View Source Evision.ML.TrainData (Evision v0.1.7)
Link to this section Summary
cv.ml
Creates training data from in-memory arrays.
Creates training data from in-memory arrays.
Positional Arguments
- vi:
int
Python prototype (for reference):
Python prototype (for reference):
Python prototype (for reference):
Returns the vector of class labels
Python prototype (for reference):
Python prototype (for reference):
Python prototype (for reference):
Python prototype (for reference):
Returns vector of symbolic names captured in loadFromCSV()
Python prototype (for reference):
Python prototype (for reference):
Python prototype (for reference):
Python prototype (for reference):
Python prototype (for reference):
Python prototype (for reference):
Python prototype (for reference):
Python prototype (for reference):
Python prototype (for reference):
Extract from matrix rows/cols specified by passed indexes.
Extract from 1D vector elements specified by passed indexes.
Python prototype (for reference):
Python prototype (for reference):
Python prototype (for reference):
Returns matrix of test samples
Python prototype (for reference):
Returns the vector of normalized categorical responses
Returns the vector of responses
Python prototype (for reference):
Returns matrix of train samples
Python prototype (for reference):
Python prototype (for reference):
Python prototype (for reference):
Python prototype (for reference):
Splits the training data into the training and test parts
Splits the training data into the training and test parts
Splits the training data into the training and test parts
Splits the training data into the training and test parts
Python prototype (for reference):
Functions
Raising version of create/3
.
Raising version of create/4
.
Raising version of getCatCount/2
.
Raising version of getCatMap/1
.
Raising version of getCatOfs/1
.
Raising version of getClassLabels/1
.
Raising version of getDefaultSubstValues/1
.
Raising version of getLayout/1
.
Raising version of getMissing/1
.
Raising version of getNAllVars/1
.
Raising version of getNames/2
.
Raising version of getNormCatResponses/1
.
Raising version of getNSamples/1
.
Raising version of getNTestSamples/1
.
Raising version of getNTrainSamples/1
.
Raising version of getNVars/1
.
Raising version of getResponses/1
.
Raising version of getResponseType/1
.
Raising version of getSample/4
.
Raising version of getSamples/1
.
Raising version of getSampleWeights/1
.
Raising version of getSubMatrix/3
.
Raising version of getSubVector/2
.
Raising version of getTestNormCatResponses/1
.
Raising version of getTestResponses/1
.
Raising version of getTestSampleIdx/1
.
Raising version of getTestSamples/1
.
Raising version of getTestSampleWeights/1
.
Raising version of getTrainNormCatResponses/1
.
Raising version of getTrainResponses/1
.
Raising version of getTrainSampleIdx/1
.
Raising version of getTrainSamples/1
.
Raising version of getTrainSampleWeights/1
.
Raising version of getValues/4
.
Raising version of getVarIdx/1
.
Raising version of getVarSymbolFlags/1
.
Raising version of getVarType/1
.
Raising version of setTrainTestSplit/2
.
Raising version of setTrainTestSplit/3
.
Raising version of setTrainTestSplitRatio/2
.
Raising version of setTrainTestSplitRatio/3
.
Raising version of shuffleTrainTest/1
.
Link to this section cv.ml
Creates training data from in-memory arrays.
Positional Arguments
samples:
Evision.Mat
.matrix of samples. It should have CV_32F type.
layout:
int
.see ml::SampleTypes.
responses:
Evision.Mat
.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)
Keyword Arguments
varIdx:
Evision.Mat
.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:
Evision.Mat
.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:
Evision.Mat
.optional vector with weights for each sample. It should have CV_32F type.
varType:
Evision.Mat
.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.
Python prototype (for reference):
create(samples, layout, responses[, varIdx[, sampleIdx[, sampleWeights[, varType]]]]) -> retval
Creates training data from in-memory arrays.
Positional Arguments
samples:
Evision.Mat
.matrix of samples. It should have CV_32F type.
layout:
int
.see ml::SampleTypes.
responses:
Evision.Mat
.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)
Keyword Arguments
varIdx:
Evision.Mat
.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:
Evision.Mat
.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:
Evision.Mat
.optional vector with weights for each sample. It should have CV_32F type.
varType:
Evision.Mat
.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.
Python prototype (for reference):
create(samples, layout, responses[, varIdx[, sampleIdx[, sampleWeights[, varType]]]]) -> retval
Positional Arguments
- vi:
int
Python prototype (for reference):
getCatCount(vi) -> retval
Python prototype (for reference):
getCatMap() -> retval
Python prototype (for reference):
getCatOfs() -> retval
Returns the vector of class labels
The function returns vector of unique labels occurred in the responses.
Python prototype (for reference):
getClassLabels() -> retval
Python prototype (for reference):
getDefaultSubstValues() -> retval
Python prototype (for reference):
getLayout() -> retval
Python prototype (for reference):
getMissing() -> retval
Python prototype (for reference):
getNAllVars() -> retval
Returns vector of symbolic names captured in loadFromCSV()
Positional Arguments
- names:
[String]
Python prototype (for reference):
getNames(names) -> None
Python prototype (for reference):
getNormCatResponses() -> retval
Python prototype (for reference):
getNSamples() -> retval
Python prototype (for reference):
getNTestSamples() -> retval
Python prototype (for reference):
getNTrainSamples() -> retval
Python prototype (for reference):
getNVars() -> retval
Python prototype (for reference):
getResponses() -> retval
Python prototype (for reference):
getResponseType() -> retval
Positional Arguments
- varIdx:
Evision.Mat
- sidx:
int
- buf:
float*
Python prototype (for reference):
getSample(varIdx, sidx, buf) -> None
Python prototype (for reference):
getSamples() -> retval
Python prototype (for reference):
getSampleWeights() -> retval
Extract from matrix rows/cols specified by passed indexes.
Positional Arguments
matrix:
Evision.Mat
.input matrix (supported types: CV_32S, CV_32F, CV_64F)
idx:
Evision.Mat
.1D index vector
layout:
int
.specifies to extract rows (cv::ml::ROW_SAMPLES) or to extract columns (cv::ml::COL_SAMPLES)
Python prototype (for reference):
getSubMatrix(matrix, idx, layout) -> retval
Extract from 1D vector elements specified by passed indexes.
Positional Arguments
vec:
Evision.Mat
.input vector (supported types: CV_32S, CV_32F, CV_64F)
idx:
Evision.Mat
.1D index vector
Python prototype (for reference):
getSubVector(vec, idx) -> retval
Python prototype (for reference):
getTestNormCatResponses() -> retval
Python prototype (for reference):
getTestResponses() -> retval
Python prototype (for reference):
getTestSampleIdx() -> retval
Returns matrix of test samples
Python prototype (for reference):
getTestSamples() -> retval
Python prototype (for reference):
getTestSampleWeights() -> retval
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.
Python prototype (for reference):
getTrainNormCatResponses() -> retval
Returns the vector of responses
The function returns ordered or the original categorical responses. Usually it's used in regression algorithms.
Python prototype (for reference):
getTrainResponses() -> retval
Python prototype (for reference):
getTrainSampleIdx() -> retval
Returns matrix of train samples
Keyword Arguments
layout:
int
.The requested layout. If it's different from the initial one, the matrix is transposed. See ml::SampleTypes.
compressSamples:
bool
.if true, the function returns only the training samples (specified by sampleIdx)
compressVars:
bool
.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).
Python prototype (for reference):
getTrainSamples([, layout[, compressSamples[, compressVars]]]) -> retval
Python prototype (for reference):
getTrainSampleWeights() -> retval
Positional Arguments
- vi:
int
- sidx:
Evision.Mat
- values:
float*
Python prototype (for reference):
getValues(vi, sidx, values) -> None
Python prototype (for reference):
getVarIdx() -> retval
Python prototype (for reference):
getVarSymbolFlags() -> retval
Python prototype (for reference):
getVarType() -> retval
Splits the training data into the training and test parts
Positional Arguments
- count:
int
Keyword Arguments
- shuffle:
bool
.
@sa TrainData::setTrainTestSplitRatio
Python prototype (for reference):
setTrainTestSplit(count[, shuffle]) -> None
Splits the training data into the training and test parts
Positional Arguments
- count:
int
Keyword Arguments
- shuffle:
bool
.
@sa TrainData::setTrainTestSplitRatio
Python prototype (for reference):
setTrainTestSplit(count[, shuffle]) -> None
Splits the training data into the training and test parts
Positional Arguments
- ratio:
double
Keyword Arguments
- shuffle:
bool
.
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. @sa TrainData::setTrainTestSplit
Python prototype (for reference):
setTrainTestSplitRatio(ratio[, shuffle]) -> None
Splits the training data into the training and test parts
Positional Arguments
- ratio:
double
Keyword Arguments
- shuffle:
bool
.
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. @sa TrainData::setTrainTestSplit
Python prototype (for reference):
setTrainTestSplitRatio(ratio[, shuffle]) -> None
Python prototype (for reference):
shuffleTrainTest() -> None
Link to this section Functions
Raising version of create/3
.
Raising version of create/4
.
Raising version of getCatCount/2
.
Raising version of getCatMap/1
.
Raising version of getCatOfs/1
.
Raising version of getClassLabels/1
.
Raising version of getDefaultSubstValues/1
.
Raising version of getLayout/1
.
Raising version of getMissing/1
.
Raising version of getNAllVars/1
.
Raising version of getNames/2
.
Raising version of getNormCatResponses/1
.
Raising version of getNSamples/1
.
Raising version of getNTestSamples/1
.
Raising version of getNTrainSamples/1
.
Raising version of getNVars/1
.
Raising version of getResponses/1
.
Raising version of getResponseType/1
.
Raising version of getSample/4
.
Raising version of getSamples/1
.
Raising version of getSampleWeights/1
.
Raising version of getSubMatrix/3
.
Raising version of getSubVector/2
.
Raising version of getTestNormCatResponses/1
.
Raising version of getTestResponses/1
.
Raising version of getTestSampleIdx/1
.
Raising version of getTestSamples/1
.
Raising version of getTestSampleWeights/1
.
Raising version of getTrainNormCatResponses/1
.
Raising version of getTrainResponses/1
.
Raising version of getTrainSampleIdx/1
.
Raising version of getTrainSamples/1
.
Raising version of getTrainSampleWeights/1
.
Raising version of getValues/4
.
Raising version of getVarIdx/1
.
Raising version of getVarSymbolFlags/1
.
Raising version of getVarType/1
.
Raising version of setTrainTestSplit/2
.
Raising version of setTrainTestSplit/3
.
Raising version of setTrainTestSplitRatio/2
.
Raising version of setTrainTestSplitRatio/3
.
Raising version of shuffleTrainTest/1
.