@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.StatModel.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.StatModel.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.StatModel.t()

Python prototype (for reference only):

clear() -> None

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

empty

Positional Arguments

• self: Evision.ML.StatModel.t()

Return

• retval: bool

Python prototype (for reference only):

empty() -> retval

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

getDefaultName

Positional Arguments

• self: Evision.ML.StatModel.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 getVarCount(t()) :: integer() | {:error, String.t()}

Returns the number of variables in training samples

Positional Arguments

• self: Evision.ML.StatModel.t()

Return

• retval: int

Python prototype (for reference only):

getVarCount() -> retval

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

Returns true if the model is classifier

Positional Arguments

• self: Evision.ML.StatModel.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.StatModel.t()

Return

• retval: bool

Python prototype (for reference only):

isTrained() -> 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.StatModel.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.StatModel.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.StatModel.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.StatModel.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 train(t(), Evision.ML.TrainData.t()) :: boolean() | {:error, String.t()}

Trains the statistical model

Positional Arguments

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

Has overloading in C++

Python prototype (for reference only):

write(fs, name) -> None