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

Fields
Modifier and Type	Field and Description
`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`

Constructor Summary

Constructors
Constructor and Description

EM()
The constructor of the class

EM(int nclusters, int covMatType, TermCriteria termCrit)
The constructor of the class

Constructors
Constructor and Description
`EM()` The constructor of the class
`EM(int nclusters, int covMatType, TermCriteria termCrit)` The constructor of the class

Method Summary

Methods
Modifier and Type	Method and Description
`void`	`clear()`
`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
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
- Method Detail
  - clear
```
public void clear()
```
  - 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)
```

Class EM

Field Summary

Constructor Summary

Method Summary

Methods inherited from class org.opencv.core.Algorithm

Methods inherited from class java.lang.Object

Field Detail

COV_MAT_DEFAULT

COV_MAT_DIAGONAL

COV_MAT_GENERIC

COV_MAT_SPHERICAL

DEFAULT_MAX_ITERS

DEFAULT_NCLUSTERS

START_AUTO_STEP

START_E_STEP

START_M_STEP

Constructor Detail

EM

EM

Method Detail

clear

isTrained

predict

predict

train

train

trainE

trainE

trainM

trainM