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:
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:
- 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:
- 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
ofstr
orNone
, optional) – Names of parameters to be skipped in fitting. The default is None.**kwargs – Keyword arguments passed to
scipy.optimize.minimize_scalar
when only one parameter present orscipy.optimize.minimize
.
- Returns:
Optimal parameters given by names.
- Return type:
- 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:
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:
- 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: