View Source Evision.ML.LogisticRegression (Evision v0.1.11)

Link to this section Summary

Types

t()

Type that represents an Evision.ML.LogisticRegression 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.

Return
  • retval: bool

Python prototype (for reference):

This function returns the trained parameters arranged across rows.

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 setIterations/2

Return
  • retval: double

@see setLearningRate/2

Return
  • retval: TermCriteria

@see setTermCriteria/2

Return
  • retval: int

@see setTrainMethod/2

Returns the number of variables in training samples

Returns true if the model is classifier

Returns true if the model is trained

Loads and creates a serialized LogisticRegression from a file

Loads and creates a serialized LogisticRegression from a file

Predicts responses for input samples and returns a float type.

Predicts responses for input samples and returns a float type.

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
  • val: int

@see getIterations/1

Positional Arguments
  • val: double

@see getLearningRate/1

Positional Arguments
  • val: int

@see getMiniBatchSize/1

Positional Arguments
  • val: int

@see getRegularization/1

Positional Arguments
  • val: TermCriteria

@see getTermCriteria/1

Positional Arguments
  • val: int

@see getTrainMethod/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.LogisticRegression{ref: reference()}

Type that represents an Evision.ML.LogisticRegression 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.

Return

Creates Logistic Regression model with parameters given.

Python prototype (for reference):

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

Python prototype (for reference):

empty() -> retval
@spec get_learnt_thetas(t()) :: Evision.Mat.t() | {:error, String.t()}

This function returns the trained parameters arranged across rows.

Return

For a two class classification problem, it returns a row matrix. It returns learnt parameters of the Logistic Regression as a matrix of type CV_32F.

Python prototype (for reference):

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

@see setIterations/2

Python prototype (for reference):

getIterations() -> retval
@spec getLearningRate(t()) :: number() | {:error, String.t()}
Return
  • retval: double

@see setLearningRate/2

Python prototype (for reference):

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

@see setMiniBatchSize/2

Python prototype (for reference):

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

@see setRegularization/2

Python prototype (for reference):

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

@see setTermCriteria/2

Python prototype (for reference):

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

@see setTrainMethod/2

Python prototype (for reference):

getTrainMethod() -> 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 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 LogisticRegression from a file

Positional Arguments
  • filepath: String.

    path to serialized LogisticRegression

Keyword Arguments
  • nodeName: String.

    name of node containing the classifier

Return

Use LogisticRegression::save to serialize and store an LogisticRegression to disk. Load the LogisticRegression 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 LogisticRegression from a file

Positional Arguments
  • filepath: String.

    path to serialized LogisticRegression

Keyword Arguments
  • nodeName: String.

    name of node containing the classifier

Return

Use LogisticRegression::save to serialize and store an LogisticRegression to disk. Load the LogisticRegression 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 responses for input samples and returns a float type.

Positional Arguments
  • samples: Evision.Mat.

    The input data for the prediction algorithm. Matrix [m x n], where each row contains variables (features) of one object being classified. Should have data type CV_32F.

Keyword Arguments
  • flags: int.

    Not used.

Return
  • retval: float

  • results: Evision.Mat.

    Predicted labels as a column matrix of type CV_32S.

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 responses for input samples and returns a float type.

Positional Arguments
  • samples: Evision.Mat.

    The input data for the prediction algorithm. Matrix [m x n], where each row contains variables (features) of one object being classified. Should have data type CV_32F.

Keyword Arguments
  • flags: int.

    Not used.

Return
  • retval: float

  • results: Evision.Mat.

    Predicted labels as a column matrix of type CV_32S.

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

setIterations(self, val)

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

@see getIterations/1

Python prototype (for reference):

setIterations(val) -> None
Link to this function

setLearningRate(self, val)

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

@see getLearningRate/1

Python prototype (for reference):

setLearningRate(val) -> None
Link to this function

setMiniBatchSize(self, val)

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

@see getMiniBatchSize/1

Python prototype (for reference):

setMiniBatchSize(val) -> None
Link to this function

setRegularization(self, val)

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

@see getRegularization/1

Python prototype (for reference):

setRegularization(val) -> 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
Link to this function

setTrainMethod(self, val)

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

@see getTrainMethod/1

Python prototype (for reference):

setTrainMethod(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