
Overridden.
Reimplemented from PLearn::SourceKernel.
Definition at line 149 of file ThresholdedKernel.cc.
References evaluate_random_k_x_i(), k_x_xi, k_x_xi_mat, knn_approx, knn_sub, method, MISSING_VALUE, PLearn::negateElements(), PLearn::partialSortRows(), PLERROR, PLearn::SourceKernel::source_kernel, and threshold.
{ real k_x1_x2 = source_kernel->evaluate(x1, x2); if (method == "knn") { if (knn_approx) evaluate_random_k_x_i(x1, k_x_xi); else source_kernel->evaluate_all_x_i(x1, k_x_xi); negateElements(k_x_xi); partialSortRows(k_x_xi_mat, knn_sub); if (k_x1_x2 >= - k_x_xi[knn_sub-1]) return k_x1_x2; if (knn_approx) evaluate_random_k_x_i(x2, k_x_xi); else source_kernel->evaluate_all_i_x(k_x_xi, x2); partialSortRows(k_x_xi_mat, knn_sub); negateElements(k_x_xi); if (k_x1_x2 >= -k_x_xi[knn_sub-1]) return k_x1_x2; return threshold; } PLERROR("ThresholdedKernel::evaluate: unsupported method '%s'", method.c_str()); return MISSING_VALUE; }
returns evaluate(data(i),data(j))
Reimplemented from PLearn::SourceKernel.
Definition at line 177 of file ThresholdedKernel.cc.
References knn_kernel_values, method, MISSING_VALUE, PLERROR, PLearn::SourceKernel::source_kernel, and threshold.
{ real k_i_j = source_kernel->evaluate_i_j(i, j); if (method == "knn") { if (k_i_j >= knn_kernel_values[i] || k_i_j >= knn_kernel_values[j]) return k_i_j; else return threshold; } PLERROR("ThresholdedKernel::evaluate_i_j: unsupported method '%s'", method.c_str()); return MISSING_VALUE; }
real PLearn::ThresholdedKernel::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 192 of file ThresholdedKernel.cc.
{ // Default = uses the Kernel implementation. // Alternative = return source_kernel->evaluate_i_x(i,x,squared_norm_of_x); return Kernel::evaluate_i_x(i, x, squared_norm_of_x); }
real PLearn::ThresholdedKernel::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 201 of file ThresholdedKernel.cc.
References evaluate_random_k_x_i(), k_x_threshold, k_x_xi, k_x_xi_mat, knn_approx, knn_kernel_values, knn_sub, method, MISSING_VALUE, PLearn::negateElements(), PLearn::partialSortRows(), PLERROR, PLearn::SourceKernel::source_kernel, and threshold.
{ if (method == "knn") { if (first_time) { if (knn_approx) evaluate_random_k_x_i(x, k_x_xi); else source_kernel->evaluate_all_i_x(x, k_x_xi); negateElements(k_x_xi); partialSortRows(k_x_xi_mat, knn_sub); k_x_threshold = - k_x_xi[knn_sub - 1]; } real k_i_x = source_kernel->evaluate_i_x_again(i, x, squared_norm_of_x, first_time); if (k_i_x >= k_x_threshold || k_i_x >= knn_kernel_values[i]) return k_i_x; else return threshold; } PLERROR("ThresholdedKernel::evaluate_i_x_again: unsupported method '%s'", method.c_str()); return MISSING_VALUE; }
void PLearn::ThresholdedKernel::evaluate_random_k_x_i | ( | const Vec & | x, |
const Vec & | k_x_xi | ||
) | const [protected] |
Fill 'k_x_xi' with values of the source kernel evaluated at points xi sampled randomly from the dataset.
Definition at line 226 of file ThresholdedKernel.cc.
References PLearn::PRandom::common(), i, j, PLearn::TVec< T >::length(), PLearn::Kernel::n_examples, and PLearn::SourceKernel::source_kernel.
Referenced by evaluate(), evaluate_i_x_again(), and evaluate_x_i_again().
{ PP<PRandom> random = PRandom::common(false); // PRandom with fixed seed. int k = k_x_xi.length(); for (int j = 0; j < k; j++) { int i = random->uniform_multinomial_sample(n_examples); k_x_xi[j] = source_kernel->evaluate_x_i(x,i); } }
real PLearn::ThresholdedKernel::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 240 of file ThresholdedKernel.cc.
{ return Kernel::evaluate_x_i(x, i, squared_norm_of_x); }
real PLearn::ThresholdedKernel::evaluate_x_i_again | ( | const Vec & | x, |
int | i, | ||
real | squared_norm_of_x = -1 , |
||
bool | first_time = false |
||
) | const [virtual] |
Reimplemented from PLearn::Kernel.
Definition at line 247 of file ThresholdedKernel.cc.
References evaluate_random_k_x_i(), k_x_threshold, k_x_xi, k_x_xi_mat, knn_approx, knn_kernel_values, knn_sub, method, MISSING_VALUE, PLearn::negateElements(), PLearn::partialSortRows(), PLearn::SourceKernel::source_kernel, and threshold.
{ if (method == "knn") { if (first_time) { if (knn_approx) evaluate_random_k_x_i(x, k_x_xi); else source_kernel->evaluate_all_x_i(x, k_x_xi); negateElements(k_x_xi); partialSortRows(k_x_xi_mat, knn_sub); k_x_threshold = - k_x_xi[knn_sub - 1]; } real k_x_i = source_kernel->evaluate_x_i_again(x, i, squared_norm_of_x, first_time); if (k_x_i >= k_x_threshold || k_x_i >= knn_kernel_values[i]) return k_x_i; else return threshold; } return MISSING_VALUE; }
OptionList & PLearn::ThresholdedKernel::getOptionList | ( | ) | const [virtual] |
Reimplemented from PLearn::SourceKernel.
Definition at line 65 of file ThresholdedKernel.cc.
OptionMap & PLearn::ThresholdedKernel::getOptionMap | ( | ) | const [virtual] |
Reimplemented from PLearn::SourceKernel.
Definition at line 65 of file ThresholdedKernel.cc.
RemoteMethodMap & PLearn::ThresholdedKernel::getRemoteMethodMap | ( | ) | const [virtual] |
Reimplemented from PLearn::SourceKernel.
Definition at line 65 of file ThresholdedKernel.cc.
void PLearn::ThresholdedKernel::makeDeepCopyFromShallowCopy | ( | CopiesMap & | copies | ) | [virtual] |
Transforms a shallow copy into a deep copy.
Reimplemented from PLearn::SourceKernel.
Definition at line 270 of file ThresholdedKernel.cc.
References PLearn::deepCopyField(), k_x_xi, k_x_xi_mat, knn_kernel_values, and PLearn::SourceKernel::makeDeepCopyFromShallowCopy().
{ inherited::makeDeepCopyFromShallowCopy(copies); deepCopyField(knn_kernel_values, copies); deepCopyField(k_x_xi, copies); deepCopyField(k_x_xi_mat, copies); }
void PLearn::ThresholdedKernel::setDataForKernelMatrix | ( | VMat | the_data | ) | [virtual] |
Overridden to precompute nearest neighbors in the dataset.
Reimplemented from PLearn::SourceKernel.
Definition at line 281 of file ThresholdedKernel.cc.
References PLearn::TMat< T >::appendRow(), PLearn::Kernel::cache_gram_matrix, PLearn::PRandom::common(), PLearn::fast_exact_is_equal(), PLearn::TVec< T >::find(), PLearn::Kernel::gram_matrix, PLearn::Kernel::gram_matrix_is_cached, PLearn::TVec< T >::hasMissing(), i, j, k_x_xi, k_x_xi_mat, knn, knn_approx, knn_approximation, knn_kernel_values, knn_sub, PLearn::TVec< T >::length(), PLearn::TMat< T >::length(), PLearn::VMat::length(), max_size_for_full_gram, method, n, n_approx, PLearn::negateElements(), PLearn::partialSortRows(), PLASSERT, PLERROR, PLWARNING, PLearn::Kernel::report_progress, PLearn::TMat< T >::resize(), PLearn::TVec< T >::resize(), PLearn::SourceKernel::setDataForKernelMatrix(), PLearn::SourceKernel::source_kernel, PLearn::Kernel::sparse_gram_matrix, PLearn::Kernel::sparse_gram_matrix_is_cached, threshold, thresholdGramMatrix(), and PLearn::TVec< T >::toMat().
{ inherited::setDataForKernelMatrix(the_data); int n = the_data->length(); PP<ProgressBar> pb; knn_sub = knn_approx ? int(round(knn * real(n_approx) / real(n))) : knn; if (knn_sub <= 0) PLERROR("In ThresholdedKernel::setDataForKernelMatrix - Not " "enough neighbors considered"); if (method == "knn") { knn_kernel_values.resize(n); if (knn_approx) { if (knn_approximation <= 1) { n_approx = int(round(knn_approximation * n)); PLASSERT( n_approx >= 1 ); } else { int k_int = int(round(knn_approximation)); if (k_int > n) PLERROR("In ThresholdedKernel::setDataForKernelMatrix - " "'knn_approximation' (%d) cannot be more than the " " number of data points (%d)", k_int, n); } } if (knn_sub > n) PLERROR("In ThresholdedKernel::setDataForKernelMatrix - The number" " of nearest neighbors to compute (%d) must be less than " "the length of the dataset (%d)", knn_sub, n); if (n <= max_size_for_full_gram && !knn_approx) { // Can afford to store the Gram matrix in memory. gram_matrix.resize(n,n); source_kernel->computeGramMatrix(gram_matrix); if (report_progress) pb = new ProgressBar("Finding nearest neighbors", n); Mat sorted_k_i(n, 1); for (int i = 0; i < n; i++) { sorted_k_i << gram_matrix(i); negateElements(sorted_k_i); // For sorting. partialSortRows(sorted_k_i, knn); knn_kernel_values[i] = - sorted_k_i(knn - 1, 0); if (report_progress) pb->update(i+1); } if (cache_gram_matrix) { // Since we have the Gram matrix at hand, we may cache it now. thresholdGramMatrix(gram_matrix); gram_matrix_is_cached = true; } else // Free memory. gram_matrix = Mat(); } else { // Computing the whole Gram matrix will probably not fit in memory, // or we do not even want / afford to compute it. if (cache_gram_matrix) { // We will cache the sparse Gram matrix. sparse_gram_matrix.resize(n); for (int i = 0; i < n; i++) sparse_gram_matrix[i].resize(0,2); } if (report_progress) pb = new ProgressBar("Computing Gram matrix of source kernel and " "finding nearest neighbors", n); int n_used = knn_approx ? n_approx : n; Mat k_i_mat(n_used, 1); Vec k_i(n_used); Vec row(2); TVec<int> neighb_i, neighb_j; PP<PRandom> random = PRandom::common(false); // Has fixed seed. for (int i = 0; i < n; i++) { if (knn_approx) { for (int j = 0; j < n_approx; j++) { int k = random->uniform_multinomial_sample(n); k_i[j] = source_kernel->evaluate_i_j(i,k); } } else { for (int j = 0; j < n; j++) k_i[j] = source_kernel->evaluate_i_j(i,j); } k_i_mat << k_i; negateElements(k_i_mat); // For sorting. partialSortRows(k_i_mat, knn_sub); knn_kernel_values[i] = - k_i_mat(knn_sub - 1, 0); if (report_progress) pb->update(i+1); if (cache_gram_matrix) { if (knn_approx) PLERROR("In ThresholdedKernel::setDataForKernelMatrix " "- Cannot currently cache the Gram matrix when" " using the knn approximation"); PLASSERT( !knn_approx ); // Let us cache the sparse Gram matrix. if (!fast_exact_is_equal(threshold, 0)) PLWARNING("In ThresholdedKernel::setDataForKernelMatrix - The sparse " "Gram matrix will be cached based on a non-zero threshold"); real k_min = knn_kernel_values[i]; Mat& g_i = sparse_gram_matrix[i]; int ki = g_i.length(); neighb_i.resize(ki); for (int j = 0; j < ki; j++) neighb_i[j] = int(g_i(j,0)); for (int j = 0; j < n; j++) if (k_i[j] >= k_min && neighb_i.find(j) == -1) { row[0] = j; row[1] = k_i[j]; g_i.appendRow(row); Mat& g_j = sparse_gram_matrix[j]; int kj = g_j.length(); bool already_there = false; for (int l = 0; l < kj; l++) if (i == int(g_j(0,1))) { already_there = true; break; } if (!already_there) { row[0] = i; g_j.appendRow(row); } } sparse_gram_matrix_is_cached = true; } } } } k_x_xi.resize(knn_approx ? n_approx : n); k_x_xi_mat = k_x_xi.toMat(k_x_xi.length(), 1); PLASSERT( !knn_kernel_values.hasMissing() ); }
void PLearn::ThresholdedKernel::thresholdGramMatrix | ( | const Mat & | K | ) | const [protected, virtual] |
Replace all elements in the Gram matrix K by 'threshold' when they meet the thresholding condition defined by the thresholding method.
Definition at line 411 of file ThresholdedKernel.cc.
References i, j, knn_kernel_values, PLearn::TMat< T >::length(), method, n, PLERROR, PLearn::Kernel::report_progress, threshold, and PLearn::TMat< T >::width().
Referenced by computeGramMatrix(), and setDataForKernelMatrix().
{ PP<ProgressBar> pb; int n = K.length(); if (K.width() != n) PLERROR("In ThresholdedKernel::thresholdGramMatrix - A square matrix is expected"); if (report_progress) pb = new ProgressBar("Thresholding Gram matrix", n); if (method == "knn") { for (int i = 0; i < n; i++) { real* K_i = K[i]; real knn_kernel_values_i = knn_kernel_values[i]; for (int j = 0; j < n; j++, K_i++) if (*K_i < knn_kernel_values_i && *K_i < knn_kernel_values[j]) *K_i = threshold; if (report_progress) pb->update(i+1); } } }
Reimplemented from PLearn::SourceKernel.
Definition at line 144 of file ThresholdedKernel.h.
real PLearn::ThresholdedKernel::k_x_threshold [mutable, protected] |
The value K(x, n_knn(x)).
Definition at line 86 of file ThresholdedKernel.h.
Referenced by evaluate_i_x_again(), and evaluate_x_i_again().
Vec PLearn::ThresholdedKernel::k_x_xi [protected] |
Used to store kernel values.
Definition at line 80 of file ThresholdedKernel.h.
Referenced by evaluate(), evaluate_i_x_again(), evaluate_x_i_again(), makeDeepCopyFromShallowCopy(), and setDataForKernelMatrix().
Mat PLearn::ThresholdedKernel::k_x_xi_mat [protected] |
Points to the same data as k_x_xi;.
Definition at line 83 of file ThresholdedKernel.h.
Referenced by evaluate(), evaluate_i_x_again(), evaluate_x_i_again(), makeDeepCopyFromShallowCopy(), and setDataForKernelMatrix().
Definition at line 94 of file ThresholdedKernel.h.
Referenced by declareOptions(), and setDataForKernelMatrix().
bool PLearn::ThresholdedKernel::knn_approx [protected] |
Set to 'true' at build time iff we use the knn approximation, i.e.
knn_approximation > 0.
Definition at line 62 of file ThresholdedKernel.h.
Referenced by build_(), evaluate(), evaluate_i_x_again(), evaluate_x_i_again(), and setDataForKernelMatrix().
Definition at line 95 of file ThresholdedKernel.h.
Referenced by build_(), declareOptions(), and setDataForKernelMatrix().
Vec PLearn::ThresholdedKernel::knn_kernel_values [protected] |
Element i is equal to K(x_i, n_knn(x_i)) with K the source kernel.
Definition at line 77 of file ThresholdedKernel.h.
Referenced by evaluate_i_j(), evaluate_i_x_again(), evaluate_x_i_again(), makeDeepCopyFromShallowCopy(), setDataForKernelMatrix(), and thresholdGramMatrix().
int PLearn::ThresholdedKernel::knn_sub [protected] |
Number of actual nearest neighbors to consider (will be less than 'knn' when using the knn approximation, otherwise will be equal to 'knn').
Definition at line 66 of file ThresholdedKernel.h.
Referenced by evaluate(), evaluate_i_x_again(), evaluate_x_i_again(), and setDataForKernelMatrix().
Definition at line 96 of file ThresholdedKernel.h.
Referenced by declareOptions(), and setDataForKernelMatrix().
Definition at line 97 of file ThresholdedKernel.h.
Referenced by declareOptions(), evaluate(), evaluate_i_j(), evaluate_i_x_again(), evaluate_x_i_again(), setDataForKernelMatrix(), and thresholdGramMatrix().
int PLearn::ThresholdedKernel::n_approx [protected] |
Set to the number of points to sample when using the knn approximation.
When it is not the case, it is set to -1.
Definition at line 70 of file ThresholdedKernel.h.
Referenced by build_(), and setDataForKernelMatrix().
Definition at line 98 of file ThresholdedKernel.h.
Referenced by declareOptions(), evaluate(), evaluate_i_j(), evaluate_i_x_again(), evaluate_x_i_again(), setDataForKernelMatrix(), and thresholdGramMatrix().