PLearn 0.1
Namespaces | Functions
VMat_basic_stats.cc File Reference
#include <plearn/base/Object.h>
#include "VMat_basic_stats.h"
#include "MemoryVMatrix.h"
#include "ShiftAndRescaleVMatrix.h"
#include <plearn/math/stats_utils.h>
#include <plearn/math/VecStatsCollector.h>
#include <plearn/math/TMat_maths.h>
Include dependency graph for VMat_basic_stats.cc:

Go to the source code of this file.

Namespaces

namespace  PLearn
 

< for swap


Functions

void PLearn::computeWeightedMean (const Vec &weights, const VMat &d, Vec &meanvec)
void PLearn::computeRange (const VMat &d, Vec &minvec, Vec &maxvec)
void PLearn::computeRowMean (const VMat &d, Vec &meanvec)
 Compute mean of each row (the returned vector has length d->length()).
void PLearn::computeMean (const VMat &d, Vec &meanvec)
 Compute basic statistics over all samples.
Mat PLearn::computeBasicStats (const VMat &m)
TVec< Mat > PLearn::computeConditionalMeans (const VMat &trainset, int targetsize, Mat &basic_stats)
void PLearn::computeMeanAndVariance (const VMat &d, Vec &meanvec, Vec &variancevec, real epsilon)
void PLearn::computeInputMean (const VMat &d, Vec &meanvec)
void PLearn::computeInputMeanAndCovar (const VMat &d, Vec &meanvec, Mat &covarmat, real epsilon)
void PLearn::computeInputMeanAndVariance (const VMat &d, Vec &meanvec, Vec &var, real epsilon)
void PLearn::computeInputMeanAndStddev (const VMat &d, Vec &meanvec, Vec &stddev, real epsilon)
void PLearn::computeWeightedMeanAndCovar (const Vec &weights, const VMat &d, Vec &meanvec, Mat &covarmat, real epsilon)
void PLearn::computeMeanAndCovar (const VMat &d, Vec &meanvec, Mat &covarmat, real epsilon)
void PLearn::computeCovar (const VMat &d, const Vec &mu, Mat &covarmat, real epsilon=0.0)
 Computes covariance matrix given mean mu.
void PLearn::computeInputCovar (const VMat &d, const Vec &mu, Mat &covarmat, real epsilon=0.0)
 Computes covariance matrix given mean mu.
void PLearn::computeMeanAndStddev (const VMat &d, Vec &meanvec, Vec &stddevvec, real epsilon)
void PLearn::autocorrelation_function (const VMat &data, Mat &acf)
VMat PLearn::normalize (const VMat &d, const Vec &meanvec, const Vec &stddevvec)
VMat PLearn::normalize (const VMat &d, int inputsize, int ntrain)
 Here, mean and stddev are estimated on d.subMat(0,0,ntrain,inputsize).
VMat PLearn::normalize (VMat d, int inputsize)
void PLearn::correlations (const VMat &x, const VMat &y, Mat &r, Mat &pvalues, bool ignore_missing=false)
 Compute the correlations between each of the columns of x and each of the columns of y.

Detailed Description

Definition in file VMat_basic_stats.cc.

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