View Source Evision.ML.TrainData (Evision v0.1.11)
Link to this section Summary
Types
Type that represents an Evision.ML.TrainData
struct.
Functions
Creates training data from in-memory arrays.
Creates training data from in-memory arrays.
Positional Arguments
- vi:
int
Return
- retval:
int
Python prototype (for reference):
Returns the vector of class labels
Return
- retval:
int
Python prototype (for reference):
Return
- retval:
int
Python prototype (for reference):
Returns vector of symbolic names captured in loadFromCSV()
Return
- retval:
int
Python prototype (for reference):
Return
- retval:
int
Python prototype (for reference):
Return
- retval:
int
Python prototype (for reference):
Return
- retval:
int
Python prototype (for reference):
Return
- retval:
int
Python prototype (for reference):
Extract from matrix rows/cols specified by passed indexes.
Extract from 1D vector elements specified by passed indexes.
Returns matrix of test samples
Returns the vector of normalized categorical responses
Returns the vector of responses
Returns matrix of train samples
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):
Link to this section Types
@type t() :: %Evision.ML.TrainData{ref: reference()}
Type that represents an Evision.ML.TrainData
struct.
ref.
reference()
The underlying erlang resource variable.
Link to this section Functions
@spec create(Evision.Mat.maybe_mat_in(), integer(), Evision.Mat.maybe_mat_in()) :: t() | {:error, String.t()}
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.
Return
- retval:
Evision.ML.TrainData
Python prototype (for reference):
create(samples, layout, responses[, varIdx[, sampleIdx[, sampleWeights[, varType]]]]) -> retval
@spec create( Evision.Mat.maybe_mat_in(), integer(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil ) :: t() | {:error, String.t()}
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.
Return
- retval:
Evision.ML.TrainData
Python prototype (for reference):
create(samples, layout, responses[, varIdx[, sampleIdx[, sampleWeights[, varType]]]]) -> retval
Positional Arguments
- vi:
int
Return
- retval:
int
Python prototype (for reference):
getCatCount(vi) -> retval
@spec getCatMap(t()) :: Evision.Mat.t() | {:error, String.t()}
Return
- retval:
Evision.Mat
Python prototype (for reference):
getCatMap() -> retval
@spec getCatOfs(t()) :: Evision.Mat.t() | {:error, String.t()}
Return
- retval:
Evision.Mat
Python prototype (for reference):
getCatOfs() -> retval
@spec getClassLabels(t()) :: Evision.Mat.t() | {:error, String.t()}
Returns the vector of class labels
Return
- retval:
Evision.Mat
The function returns vector of unique labels occurred in the responses.
Python prototype (for reference):
getClassLabels() -> retval
@spec getDefaultSubstValues(t()) :: Evision.Mat.t() | {:error, String.t()}
Return
- retval:
Evision.Mat
Python prototype (for reference):
getDefaultSubstValues() -> retval
Return
- retval:
int
Python prototype (for reference):
getLayout() -> retval
@spec getMissing(t()) :: Evision.Mat.t() | {:error, String.t()}
Return
- retval:
Evision.Mat
Python prototype (for reference):
getMissing() -> retval
Return
- retval:
int
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
@spec getNormCatResponses(t()) :: Evision.Mat.t() | {:error, String.t()}
Return
- retval:
Evision.Mat
Python prototype (for reference):
getNormCatResponses() -> retval
Return
- retval:
int
Python prototype (for reference):
getNSamples() -> retval
Return
- retval:
int
Python prototype (for reference):
getNTestSamples() -> retval
Return
- retval:
int
Python prototype (for reference):
getNTrainSamples() -> retval
Return
- retval:
int
Python prototype (for reference):
getNVars() -> retval
@spec getResponses(t()) :: Evision.Mat.t() | {:error, String.t()}
Return
- retval:
Evision.Mat
Python prototype (for reference):
getResponses() -> retval
Return
- retval:
int
Python prototype (for reference):
getResponseType() -> retval
@spec getSample(t(), Evision.Mat.maybe_mat_in(), integer(), number()) :: :ok | {:error, String.t()}
Positional Arguments
- varIdx:
Evision.Mat
- sidx:
int
- buf:
float*
Python prototype (for reference):
getSample(varIdx, sidx, buf) -> None
@spec getSamples(t()) :: Evision.Mat.t() | {:error, String.t()}
Return
- retval:
Evision.Mat
Python prototype (for reference):
getSamples() -> retval
@spec getSampleWeights(t()) :: Evision.Mat.t() | {:error, String.t()}
Return
- retval:
Evision.Mat
Python prototype (for reference):
getSampleWeights() -> retval
@spec getSubMatrix(Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in(), integer()) :: Evision.Mat.t() | {:error, String.t()}
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)
Return
- retval:
Evision.Mat
Python prototype (for reference):
getSubMatrix(matrix, idx, layout) -> retval
@spec getSubVector(Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in()) :: Evision.Mat.t() | {:error, String.t()}
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
Return
- retval:
Evision.Mat
Python prototype (for reference):
getSubVector(vec, idx) -> retval
@spec getTestNormCatResponses(t()) :: Evision.Mat.t() | {:error, String.t()}
Return
- retval:
Evision.Mat
Python prototype (for reference):
getTestNormCatResponses() -> retval
@spec getTestResponses(t()) :: Evision.Mat.t() | {:error, String.t()}
Return
- retval:
Evision.Mat
Python prototype (for reference):
getTestResponses() -> retval
@spec getTestSampleIdx(t()) :: Evision.Mat.t() | {:error, String.t()}
Return
- retval:
Evision.Mat
Python prototype (for reference):
getTestSampleIdx() -> retval
@spec getTestSamples(t()) :: Evision.Mat.t() | {:error, String.t()}
Returns matrix of test samples
Return
- retval:
Evision.Mat
Python prototype (for reference):
getTestSamples() -> retval
@spec getTestSampleWeights(t()) :: Evision.Mat.t() | {:error, String.t()}
Return
- retval:
Evision.Mat
Python prototype (for reference):
getTestSampleWeights() -> retval
@spec getTrainNormCatResponses(t()) :: Evision.Mat.t() | {:error, String.t()}
Returns the vector of normalized categorical responses
Return
- retval:
Evision.Mat
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
@spec getTrainResponses(t()) :: Evision.Mat.t() | {:error, String.t()}
Returns the vector of responses
Return
- retval:
Evision.Mat
The function returns ordered or the original categorical responses. Usually it's used in regression algorithms.
Python prototype (for reference):
getTrainResponses() -> retval
@spec getTrainSampleIdx(t()) :: Evision.Mat.t() | {:error, String.t()}
Return
- retval:
Evision.Mat
Python prototype (for reference):
getTrainSampleIdx() -> retval
@spec getTrainSamples(t()) :: Evision.Mat.t() | {:error, String.t()}
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.
Return
- retval:
Evision.Mat
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
@spec getTrainSampleWeights(t()) :: Evision.Mat.t() | {:error, String.t()}
Return
- retval:
Evision.Mat
Python prototype (for reference):
getTrainSampleWeights() -> retval
@spec getValues(t(), integer(), Evision.Mat.maybe_mat_in(), number()) :: :ok | {:error, String.t()}
Positional Arguments
- vi:
int
- sidx:
Evision.Mat
- values:
float*
Python prototype (for reference):
getValues(vi, sidx, values) -> None
@spec getVarIdx(t()) :: Evision.Mat.t() | {:error, String.t()}
Return
- retval:
Evision.Mat
Python prototype (for reference):
getVarIdx() -> retval
@spec getVarSymbolFlags(t()) :: Evision.Mat.t() | {:error, String.t()}
Return
- retval:
Evision.Mat
Python prototype (for reference):
getVarSymbolFlags() -> retval
@spec getVarType(t()) :: Evision.Mat.t() | {:error, String.t()}
Return
- retval:
Evision.Mat
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
@spec setTrainTestSplit(t(), integer(), [{atom(), term()}, ...] | nil) :: :ok | {:error, String.t()}
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
@spec setTrainTestSplitRatio(t(), number(), [{atom(), term()}, ...] | nil) :: :ok | {:error, String.t()}
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