| 
    PLearn 0.1 
   | 
 


Go to the source code of this file.
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.   | |
Definition in file VMat_basic_stats.h.
| #define MAX_ROW 3 | 
Definition at line 61 of file VMat_basic_stats.h.
Referenced by PLearn::computeBasicStats(), and PLearn::computeConditionalMeans().
| #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 | 
Definition at line 60 of file VMat_basic_stats.h.
Referenced by PLearn::computeBasicStats(), and PLearn::computeConditionalMeans().
| #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().
 1.7.4