PLearn 0.1
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VMat_basic_stats.h File Reference
#include <plearn/math/TVec.h>
#include <plearn/math/TMat.h>
#include <plearn/base/Array.h>
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Namespaces

namespace  PLearn
 

< for swap


Defines

#define MEAN_ROW   0
#define STDDEV_ROW   1
#define MIN_ROW   2
#define MAX_ROW   3
#define NMISSING_ROW   4
#define NZERO_ROW   5
#define NPOSITIVE_ROW   6
#define NNEGATIVE_ROW   7
#define MEANPOS_ROW   8
#define STDDEVPOS_ROW   9

Functions

Mat PLearn::computeBasicStats (const VMat &m)
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.
void PLearn::computeMeanAndVariance (const VMat &d, Vec &meanvec, Vec &variancevec, real epsilon)
void PLearn::computeMeanAndStddev (const VMat &d, Vec &meanvec, Vec &stddevvec, 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::computeWeightedMean (const Vec &weights, const VMat &d, Vec &meanvec)
void PLearn::computeWeightedMeanAndCovar (const Vec &weights, const VMat &d, Vec &meanvec, Mat &covarmat, real epsilon)
void PLearn::computeInputMean (const VMat &d, Vec &meanvec)
void PLearn::computeInputCovar (const VMat &d, const Vec &mu, Mat &covarmat, real epsilon=0.0)
 Computes covariance matrix given mean mu.
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::autocorrelation_function (const VMat &data, Mat &acf)
void PLearn::computeRange (const VMat &d, Vec &minvec, Vec &maxvec)
TVec< Mat > PLearn::computeConditionalMeans (VMat trainset, int targetsize, Mat &basic_stats)
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 (const VMat &d, int inputsize)
 Here, mean and stddev are estimated on the whole dataset d.
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.h.


Define Documentation

#define MAX_ROW   3
#define MEAN_ROW   0

Definition at line 58 of file VMat_basic_stats.h.

Referenced by PLearn::computeBasicStats().

#define MEANPOS_ROW   8

Definition at line 66 of file VMat_basic_stats.h.

Referenced by PLearn::computeBasicStats().

#define MIN_ROW   2
#define NMISSING_ROW   4

Definition at line 62 of file VMat_basic_stats.h.

Referenced by PLearn::computeBasicStats().

#define NNEGATIVE_ROW   7

Definition at line 65 of file VMat_basic_stats.h.

Referenced by PLearn::computeBasicStats().

#define NPOSITIVE_ROW   6

Definition at line 64 of file VMat_basic_stats.h.

Referenced by PLearn::computeBasicStats().

#define NZERO_ROW   5

Definition at line 63 of file VMat_basic_stats.h.

Referenced by PLearn::computeBasicStats().

#define STDDEV_ROW   1

Definition at line 59 of file VMat_basic_stats.h.

Referenced by PLearn::computeBasicStats().

#define STDDEVPOS_ROW   9

Definition at line 67 of file VMat_basic_stats.h.

Referenced by PLearn::computeBasicStats().

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