@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

• self: Evision.ML.SVMSGD.t()

• data: Evision.ML.TrainData.t().

  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.t().

  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 only):

calcError(data, test[, resp]) -> retval, resp

@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

• self: Evision.ML.SVMSGD.t()

• data: Evision.ML.TrainData.t().

  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.t().

  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 only):

calcError(data, test[, resp]) -> retval, resp

@spec clear(t()) :: t() | {:error, String.t()}

Clears the algorithm state

Positional Arguments

• self: Evision.ML.SVMSGD.t()

Python prototype (for reference only):

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

• retval: Evision.ML.SVMSGD.t()

Python prototype (for reference only):

create() -> retval

@spec empty(t()) :: boolean() | {:error, String.t()}

empty

Positional Arguments

• self: Evision.ML.SVMSGD.t()

Return

• retval: bool

Python prototype (for reference only):

empty() -> retval

@spec getDefaultName(t()) :: binary() | {:error, String.t()}

getDefaultName

Positional Arguments

• self: Evision.ML.SVMSGD.t()

Return

• retval: String

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 only):

getDefaultName() -> retval

@spec getInitialStepSize(t()) :: number() | {:error, String.t()}

getInitialStepSize

Positional Arguments

• self: Evision.ML.SVMSGD.t()

Return

• retval: float

@see setInitialStepSize/2

Python prototype (for reference only):

getInitialStepSize() -> retval

@spec getMarginRegularization(t()) :: number() | {:error, String.t()}

getMarginRegularization

Positional Arguments

• self: Evision.ML.SVMSGD.t()

Return

• retval: float

@see setMarginRegularization/2

Python prototype (for reference only):

getMarginRegularization() -> retval

@spec getMarginType(t()) :: integer() | {:error, String.t()}

getMarginType

Positional Arguments

• self: Evision.ML.SVMSGD.t()

Return

• retval: int

@see setMarginType/2

Python prototype (for reference only):

getMarginType() -> retval

@spec getShift(t()) :: number() | {:error, String.t()}

getShift

Positional Arguments

• self: Evision.ML.SVMSGD.t()

Return

• retval: float

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

Python prototype (for reference only):

getShift() -> retval

@spec getStepDecreasingPower(t()) :: number() | {:error, String.t()}

getStepDecreasingPower

Positional Arguments

• self: Evision.ML.SVMSGD.t()

Return

• retval: float

@see setStepDecreasingPower/2

Python prototype (for reference only):

getStepDecreasingPower() -> retval

@spec getSvmsgdType(t()) :: integer() | {:error, String.t()}

getSvmsgdType

Positional Arguments

• self: Evision.ML.SVMSGD.t()

Return

• retval: int

@see setSvmsgdType/2

Python prototype (for reference only):

getSvmsgdType() -> retval

@spec getTermCriteria(t()) :: {integer(), integer(), number()} | {:error, String.t()}

getTermCriteria

Positional Arguments

• self: Evision.ML.SVMSGD.t()

Return

• retval: TermCriteria

@see setTermCriteria/2

Python prototype (for reference only):

getTermCriteria() -> retval

@spec getVarCount(t()) :: integer() | {:error, String.t()}

Returns the number of variables in training samples

Positional Arguments

• self: Evision.ML.SVMSGD.t()

Return

• retval: int

Python prototype (for reference only):

getVarCount() -> retval

@spec getWeights(t()) :: Evision.Mat.t() | {:error, String.t()}

getWeights

Positional Arguments

• self: Evision.ML.SVMSGD.t()

Return

• retval: Evision.Mat.t()

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

Python prototype (for reference only):

getWeights() -> retval

@spec isClassifier(t()) :: boolean() | {:error, String.t()}

Returns true if the model is classifier

Positional Arguments

• self: Evision.ML.SVMSGD.t()

Return

• retval: bool

Python prototype (for reference only):

isClassifier() -> retval

@spec isTrained(t()) :: boolean() | {:error, String.t()}

Returns true if the model is trained

Positional Arguments

• self: Evision.ML.SVMSGD.t()

Return

• retval: bool

Python prototype (for reference only):

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

• retval: Evision.ML.SVMSGD.t()

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 only):

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

• retval: Evision.ML.SVMSGD.t()

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 only):

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

• self: Evision.ML.SVMSGD.t()

• samples: Evision.Mat.t().

  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.t().

  The optional output matrix of results.

Python prototype (for reference only):

predict(samples[, results[, flags]]) -> retval, results

@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

• self: Evision.ML.SVMSGD.t()

• samples: Evision.Mat.t().

  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.t().

  The optional output matrix of results.

Python prototype (for reference only):

predict(samples[, results[, flags]]) -> retval, results

@spec read(t(), Evision.FileNode.t()) :: t() | {:error, String.t()}

Reads algorithm parameters from a file storage

Positional Arguments

• self: Evision.ML.SVMSGD.t()
• fn_: Evision.FileNode.t()

Python prototype (for reference only):

read(fn_) -> None

@spec save(t(), binary()) :: t() | {:error, String.t()}

save

Positional Arguments

• self: Evision.ML.SVMSGD.t()
• filename: String

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 only):

save(filename) -> None

@spec setInitialStepSize(t(), number()) :: t() | {:error, String.t()}

setInitialStepSize

Positional Arguments

• self: Evision.ML.SVMSGD.t()
• initialStepSize: float

@see getInitialStepSize/1

Python prototype (for reference only):

setInitialStepSize(InitialStepSize) -> None

@spec setMarginRegularization(t(), number()) :: t() | {:error, String.t()}

setMarginRegularization

Positional Arguments

• self: Evision.ML.SVMSGD.t()
• marginRegularization: float

@see getMarginRegularization/1

Python prototype (for reference only):

setMarginRegularization(marginRegularization) -> None

@spec setMarginType(t(), integer()) :: t() | {:error, String.t()}

setMarginType

Positional Arguments

• self: Evision.ML.SVMSGD.t()
• marginType: int

@see getMarginType/1

Python prototype (for reference only):

setMarginType(marginType) -> None

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

Function sets optimal parameters values for chosen SVM SGD model.

Positional Arguments

• self: Evision.ML.SVMSGD.t()

Keyword Arguments

• svmsgdType: int.

  is the type of SVMSGD classifier.

• marginType: int.

  is the type of margin constraint.

Python prototype (for reference only):

setOptimalParameters([, svmsgdType[, marginType]]) -> None

@spec setOptimalParameters(t(), [{atom(), term()}, ...] | nil) ::
  t() | {:error, String.t()}

Function sets optimal parameters values for chosen SVM SGD model.

Positional Arguments

• self: Evision.ML.SVMSGD.t()

Keyword Arguments

• svmsgdType: int.

  is the type of SVMSGD classifier.

• marginType: int.

  is the type of margin constraint.

Python prototype (for reference only):

setOptimalParameters([, svmsgdType[, marginType]]) -> None

@spec setStepDecreasingPower(t(), number()) :: t() | {:error, String.t()}

setStepDecreasingPower

Positional Arguments

• self: Evision.ML.SVMSGD.t()
• stepDecreasingPower: float

@see getStepDecreasingPower/1

Python prototype (for reference only):

setStepDecreasingPower(stepDecreasingPower) -> None

@spec setSvmsgdType(t(), integer()) :: t() | {:error, String.t()}

setSvmsgdType

Positional Arguments

• self: Evision.ML.SVMSGD.t()
• svmsgdType: int

@see getSvmsgdType/1

Python prototype (for reference only):

setSvmsgdType(svmsgdType) -> None

@spec setTermCriteria(t(), {integer(), integer(), number()}) ::
  t() | {:error, String.t()}

setTermCriteria

Positional Arguments

• self: Evision.ML.SVMSGD.t()
• val: TermCriteria

@see getTermCriteria/1

Python prototype (for reference only):

setTermCriteria(val) -> None

@spec train(t(), Evision.ML.TrainData.t()) :: boolean() | {:error, String.t()}

Trains the statistical model

Positional Arguments

• self: Evision.ML.SVMSGD.t()

• trainData: Evision.ML.TrainData.t().

  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 only):

train(trainData[, flags]) -> retval

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

Trains the statistical model

Positional Arguments

• self: Evision.ML.SVMSGD.t()

• trainData: Evision.ML.TrainData.t().

  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 only):

train(trainData[, flags]) -> retval

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

Trains the statistical model

Positional Arguments

• self: Evision.ML.SVMSGD.t()

• samples: Evision.Mat.t().

  training samples

• layout: int.

  See ml::SampleTypes.

• responses: Evision.Mat.t().

  vector of responses associated with the training samples.

Return

• retval: bool

Python prototype (for reference only):

train(samples, layout, responses) -> retval

@spec write(t(), Evision.FileStorage.t()) :: t() | {:error, String.t()}

Stores algorithm parameters in a file storage

Positional Arguments

• self: Evision.ML.SVMSGD.t()
• fs: Evision.FileStorage.t()

Python prototype (for reference only):

write(fs) -> None

@spec write(t(), Evision.FileStorage.t(), binary()) :: t() | {:error, String.t()}

write

Positional Arguments

• self: Evision.ML.SVMSGD.t()
• fs: Evision.FileStorage.t()
• name: String

Has overloading in C++

Python prototype (for reference only):

write(fs, name) -> None