PLearn 0.1
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#include "databases.h"
#include <plearn/vmat/ConcatRowsVMatrix.h>
#include <plearn/db/NistDB.h>
#include <plearn/math/random.h>
#include <plearn/vmat/RemapLastColumnVMatrix.h>
#include <plearn/vmat/ShiftAndRescaleVMatrix.h>
#include <plearn/vmat/Splitter.h>
#include <plearn/vmat/VMat_basic_stats.h>
#include <plearn/io/MatIO.h>
#include <plearn/base/stringutils.h>
#include <plearn/math/TMat_maths.h>
Go to the source code of this file.
Namespaces | |
namespace | PLearn |
< for swap | |
Defines | |
#define | JAVA "java" |
Functions | |
Mat | PLearn::input2dSet (const PPath &filename="data2d.amat") |
This will input a 2d binary classification problem (launches a java applet) | |
void | PLearn::normalizeDataSets (Mat &training_set, Mat &validation_set, Mat &test_set) |
void | PLearn::normalizeDataSets (VMat &training_set, VMat &validation_set, VMat &test_set) |
void | PLearn::normalizeDataSets (Mat &training_set, Mat &test_set) |
normalize both training_set and test_set according to mean and stddev computed on training_set | |
void | PLearn::normalizeDataSet (Mat &m) |
void | PLearn::splitTrainValidTest (VMat &data_set, VMat &train_set, VMat &valid_set, real valid_fraction, VMat &test_set, real test_fraction, bool normalize) |
VMat | PLearn::reduceInputSize (real fraction, VMat data) |
VMat | PLearn::reduceDataSetSize (real fraction, VMat data) |
void | PLearn::remapClassnums (VMat &data, real remap_minval_to, real remap_maxval_to) |
remaps classnums to {0,1} or to {-1,+1} | |
VMat | PLearn::loadBreastCancerWisconsin (bool normalize, bool uniq) |
int | PLearn::loadBreastCancer (VMat &training_set, VMat &validation_set, VMat &test_set, int ntrain, int nvalid, bool uniq=true) |
These calls return the number of classes... | |
VMat | PLearn::loadPimaIndians (bool normalize) |
VMat | PLearn::loadHousing (bool normalize) |
VMat | PLearn::loadSonar () |
VMat | PLearn::loadIonosphere () |
VMat | PLearn::loadDiabetes (bool normalize) |
int | PLearn::loadDiabetes (VMat &training_set, VMat &validation_set, VMat &test_set, int ntrain, int nvalid) |
int | PLearn::loadATT800 (VMat &training_set, VMat &test_set) |
VMat | PLearn::loadLetters (bool normalize) |
VMat | PLearn::loadLetters (const char *class0, const char *class1, bool normalize) |
int | PLearn::loadLetters (VMat &training_set, VMat &validation_set, VMat &test_set, char *which_letters, real validation_fraction, real test_fraction, bool do_shuffle) |
VMat | PLearn::loadLetters (int n_letters, bool do_shuffle) |
int | PLearn::loadLetters (VMat &training_set, VMat &validation_set, VMat &test_set, int n_letters, real validation_fraction, real test_fraction, bool do_shuffle) |
void | PLearn::loadCorelDatamat (int classnum, Mat &train, Mat &valid, Mat &test) |
Mat | PLearn::smoothCorelHisto (Mat &data) |
void | PLearn::loadCorel (Mat &training_set, Mat &validation_set, Mat &test_set, int negative_class, int positive_class) |
void | PLearn::loadCallxx (int year, VMat &d) |
void | PLearn::loadUSPS (VMat &trainset, VMat &testset, bool use_smooth) |
VMat | PLearn::loadUSPS (bool use_smooth) |
void | PLearn::loadLetters (int &inputsize, int &nclasses, VMat &trainset, VMat &testset) |
void | PLearn::loadClassificationDataset (const string &datasetname, int &inputsize, int &nclasses, VMat &trainset, VMat &testset, bool normalizeinputs, VMat &allset) |
void | PLearn::loadUCI (VMat &trainset, VMat &testset, VMat &allset, string db_spec, string id, bool &normalize, const string &type) |
Load the train, test and all datasets for a UCI database. | |
void | PLearn::loadUCIAMat (VMat &data, string file, PP< UCISpecification > uci_spec) |
Load a AMAT format UCI dataset in the given VMatrix. | |
void | PLearn::loadUCISet (VMat &data, PP< UCISpecification > uci_spec) |
Load a specific UCI dataset in the given VMatrix. | |
void | PLearn::loadUCISet (VMat &data, string file, PP< UCISpecification > uci_spec) |
Load a specific UCI dataset in the given VMatrix. |
#define JAVA "java" |
Definition at line 60 of file databases.cc.
Referenced by PLearn::input2dSet().