View Source Evision.ML.SVMSGD (Evision v0.1.14)

Link to this section Summary

Types

t()

Type that represents an Evision.ML.SVMSGD struct.

Functions

Computes error on the training or test dataset

Computes error on the training or test dataset

Clears the algorithm state

Creates empty model. Use StatModel::train to train the model. Since %SVMSGD has several parameters, you may want to find the best parameters for your problem or use setOptimalParameters() to set some default parameters.

Return
  • retval: bool

Python prototype (for reference):

Return

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

Return
  • retval: int

@see setMarginType/2

Return
  • retval: float

@return the shift of the trained model (decision function f(x) = weights * x + shift).

Return
  • retval: int

@see setSvmsgdType/2

Return
  • retval: TermCriteria

@see setTermCriteria/2

Returns the number of variables in training samples

Return

@return the weights of the trained model (decision function f(x) = weights * x + shift).

Returns true if the model is classifier

Returns true if the model is trained

Loads and creates a serialized SVMSGD from a file

Loads and creates a serialized SVMSGD from a file

Predicts response(s) for the provided sample(s)

Predicts response(s) for the provided sample(s)

Reads algorithm parameters from a file storage

Positional Arguments

Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

Positional Arguments
  • initialStepSize: float

@see getInitialStepSize/1

Positional Arguments
  • marginRegularization: float

@see getMarginRegularization/1

Positional Arguments
  • marginType: int

@see getMarginType/1

Function sets optimal parameters values for chosen SVM SGD model.

Positional Arguments
  • stepDecreasingPower: float

@see getStepDecreasingPower/1

Positional Arguments
  • svmsgdType: int

@see getSvmsgdType/1

Positional Arguments
  • val: TermCriteria

@see getTermCriteria/1

Trains the statistical model

Trains the statistical model

Trains the statistical model

simplified API for language bindings

simplified API for language bindings

Link to this section Types

@type t() :: %Evision.ML.SVMSGD{ref: reference()}

Type that represents an Evision.ML.SVMSGD struct.

  • ref. reference()

    The underlying erlang resource variable.

Link to this section Functions

Link to this function

calcError(self, data, test)

View Source
@spec calcError(t(), Evision.ML.TrainData.t(), boolean()) ::
  {number(), Evision.Mat.t()} | {:error, String.t()}

Computes error on the training or test dataset

Positional Arguments
  • data: Evision.ML.TrainData.

    the training data

  • test: bool.

    if true, the error is computed over the test subset of the data, otherwise it's computed over the training subset of the data. Please note that if you loaded a completely different dataset to evaluate already trained classifier, you will probably want not to set the test subset at all with TrainData::setTrainTestSplitRatio and specify test=false, so that the error is computed for the whole new set. Yes, this sounds a bit confusing.

Return
  • retval: float

  • resp: Evision.Mat.

    the optional output responses.

The method uses StatModel::predict to compute the error. For regression models the error is computed as RMS, for classifiers - as a percent of missclassified samples (0%-100%).

Python prototype (for reference):

calcError(data, test[, resp]) -> retval, resp
Link to this function

calcError(self, data, test, opts)

View Source
@spec calcError(
  t(),
  Evision.ML.TrainData.t(),
  boolean(),
  [{atom(), term()}, ...] | nil
) ::
  {number(), Evision.Mat.t()} | {:error, String.t()}

Computes error on the training or test dataset

Positional Arguments
  • data: Evision.ML.TrainData.

    the training data

  • test: bool.

    if true, the error is computed over the test subset of the data, otherwise it's computed over the training subset of the data. Please note that if you loaded a completely different dataset to evaluate already trained classifier, you will probably want not to set the test subset at all with TrainData::setTrainTestSplitRatio and specify test=false, so that the error is computed for the whole new set. Yes, this sounds a bit confusing.

Return
  • retval: float

  • resp: Evision.Mat.

    the optional output responses.

The method uses StatModel::predict to compute the error. For regression models the error is computed as RMS, for classifiers - as a percent of missclassified samples (0%-100%).

Python prototype (for reference):

calcError(data, test[, resp]) -> retval, resp
@spec clear(t()) :: :ok | {:error, String.t()}

Clears the algorithm state

Python prototype (for reference):

clear() -> None
@spec create() :: t() | {:error, String.t()}

Creates empty model. Use StatModel::train to train the model. Since %SVMSGD has several parameters, you may want to find the best parameters for your problem or use setOptimalParameters() to set some default parameters.

Return

Python prototype (for reference):

create() -> retval
@spec empty(t()) :: boolean() | {:error, String.t()}
Return
  • retval: bool

Python prototype (for reference):

empty() -> retval
@spec getDefaultName(t()) :: binary() | {:error, String.t()}
Return

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

Python prototype (for reference):

getDefaultName() -> retval
Link to this function

getInitialStepSize(self)

View Source
@spec getInitialStepSize(t()) :: number() | {:error, String.t()}
Return
  • retval: float

@see setInitialStepSize/2

Python prototype (for reference):

getInitialStepSize() -> retval
Link to this function

getMarginRegularization(self)

View Source
@spec getMarginRegularization(t()) :: number() | {:error, String.t()}
Return
  • retval: float

@see setMarginRegularization/2

Python prototype (for reference):

getMarginRegularization() -> retval
@spec getMarginType(t()) :: integer() | {:error, String.t()}
Return
  • retval: int

@see setMarginType/2

Python prototype (for reference):

getMarginType() -> retval
@spec getShift(t()) :: number() | {:error, String.t()}
Return
  • retval: float

@return the shift of the trained model (decision function f(x) = weights * x + shift).

Python prototype (for reference):

getShift() -> retval
Link to this function

getStepDecreasingPower(self)

View Source
@spec getStepDecreasingPower(t()) :: number() | {:error, String.t()}
Return
  • retval: float

@see setStepDecreasingPower/2

Python prototype (for reference):

getStepDecreasingPower() -> retval
@spec getSvmsgdType(t()) :: integer() | {:error, String.t()}
Return
  • retval: int

@see setSvmsgdType/2

Python prototype (for reference):

getSvmsgdType() -> retval
@spec getTermCriteria(t()) :: {integer(), integer(), number()} | {:error, String.t()}
Return
  • retval: TermCriteria

@see setTermCriteria/2

Python prototype (for reference):

getTermCriteria() -> retval
@spec getVarCount(t()) :: integer() | {:error, String.t()}

Returns the number of variables in training samples

Return
  • retval: int

Python prototype (for reference):

getVarCount() -> retval
@spec getWeights(t()) :: Evision.Mat.t() | {:error, String.t()}
Return

@return the weights of the trained model (decision function f(x) = weights * x + shift).

Python prototype (for reference):

getWeights() -> retval
@spec isClassifier(t()) :: boolean() | {:error, String.t()}

Returns true if the model is classifier

Return
  • retval: bool

Python prototype (for reference):

isClassifier() -> retval
@spec isTrained(t()) :: boolean() | {:error, String.t()}

Returns true if the model is trained

Return
  • retval: bool

Python prototype (for reference):

isTrained() -> retval
@spec load(binary()) :: t() | {:error, String.t()}

Loads and creates a serialized SVMSGD from a file

Positional Arguments
  • filepath: String.

    path to serialized SVMSGD

Keyword Arguments
  • nodeName: String.

    name of node containing the classifier

Return

Use SVMSGD::save to serialize and store an SVMSGD to disk. Load the SVMSGD from this file again, by calling this function with the path to the file. Optionally specify the node for the file containing the classifier

Python prototype (for reference):

load(filepath[, nodeName]) -> retval
@spec load(binary(), [{atom(), term()}, ...] | nil) :: t() | {:error, String.t()}

Loads and creates a serialized SVMSGD from a file

Positional Arguments
  • filepath: String.

    path to serialized SVMSGD

Keyword Arguments
  • nodeName: String.

    name of node containing the classifier

Return

Use SVMSGD::save to serialize and store an SVMSGD to disk. Load the SVMSGD from this file again, by calling this function with the path to the file. Optionally specify the node for the file containing the classifier

Python prototype (for reference):

load(filepath[, nodeName]) -> retval
@spec predict(t(), Evision.Mat.maybe_mat_in()) ::
  {number(), Evision.Mat.t()} | {:error, String.t()}

Predicts response(s) for the provided sample(s)

Positional Arguments
  • samples: Evision.Mat.

    The input samples, floating-point matrix

Keyword Arguments
  • flags: int.

    The optional flags, model-dependent. See cv::ml::StatModel::Flags.

Return
  • retval: float

  • results: Evision.Mat.

    The optional output matrix of results.

Python prototype (for reference):

predict(samples[, results[, flags]]) -> retval, results
Link to this function

predict(self, samples, opts)

View Source
@spec predict(t(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) ::
  {number(), Evision.Mat.t()} | {:error, String.t()}

Predicts response(s) for the provided sample(s)

Positional Arguments
  • samples: Evision.Mat.

    The input samples, floating-point matrix

Keyword Arguments
  • flags: int.

    The optional flags, model-dependent. See cv::ml::StatModel::Flags.

Return
  • retval: float

  • results: Evision.Mat.

    The optional output matrix of results.

Python prototype (for reference):

predict(samples[, results[, flags]]) -> retval, results
@spec read(t(), Evision.FileNode.t()) :: :ok | {:error, String.t()}

Reads algorithm parameters from a file storage

Positional Arguments

Python prototype (for reference):

read(fn_) -> None
@spec save(t(), binary()) :: :ok | {:error, String.t()}
Positional Arguments

Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

Python prototype (for reference):

save(filename) -> None
Link to this function

setInitialStepSize(self, initialStepSize)

View Source
@spec setInitialStepSize(t(), number()) :: :ok | {:error, String.t()}
Positional Arguments
  • initialStepSize: float

@see getInitialStepSize/1

Python prototype (for reference):

setInitialStepSize(InitialStepSize) -> None
Link to this function

setMarginRegularization(self, marginRegularization)

View Source
@spec setMarginRegularization(t(), number()) :: :ok | {:error, String.t()}
Positional Arguments
  • marginRegularization: float

@see getMarginRegularization/1

Python prototype (for reference):

setMarginRegularization(marginRegularization) -> None
Link to this function

setMarginType(self, marginType)

View Source
@spec setMarginType(t(), integer()) :: :ok | {:error, String.t()}
Positional Arguments
  • marginType: int

@see getMarginType/1

Python prototype (for reference):

setMarginType(marginType) -> None
Link to this function

setOptimalParameters(self)

View Source
@spec setOptimalParameters(t()) :: :ok | {:error, String.t()}

Function sets optimal parameters values for chosen SVM SGD model.

Keyword Arguments
  • svmsgdType: int.

    is the type of SVMSGD classifier.

  • marginType: int.

    is the type of margin constraint.

Python prototype (for reference):

setOptimalParameters([, svmsgdType[, marginType]]) -> None
Link to this function

setStepDecreasingPower(self, stepDecreasingPower)

View Source
@spec setStepDecreasingPower(t(), number()) :: :ok | {:error, String.t()}
Positional Arguments
  • stepDecreasingPower: float

@see getStepDecreasingPower/1

Python prototype (for reference):

setStepDecreasingPower(stepDecreasingPower) -> None
Link to this function

setSvmsgdType(self, svmsgdType)

View Source
@spec setSvmsgdType(t(), integer()) :: :ok | {:error, String.t()}
Positional Arguments
  • svmsgdType: int

@see getSvmsgdType/1

Python prototype (for reference):

setSvmsgdType(svmsgdType) -> None
Link to this function

setTermCriteria(self, val)

View Source
@spec setTermCriteria(t(), {integer(), integer(), number()}) ::
  :ok | {:error, String.t()}
Positional Arguments
  • val: TermCriteria

@see getTermCriteria/1

Python prototype (for reference):

setTermCriteria(val) -> None
@spec train(t(), Evision.ML.TrainData.t()) :: boolean() | {:error, String.t()}

Trains the statistical model

Positional Arguments
  • trainData: Evision.ML.TrainData.

    training data that can be loaded from file using TrainData::loadFromCSV or created with TrainData::create.

Keyword Arguments
  • flags: int.

    optional flags, depending on the model. Some of the models can be updated with the new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).

Return
  • retval: bool

Python prototype (for reference):

train(trainData[, flags]) -> retval
Link to this function

train(self, trainData, opts)

View Source
@spec train(t(), Evision.ML.TrainData.t(), [{atom(), term()}, ...] | nil) ::
  boolean() | {:error, String.t()}

Trains the statistical model

Positional Arguments
  • trainData: Evision.ML.TrainData.

    training data that can be loaded from file using TrainData::loadFromCSV or created with TrainData::create.

Keyword Arguments
  • flags: int.

    optional flags, depending on the model. Some of the models can be updated with the new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).

Return
  • retval: bool

Python prototype (for reference):

train(trainData[, flags]) -> retval
Link to this function

train(self, samples, layout, responses)

View Source
@spec train(t(), Evision.Mat.maybe_mat_in(), integer(), Evision.Mat.maybe_mat_in()) ::
  boolean() | {:error, String.t()}

Trains the statistical model

Positional Arguments
  • samples: Evision.Mat.

    training samples

  • layout: int.

    See ml::SampleTypes.

  • responses: Evision.Mat.

    vector of responses associated with the training samples.

Return
  • retval: bool

Python prototype (for reference):

train(samples, layout, responses) -> retval
@spec write(t(), Evision.FileStorage.t()) :: :ok | {:error, String.t()}

simplified API for language bindings

Positional Arguments
Keyword Arguments

Has overloading in C++

Python prototype (for reference):

write(fs[, name]) -> None
@spec write(t(), Evision.FileStorage.t(), [{atom(), term()}, ...] | nil) ::
  :ok | {:error, String.t()}

simplified API for language bindings

Positional Arguments
Keyword Arguments

Has overloading in C++

Python prototype (for reference):

write(fs[, name]) -> None