View Source Evision.ML.EM (Evision v0.1.15)

Link to this section Summary

Types

t()

Type that represents an Evision.ML.EM struct.

Functions

Computes error on the training or test dataset

Computes error on the training or test dataset

Clears the algorithm state

Return

Creates empty %EM model. The model should be trained then using StatModel::train(traindata, flags) method. Alternatively, you can use one of the EM::train* methods or load it from file using Algorithm::load\<EM>(filename).

Return
  • retval: bool

Python prototype (for reference):

Returns covariation matrices

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.

Returns the cluster centers (means of the Gaussian mixture)

Return
  • retval: TermCriteria

@see setTermCriteria/2

Returns the number of variables in training samples

Returns weights of the mixtures

Returns true if the model is classifier

Returns true if the model is trained

Loads and creates a serialized EM from a file

Loads and creates a serialized EM from a file

Returns a likelihood logarithm value and an index of the most probable mixture component for the given sample.

Returns a likelihood logarithm value and an index of the most probable mixture component for the given sample.

Returns posterior probabilities for the provided samples

Returns posterior probabilities for the provided samples

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 getClustersNumber/1

Positional Arguments
  • val: TermCriteria

@see getTermCriteria/1

Trains the statistical model

Trains the statistical model

Trains the statistical model

Estimate the Gaussian mixture parameters from a samples set.

Estimate the Gaussian mixture parameters from a samples set.

Estimate the Gaussian mixture parameters from a samples set.

Estimate the Gaussian mixture parameters from a samples set.

Estimate the Gaussian mixture parameters from a samples set.

Estimate the Gaussian mixture parameters from a samples set.

simplified API for language bindings

simplified API for language bindings

Link to this section Types

@type t() :: %Evision.ML.EM{ref: reference()}

Type that represents an Evision.ML.EM 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()}
Return

Creates empty %EM model. The model should be trained then using StatModel::train(traindata, flags) method. Alternatively, you can use one of the EM::train* methods or load it from file using Algorithm::load\<EM>(filename).

Python prototype (for reference):

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

Python prototype (for reference):

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

@see setClustersNumber/2

Python prototype (for reference):

getClustersNumber() -> retval
Link to this function

getCovarianceMatrixType(self)

View Source
@spec getCovarianceMatrixType(t()) :: integer() | {:error, String.t()}
Return
  • retval: int

@see setCovarianceMatrixType/2

Python prototype (for reference):

getCovarianceMatrixType() -> retval
@spec getCovs(t()) :: [Evision.Mat.t()] | {:error, String.t()}

Returns covariation matrices

Return
  • covs: [Evision.Mat].

Returns vector of covariation matrices. Number of matrices is the number of gaussian mixtures, each matrix is a square floating-point matrix NxN, where N is the space dimensionality.

Python prototype (for reference):

getCovs([, covs]) -> covs
@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 getMeans(t()) :: Evision.Mat.t() | {:error, String.t()}

Returns the cluster centers (means of the Gaussian mixture)

Return

Returns matrix with the number of rows equal to the number of mixtures and number of columns equal to the space dimensionality.

Python prototype (for reference):

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

@see setTermCriteria/2

Python prototype (for reference):

getTermCriteria() -> 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 getWeights(t()) :: Evision.Mat.t() | {:error, String.t()}

Returns weights of the mixtures

Return

Returns vector with the number of elements equal to the number of mixtures.

Python prototype (for reference):

getWeights() -> 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 EM from a file

Positional Arguments
  • filepath: String.

    path to serialized EM

Keyword Arguments
  • nodeName: String.

    name of node containing the classifier

Return

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

Positional Arguments
  • filepath: String.

    path to serialized EM

Keyword Arguments
  • nodeName: String.

    name of node containing the classifier

Return

Use EM::save to serialize and store an EM to disk. Load the EM 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 predict2(t(), Evision.Mat.maybe_mat_in()) ::
  {{number(), number()}, Evision.Mat.t()} | {:error, String.t()}

Returns a likelihood logarithm value and an index of the most probable mixture component for the given sample.

Positional Arguments
  • sample: Evision.Mat.

    A sample for classification. It should be a one-channel matrix of \f$1 \times dims\f$ or \f$dims \times 1\f$ size.

Return
  • retval: Vec2d

  • probs: Evision.Mat.

    Optional output matrix that contains posterior probabilities of each component given the sample. It has \f$1 \times nclusters\f$ size and CV_64FC1 type.

The method returns a two-element double vector. Zero element is a likelihood logarithm value for the sample. First element is an index of the most probable mixture component for the given sample.

Python prototype (for reference):

predict2(sample[, probs]) -> retval, probs
Link to this function

predict2(self, sample, opts)

View Source
@spec predict2(t(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) ::
  {{number(), number()}, Evision.Mat.t()} | {:error, String.t()}

Returns a likelihood logarithm value and an index of the most probable mixture component for the given sample.

Positional Arguments
  • sample: Evision.Mat.

    A sample for classification. It should be a one-channel matrix of \f$1 \times dims\f$ or \f$dims \times 1\f$ size.

Return
  • retval: Vec2d

  • probs: Evision.Mat.

    Optional output matrix that contains posterior probabilities of each component given the sample. It has \f$1 \times nclusters\f$ size and CV_64FC1 type.

The method returns a two-element double vector. Zero element is a likelihood logarithm value for the sample. First element is an index of the most probable mixture component for the given sample.

Python prototype (for reference):

predict2(sample[, probs]) -> retval, probs
@spec predict(t(), Evision.Mat.maybe_mat_in()) ::
  {number(), Evision.Mat.t()} | {:error, String.t()}

Returns posterior probabilities for the provided samples

Positional Arguments
  • samples: Evision.Mat.

    The input samples, floating-point matrix

Keyword Arguments
  • flags: int.

    This parameter will be ignored

Return
  • retval: float

  • results: Evision.Mat.

    The optional output \f$ nSamples \times nClusters\f$ matrix of results. It contains posterior probabilities for each sample from the input

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()}

Returns posterior probabilities for the provided samples

Positional Arguments
  • samples: Evision.Mat.

    The input samples, floating-point matrix

Keyword Arguments
  • flags: int.

    This parameter will be ignored

Return
  • retval: float

  • results: Evision.Mat.

    The optional output \f$ nSamples \times nClusters\f$ matrix of results. It contains posterior probabilities for each sample from the input

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

setClustersNumber(self, val)

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

@see getClustersNumber/1

Python prototype (for reference):

setClustersNumber(val) -> None
Link to this function

setCovarianceMatrixType(self, val)

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

@see getCovarianceMatrixType/1

Python prototype (for reference):

setCovarianceMatrixType(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
@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
Link to this function

trainE(self, samples, means0)

View Source
@spec trainE(t(), Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in()) ::
  {Evision.Mat.t(), Evision.Mat.t(), Evision.Mat.t()}
  | false
  | {:error, String.t()}

Estimate the Gaussian mixture parameters from a samples set.

Positional Arguments
  • samples: Evision.Mat.

    Samples from which the Gaussian mixture model will be estimated. It should be a one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type it will be converted to the inner matrix of such type for the further computing.

  • means0: Evision.Mat.

    Initial means \f$a_k\f$ of mixture components. It is a one-channel matrix of \f$nclusters \times dims\f$ size. If the matrix does not have CV_64F type it will be converted to the inner matrix of such type for the further computing.

Keyword Arguments
  • covs0: Evision.Mat.

    The vector of initial covariance matrices \f$S_k\f$ of mixture components. Each of covariance matrices is a one-channel matrix of \f$dims \times dims\f$ size. If the matrices do not have CV_64F type they will be converted to the inner matrices of such type for the further computing.

  • weights0: Evision.Mat.

    Initial weights \f$\pi_k\f$ of mixture components. It should be a one-channel floating-point matrix with \f$1 \times nclusters\f$ or \f$nclusters \times 1\f$ size.

Return
  • retval: bool

  • logLikelihoods: Evision.Mat.

    The optional output matrix that contains a likelihood logarithm value for each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type.

  • labels: Evision.Mat.

    The optional output "class label" for each sample: \f$\texttt{labels}i=\texttt{arg max}_k(p{i,k}), i=1..N\f$ (indices of the most probable mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type.

  • probs: Evision.Mat.

    The optional output matrix that contains posterior probabilities of each Gaussian mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and CV_64FC1 type.

This variation starts with Expectation step. You need to provide initial means \f$a_k\f$ of mixture components. Optionally you can pass initial weights \f$\pi_k\f$ and covariance matrices \f$S_k\f$ of mixture components.

Python prototype (for reference):

trainE(samples, means0[, covs0[, weights0[, logLikelihoods[, labels[, probs]]]]]) -> retval, logLikelihoods, labels, probs
Link to this function

trainE(self, samples, means0, opts)

View Source
@spec trainE(
  t(),
  Evision.Mat.maybe_mat_in(),
  Evision.Mat.maybe_mat_in(),
  [{atom(), term()}, ...] | nil
) ::
  {Evision.Mat.t(), Evision.Mat.t(), Evision.Mat.t()}
  | false
  | {:error, String.t()}

Estimate the Gaussian mixture parameters from a samples set.

Positional Arguments
  • samples: Evision.Mat.

    Samples from which the Gaussian mixture model will be estimated. It should be a one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type it will be converted to the inner matrix of such type for the further computing.

  • means0: Evision.Mat.

    Initial means \f$a_k\f$ of mixture components. It is a one-channel matrix of \f$nclusters \times dims\f$ size. If the matrix does not have CV_64F type it will be converted to the inner matrix of such type for the further computing.

Keyword Arguments
  • covs0: Evision.Mat.

    The vector of initial covariance matrices \f$S_k\f$ of mixture components. Each of covariance matrices is a one-channel matrix of \f$dims \times dims\f$ size. If the matrices do not have CV_64F type they will be converted to the inner matrices of such type for the further computing.

  • weights0: Evision.Mat.

    Initial weights \f$\pi_k\f$ of mixture components. It should be a one-channel floating-point matrix with \f$1 \times nclusters\f$ or \f$nclusters \times 1\f$ size.

Return
  • retval: bool

  • logLikelihoods: Evision.Mat.

    The optional output matrix that contains a likelihood logarithm value for each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type.

  • labels: Evision.Mat.

    The optional output "class label" for each sample: \f$\texttt{labels}i=\texttt{arg max}_k(p{i,k}), i=1..N\f$ (indices of the most probable mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type.

  • probs: Evision.Mat.

    The optional output matrix that contains posterior probabilities of each Gaussian mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and CV_64FC1 type.

This variation starts with Expectation step. You need to provide initial means \f$a_k\f$ of mixture components. Optionally you can pass initial weights \f$\pi_k\f$ and covariance matrices \f$S_k\f$ of mixture components.

Python prototype (for reference):

trainE(samples, means0[, covs0[, weights0[, logLikelihoods[, labels[, probs]]]]]) -> retval, logLikelihoods, labels, probs
@spec trainEM(t(), Evision.Mat.maybe_mat_in()) ::
  {Evision.Mat.t(), Evision.Mat.t(), Evision.Mat.t()}
  | false
  | {:error, String.t()}

Estimate the Gaussian mixture parameters from a samples set.

Positional Arguments
  • samples: Evision.Mat.

    Samples from which the Gaussian mixture model will be estimated. It should be a one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type it will be converted to the inner matrix of such type for the further computing.

Return
  • retval: bool

  • logLikelihoods: Evision.Mat.

    The optional output matrix that contains a likelihood logarithm value for each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type.

  • labels: Evision.Mat.

    The optional output "class label" for each sample: \f$\texttt{labels}i=\texttt{arg max}_k(p{i,k}), i=1..N\f$ (indices of the most probable mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type.

  • probs: Evision.Mat.

    The optional output matrix that contains posterior probabilities of each Gaussian mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and CV_64FC1 type.

This variation starts with Expectation step. Initial values of the model parameters will be estimated by the k-means algorithm. Unlike many of the ML models, %EM is an unsupervised learning algorithm and it does not take responses (class labels or function values) as input. Instead, it computes the Maximum Likelihood Estimate of the Gaussian mixture parameters from an input sample set, stores all the parameters inside the structure: \f$p_{i,k}\f$ in probs, \f$a_k\f$ in means , \f$S_k\f$ in covs[k], \f$\pi_k\f$ in weights , and optionally computes the output "class label" for each sample: \f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable mixture component for each sample). The trained model can be used further for prediction, just like any other classifier. The trained model is similar to the NormalBayesClassifier.

Python prototype (for reference):

trainEM(samples[, logLikelihoods[, labels[, probs]]]) -> retval, logLikelihoods, labels, probs
Link to this function

trainEM(self, samples, opts)

View Source
@spec trainEM(t(), Evision.Mat.maybe_mat_in(), [{atom(), term()}, ...] | nil) ::
  {Evision.Mat.t(), Evision.Mat.t(), Evision.Mat.t()}
  | false
  | {:error, String.t()}

Estimate the Gaussian mixture parameters from a samples set.

Positional Arguments
  • samples: Evision.Mat.

    Samples from which the Gaussian mixture model will be estimated. It should be a one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type it will be converted to the inner matrix of such type for the further computing.

Return
  • retval: bool

  • logLikelihoods: Evision.Mat.

    The optional output matrix that contains a likelihood logarithm value for each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type.

  • labels: Evision.Mat.

    The optional output "class label" for each sample: \f$\texttt{labels}i=\texttt{arg max}_k(p{i,k}), i=1..N\f$ (indices of the most probable mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type.

  • probs: Evision.Mat.

    The optional output matrix that contains posterior probabilities of each Gaussian mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and CV_64FC1 type.

This variation starts with Expectation step. Initial values of the model parameters will be estimated by the k-means algorithm. Unlike many of the ML models, %EM is an unsupervised learning algorithm and it does not take responses (class labels or function values) as input. Instead, it computes the Maximum Likelihood Estimate of the Gaussian mixture parameters from an input sample set, stores all the parameters inside the structure: \f$p_{i,k}\f$ in probs, \f$a_k\f$ in means , \f$S_k\f$ in covs[k], \f$\pi_k\f$ in weights , and optionally computes the output "class label" for each sample: \f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable mixture component for each sample). The trained model can be used further for prediction, just like any other classifier. The trained model is similar to the NormalBayesClassifier.

Python prototype (for reference):

trainEM(samples[, logLikelihoods[, labels[, probs]]]) -> retval, logLikelihoods, labels, probs
Link to this function

trainM(self, samples, probs0)

View Source
@spec trainM(t(), Evision.Mat.maybe_mat_in(), Evision.Mat.maybe_mat_in()) ::
  {Evision.Mat.t(), Evision.Mat.t(), Evision.Mat.t()}
  | false
  | {:error, String.t()}

Estimate the Gaussian mixture parameters from a samples set.

Positional Arguments
  • samples: Evision.Mat.

    Samples from which the Gaussian mixture model will be estimated. It should be a one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type it will be converted to the inner matrix of such type for the further computing.

  • probs0: Evision.Mat.

    the probabilities

Return
  • retval: bool

  • logLikelihoods: Evision.Mat.

    The optional output matrix that contains a likelihood logarithm value for each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type.

  • labels: Evision.Mat.

    The optional output "class label" for each sample: \f$\texttt{labels}i=\texttt{arg max}_k(p{i,k}), i=1..N\f$ (indices of the most probable mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type.

  • probs: Evision.Mat.

    The optional output matrix that contains posterior probabilities of each Gaussian mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and CV_64FC1 type.

This variation starts with Maximization step. You need to provide initial probabilities \f$p_{i,k}\f$ to use this option.

Python prototype (for reference):

trainM(samples, probs0[, logLikelihoods[, labels[, probs]]]) -> retval, logLikelihoods, labels, probs
Link to this function

trainM(self, samples, probs0, opts)

View Source
@spec trainM(
  t(),
  Evision.Mat.maybe_mat_in(),
  Evision.Mat.maybe_mat_in(),
  [{atom(), term()}, ...] | nil
) ::
  {Evision.Mat.t(), Evision.Mat.t(), Evision.Mat.t()}
  | false
  | {:error, String.t()}

Estimate the Gaussian mixture parameters from a samples set.

Positional Arguments
  • samples: Evision.Mat.

    Samples from which the Gaussian mixture model will be estimated. It should be a one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type it will be converted to the inner matrix of such type for the further computing.

  • probs0: Evision.Mat.

    the probabilities

Return
  • retval: bool

  • logLikelihoods: Evision.Mat.

    The optional output matrix that contains a likelihood logarithm value for each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type.

  • labels: Evision.Mat.

    The optional output "class label" for each sample: \f$\texttt{labels}i=\texttt{arg max}_k(p{i,k}), i=1..N\f$ (indices of the most probable mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type.

  • probs: Evision.Mat.

    The optional output matrix that contains posterior probabilities of each Gaussian mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and CV_64FC1 type.

This variation starts with Maximization step. You need to provide initial probabilities \f$p_{i,k}\f$ to use this option.

Python prototype (for reference):

trainM(samples, probs0[, logLikelihoods[, labels[, probs]]]) -> retval, logLikelihoods, labels, probs
@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