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OpenCV 2.4.3.1
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## org.opencv.ml
Class EM

    java.lang.Object
      org.opencv.core.Algorithm
          org.opencv.ml.EM

    public class EM

extends Algorithm

The class implements the EM algorithm as described in the beginning of this section. It is inherited from "Algorithm".

See Also:
  org.opencv.ml.EM : public Algorithm

Field Summary
`static int` | `COV_MAT_DEFAULT`
`static int` | `COV_MAT_DIAGONAL`
`static int` | `COV_MAT_GENERIC`
`static int` | `COV_MAT_SPHERICAL`
`static int` | `DEFAULT_MAX_ITERS`
`static int` | `DEFAULT_NCLUSTERS`
`static int` | `START_AUTO_STEP`
`static int` | `START_E_STEP`
`static int` | `START_M_STEP`

Fields inherited from class org.opencv.core.Algorithm
`nativeObj`

Constructor Summary
`` | `EM()` The constructor of the class
`` | `EM(int nclusters, int covMatType, TermCriteria termCrit)` The constructor of the class
`protected` | `EM(long addr)`

Method Summary
`void` | `clear()`
`protected void` | `finalize()`
`boolean` | `isTrained()`
`double[]` | `predict(Mat sample)` Returns a likelihood logarithm value and an index of the most probable mixture component for the given sample.
`double[]` | `predict(Mat sample, Mat probs)` Returns a likelihood logarithm value and an index of the most probable mixture component for the given sample.
`boolean` | `train(Mat samples)` Estimates the Gaussian mixture parameters from a samples set.
`boolean` | `train(Mat samples, Mat logLikelihoods, Mat labels, Mat probs)` Estimates the Gaussian mixture parameters from a samples set.
`boolean` | `trainE(Mat samples, Mat means0)`
`boolean` | `trainE(Mat samples, Mat means0, Mat covs0, Mat weights0, Mat logLikelihoods, Mat labels, Mat probs)`
`boolean` | `trainM(Mat samples, Mat probs0)`
`boolean` | `trainM(Mat samples, Mat probs0, Mat logLikelihoods, Mat labels, Mat probs)`

Methods inherited from class org.opencv.core.Algorithm
`getBool, getDouble, getInt, getMat, getMatVector, getString, paramHelp, paramType, setBool, setDouble, setInt, setMat, setMatVector, setString`

Methods inherited from class java.lang.Object
`clone, equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

Field Detail

### COV_MAT_DEFAULT

    public static final int COV_MAT_DEFAULT

See Also:
  Constant Field Values

### COV_MAT_DIAGONAL

    public static final int COV_MAT_DIAGONAL

See Also:
  Constant Field Values

### COV_MAT_GENERIC

    public static final int COV_MAT_GENERIC

See Also:
  Constant Field Values

### COV_MAT_SPHERICAL

    public static final int COV_MAT_SPHERICAL

See Also:
  Constant Field Values

### DEFAULT_MAX_ITERS

    public static final int DEFAULT_MAX_ITERS

See Also:
  Constant Field Values

### DEFAULT_NCLUSTERS

    public static final int DEFAULT_NCLUSTERS

See Also:
  Constant Field Values

### START_AUTO_STEP

    public static final int START_AUTO_STEP

See Also:
  Constant Field Values

### START_E_STEP

    public static final int START_E_STEP

See Also:
  Constant Field Values

### START_M_STEP

    public static final int START_M_STEP

See Also:
  Constant Field Values

Constructor Detail

### EM

    public EM()

  The constructor of the class

  See Also:
    org.opencv.ml.EM.EM

### EM

    public EM(int nclusters,
              int covMatType,
              TermCriteria termCrit)

  The constructor of the class

  Parameters:
    `nclusters` - The number of mixture components in the Gaussian mixture model. Default value of the parameter is `EM.DEFAULT_NCLUSTERS=5`. Some of EM implementation could determine the optimal number of mixtures within a specified value range, but that is not the case in ML yet.
    `covMatType` - Constraint on covariance matrices which defines type of matrices. Possible values are:
    - EM.COV_MAT_SPHERICAL A scaled identity matrix mu_k * I. There is the only parameter mu_k to be estimated for each matrix. The option may be used in special cases, when the constraint is relevant, or as a first step in the optimization (for example in case when the data is preprocessed with PCA). The results of such preliminary estimation may be passed again to the optimization procedure, this time with `covMatType=EM.COV_MAT_DIAGONAL`.
    - EM.COV_MAT_DIAGONAL A diagonal matrix with positive diagonal elements. The number of free parameters is `d` for each matrix. This is most commonly used option yielding good estimation results.
    - EM.COV_MAT_GENERIC A symmetric positively defined matrix. The number of free parameters in each matrix is about d^2/2. It is not recommended to use this option, unless there is pretty accurate initial estimation of the parameters and/or a huge number of training samples.
    `termCrit` - The termination criteria of the EM algorithm. The EM algorithm can be terminated by the number of iterations `termCrit.maxCount` (number of M-steps) or when relative change of likelihood logarithm is less than `termCrit.epsilon`. Default maximum number of iterations is `EM.DEFAULT_MAX_ITERS=100`.
  See Also:
    org.opencv.ml.EM.EM

### EM

    protected EM(long addr)

Method Detail

### clear

    public void clear()

### finalize

    protected void finalize()
                     throws java.lang.Throwable

  Overrides:
    `finalize` in class `Algorithm`
  Throws:
    `java.lang.Throwable`

### isTrained

    public boolean isTrained()

### predict

    public double[] predict(Mat sample)

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

  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.

  Parameters:
    `sample` - A sample for classification. It should be a one-channel matrix of 1 x dims or dims x 1 size.
  See Also:
    org.opencv.ml.EM.predict

### predict

    public double[] predict(Mat sample,
                            Mat probs)

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

  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.

  Parameters:
    `sample` - A sample for classification. It should be a one-channel matrix of 1 x dims or dims x 1 size.
    `probs` - Optional output matrix that contains posterior probabilities of each component given the sample. It has 1 x nclusters size and `CV_64FC1` type.
  See Also:
    org.opencv.ml.EM.predict

### train

    public boolean train(Mat samples)

  Estimates the Gaussian mixture parameters from a samples set.

  Three versions of training method differ in the initialization of Gaussian mixture model parameters and start step:

  - train - Starts with Expectation step. Initial values of the model parameters will be estimated by the k-means algorithm.
  - trainE - Starts with Expectation step. You need to provide initial means a_k of mixture components. Optionally you can pass initial weights pi_k and covariance matrices S_k of mixture components.
  - trainM - Starts with Maximization step. You need to provide initial probabilities p_(i,k) to use this option.

  The methods return `true` if the Gaussian mixture model was trained successfully, otherwise it returns `false`.

  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: p_(i,k) in `probs`, a_k in `means`, S_k in `covs[k]`, pi_k in `weights`, and optionally computes the output "class label" for each sample: labels_i=arg max_k(p_(i,k)), i=1..N (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 "CvNormalBayesClassifier".

  Parameters:
    `samples` - 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.
  See Also:
    org.opencv.ml.EM.train

### train

    public boolean train(Mat samples,
                         Mat logLikelihoods,
                         Mat labels,
                         Mat probs)

  Estimates the Gaussian mixture parameters from a samples set.

  Three versions of training method differ in the initialization of Gaussian mixture model parameters and start step:

  - train - Starts with Expectation step. Initial values of the model parameters will be estimated by the k-means algorithm.
  - trainE - Starts with Expectation step. You need to provide initial means a_k of mixture components. Optionally you can pass initial weights pi_k and covariance matrices S_k of mixture components.
  - trainM - Starts with Maximization step. You need to provide initial probabilities p_(i,k) to use this option.

  The methods return `true` if the Gaussian mixture model was trained successfully, otherwise it returns `false`.

  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: p_(i,k) in `probs`, a_k in `means`, S_k in `covs[k]`, pi_k in `weights`, and optionally computes the output "class label" for each sample: labels_i=arg max_k(p_(i,k)), i=1..N (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 "CvNormalBayesClassifier".

  Parameters:
    `samples` - 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.
    `logLikelihoods` - The optional output matrix that contains a likelihood logarithm value for each sample. It has nsamples x 1 size and `CV_64FC1` type.
    `labels` - The optional output "class label" for each sample: labels_i=arg max_k(p_(i,k)), i=1..N (indices of the most probable mixture component for each sample). It has nsamples x 1 size and `CV_32SC1` type.
    `probs` - The optional output matrix that contains posterior probabilities of each Gaussian mixture component given the each sample. It has nsamples x nclusters size and `CV_64FC1` type.
  See Also:
    org.opencv.ml.EM.train

### trainE

    public boolean trainE(Mat samples,
                          Mat means0)

### trainE

    public boolean trainE(Mat samples,
                          Mat means0,
                          Mat covs0,
                          Mat weights0,
                          Mat logLikelihoods,
                          Mat labels,
                          Mat probs)

### trainM

    public boolean trainM(Mat samples,
                          Mat probs0)

### trainM

    public boolean trainM(Mat samples,
                          Mat probs0,
                          Mat logLikelihoods,
                          Mat labels,
                          Mat probs)

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