PLearn 0.1
Classes | Namespaces | Defines | Typedefs | Functions | Variables
pl_math.h File Reference
#include <cmath>
#include <cfloat>
#include <climits>
#include <plearn/base/plerror.h>
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Go to the source code of this file.

Classes

union  PLearn::_plearn_nan_type
class  PLearn::PLMathInitializer

Namespaces

namespace  PLearn
 

< for swap


Defines

#define MISSING_VALUE   (plearn_nan.d)
 Quiet NaN (float pattern) Intel Compiler seems to have a bug when initializing a class' members with NAN.
#define M_SQRT2   1.41421356237309504880
 INFINITY is not defined under Windows, or its definition may produce a compilation warning when used.
#define M_PI   3.14159265358979323846
 Define M_PI as Pi (may not be defined on all systems).
#define pl_log   std::log
#define MIN(a, b)   ((a)<(b)?(a):(b))
 Deprecated, use std::min and std::max instead.
#define MAX(a, b)   ((a)>(b)?(a):(b))
#define SIGN(a)   ((a)>=0?1:-1)
#define Pi   3.141592653589793
#define LogPi   1.14472988585
#define Log2Pi   1.837877066409
#define LOG_2   0.693147180559945
#define LOG_INIT   -REAL_MAX
#define MINUS_LOG_THRESHOLD   -18.42
#define DEG2RAD   Pi/180.0
#define RAD2DEG   57.29578
#define TANHTABLESIZE   5000
#define MAXTANHX   10.
#define DOUBLE_TO_INT(in, out)   out = int(round(in))
#define FSWAP(a, b)   do {real _c; _c = *(a); *(a) = *(b); *(b) = _c;} while(0)

Typedefs

typedef real(* PLearn::tRealFunc )(real)
typedef real(* PLearn::tRealReadFunc )(real, real)

Functions

real PLearn::log_force_nan_if_negative (real a)
 Under Cygwin with GCC, log(x) with x < 0 returns -Inf instead of NaN.
real PLearn::sign (real a)
real PLearn::positive (real a)
real PLearn::negative (real a)
bool PLearn::is_equal (real a, real b, real absolute_tolerance_threshold=1.0, real absolute_tolerance=ABSOLUTE_TOLERANCE, real relative_tolerance=RELATIVE_TOLERANCE)
 Test float equality (correctly deals with 'nan' and 'inf' values).
bool PLearn::fast_is_equal (real a, real b, real absolute_tolerance_threshold=1.0, real absolute_tolerance=ABSOLUTE_TOLERANCE, real relative_tolerance=RELATIVE_TOLERANCE)
 Test float equality (but does not deal with 'nan' and 'inf' values).
bool PLearn::fast_exact_is_equal (real a, real b)
 Test exact float equality.
bool PLearn::fast_is_more (real a, real b, real absolute_tolerance_threshold=1.0, real absolute_tolerance=ABSOLUTE_TOLERANCE, real relative_tolerance=RELATIVE_TOLERANCE)
 Test float inequality (but does not deal with 'nan' and 'inf' values).
bool PLearn::fast_is_less (real a, real b, real absolute_tolerance_threshold=1.0, real absolute_tolerance=ABSOLUTE_TOLERANCE, real relative_tolerance=RELATIVE_TOLERANCE)
 Test float inequality (but does not deal with 'nan' and 'inf' values).
bool PLearn::is_more (real a, real b, real absolute_tolerance_threshold=1.0, real absolute_tolerance=ABSOLUTE_TOLERANCE, real relative_tolerance=RELATIVE_TOLERANCE)
 Test float inequality while dealling with 'nan' and 'inf' values.
template<class T >
PLearn::square (const T &x)
real PLearn::square_f (real x)
template<class T >
PLearn::two (const T &x)
real PLearn::fasttanh (const real &x)
real PLearn::fastsigmoid (const real &x)
real PLearn::ultrafasttanh (const real &x)
real PLearn::ultrafastsigmoid (const real &x)
real PLearn::hinge_loss (const real &output, int target)
real PLearn::d_hinge_loss (const real &output, int target)
template<class T >
bool PLearn::is_missing (const T &x)
 Tells if the passed value means "missing" for its data-type.
bool PLearn::is_missing (double x)
 Missing value for double and float are represented by NaN.
bool PLearn::is_missing (float x)
 Missing value for double and float are represented by NaN.
bool PLearn::is_integer (real x)
real PLearn::FABS (real x)
real PLearn::mypow (real x, real p)
real PLearn::ipow (real x, int p)
int PLearn::ipow (int x, int p)
real PLearn::sigmoid (real x)
 numerically stable version of sigmoid(x) = 1.0/(1.0+exp(-x))
real PLearn::is_positive (real x)
 "hard" version of the sigmoid, i.e.
real PLearn::inverse_sigmoid (real x)
 Numerically stable version of inverse_sigmoid(x) = log(x/(1-x)).
real PLearn::softplus (real x)
 numerically stable computation of log(1+exp(x))
real PLearn::tabulated_softplus (real x)
real PLearn::inverse_softplus (real y)
 inverse of softplus function
real PLearn::hard_slope (real x, real left=0, real right=1)
real PLearn::log_sigmoid (real x)
 to avoid 0 or 1 probability, work in the log-domain
real PLearn::soft_slope (real x, real smoothness=1, real left=0, real right=1)
real PLearn::tabulated_soft_slope (real x, real smoothness=1, real left=0, real right=1)
real PLearn::d_soft_slope (real x, real smoothness=1, real left=0, real right=1)
int PLearn::n_choose (int M, int N)
 Return M choose N, i.e., M! / ( N! (M-N)! )
real PLearn::safeflog (real a)
real PLearn::safelog (real a)
real PLearn::safeexp (real a)
real PLearn::log (real base, real a)
real PLearn::logtwo (real a)
real PLearn::safeflog (real base, real a)
real PLearn::safeflog2 (real a)
real PLearn::logadd (double log_a, double log_b)
 compute log(exp(log_a)+exp(log_b)) without losing too much precision (doing the computation in double precision)
real PLearn::logsub (real log_a, real log_b)
 compute log(exp(log_a)-exp(log_b)) without losing too much precision
real PLearn::dilogarithm (real x)
 It is also useful because -dilogarithm(-exp(x)) is the primitive of the softplus function log(1+exp(x)).
real PLearn::softplus_primitive (real x)
real PLearn::tabulated_softplus_primitive (real x)
real PLearn::hard_slope_integral (real l, real r, real a, real b)
real PLearn::soft_slope_integral (real smoothness, real left, real right, real a, real b)
real PLearn::tabulated_soft_slope_integral (real smoothness, real left, real right, real a, real b)

Variables

_plearn_nan_type PLearn::plearn_nan

Detailed Description

Definition in file pl_math.h.


Define Documentation

#define DEG2RAD   Pi/180.0
#define DOUBLE_TO_INT (   in,
  out 
)    out = int(round(in))
#define FSWAP (   a,
  b 
)    do {real _c; _c = *(a); *(a) = *(b); *(b) = _c;} while(0)

Definition at line 414 of file pl_math.h.

#define Log2Pi   1.837877066409

Definition at line 162 of file pl_math.h.

#define LOG_2   0.693147180559945

Definition at line 166 of file pl_math.h.

Referenced by PLearn::logadd(), PLearn::logtwo(), and PLearn::safeflog2().

#define LOG_INIT   -REAL_MAX

Definition at line 170 of file pl_math.h.

Referenced by PLearn::logadd().

#define LogPi   1.14472988585

Definition at line 158 of file pl_math.h.

Referenced by PLearn::RBMGaussianLayer::freeEnergyContribution().

#define M_PI   3.14159265358979323846

Define M_PI as Pi (may not be defined on all systems).

Definition at line 107 of file pl_math.h.

Referenced by PLearn::GaussianProcessNLLVariable::fprop(), PLearn::logOfCompactGaussian(), and PLearn::logOfNormal().

#define M_SQRT2   1.41421356237309504880

INFINITY is not defined under Windows, or its definition may produce a compilation warning when used.

We instead use the STL numeric limits. Define M_SQRT2 as the square root of 2 (may not be defined on all systems).

Definition at line 102 of file pl_math.h.

Referenced by PLearn::RBMGaussianLayer::computeStdDeviation(), PLearn::GhostScript::drawCross(), and PLearn::RBMGaussianLayer::forget().

#define MAX (   a,
  b 
)    ((a)>(b)?(a):(b))
#define MAXTANHX   10.

Definition at line 279 of file pl_math.h.

Referenced by PLearn::fasttanh(), and PLearn::PLMathInitializer::PLMathInitializer().

#define MIN (   a,
  b 
)    ((a)<(b)?(a):(b))
#define MINUS_LOG_THRESHOLD   -18.42

Definition at line 174 of file pl_math.h.

Referenced by PLearn::logadd(), and PLearn::logsub().

#define MISSING_VALUE   (plearn_nan.d)

Quiet NaN (float pattern) Intel Compiler seems to have a bug when initializing a class' members with NAN.

Definition at line 88 of file pl_math.h.

Referenced by PLearn::VecStatsCollector::append(), PLearn::BasisSelectionRegressor::appendCandidateFunctionsOfSingleField(), PLearn::argmax(), PLearn::argmin(), PLearn::avgdev(), PLearn::StackedSVDNet::build_(), PLearn::SequentialLearner::build_(), PLearn::SelectColumnsVMatrix::build_(), PLearn::ReIndexedTargetVariable::build_(), PLearn::NLLNeighborhoodWeightsVariable::build_(), PLearn::NeuralProbabilisticLanguageModel::build_(), PLearn::NeuralNet::build_(), PLearn::MovingAverageVMatrix::build_(), PLearn::LinearInductiveTransferClassifier::build_(), PLearn::GaussianContinuumDistribution::build_(), PLearn::FeatureSetSequentialCRF::build_(), PLearn::FeatureSetNNet::build_(), PLearn::FeatureSetNaiveBayesClassifier::build_(), PLearn::DictionaryVMatrix::build_(), PLearn::CumVMatrix::build_(), PLearn::GaussMix::cdf(), PLearn::MergeDond2Files::combineAndPut(), PLearn::VPLPreprocessedLearner2::computeConfidenceFromOutput(), PLearn::VPLPreprocessedLearner::computeConfidenceFromOutput(), PLearn::PLearner::computeConfidenceFromOutput(), PLearn::VariableSelectionWithDirectedGradientDescent::computeCostsFromOutputs(), PLearn::SubsamplingDBN::computeCostsFromOutputs(), PLearn::StructuralLearner::computeCostsFromOutputs(), PLearn::StackedSVDNet::computeCostsFromOutputs(), PLearn::StackedFocusedAutoassociatorsNet::computeCostsFromOutputs(), PLearn::StackedAutoassociatorsNet::computeCostsFromOutputs(), PLearn::PseudolikelihoodRBM::computeCostsFromOutputs(), PLearn::MultiInstanceNNet::computeCostsFromOutputs(), PLearn::ManifoldParzen::computeCostsFromOutputs(), PLearn::KernelProjection::computeCostsFromOutputs(), PLearn::DiscriminativeRBM::computeCostsFromOutputs(), PLearn::DiscriminativeDeepBeliefNet::computeCostsFromOutputs(), PLearn::DeepNonLocalManifoldParzen::computeCostsFromOutputs(), PLearn::DeepBeliefNet::computeCostsFromOutputs(), PLearn::CompareLearner::computeCostsFromOutputs(), PLearn::AddCostToLearner::computeCostsFromOutputs(), PLearn::AdaBoost::computeCostsFromOutputs(), PLearn::RegressionTree::computeCostsFromOutputsAndNodes(), PLearn::MemoryCachedKernel::computeGramMatrixDerivNV(), PLearn::PLearner::computeInputOutputConfMat(), PLearn::ModuleLearner::computeOutput(), PLearn::DistRepNNet::computeOutput(), PLearn::NNet::computeOutputAndCosts(), PLearn::MultiInstanceNNet::computeOutputAndCosts(), PLearn::MultiClassAdaBoost::computeOutputAndCosts(), PLearn::ExhaustiveNearestNeighbors::computeOutputAndCosts(), PLearn::DistRepNNet::computeOutputAndCosts(), PLearn::BallTreeNearestNeighbors::computeOutputAndCosts(), PLearn::AdaBoost::computeOutputAndCosts(), PLearn::GaussianProcessRegressor::computeOutputAux(), PLearn::PLearner::computeOutputConfMat(), PLearn::PLearner::computeOutputCovMat(), PLearn::GaussianProcessRegressor::computeOutputCovMat(), PLearn::DeepBeliefNet::computeOutputsAndCosts(), PLearn::GenericNearestNeighbors::constructOutputVector(), PLearn::SDBVMFieldDateDiff::convertField(), PLearn::covariance(), PLearn::date_to_double(), PLearn::date_to_float(), PLearn::datetime_to_double(), PLearn::det(), PLearn::DichotomizeDond2DiscreteVariables::dichotomizeDiscreteVariables(), PLearn::dilogarithm(), PLearn::StatsCollector::dmode(), PLearn::ThresholdedKernel::evaluate(), PLearn::ClassErrorCostFunction::evaluate(), PLearn::ThresholdedKernel::evaluate_i_j(), PLearn::ThresholdedKernel::evaluate_i_x_again(), PLearn::ThresholdedKernel::evaluate_x_i_again(), PLearn::ProcessSymbolicSequenceVMatrix::fill_current_row(), PLearn::ManifoldParzen2::find_nearest_neighbor(), PLearn::QuantilesStatsIterator::finish(), PLearn::StatsCollector::forget(), PLearn::SequentialLearner::forget(), PLearn::SoftmaxNLLCostModule::fprop(), PLearn::RBMModule::fprop(), PLearn::OnBagsModule::fprop(), PLearn::NLLCostModule::fprop(), PLearn::LiftOutputVariable::fprop(), PLearn::LayerCostModule::fprop(), PLearn::geometric_mean(), PLearn::SelectColumnsVMatrix::get(), PLearn::MeanMedianModeImputationVMatrix::get(), PLearn::VecStatsCollector::getCovariance(), PLearn::AutoSDBVMatrix::getMappings(), PLearn::ProcessSymbolicSequenceVMatrix::getNewRow(), PLearn::MovingAverageVMatrix::getNewRow(), PLearn::MixUnlabeledNeighbourVMatrix::getNewRow(), PLearn::MissingInstructionVMatrix::getNewRow(), PLearn::MeanImputationVMatrix::getNewRow(), PLearn::DichotomizeVMatrix::getNewRow(), PLearn::DatedJoinVMatrix::getNewRow(), PLearn::AutoSDBVMatrix::getNewRow(), PLearn::AddMissingVMatrix::getNewRow(), PLearn::RegressionTreeMulticlassLeaveProb::getOutputAndError(), PLearn::RegressionTreeMulticlassLeaveFast::getOutputAndError(), PLearn::RegressionTreeMulticlassLeave::getOutputAndError(), PLearn::RegressionTreeLeave::getOutputAndError(), PLearn::TextFilesVMatrix::getPostalEncoding(), PLearn::MultiTaskSeparationSplitter::getSplit(), PLearn::VecStatsCollector::getStat(), PLearn::VMatrix::getStringVal(), PLearn::SelectColumnsVMatrix::getStringVal(), PLearn::ProcessSymbolicSequenceVMatrix::getStringVal(), PLearn::LemmatizeVMatrix::getStringVal(), PLearn::DictionaryVMatrix::getStringVal(), PLearn::SelectColumnsVMatrix::getSubRow(), PLearn::ProcessSymbolicSequenceVMatrix::getValues(), PLearn::harmonic_mean(), PLearn::GaussianContinuumDistribution::initializeParams(), PLearn::inverse_softplus(), PLearn::isOverlapping(), PLearn::PartSupervisedDBN::jointGreedyStep(), PLearn::GaussPartSupervisedDBN::jointGreedyStep(), PLearn::loadAscii(), PLearn::loadSTATLOG(), PLearn::loadUCIMLDB(), PLearn::log_force_nan_if_negative(), PLearn::matlabSave(), PLearn::mean(), PLearn::new_read_compressed(), PLearn::HyperOptimize::optimize(), PLearn::NeuralProbabilisticLanguageModel::output_gradient_verification(), PLearn::FeatureSetSequentialCRF::output_gradient_verification(), PLearn::FeatureSetNNet::output_gradient_verification(), PLearn::p_value(), PLearn::paired_t_test(), PLearn::PLMathTest::perform(), PLearn::FileVMatrixTest::perform(), PLearn::PTester::perform1Split(), PLearn::pl_isnumber(), PLearn::pl_strtod(), PLearn::StatsCollector::pseudo_quantile(), PLearn::RealMapping::read(), PLearn::PStream::readAsciiNum(), PLearn::StatsCollector::remove_observation(), PLearn::VMatLanguage::run(), PLearn::SequentialModelSelector::sequenceCost(), PLearn::SDBVMOutputCoder::setOutput(), PLearn::StatsCollector::sharperatio(), PLearn::SpearmanRankCorrelation(), PLearn::sum(), PLearn::SupervisedDBN::supervisedContrastiveDivergenceStep(), PLearn::PartSupervisedDBN::supervisedContrastiveDivergenceStep(), PLearn::GaussPartSupervisedDBN::supervisedContrastiveDivergenceStep(), PLearn::GaussMix::survival_fn(), PLearn::SubsamplingDBN::test(), PLearn::StructuralLearner::test(), PLearn::SequentialModelSelector::test(), PLearn::PseudolikelihoodRBM::test(), PLearn::DynamicallyLinkedRBMsModel::test(), PLearn::DenoisingRecurrentNet::test(), PLearn::DeepBeliefNet::test(), PLearn::RowIterator::toDouble(), PLearn::FieldValue::toDouble(), PLearn::todouble(), PLearn::TopDownAsymetricDeepNetwork::train(), PLearn::SubsamplingDBN::train(), PLearn::StructuralLearner::train(), PLearn::StackedSVDNet::train(), PLearn::StackedFocusedAutoassociatorsNet::train(), PLearn::StackedAutoassociatorsNet::train(), PLearn::PseudolikelihoodRBM::train(), PLearn::NatGradSMPNNet::train(), PLearn::NatGradNNet::train(), PLearn::mNNet::train(), PLearn::ManifoldParzen::train(), PLearn::DynamicallyLinkedRBMsModel::train(), PLearn::DiscriminativeRBM::train(), PLearn::DiscriminativeDeepBeliefNet::train(), PLearn::DenoisingRecurrentNet::train(), PLearn::DeepNonLocalManifoldParzen::train(), PLearn::DeepBeliefNet::train(), PLearn::TextFilesVMatrix::transformStringToValue(), PLearn::ToBagClassifier::updateCostAndBagOutput(), PLearn::variance(), PLearn::Function::verifyGradient(), PLearn::weighted_mean(), PLearn::weighted_variance(), and PLearn::StatsCollector::zpr1t().

#define Pi   3.141592653589793

Definition at line 154 of file pl_math.h.

#define pl_log   std::log

Definition at line 118 of file pl_math.h.

Referenced by PLearn::bnldev(), PLearn::SumEntropyOfCategoricals::bprop(), PLearn::SumEntropyOfBernoullis::bprop(), PLearn::NonLocalManifoldParzen::build_(), PLearn::NLLNeighborhoodWeightsVariable::build_(), PLearn::AsciiVMatrix::build_(), PLearn::choleskyInvert(), PLearn::compute_log(), PLearn::PseudolikelihoodRBM::compute_Z(), PLearn::VariableSelectionWithDirectedGradientDescent::computeCostsFromOutputs(), PLearn::PseudolikelihoodRBM::computeCostsFromOutputs(), PLearn::PDistribution::computeCostsFromOutputs(), PLearn::PartSupervisedDBN::computeCostsFromOutputs(), PLearn::ManifoldParzen::computeCostsFromOutputs(), PLearn::KNNClassifier::computeCostsFromOutputs(), PLearn::KFoldLogisticClassifier::computeCostsFromOutputs(), PLearn::HintonDeepBeliefNet::computeCostsFromOutputs(), PLearn::GaussPartSupervisedDBN::computeCostsFromOutputs(), PLearn::GaussianDBNClassification::computeCostsFromOutputs(), PLearn::DiscriminativeRBM::computeCostsFromOutputs(), PLearn::DeepNonLocalManifoldParzen::computeCostsFromOutputs(), PLearn::AddCostToLearner::computeCostsFromOutputs(), PLearn::RationalQuadraticARDKernel::computeGramMatrixDerivIspAlpha(), PLearn::LinearRegressor::computeInformationCriteria(), PLearn::GaussMix::computeLogLikelihood(), PLearn::ManifoldParzen::computeOutput(), PLearn::LocalGaussianClassifier::computeOutput(), PLearn::DeepNonLocalManifoldParzen::computeOutput(), PLearn::GaussMix::computePosteriors(), PLearn::RationalQuadraticARDKernel::derivIspAlpha(), PLearn::SquaredExponentialARDKernel::derivIspGlobalSigma(), PLearn::NeuralNetworkARDKernel::derivIspGlobalSigma(), PLearn::Matern1ARDKernel::derivIspGlobalSigma(), PLearn::det(), PLearn::entropy(), PLearn::GaussianDensityKernel::evaluate(), PLearn::CorrelationKernel::evaluate(), PLearn::expdev(), PLearn::NatGradSMPNNet::fbpropLoss(), PLearn::NatGradNNet::fbpropLoss(), PLearn::mNNet::fbpropLoss(), PLearn::findSmallestEigenPairOfSymmMat(), PLearn::PartSupervisedDBN::fineTuneByGradientDescent(), PLearn::HintonDeepBeliefNet::fineTuneByGradientDescent(), PLearn::GaussPartSupervisedDBN::fineTuneByGradientDescent(), PLearn::DeepNonLocalManifoldParzen::fineTuningStep(), PLearn::WeightedLogGaussian::fprop(), PLearn::SumEntropyOfCategoricals::fprop(), PLearn::SumEntropyOfBernoullis::fprop(), PLearn::NllGeneralGaussianVariable::fprop(), PLearn::NLLErrModule::fprop(), PLearn::NLLCostModule::fprop(), PLearn::NegLogPoissonVariable::fprop(), PLearn::NegCrossEntropySigmoidVariable::fprop(), PLearn::GaussianProcessNLLVariable::fprop(), PLearn::CrossEntropyVariable::fprop(), PLearn::RBMMultinomialLayer::fpropNLL(), PLearn::RBMLocalMultinomialLayer::fpropNLL(), PLearn::RBMGaussianLayer::fpropNLL(), PLearn::RBMGaussianLayer::freeEnergyContribution(), PLearn::gamdev(), PLearn::TransformationLearner::gamma_sample(), PLearn::gauss_log_density_stddev(), PLearn::gauss_log_density_var(), PLearn::gaussian_01(), PLearn::GDFindSmallEigenPairs(), PLearn::RBMTruncExpLayer::generateSample(), PLearn::RBMTruncExpLayer::generateSamples(), PLearn::geometric_mean(), PLearn::incomplete_beta(), PLearn::TransformationLearner::INIT_weight(), PLearn::inverse_sigmoid(), PLearn::inverse_softplus(), PLearn::PartSupervisedDBN::jointGreedyStep(), PLearn::GaussPartSupervisedDBN::jointGreedyStep(), PLearn::log(), PLearn::UniformDistribution::log_density(), PLearn::TransformationLearner::log_density(), PLearn::SupervisedDBN::log_density(), PLearn::PartSupervisedDBN::log_density(), PLearn::NonLocalManifoldParzen::log_density(), PLearn::NeighborhoodBoxVolumeDensityEstimator::log_density(), PLearn::MixtureDistribution::log_density(), PLearn::ManifoldKNNDistribution::log_density(), PLearn::LocallyMagnifiedDistribution::log_density(), PLearn::KernelDensityEstimator::log_density(), PLearn::HistogramDistribution::log_density(), PLearn::HintonDeepBeliefNet::log_density(), PLearn::GaussPartSupervisedDBN::log_density(), PLearn::GaussMix::log_density(), PLearn::GaussianDBNRegression::log_density(), PLearn::GaussianDBNClassification::log_density(), PLearn::ConditionalDensityNet::log_density(), PLearn::log_gamma(), PLearn::logOfCompactGaussian(), PLearn::logOfNormal(), PLearn::logtwo(), PLearn::PLMathTest::perform(), PLearn::pl_dgammlndz(), PLearn::pl_gammln(), PLearn::pl_gcf(), PLearn::pl_gser(), PLearn::poidev(), PLearn::positive_dilogarithm(), PLearn::GaussMix::precomputeGaussianLogCoefficient(), PLearn::ridgeRegressionByGCV(), PLearn::VMatLanguage::run(), PLearn::safeflog(), PLearn::GaussMix::setPredictor(), PLearn::KroneckerBaseKernel::softplusFloor(), PLearn::sum_of_log(), PLearn::SymmMatNullSpaceByInversePowerIteration(), PLearn::VariableSelectionWithDirectedGradientDescent::train(), PLearn::UnfrozenDeepBeliefNet::train(), PLearn::ClassifierFromDensity::train(), and PLearn::weightedRidgeRegressionByGCV().

#define RAD2DEG   57.29578

Definition at line 182 of file pl_math.h.

#define SIGN (   a)    ((a)>=0?1:-1)

Definition at line 150 of file pl_math.h.

#define TANHTABLESIZE   5000

Definition at line 278 of file pl_math.h.

Referenced by PLearn::fasttanh(), and PLearn::PLMathInitializer::PLMathInitializer().

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