@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.NormalBayesClassifier.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.NormalBayesClassifier.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(Keyword.t()) :: any() | {:error, String.t()}

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

Clears the algorithm state

Positional Arguments

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

Python prototype (for reference only):

clear() -> None

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

create

Return

• retval: Evision.ML.NormalBayesClassifier.t()

Creates empty model Use StatModel::train to train the model after creation.

Python prototype (for reference only):

create() -> retval

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

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

empty

Positional Arguments

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

Return

• retval: bool

Python prototype (for reference only):

empty() -> retval

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

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

getDefaultName

Positional Arguments

• self: Evision.ML.NormalBayesClassifier.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(Keyword.t()) :: any() | {:error, String.t()}

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

Returns the number of variables in training samples

Positional Arguments

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

Return

• retval: integer()

Python prototype (for reference only):

getVarCount() -> retval

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

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

Returns true if the model is classifier

Positional Arguments

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

Return

• retval: bool

Python prototype (for reference only):

isClassifier() -> retval

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

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

Returns true if the model is trained

Positional Arguments

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

Return

• retval: bool

Python prototype (for reference only):

isTrained() -> retval

@spec load(Keyword.t()) :: any() | {:error, String.t()}

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

Loads and creates a serialized NormalBayesClassifier from a file

Positional Arguments

• filepath: String.

  path to serialized NormalBayesClassifier

Keyword Arguments

• nodeName: String.

  name of node containing the classifier

Return

• retval: Evision.ML.NormalBayesClassifier.t()

Use NormalBayesClassifier::save to serialize and store an NormalBayesClassifier to disk. Load the NormalBayesClassifier 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(), [{:nodeName, term()}] | nil) :: t() | {:error, String.t()}

Loads and creates a serialized NormalBayesClassifier from a file

Positional Arguments

• filepath: String.

  path to serialized NormalBayesClassifier

Keyword Arguments

• nodeName: String.

  name of node containing the classifier

Return

• retval: Evision.ML.NormalBayesClassifier.t()

Use NormalBayesClassifier::save to serialize and store an NormalBayesClassifier to disk. Load the NormalBayesClassifier 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.NormalBayesClassifier.t()

• samples: Evision.Mat.

  The input samples, floating-point matrix

Keyword Arguments

• flags: integer().

  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(), [{:flags, term()}] | nil) ::
  {number(), Evision.Mat.t()} | {:error, String.t()}

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

Positional Arguments

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

• samples: Evision.Mat.

  The input samples, floating-point matrix

Keyword Arguments

• flags: integer().

  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 predictProb(t(), Evision.Mat.maybe_mat_in()) ::
  {number(), Evision.Mat.t(), Evision.Mat.t()} | {:error, String.t()}

Predicts the response for sample(s).

Positional Arguments

• self: Evision.ML.NormalBayesClassifier.t()
• inputs: Evision.Mat

Keyword Arguments

• flags: integer().

Return

• retval: float
• outputs: Evision.Mat.t().
• outputProbs: Evision.Mat.t().

The method estimates the most probable classes for input vectors. Input vectors (one or more) are stored as rows of the matrix inputs. In case of multiple input vectors, there should be one output vector outputs. The predicted class for a single input vector is returned by the method. The vector outputProbs contains the output probabilities corresponding to each element of result.

Python prototype (for reference only):

predictProb(inputs[, outputs[, outputProbs[, flags]]]) -> retval, outputs, outputProbs

@spec predictProb(t(), Evision.Mat.maybe_mat_in(), [{:flags, term()}] | nil) ::
  {number(), Evision.Mat.t(), Evision.Mat.t()} | {:error, String.t()}

Predicts the response for sample(s).

Positional Arguments

• self: Evision.ML.NormalBayesClassifier.t()
• inputs: Evision.Mat

Keyword Arguments

• flags: integer().

Return

• retval: float
• outputs: Evision.Mat.t().
• outputProbs: Evision.Mat.t().

The method estimates the most probable classes for input vectors. Input vectors (one or more) are stored as rows of the matrix inputs. In case of multiple input vectors, there should be one output vector outputs. The predicted class for a single input vector is returned by the method. The vector outputProbs contains the output probabilities corresponding to each element of result.

Python prototype (for reference only):

predictProb(inputs[, outputs[, outputProbs[, flags]]]) -> retval, outputs, outputProbs

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

Reads algorithm parameters from a file storage

Positional Arguments

• self: Evision.ML.NormalBayesClassifier.t()
• func: Evision.FileNode

Python prototype (for reference only):

read(fn) -> None

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

save

Positional Arguments

• self: Evision.ML.NormalBayesClassifier.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.NormalBayesClassifier.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: integer().

  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(), [{:flags, term()}] | nil) ::
  boolean() | {:error, String.t()}

Trains the statistical model

Positional Arguments

• self: Evision.ML.NormalBayesClassifier.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: integer().

  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.NormalBayesClassifier.t()

• samples: Evision.Mat.

  training samples

• layout: integer().

  See ml::SampleTypes.

• responses: Evision.Mat.

  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.NormalBayesClassifier.t()
• fs: Evision.FileStorage

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.NormalBayesClassifier.t()
• fs: Evision.FileStorage
• name: String

Has overloading in C++

Python prototype (for reference only):

write(fs, name) -> None