gstools.normalizer.Normalizer¶

class gstools.normalizer.Normalizer(data=None, **parameter)[source]¶

Bases: object

Normalizer class.

Parameters

data (array_like, optional) – Input data to fit the transformation to in order to gain normality. The default is None.
**parameter – Specified parameters given by name. If not given, default parameters will be used.

Attributes

name: str: The name of the normalizer class.

Methods

`denormalize`(data)	Transform to input distribution.
`derivative`(data)	Factor for normal PDF to gain target PDF.
`fit`(data[, skip])	Fitting the transformation to data by maximizing Log-Likelihood.
`kernel_loglikelihood`(data)	Kernel Log-Likelihood for given data with current parameters.
`likelihood`(data)	Likelihood for given data with current parameters.
`loglikelihood`(data)	Log-Likelihood for given data with current parameters.
`normalize`(data)	Transform to normal distribution.

denormalize(data)[source]¶

Transform to input distribution.

Parameters: data (array_like) – Input data (normal distributed).
Returns: Denormalized data.
Return type: numpy.ndarray

derivative(data)[source]¶

Factor for normal PDF to gain target PDF.

Parameters: data (array_like) – Input data (not normal distributed).
Returns: Derivative of the normalization transformation function.
Return type: numpy.ndarray

fit(data, skip=None, **kwargs)[source]¶

Fitting the transformation to data by maximizing Log-Likelihood.

Parameters

data (array_like) – Input data to fit the transformation to in order to gain normality.
skip (list of str or None, optional) – Names of parameters to be skiped in fitting. The default is None.
**kwargs – Keyword arguments passed to scipy.optimize.minimize_scalar when only one parameter present or scipy.optimize.minimize.

Returns

Optimal paramters given by names.

Return type

dict

kernel_loglikelihood(data)[source]¶

Kernel Log-Likelihood for given data with current parameters.

Parameters: data (array_like) – Input data to fit the transformation to in order to gain normality.
Returns: Kernel Log-Likelihood of the given data.
Return type: float

Notes

This loglikelihood function is neglecting additive constants, that are not needed for optimization.

likelihood(data)[source]¶

Likelihood for given data with current parameters.

Parameters: data (array_like) – Input data to fit the transformation to in order to gain normality.
Returns: Likelihood of the given data.
Return type: float

loglikelihood(data)[source]¶

Log-Likelihood for given data with current parameters.

Parameters: data (array_like) – Input data to fit the transformation to in order to gain normality.
Returns: Log-Likelihood of the given data.
Return type: float

normalize(data)[source]¶

Transform to normal distribution.

Parameters: data (array_like) – Input data (not normal distributed).
Returns: Normalized data.
Return type: numpy.ndarray

default_parameter = {}¶

Default parameters of the Normalizer.

Type: dict

denormalize_range = (-inf, inf)¶

Valid range for output/normal data.

Type: tuple

property name¶

The name of the normalizer class.

Type: str

normalize_range = (-inf, inf)¶

Valid range for input data.

Type: tuple