gstools.normalizer.LogNormal¶
-
class
gstools.normalizer.
LogNormal
(data=None, **parameter)[source]¶ Bases:
gstools.normalizer.base.Normalizer
Log-normal fields.
Notes
This parameter-free transformation is given by:
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.
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denormalize
(data)¶ Transform to input distribution.
- Parameters
data (array_like) – Input data (normal distributed).
- Returns
Denormalized data.
- Return type
-
derivative
(data)¶ 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)¶ 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 skiped 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 paramters given by names.
- Return type
-
kernel_loglikelihood
(data)¶ 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)¶ 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)¶ 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
-
normalize
(data)¶ Transform to normal distribution.
- Parameters
data (array_like) – Input data (not normal distributed).
- Returns
Normalized data.
- Return type
-
normalize_range
= (0.0, inf)¶ Valid range for input data.
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