View Source Evision.ML.RTrees (Evision v0.1.21)

Link to this section Summary

Types

t()

Type that represents an Evision.ML.RTrees struct.

Functions

Computes error on the training or test dataset

Computes error on the training or test dataset

Clears the algorithm state

create

empty

getActiveVarCount

getCalculateVarImportance

getCVFolds

getDefaultName

getMaxCategories

getMaxDepth

getMinSampleCount

getOOBError

getPriors

getRegressionAccuracy

getTermCriteria

getTruncatePrunedTree

getUse1SERule

getUseSurrogates

Returns the number of variables in training samples

getVarImportance

Returns true if the model is classifier

Returns true if the model is trained

Loads and creates a serialized RTree from a file

Loads and creates a serialized RTree 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

setActiveVarCount

setCalculateVarImportance

setMaxCategories

setMinSampleCount

setRegressionAccuracy

setTermCriteria

setTruncatePrunedTree

setUse1SERule

setUseSurrogates

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.RTrees{ref: reference()}

Type that represents an Evision.ML.RTrees struct.

  • ref. reference()

    The underlying erlang resource variable.

Link to this section Functions

Link to this function

calcError(self, data, test)

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@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.RTrees.t()

  • 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 only):

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

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@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.RTrees.t()

  • 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 only):

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

Clears the algorithm state

Positional Arguments
  • self: Evision.ML.RTrees.t()

Python prototype (for reference only):

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

create

Return

Creates the empty model. Use StatModel::train to train the model, StatModel::train to create and train the model, Algorithm::load to load the pre-trained model.

Python prototype (for reference only):

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

empty

Positional Arguments
  • self: Evision.ML.RTrees.t()
Return
  • retval: bool

Python prototype (for reference only):

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

getActiveVarCount

Positional Arguments
  • self: Evision.ML.RTrees.t()
Return
  • retval: int

@see setActiveVarCount/2

Python prototype (for reference only):

getActiveVarCount() -> retval
Link to this function

getCalculateVarImportance(self)

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

getCalculateVarImportance

Positional Arguments
  • self: Evision.ML.RTrees.t()
Return
  • retval: bool

@see setCalculateVarImportance/2

Python prototype (for reference only):

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

getCVFolds

Positional Arguments
  • self: Evision.ML.RTrees.t()
Return
  • retval: int

@see setCVFolds/2

Python prototype (for reference only):

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

getDefaultName

Positional Arguments
  • self: Evision.ML.RTrees.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 only):

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

getMaxCategories

Positional Arguments
  • self: Evision.ML.RTrees.t()
Return
  • retval: int

@see setMaxCategories/2

Python prototype (for reference only):

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

getMaxDepth

Positional Arguments
  • self: Evision.ML.RTrees.t()
Return
  • retval: int

@see setMaxDepth/2

Python prototype (for reference only):

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

getMinSampleCount

Positional Arguments
  • self: Evision.ML.RTrees.t()
Return
  • retval: int

@see setMinSampleCount/2

Python prototype (for reference only):

getMinSampleCount() -> retval
@spec getOOBError(t()) :: number() | {:error, String.t()}

getOOBError

Positional Arguments
  • self: Evision.ML.RTrees.t()
Return
  • retval: double

Returns the OOB error value, computed at the training stage when calcOOBError is set to true. If this flag was set to false, 0 is returned. The OOB error is also scaled by sample weighting.

Python prototype (for reference only):

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

getPriors

Positional Arguments
  • self: Evision.ML.RTrees.t()
Return
  • retval: cv::Mat

@see setPriors/2

Python prototype (for reference only):

getPriors() -> retval
Link to this function

getRegressionAccuracy(self)

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

getRegressionAccuracy

Positional Arguments
  • self: Evision.ML.RTrees.t()
Return
  • retval: float

@see setRegressionAccuracy/2

Python prototype (for reference only):

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

getTermCriteria

Positional Arguments
  • self: Evision.ML.RTrees.t()
Return
  • retval: TermCriteria

@see setTermCriteria/2

Python prototype (for reference only):

getTermCriteria() -> retval
Link to this function

getTruncatePrunedTree(self)

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

getTruncatePrunedTree

Positional Arguments
  • self: Evision.ML.RTrees.t()
Return
  • retval: bool

@see setTruncatePrunedTree/2

Python prototype (for reference only):

getTruncatePrunedTree() -> retval
@spec getUse1SERule(t()) :: boolean() | {:error, String.t()}

getUse1SERule

Positional Arguments
  • self: Evision.ML.RTrees.t()
Return
  • retval: bool

@see setUse1SERule/2

Python prototype (for reference only):

getUse1SERule() -> retval
@spec getUseSurrogates(t()) :: boolean() | {:error, String.t()}

getUseSurrogates

Positional Arguments
  • self: Evision.ML.RTrees.t()
Return
  • retval: bool

@see setUseSurrogates/2

Python prototype (for reference only):

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

Returns the number of variables in training samples

Positional Arguments
  • self: Evision.ML.RTrees.t()
Return
  • retval: int

Python prototype (for reference only):

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

getVarImportance

Positional Arguments
  • self: Evision.ML.RTrees.t()
Return

Returns the variable importance array. The method returns the variable importance vector, computed at the training stage when CalculateVarImportance is set to true. If this flag was set to false, the empty matrix is returned.

Python prototype (for reference only):

getVarImportance() -> retval
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getVotes(self, samples, flags)

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

getVotes

Positional Arguments
  • self: Evision.ML.RTrees.t()

  • samples: Evision.Mat.

    Array containing the samples for which votes will be calculated.

  • flags: int.

    Flags for defining the type of RTrees.

Return
  • results: Evision.Mat.

    Array where the result of the calculation will be written.

Returns the result of each individual tree in the forest. In case the model is a regression problem, the method will return each of the trees' results for each of the sample cases. If the model is a classifier, it will return a Mat with samples + 1 rows, where the first row gives the class number and the following rows return the votes each class had for each sample.

Python prototype (for reference only):

getVotes(samples, flags[, results]) -> results
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getVotes(self, samples, flags, opts)

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@spec getVotes(
  t(),
  Evision.Mat.maybe_mat_in(),
  integer(),
  [{atom(), term()}, ...] | nil
) ::
  Evision.Mat.t() | {:error, String.t()}

getVotes

Positional Arguments
  • self: Evision.ML.RTrees.t()

  • samples: Evision.Mat.

    Array containing the samples for which votes will be calculated.

  • flags: int.

    Flags for defining the type of RTrees.

Return
  • results: Evision.Mat.

    Array where the result of the calculation will be written.

Returns the result of each individual tree in the forest. In case the model is a regression problem, the method will return each of the trees' results for each of the sample cases. If the model is a classifier, it will return a Mat with samples + 1 rows, where the first row gives the class number and the following rows return the votes each class had for each sample.

Python prototype (for reference only):

getVotes(samples, flags[, results]) -> results
@spec isClassifier(t()) :: boolean() | {:error, String.t()}

Returns true if the model is classifier

Positional Arguments
  • self: Evision.ML.RTrees.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.RTrees.t()
Return
  • retval: bool

Python prototype (for reference only):

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

Loads and creates a serialized RTree from a file

Positional Arguments
  • filepath: String.

    path to serialized RTree

Keyword Arguments
  • nodeName: String.

    name of node containing the classifier

Return

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

Positional Arguments
  • filepath: String.

    path to serialized RTree

Keyword Arguments
  • nodeName: String.

    name of node containing the classifier

Return

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

  • 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 only):

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

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@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.RTrees.t()

  • 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 only):

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 only):

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

save

Positional Arguments
  • self: Evision.ML.RTrees.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
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setActiveVarCount(self, val)

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

setActiveVarCount

Positional Arguments
  • self: Evision.ML.RTrees.t()
  • val: int

@see getActiveVarCount/1

Python prototype (for reference only):

setActiveVarCount(val) -> None
Link to this function

setCalculateVarImportance(self, val)

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

setCalculateVarImportance

Positional Arguments
  • self: Evision.ML.RTrees.t()
  • val: bool

@see getCalculateVarImportance/1

Python prototype (for reference only):

setCalculateVarImportance(val) -> None
@spec setCVFolds(t(), integer()) :: :ok | {:error, String.t()}

setCVFolds

Positional Arguments
  • self: Evision.ML.RTrees.t()
  • val: int

@see getCVFolds/1

Python prototype (for reference only):

setCVFolds(val) -> None
Link to this function

setMaxCategories(self, val)

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

setMaxCategories

Positional Arguments
  • self: Evision.ML.RTrees.t()
  • val: int

@see getMaxCategories/1

Python prototype (for reference only):

setMaxCategories(val) -> None
@spec setMaxDepth(t(), integer()) :: :ok | {:error, String.t()}

setMaxDepth

Positional Arguments
  • self: Evision.ML.RTrees.t()
  • val: int

@see getMaxDepth/1

Python prototype (for reference only):

setMaxDepth(val) -> None
Link to this function

setMinSampleCount(self, val)

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

setMinSampleCount

Positional Arguments
  • self: Evision.ML.RTrees.t()
  • val: int

@see getMinSampleCount/1

Python prototype (for reference only):

setMinSampleCount(val) -> None
@spec setPriors(t(), Evision.Mat.maybe_mat_in()) :: :ok | {:error, String.t()}

setPriors

Positional Arguments

@see getPriors/1

Python prototype (for reference only):

setPriors(val) -> None
Link to this function

setRegressionAccuracy(self, val)

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

setRegressionAccuracy

Positional Arguments
  • self: Evision.ML.RTrees.t()
  • val: float

@see getRegressionAccuracy/1

Python prototype (for reference only):

setRegressionAccuracy(val) -> None
Link to this function

setTermCriteria(self, val)

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

setTermCriteria

Positional Arguments
  • self: Evision.ML.RTrees.t()
  • val: TermCriteria

@see getTermCriteria/1

Python prototype (for reference only):

setTermCriteria(val) -> None
Link to this function

setTruncatePrunedTree(self, val)

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

setTruncatePrunedTree

Positional Arguments
  • self: Evision.ML.RTrees.t()
  • val: bool

@see getTruncatePrunedTree/1

Python prototype (for reference only):

setTruncatePrunedTree(val) -> None
Link to this function

setUse1SERule(self, val)

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@spec setUse1SERule(t(), boolean()) :: :ok | {:error, String.t()}

setUse1SERule

Positional Arguments
  • self: Evision.ML.RTrees.t()
  • val: bool

@see getUse1SERule/1

Python prototype (for reference only):

setUse1SERule(val) -> None
Link to this function

setUseSurrogates(self, val)

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

setUseSurrogates

Positional Arguments
  • self: Evision.ML.RTrees.t()
  • val: bool

@see getUseSurrogates/1

Python prototype (for reference only):

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

Trains the statistical model

Positional Arguments
  • self: Evision.ML.RTrees.t()

  • 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 only):

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

train(self, trainData, opts)

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

Trains the statistical model

Positional Arguments
  • self: Evision.ML.RTrees.t()

  • 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 only):

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

train(self, samples, layout, responses)

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@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.RTrees.t()

  • 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 only):

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 only):

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 only):

write(fs[, name]) -> None