Class encapsulating training data.
More...
#include "ml.hpp"
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| virtual | ~TrainData () |
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| virtual int | getCatCount (int vi) const =0 |
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| virtual Mat | getCatMap () const =0 |
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| virtual Mat | getCatOfs () const =0 |
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| virtual Mat | getClassLabels () const =0 |
| | Returns the vector of class labels. More...
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| virtual Mat | getDefaultSubstValues () const =0 |
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| virtual int | getLayout () const =0 |
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| virtual Mat | getMissing () const =0 |
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| virtual int | getNAllVars () const =0 |
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| virtual Mat | getNormCatResponses () const =0 |
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| virtual void | getNormCatValues (int vi, InputArray sidx, int *values) const =0 |
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| virtual int | getNSamples () const =0 |
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| virtual int | getNTestSamples () const =0 |
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| virtual int | getNTrainSamples () const =0 |
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| virtual int | getNVars () const =0 |
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| virtual Mat | getResponses () const =0 |
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| virtual int | getResponseType () const =0 |
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| virtual void | getSample (InputArray varIdx, int sidx, float *buf) const =0 |
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| virtual Mat | getSamples () const =0 |
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| virtual Mat | getSampleWeights () const =0 |
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| virtual Mat | getTestNormCatResponses () const =0 |
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| virtual Mat | getTestResponses () const =0 |
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| virtual Mat | getTestSampleIdx () const =0 |
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| virtual Mat | getTestSampleWeights () const =0 |
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| virtual Mat | getTrainNormCatResponses () const =0 |
| | Returns the vector of normalized categorical responses. More...
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| virtual Mat | getTrainResponses () const =0 |
| | Returns the vector of responses. More...
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| virtual Mat | getTrainSampleIdx () const =0 |
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| virtual Mat | getTrainSamples (int layout=ROW_SAMPLE, bool compressSamples=true, bool compressVars=true) const =0 |
| | Returns matrix of train samples. More...
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| virtual Mat | getTrainSampleWeights () const =0 |
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| virtual void | getValues (int vi, InputArray sidx, float *values) const =0 |
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| virtual Mat | getVarIdx () const =0 |
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| virtual Mat | getVarType () const =0 |
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| virtual void | setTrainTestSplit (int count, bool shuffle=true)=0 |
| | Splits the training data into the training and test parts. More...
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| virtual void | setTrainTestSplitRatio (double ratio, bool shuffle=true)=0 |
| | Splits the training data into the training and test parts. More...
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| virtual void | shuffleTrainTest ()=0 |
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| static Ptr< TrainData > | create (InputArray samples, int layout, InputArray responses, InputArray varIdx=noArray(), InputArray sampleIdx=noArray(), InputArray sampleWeights=noArray(), InputArray varType=noArray()) |
| | Creates training data from in-memory arrays. More...
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| static Mat | getSubVector (const Mat &vec, const Mat &idx) |
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| static Ptr< TrainData > | loadFromCSV (const String &filename, int headerLineCount, int responseStartIdx=-1, int responseEndIdx=-1, const String &varTypeSpec=String(), char delimiter=',', char missch='?') |
| | Reads the dataset from a .csv file and returns the ready-to-use training data. More...
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| static float | missingValue () |
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Class encapsulating training data.
Please note that the class only specifies the interface of training data, but not implementation. All the statistical model classes in ml module accepts Ptr<TrainData> as parameter. In other words, you can create your own class derived from TrainData and pass smart pointer to the instance of this class into StatModel::train.
- See also
- Training Data
| virtual cv::ml::TrainData::~TrainData |
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virtual |
Creates training data from in-memory arrays.
- Parameters
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| samples | matrix of samples. It should have CV_32F type. |
| layout | see ml::SampleTypes. |
| responses | matrix of responses. If the responses are scalar, they should be stored as a single row or as a single column. The matrix should have type CV_32F or CV_32S (in the former case the responses are considered as ordered by default; in the latter case - as categorical) |
| varIdx | vector specifying which variables to use for training. It can be an integer vector (CV_32S) containing 0-based variable indices or byte vector (CV_8U) containing a mask of active variables. |
| sampleIdx | vector specifying which samples to use for training. It can be an integer vector (CV_32S) containing 0-based sample indices or byte vector (CV_8U) containing a mask of training samples. |
| sampleWeights | optional vector with weights for each sample. It should have CV_32F type. |
| varType | optional vector of type CV_8U and size <number_of_variables_in_samples> + <number_of_variables_in_responses>, containing types of each input and output variable. See ml::VariableTypes. |
| virtual int cv::ml::TrainData::getCatCount |
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int |
vi | ) |
const |
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pure virtual |
| virtual Mat cv::ml::TrainData::getCatMap |
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const |
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pure virtual |
| virtual Mat cv::ml::TrainData::getCatOfs |
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const |
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pure virtual |
| virtual Mat cv::ml::TrainData::getClassLabels |
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const |
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pure virtual |
Returns the vector of class labels.
The function returns vector of unique labels occurred in the responses.
| virtual Mat cv::ml::TrainData::getDefaultSubstValues |
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const |
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pure virtual |
| virtual int cv::ml::TrainData::getLayout |
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const |
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pure virtual |
| virtual Mat cv::ml::TrainData::getMissing |
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const |
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pure virtual |
| virtual int cv::ml::TrainData::getNAllVars |
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const |
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pure virtual |
| virtual Mat cv::ml::TrainData::getNormCatResponses |
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const |
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pure virtual |
| virtual void cv::ml::TrainData::getNormCatValues |
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int |
vi, |
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InputArray |
sidx, |
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int * |
values |
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| const |
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pure virtual |
| virtual int cv::ml::TrainData::getNSamples |
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const |
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pure virtual |
| virtual int cv::ml::TrainData::getNTestSamples |
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const |
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pure virtual |
| virtual int cv::ml::TrainData::getNTrainSamples |
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const |
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pure virtual |
| virtual int cv::ml::TrainData::getNVars |
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const |
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pure virtual |
| virtual Mat cv::ml::TrainData::getResponses |
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const |
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pure virtual |
| virtual int cv::ml::TrainData::getResponseType |
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const |
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pure virtual |
| virtual void cv::ml::TrainData::getSample |
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InputArray |
varIdx, |
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int |
sidx, |
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float * |
buf |
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) |
| const |
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pure virtual |
| virtual Mat cv::ml::TrainData::getSamples |
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const |
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pure virtual |
| virtual Mat cv::ml::TrainData::getSampleWeights |
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const |
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pure virtual |
| static Mat cv::ml::TrainData::getSubVector |
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const Mat & |
vec, |
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const Mat & |
idx |
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) |
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static |
| virtual Mat cv::ml::TrainData::getTestNormCatResponses |
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const |
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pure virtual |
| virtual Mat cv::ml::TrainData::getTestResponses |
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const |
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pure virtual |
| virtual Mat cv::ml::TrainData::getTestSampleIdx |
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const |
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pure virtual |
| virtual Mat cv::ml::TrainData::getTestSampleWeights |
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const |
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pure virtual |
| virtual Mat cv::ml::TrainData::getTrainNormCatResponses |
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const |
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pure virtual |
Returns the vector of normalized categorical responses.
The function returns vector of responses. Each response is integer from 0 to <number of classes>-1. The actual label value can be retrieved then from the class label vector, see TrainData::getClassLabels.
| virtual Mat cv::ml::TrainData::getTrainResponses |
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const |
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pure virtual |
Returns the vector of responses.
The function returns ordered or the original categorical responses. Usually it's used in regression algorithms.
| virtual Mat cv::ml::TrainData::getTrainSampleIdx |
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const |
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pure virtual |
| virtual Mat cv::ml::TrainData::getTrainSamples |
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int |
layout = ROW_SAMPLE, |
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bool |
compressSamples = true, |
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bool |
compressVars = true |
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| const |
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pure virtual |
Returns matrix of train samples.
- Parameters
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| layout | The requested layout. If it's different from the initial one, the matrix is transposed. See ml::SampleTypes. |
| compressSamples | if true, the function returns only the training samples (specified by sampleIdx) |
| compressVars | if true, the function returns the shorter training samples, containing only the active variables. |
In current implementation the function tries to avoid physical data copying and returns the matrix stored inside TrainData (unless the transposition or compression is needed).
| virtual Mat cv::ml::TrainData::getTrainSampleWeights |
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const |
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pure virtual |
| virtual void cv::ml::TrainData::getValues |
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int |
vi, |
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InputArray |
sidx, |
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float * |
values |
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| const |
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pure virtual |
| virtual Mat cv::ml::TrainData::getVarIdx |
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const |
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pure virtual |
| virtual Mat cv::ml::TrainData::getVarType |
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const |
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pure virtual |
| static Ptr<TrainData> cv::ml::TrainData::loadFromCSV |
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const String & |
filename, |
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int |
headerLineCount, |
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int |
responseStartIdx = -1, |
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int |
responseEndIdx = -1, |
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const String & |
varTypeSpec = String(), |
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char |
delimiter = ',', |
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char |
missch = '?' |
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static |
Reads the dataset from a .csv file and returns the ready-to-use training data.
- Parameters
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| filename | The input file name |
| headerLineCount | The number of lines in the beginning to skip; besides the header, the function also skips empty lines and lines staring with # |
| responseStartIdx | Index of the first output variable. If -1, the function considers the last variable as the response |
| responseEndIdx | Index of the last output variable + 1. If -1, then there is single response variable at responseStartIdx. |
| varTypeSpec | The optional text string that specifies the variables' types. It has the format ord[n1-n2,n3,n4-n5,...]cat[n6,n7-n8,...]. That is, variables from n1 to n2 (inclusive range), n3, n4 to n5 ... are considered ordered and n6, n7 to n8 ... are considered as categorical. The range [n1..n2] + [n3] + [n4..n5] + ... + [n6] + [n7..n8] should cover all the variables. If varTypeSpec is not specified, then algorithm uses the following rules:
- all input variables are considered ordered by default. If some column contains has non- numerical values, e.g. 'apple', 'pear', 'apple', 'apple', 'mango', the corresponding variable is considered categorical.
- if there are several output variables, they are all considered as ordered. Error is reported when non-numerical values are used.
- if there is a single output variable, then if its values are non-numerical or are all integers, then it's considered categorical. Otherwise, it's considered ordered.
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| delimiter | The character used to separate values in each line. |
| missch | The character used to specify missing measurements. It should not be a digit. Although it's a non-numerical value, it surely does not affect the decision of whether the variable ordered or categorical. |
- Note
- If the dataset only contains input variables and no responses, use responseStartIdx = -2 and responseEndIdx = 0. The output variables vector will just contain zeros.
| static float cv::ml::TrainData::missingValue |
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inlinestatic |
| virtual void cv::ml::TrainData::setTrainTestSplit |
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int |
count, |
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bool |
shuffle = true |
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pure virtual |
| virtual void cv::ml::TrainData::setTrainTestSplitRatio |
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double |
ratio, |
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bool |
shuffle = true |
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pure virtual |
Splits the training data into the training and test parts.
The function selects a subset of specified relative size and then returns it as the training set. If the function is not called, all the data is used for training. Please, note that for each of TrainData::getTrain* there is corresponding TrainData::getTest*, so that the test subset can be retrieved and processed as well.
- See also
- TrainData::setTrainTestSplit
| virtual void cv::ml::TrainData::shuffleTrainTest |
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pure virtual |
The documentation for this class was generated from the following file:
1.8.9.1
