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java.lang.Objectedu.umd.cloud9.example.clustering.UnivariateGaussianMixtureModel
public class UnivariateGaussianMixtureModel
| Field Summary | |
|---|---|
PVector[] |
param
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int[] |
pos
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int |
size
|
double[] |
weight
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| Constructor Summary | |
|---|---|
UnivariateGaussianMixtureModel()
Class constructor. |
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UnivariateGaussianMixtureModel(int n)
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| Method Summary | |
|---|---|
UnivariateGaussianMixtureModel |
clone()
Creates and returns a copy of the instance. |
double |
density(Point x)
Computes the density value of this mixture value. |
static double |
densityOfGaussian(Point x,
PVector param)
Computes the density value \f$ f(x;\mu,\sigma^2) \f$. |
static Point |
drawRandomPointFromGaussian(PVector L)
Draws a point from the considered distribution. |
Point[] |
drawRandomPoints(int m)
Draws points from the considered mixture model. |
int |
getDimension()
Return the dimension of the parameters of the mixture model. |
void |
normalizeWeights()
Normalizes the weights of the mixture models \f$ \alpha_i \f$ such as \f$ \sum_{i=1}^n \alpha_i = 1 \f$. |
void |
setSize(int n)
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String |
toString()
Method toString. |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
| Field Detail |
|---|
public int size
public double[] weight
public int[] pos
public PVector[] param
| Constructor Detail |
|---|
public UnivariateGaussianMixtureModel()
n - number of components in the mixture modelpublic UnivariateGaussianMixtureModel(int n)
| Method Detail |
|---|
public void setSize(int n)
public double density(Point x)
x - a point
public void normalizeWeights()
public String toString()
toString in class Objectpublic UnivariateGaussianMixtureModel clone()
clone in class Objectpublic int getDimension()
public Point[] drawRandomPoints(int m)
m - number of points to draw
public static double densityOfGaussian(Point x,
PVector param)
x - pointparam - parameters (source, natural, or expectation)
public static Point drawRandomPointFromGaussian(PVector L)
L - source parameters \f$ \mathbf{\Lambda} = ( \mu , \sigma^2 )\f$
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