Modifier and Type	Class and Description
public static class	Gaussian.Parametric Parametric function where the input array contains the parameters of the Gaussian, ordered as follows: Norm Mean Standard deviation

Modifier and Type	Field and Description
private final double	i2s2 Inverse of twice the square of the standard deviation.
private final double	is Inverse of the standard deviation.
private final double	mean Mean.
private final double	norm Normalization factor.

Access	Constructor and Description
public	Gaussian(double Normalization factor. norm, double Mean. mean, double Standard deviation. sigma) Gaussian with given normalization factor, mean and standard deviation.
public	Gaussian(double Mean. mean, double Standard deviation. sigma) Normalized gaussian with given mean and standard deviation.
public	Gaussian() Normalized gaussian with zero mean and unit standard deviation.

Method Summary

Modifier and Type	Method and Description
public UnivariateFunction	derivative() Implements org.apache.commons.math3.analysis.DifferentiableUnivariateFunction.derivative. Deprecated as of 3.1, replaced by `value(DerivativeStructure)` Returns the derivative of the function
public double	value(double Point at which the function value should be computed. x) Implements org.apache.commons.math3.analysis.UnivariateFunction.value. Compute the value of the function.
private static double	Returns: the value of the Gaussian at `x`. value(double `x - mean`. xMinusMean, double Normalization factor. norm, double Inverse of twice the square of the standard deviation. i2s2)
public DerivativeStructure	value(final DerivativeStructure function input value t) Implements org.apache.commons.math3.analysis.differentiation.UnivariateDifferentiableFunction.value. Simple mathematical function.

Inherited from java.lang.Object:: clone equals finalize getClass hashCode notify notifyAll toString wait wait wait

i2s2	back to summary
private final double i2s2 Inverse of twice the square of the standard deviation.

is	back to summary
private final double is Inverse of the standard deviation.

mean	back to summary
private final double mean Mean.

norm	back to summary
private final double norm Normalization factor.

Constructor Detail

Gaussian back to summary

Gaussian	back to summary
public Gaussian(double norm, double mean, double sigma) throws NotStrictlyPositiveException Gaussian with given normalization factor, mean and standard deviation. Parameters norm:double Normalization factor. mean:double Mean. sigma:double Standard deviation. Exceptions NotStrictlyPositiveException: if `sigma <= 0`.

public Gaussian(double norm, double mean, double sigma) throws NotStrictlyPositiveException

Gaussian with given normalization factor, mean and standard deviation.

Parameters

norm:double: Normalization factor.
mean:double: Mean.
sigma:double: Standard deviation.

Exceptions

NotStrictlyPositiveException:: if sigma <= 0.

Gaussian back to summary

Gaussian	back to summary
public Gaussian(double mean, double sigma) throws NotStrictlyPositiveException Normalized gaussian with given mean and standard deviation. Parameters mean:double Mean. sigma:double Standard deviation. Exceptions NotStrictlyPositiveException: if `sigma <= 0`.

public Gaussian(double mean, double sigma) throws NotStrictlyPositiveException

Normalized gaussian with given mean and standard deviation.

Parameters

mean:double: Mean.
sigma:double: Standard deviation.

Exceptions

NotStrictlyPositiveException:: if sigma <= 0.

Gaussian	back to summary
public Gaussian() Normalized gaussian with zero mean and unit standard deviation.

Method Detail

derivative back to summary

derivative	back to summary
public UnivariateFunction derivative() Implements org.apache.commons.math3.analysis.DifferentiableUnivariateFunction.derivative. Doc from org.apache.commons.math3.analysis.DifferentiableUnivariateFunction.derivative. Deprecated as of 3.1, replaced by `value(DerivativeStructure)` Returns the derivative of the function Returns:UnivariateFunction the derivative function Annotations @Deprecated

public UnivariateFunction derivative()

Implements org.apache.commons.math3.analysis.DifferentiableUnivariateFunction.derivative.

Doc from org.apache.commons.math3.analysis.DifferentiableUnivariateFunction.derivative.

Deprecated

as of 3.1, replaced by value(DerivativeStructure)

Returns the derivative of the function

Returns:UnivariateFunction

the derivative function

Annotations

@Deprecated

value	back to summary
public double value(double x) Implements org.apache.commons.math3.analysis.UnivariateFunction.value. Doc from org.apache.commons.math3.analysis.UnivariateFunction.value. Compute the value of the function. Parameters x:double Point at which the function value should be computed. Returns:double the value of the function.

value

back to summary

public double value(double x)

Implements org.apache.commons.math3.analysis.UnivariateFunction.value.

Doc from org.apache.commons.math3.analysis.UnivariateFunction.value.

Compute the value of the function.

Parameters

x:double: Point at which the function value should be computed.

Returns:double

the value of the function.

value	back to summary
private static double value(double xMinusMean, double norm, double i2s2) Parameters xMinusMean:double `x - mean`. norm:double Normalization factor. i2s2:double Inverse of twice the square of the standard deviation. Returns:double the value of the Gaussian at `x`.

value back to summary

value	back to summary
public DerivativeStructure value(final DerivativeStructure t) throws DimensionMismatchException Implements org.apache.commons.math3.analysis.differentiation.UnivariateDifferentiableFunction.value. Doc from org.apache.commons.math3.analysis.differentiation.UnivariateDifferentiableFunction.value. Simple mathematical function. `UnivariateDifferentiableFunction` classes compute both the value and the first derivative of the function. Parameters t:DerivativeStructure function input value Returns:DerivativeStructure function result Exceptions DimensionMismatchException: if t is inconsistent with the function's free parameters or order Since 3.1

public DerivativeStructure value(final DerivativeStructure t) throws DimensionMismatchException

Implements org.apache.commons.math3.analysis.differentiation.UnivariateDifferentiableFunction.value.

Doc from org.apache.commons.math3.analysis.differentiation.UnivariateDifferentiableFunction.value.

Simple mathematical function.

UnivariateDifferentiableFunction classes compute both the value and the first derivative of the function.

Parameters

t:DerivativeStructure: function input value

Returns:DerivativeStructure

function result

Exceptions

DimensionMismatchException:: if t is inconsistent with the function's free parameters or order

Since

3.1

org.apache.commons.math3.analysis.function back to summary

public Class Gaussian.Parametric

Class Inheritance

extends Object
implements ParametricUnivariateFunction

java.lang.Object
org.apache.commons.math3.analysis.function.Gaussian.Parametric

All Implemented Interfaces

org.apache.commons.math3.analysis.ParametricUnivariateFunction

Parametric function where the input array contains the parameters of the Gaussian, ordered as follows:

Norm
Mean
Standard deviation

Constructor Summary

Access	Constructor and Description
public	Parametric()

Method Summary

Modifier and Type	Method and Description
public double[]	Returns: the gradient vector at `x`. gradient(double Value at which the gradient must be computed. x, double... Values of norm, mean and standard deviation. param) Implements org.apache.commons.math3.analysis.ParametricUnivariateFunction.gradient. Computes the value of the gradient at `x`.
private void	validateParameters(double[] Values of norm, mean and standard deviation. param) Validates parameters to ensure they are appropriate for the evaluation of the `value(double, double[])` and `gradient(double, double[])` methods.
public double	Returns: the value of the function. value(double Value for which the function must be computed. x, double... Values of norm, mean and standard deviation. param) Implements org.apache.commons.math3.analysis.ParametricUnivariateFunction.value. Computes the value of the Gaussian at `x`.

Inherited from java.lang.Object:: clone equals finalize getClass hashCode notify notifyAll toString wait wait wait

Constructor Detail

Parametric	back to summary
public Parametric()

Method Detail

gradient back to summary

gradient	back to summary
public double[] gradient(double x, double... param) throws NullArgumentException, DimensionMismatchException, NotStrictlyPositiveException Implements org.apache.commons.math3.analysis.ParametricUnivariateFunction.gradient. Computes the value of the gradient at `x`. The components of the gradient vector are the partial derivatives of the function with respect to each of the parameters (norm, mean and standard deviation). Parameters x:double Value at which the gradient must be computed. param:double[] Values of norm, mean and standard deviation. Returns:double[] the gradient vector at `x`. Exceptions NullArgumentException: if `param` is `null`. DimensionMismatchException: if the size of `param` is not 3. NotStrictlyPositiveException: if `param[2]` is negative.

public double[] gradient(double x, double... param) throws NullArgumentException, DimensionMismatchException, NotStrictlyPositiveException

Implements org.apache.commons.math3.analysis.ParametricUnivariateFunction.gradient.

Computes the value of the gradient at x. The components of the gradient vector are the partial derivatives of the function with respect to each of the parameters (norm, mean and standard deviation).

Parameters

x:double: Value at which the gradient must be computed.
param:double[]: Values of norm, mean and standard deviation.

Returns:double[]

the gradient vector at x.

Exceptions

NullArgumentException:: if param is null.
DimensionMismatchException:: if the size of param is not 3.
NotStrictlyPositiveException:: if param[2] is negative.

validateParameters back to summary

validateParameters	back to summary
private void validateParameters(double[] param) throws NullArgumentException, DimensionMismatchException, NotStrictlyPositiveException Validates parameters to ensure they are appropriate for the evaluation of the `value(double, double[])` and `gradient(double, double[])` methods. Parameters param:double[] Values of norm, mean and standard deviation. Exceptions NullArgumentException: if `param` is `null`. DimensionMismatchException: if the size of `param` is not 3. NotStrictlyPositiveException: if `param[2]` is negative.

private void validateParameters(double[] param) throws NullArgumentException, DimensionMismatchException, NotStrictlyPositiveException

Validates parameters to ensure they are appropriate for the evaluation of the value(double, double[]) and gradient(double, double[]) methods.

Parameters

param:double[]: Values of norm, mean and standard deviation.

Exceptions

NullArgumentException:: if param is null.
DimensionMismatchException:: if the size of param is not 3.
NotStrictlyPositiveException:: if param[2] is negative.

value back to summary

value	back to summary
public double value(double x, double... param) throws NullArgumentException, DimensionMismatchException, NotStrictlyPositiveException Implements org.apache.commons.math3.analysis.ParametricUnivariateFunction.value. Computes the value of the Gaussian at `x`. Parameters x:double Value for which the function must be computed. param:double[] Values of norm, mean and standard deviation. Returns:double the value of the function. Exceptions NullArgumentException: if `param` is `null`. DimensionMismatchException: if the size of `param` is not 3. NotStrictlyPositiveException: if `param[2]` is negative.

public double value(double x, double... param) throws NullArgumentException, DimensionMismatchException, NotStrictlyPositiveException

Implements org.apache.commons.math3.analysis.ParametricUnivariateFunction.value.

Computes the value of the Gaussian at x.

Parameters

x:double: Value for which the function must be computed.
param:double[]: Values of norm, mean and standard deviation.

Returns:double

the value of the function.

Exceptions

NullArgumentException:: if param is null.
DimensionMismatchException:: if the size of param is not 3.
NotStrictlyPositiveException:: if param[2] is negative.

public Class Gaussian

Nested and Inner Type Summary

Field Summary

Constructor Summary

Method Summary

Field Detail

Constructor Detail

Method Detail

public Class Gaussian.Parametric

Constructor Summary

Method Summary

Constructor Detail

Method Detail