UnivariateMixtureEstimator.MM

java.lang.Object
- weka.estimators.UnivariateMixtureEstimator.MM

Enclosing class:

UnivariateMixtureEstimator
```
public class UnivariateMixtureEstimator.MM
extends java.lang.Object
```
Fast univariate mixture model implementation.

Constructor Summary

Constructors
Constructor Description

MM()

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`void`	`initializeModel(int K, double[] values, double[] weights, java.util.Random r)`	Initializes the model.
`double`	`logDensity(double value)`	Computes log of density for given value.
`double`	`loglikelihood(double[] values, double[] weights)`	Computes loglikelihood of current model.
`double`	`MSE()`	Returns average of squared errors for current model.
`double[][]`	`predictIntervals(double conf)`	Returns the interval for the given confidence value.
`double`	`predictQuantile(double percentage)`	Returns the quantile for the given percentage.
`java.lang.String`	`toString()`	Returns string describing the estimator.

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

- Constructor Detail
  - MM
```
public MM()
```
- Method Detail
  - toString
```
public java.lang.String toString()
```
    Returns string describing the estimator.
    
    Overrides:
    
    toString in class java.lang.Object
  - initializeModel
```
public void initializeModel(int K,
                            double[] values,
                            double[] weights,
                            java.util.Random r)
```
    Initializes the model. Assumes K >= 1, values.length >= 1, and values.length = weights.length.
  - loglikelihood
```
public double loglikelihood(double[] values,
                            double[] weights)
```
    Computes loglikelihood of current model.
  - MSE
```
public double MSE()
```
    Returns average of squared errors for current model.
  - logDensity
```
public double logDensity(double value)
```
    Computes log of density for given value.
  - predictIntervals
```
public double[][] predictIntervals(double conf)
```
    Returns the interval for the given confidence value.
    
    Parameters:
    
    conf - the confidence value in the interval [0, 1]
    
    Returns:
    
    the interval
  - predictQuantile
```
public double predictQuantile(double percentage)
```
    Returns the quantile for the given percentage.
    
    Parameters:
    
    percentage - the percentage
    
    Returns:
    
    the quantile