From 413cd6df3c34d31e25c04685a4e944d3b922224e Mon Sep 17 00:00:00 2001 From: Tyler Date: Sat, 4 Nov 2023 18:05:17 +0000 Subject: [PATCH] renaming models to be less verbose --- examples/copula_examples/copula_example.py | 4 +- .../multivariate_example.py | 13 +- sklarpy/copulas/README.md | 34 ++--- sklarpy/copulas/__init__.py | 11 +- sklarpy/copulas/distributions.py | 116 +++++++------- sklarpy/copulas/distributions_map.py | 12 +- sklarpy/multivariate/README.md | 28 ++-- sklarpy/multivariate/__init__.py | 9 +- sklarpy/multivariate/_params/_archimedean.py | 12 +- sklarpy/multivariate/_params/_gaussian_kde.py | 4 +- .../_params/_generalized_hyperbolic.py | 6 +- sklarpy/multivariate/_params/_hyperbolics.py | 12 +- sklarpy/multivariate/_params/_normal.py | 4 +- sklarpy/multivariate/_params/_skewed_t.py | 10 +- sklarpy/multivariate/_params/_student_t.py | 6 +- .../_symmetric_generalized_hyperbolic.py | 6 +- .../_params/_symmetric_hyperbolics.py | 19 +-- sklarpy/multivariate/distributions.py | 143 +++++++++--------- sklarpy/multivariate/distributions_map.py | 24 +-- sklarpy/tests/copulas/conftest.py | 24 +-- sklarpy/tests/multivariate/conftest.py | 44 +++--- 21 files changed, 253 insertions(+), 288 deletions(-) diff --git a/examples/copula_examples/copula_example.py b/examples/copula_examples/copula_example.py index 5604e86..eac1a8f 100644 --- a/examples/copula_examples/copula_example.py +++ b/examples/copula_examples/copula_example.py @@ -15,10 +15,10 @@ my_params = (my_chi, my_psi, my_loc, my_shape, my_gamma) # generating multivariate hyperbolic random variables -from sklarpy.multivariate import multivariate_hyperbolic +from sklarpy.multivariate import mvt_hyperbolic num_generate: int = 1000 -rvs: np.ndarray = multivariate_hyperbolic.rvs(num_generate, my_params) +rvs: np.ndarray = mvt_hyperbolic.rvs(num_generate, my_params) rvs_df: pd.DataFrame = pd.DataFrame(rvs, columns=['Process A', 'Process B'], dtype=float) diff --git a/examples/multivariate_examples/multivariate_example.py b/examples/multivariate_examples/multivariate_example.py index c69f4f9..ed1093b 100644 --- a/examples/multivariate_examples/multivariate_example.py +++ b/examples/multivariate_examples/multivariate_example.py @@ -15,18 +15,17 @@ my_mvn_params: tuple = (my_mu, my_cov) # generating multivariate random normal variables -from sklarpy.multivariate import multivariate_normal +from sklarpy.multivariate import mvt_normal -rvs: np.ndarray = multivariate_normal.rvs(1000, my_mvn_params) +rvs: np.ndarray = mvt_normal.rvs(1000, my_mvn_params) rvs_df: pd.DataFrame = pd.DataFrame(rvs, columns=['Wife Age', 'Husband Age'], dtype=float) # fitting a symmetric hyperbolic dist to our generated data using # Maximum Likelihood Estimation -from sklarpy.multivariate import multivariate_sym_hyperbolic +from sklarpy.multivariate import mvt_shyperbolic -fitted_msh = multivariate_sym_hyperbolic.fit(rvs_df, method='mle', - show_progress=True) +fitted_msh = mvt_shyperbolic.fit(rvs_df, method='mle', show_progress=True) # printing our fitted parameters print(fitted_msh.params.to_dict) @@ -48,7 +47,7 @@ # distribution of the same type from sklarpy import load -loaded_msh_params = load('multivariate_sym_hyperbolic.pickle') -param_fitted_msh = multivariate_sym_hyperbolic.fit(params=loaded_msh_params) +loaded_msh_params = load('mvt_shyperbolic.pickle') +param_fitted_msh = mvt_shyperbolic.fit(params=loaded_msh_params) print(param_fitted_msh.params.to_dict) param_fitted_msh.pdf_plot(axes_names=rvs_df.columns) diff --git a/sklarpy/copulas/README.md b/sklarpy/copulas/README.md index b3de643..229dfb6 100644 --- a/sklarpy/copulas/README.md +++ b/sklarpy/copulas/README.md @@ -6,23 +6,23 @@ Examples of how to use these models can be found in the `examples` folder. ## SklarPy Copula Models - | Family | Name | Dimensions | SklarPy Model | -|----------------|-----------------------------------|--------------|--------------------------------| -| Normal Mixture | Normal / Gaussian | Multivariate | gaussian_copula | -| Normal Mixture | Student-T | Multivariate | student_t_copula | -| Normal Mixture | Skewed-T | Multivariate | skewed_t_copula | -| Normal Mixture | Generalized Hyperbolic | Multivariate | gen_hyperbolic_copula | -| Normal Mixture | Symmetric Generalized Hyperbolic | Multivariate | sym_gen_hyperbolic_copula | -| Normal Mixture | Hyperbolic | Multivariate | hyperbolic_copula | -| Normal Mixture | Symmetric Hyperbolic | Multivariate | sym_hyperbolic_copula | -| Normal Mixture | Normal-Inverse Gaussian (NIG) | Multivariate | nig_copula | -| Normal Mixture | Symmetric Normal-Inverse Gaussian | Multivariate | sym_nig_copula | -| Normal Mixture | Marginal Hyperbolic | Multivariate | marginal_hyperbolic_copula | -| Normal Mixture | Symmetric Marginal Hyperbolic | Multivariate | sym_marginal_hyperbolic_copula | -| Archimedean | Clayton | Multivariate | clayton_copula | -| Archimedean | Gumbel | Multivariate | gumbel_copula | -| Archimedean | Frank | Bivariate | frank_copula | -| Numerical | Gaussian KDE | Multivariate | gaussian_kde_copula | + | Family | Name | Dimensions | SklarPy Model | +|----------------|-----------------------------------|--------------|---------------------| +| Normal Mixture | Normal / Gaussian | Multivariate | gaussian_copula | +| Normal Mixture | Student-T | Multivariate | student_t_copula | +| Normal Mixture | Skewed-T | Multivariate | skewed_t_copula | +| Normal Mixture | Generalized Hyperbolic | Multivariate | gh_copula | +| Normal Mixture | Symmetric Generalized Hyperbolic | Multivariate | sgh_copula | +| Normal Mixture | Hyperbolic | Multivariate | hyperbolic_copula | +| Normal Mixture | Symmetric Hyperbolic | Multivariate | shyperbolic_copula | +| Normal Mixture | Normal-Inverse Gaussian (NIG) | Multivariate | nig_copula | +| Normal Mixture | Symmetric Normal-Inverse Gaussian | Multivariate | snig_copula | +| Normal Mixture | Marginal Hyperbolic | Multivariate | mh_copula | +| Normal Mixture | Symmetric Marginal Hyperbolic | Multivariate | smh_copula | +| Archimedean | Clayton | Multivariate | clayton_copula | +| Archimedean | Gumbel | Multivariate | gumbel_copula | +| Archimedean | Frank | Bivariate | frank_copula | +| Numerical | Gaussian KDE | Multivariate | gaussian_kde_copula | ### Implementation Status - [x] Normal Mixture diff --git a/sklarpy/copulas/__init__.py b/sklarpy/copulas/__init__.py index e0b213b..42b2963 100644 --- a/sklarpy/copulas/__init__.py +++ b/sklarpy/copulas/__init__.py @@ -1,8 +1,7 @@ from sklarpy.copulas.marginal_fitter import MarginalFitter -from sklarpy.copulas.distributions import gaussian_copula, \ - gaussian_kde_copula, gen_hyperbolic_copula, marginal_hyperbolic_copula, \ - hyperbolic_copula, nig_copula, skewed_t_copula, student_t_copula, \ - sym_gen_hyperbolic_copula, sym_marginal_hyperbolic_copula, \ - sym_hyperbolic_copula, sym_nig_copula, clayton_copula, gumbel_copula, \ - frank_copula +from sklarpy.copulas.distributions import ( + gaussian_copula, gaussian_kde_copula, gh_copula, mh_copula, + hyperbolic_copula, nig_copula, skewed_t_copula, student_t_copula, + sgh_copula, smh_copula, shyperbolic_copula, snig_copula, clayton_copula, + gumbel_copula, frank_copula) from sklarpy.copulas.distributions_map import distributions_map diff --git a/sklarpy/copulas/distributions.py b/sklarpy/copulas/distributions.py index 56db862..9d764ff 100644 --- a/sklarpy/copulas/distributions.py +++ b/sklarpy/copulas/distributions.py @@ -1,95 +1,81 @@ # Contains code for building copula models -from sklarpy.copulas._distributions._gaussian import \ - gaussian_copula_gen -from sklarpy.copulas._distributions._gaussian_kde import \ - gaussian_kde_copula_gen -from sklarpy.copulas._distributions._generalized_hyperbolic import \ - gen_hyperbolic_copula_gen -from sklarpy.copulas._distributions._hyperbolics import \ - marginal_hyperbolic_copula_gen, hyperbolic_copula_gen, nig_copula_gen +from sklarpy.copulas._distributions._gaussian import gaussian_copula_gen +from sklarpy.copulas._distributions._gaussian_kde import ( + gaussian_kde_copula_gen) +from sklarpy.copulas._distributions._generalized_hyperbolic import ( + gen_hyperbolic_copula_gen) +from sklarpy.copulas._distributions._hyperbolics import ( + marginal_hyperbolic_copula_gen, hyperbolic_copula_gen, nig_copula_gen) from sklarpy.copulas._distributions._skewed_t import skewed_t_copula_gen from sklarpy.copulas._distributions._student_t import student_t_copula_gen -from sklarpy.copulas._distributions._symmetric_generalized_hyperbolic import \ - sym_gen_hyperbolic_copula_gen -from sklarpy.copulas._distributions._symmetric_hyperbolics import \ - sym_marginal_hyperbolic_copula_gen, sym_hyperbolic_copula_gen, \ - sym_nig_copula_gen -from sklarpy.copulas._distributions._archimedean import clayton_copula_gen, \ - gumbel_copula_gen, frank_copula_gen - -from sklarpy.multivariate import multivariate_normal, \ - multivariate_gaussian_kde, multivariate_gen_hyperbolic, \ - multivariate_marginal_hyperbolic, multivariate_hyperbolic, \ - multivariate_nig, multivariate_student_t, multivariate_skewed_t,\ - multivariate_sym_gen_hyperbolic, multivariate_sym_marginal_hyperbolic, \ - multivariate_sym_hyperbolic, multivariate_sym_nig - -from sklarpy.multivariate.distributions import multivariate_clayton, \ - multivariate_gumbel, bivariate_frank - -__all__ = ['gaussian_copula', 'gaussian_kde_copula', 'gen_hyperbolic_copula', - 'marginal_hyperbolic_copula', 'hyperbolic_copula', 'nig_copula', - 'skewed_t_copula', 'student_t_copula', 'sym_gen_hyperbolic_copula', - 'sym_marginal_hyperbolic_copula', 'sym_hyperbolic_copula', - 'sym_nig_copula','clayton_copula', 'gumbel_copula', 'frank_copula'] +from sklarpy.copulas._distributions._symmetric_generalized_hyperbolic import ( + sym_gen_hyperbolic_copula_gen) +from sklarpy.copulas._distributions._symmetric_hyperbolics import ( + sym_marginal_hyperbolic_copula_gen, sym_hyperbolic_copula_gen, + sym_nig_copula_gen) +from sklarpy.copulas._distributions._archimedean import ( + clayton_copula_gen, gumbel_copula_gen, frank_copula_gen) + +from sklarpy.multivariate import ( + mvt_normal, mvt_gaussian_kde, mvt_gh, mvt_mh, mvt_hyperbolic, mvt_nig, + mvt_student_t, mvt_skewed_t, mvt_sgh, mvt_smh, mvt_shyperbolic, mvt_snig) + +from sklarpy.multivariate.distributions import ( + mvt_clayton, mvt_gumbel, bvt_frank) + +__all__ = ['gaussian_copula', 'gaussian_kde_copula', 'gh_copula', 'mh_copula', + 'hyperbolic_copula', 'nig_copula', 'skewed_t_copula', + 'student_t_copula', 'sgh_copula', 'smh_copula', + 'shyperbolic_copula', 'snig_copula', 'clayton_copula', + 'gumbel_copula', 'frank_copula'] ############################################################################### # Numerical/Non-Parametric ############################################################################### gaussian_kde_copula: gaussian_kde_copula_gen = \ gaussian_kde_copula_gen( - name="gaussian_kde", mv_object=multivariate_gaussian_kde) + name="gaussian_kde", mv_object=mvt_gaussian_kde) ############################################################################### # Parametric ############################################################################### gaussian_copula: gaussian_copula_gen = \ - gaussian_copula_gen(name="gaussian", mv_object=multivariate_normal) + gaussian_copula_gen(name="gaussian", mv_object=mvt_normal) -gen_hyperbolic_copula: gen_hyperbolic_copula_gen = \ - gen_hyperbolic_copula_gen( - name="gen_hyperbolic", mv_object=multivariate_gen_hyperbolic) +gh_copula: gen_hyperbolic_copula_gen = gen_hyperbolic_copula_gen( + name="gh", mv_object=mvt_gh) -marginal_hyperbolic_copula: marginal_hyperbolic_copula_gen = \ - marginal_hyperbolic_copula_gen( - name="marginal_hyperbolic", mv_object=multivariate_marginal_hyperbolic) +mh_copula: marginal_hyperbolic_copula_gen = marginal_hyperbolic_copula_gen( + name="mh", mv_object=mvt_mh) -hyperbolic_copula: hyperbolic_copula_gen = \ - hyperbolic_copula_gen( - name="hyperbolic", mv_object=multivariate_hyperbolic) +hyperbolic_copula: hyperbolic_copula_gen = hyperbolic_copula_gen( + name="hyperbolic", mv_object=mvt_hyperbolic) -nig_copula: nig_copula_gen = \ - nig_copula_gen(name="nig", mv_object=multivariate_nig) +nig_copula: nig_copula_gen = nig_copula_gen(name="nig", mv_object=mvt_nig) -skewed_t_copula: skewed_t_copula_gen = \ - skewed_t_copula_gen( - name="skewed_t", mv_object=multivariate_skewed_t) +skewed_t_copula: skewed_t_copula_gen = skewed_t_copula_gen( + name="skewed_t", mv_object=mvt_skewed_t) -student_t_copula: student_t_copula_gen = \ - student_t_copula_gen( - name="student_t", mv_object=multivariate_student_t) +student_t_copula: student_t_copula_gen = student_t_copula_gen( + name="student_t", mv_object=mvt_student_t) -sym_gen_hyperbolic_copula: sym_gen_hyperbolic_copula_gen = \ - sym_gen_hyperbolic_copula_gen( - name="sym_gen_hyperbolic", mv_object=multivariate_sym_gen_hyperbolic) +sgh_copula: sym_gen_hyperbolic_copula_gen = sym_gen_hyperbolic_copula_gen( + name="sgh", mv_object=mvt_sgh) -sym_marginal_hyperbolic_copula: sym_marginal_hyperbolic_copula_gen = \ - sym_marginal_hyperbolic_copula_gen( - name="sym_marginal_hyperbolic", - mv_object=multivariate_sym_marginal_hyperbolic) +smh_copula: sym_marginal_hyperbolic_copula_gen = \ + sym_marginal_hyperbolic_copula_gen(name="smh", mv_object=mvt_smh) -sym_hyperbolic_copula: sym_hyperbolic_copula_gen = \ - sym_hyperbolic_copula_gen(name="sym_hyperbolic", - mv_object=multivariate_sym_hyperbolic) +shyperbolic_copula: sym_hyperbolic_copula_gen = sym_hyperbolic_copula_gen( + name="shyperbolic", mv_object=mvt_shyperbolic) -sym_nig_copula: sym_nig_copula_gen = \ - sym_nig_copula_gen(name="sym_nig", mv_object=multivariate_sym_nig) +snig_copula: sym_nig_copula_gen = sym_nig_copula_gen( + name="snig", mv_object=mvt_snig) clayton_copula: clayton_copula_gen = \ - clayton_copula_gen(name="clayton", mv_object=multivariate_clayton) + clayton_copula_gen(name="clayton", mv_object=mvt_clayton) gumbel_copula: gumbel_copula_gen = \ - gumbel_copula_gen(name="gumbel", mv_object=multivariate_gumbel) + gumbel_copula_gen(name="gumbel", mv_object=mvt_gumbel) frank_copula: frank_copula_gen = \ - frank_copula_gen(name="frank", mv_object=bivariate_frank) + frank_copula_gen(name="frank", mv_object=bvt_frank) diff --git a/sklarpy/copulas/distributions_map.py b/sklarpy/copulas/distributions_map.py index c823cb5..49eef37 100644 --- a/sklarpy/copulas/distributions_map.py +++ b/sklarpy/copulas/distributions_map.py @@ -9,16 +9,16 @@ 'gumbel_copula', 'frank_copula', 'gaussian_copula', - 'gen_hyperbolic_copula', - 'marginal_hyperbolic_copula', + 'gh_copula', + 'mh_copula', 'hyperbolic_copula', 'nig_copula', 'skewed_t_copula', 'student_t_copula', - 'sym_gen_hyperbolic_copula', - 'sym_marginal_hyperbolic_copula', - 'sym_hyperbolic_copula', - 'sym_nig_copula', + 'sgh_copula', + 'smh_copula', + 'shyperbolic_copula', + 'snig_copula', ) ############################################################################### diff --git a/sklarpy/multivariate/README.md b/sklarpy/multivariate/README.md index fae653b..0b14070 100644 --- a/sklarpy/multivariate/README.md +++ b/sklarpy/multivariate/README.md @@ -6,20 +6,20 @@ Examples of how to use these models can be found in the `examples` folder. ## SklarPy Multivariate Models - | Family | Name | Dimensions | SklarPy Model | -|----------------|-----------------------------------|--------------|--------------------------------------| -| Normal Mixture | Normal / Gaussian | Multivariate | multivariate_normal | -| Normal Mixture | Student-T | Multivariate | multivariate_student_t | -| Normal Mixture | Skewed-T | Multivariate | multivariate_skewed_t | -| Normal Mixture | Generalized Hyperbolic | Multivariate | multivariate_gen_hyperbolic | -| Normal Mixture | Symmetric Generalized Hyperbolic | Multivariate | multivariate_sym_gen_hyperbolic | -| Normal Mixture | Hyperbolic | Multivariate | multivariate_hyperbolic | -| Normal Mixture | Symmetric Hyperbolic | Multivariate | multivariate_sym_hyperbolic | -| Normal Mixture | Normal-Inverse Gaussian (NIG) | Multivariate | multivariate_nig | -| Normal Mixture | Symmetric Normal-Inverse Gaussian | Multivariate | multivariate_sym_nig | -| Normal Mixture | Marginal Hyperbolic | Multivariate | multivariate_marginal_hyperbolic | -| Normal Mixture | Symmetric Marginal Hyperbolic | Multivariate | multivariate_sym_marginal_hyperbolic | -| Numerical | Gaussian KDE | Multivariate | multivariate_gaussian_kde | + | Family | Name | Dimensions | SklarPy Model | +|----------------|-----------------------------------|--------------|------------------| +| Normal Mixture | Normal / Gaussian | Multivariate | mvt_normal | +| Normal Mixture | Student-T | Multivariate | mvt_student_t | +| Normal Mixture | Skewed-T | Multivariate | mvt_skewed_t | +| Normal Mixture | Generalized Hyperbolic | Multivariate | mvt_gh | +| Normal Mixture | Symmetric Generalized Hyperbolic | Multivariate | mvt_sgh | +| Normal Mixture | Hyperbolic | Multivariate | mvt_hyperbolic | +| Normal Mixture | Symmetric Hyperbolic | Multivariate | mvt_shyperbolic | +| Normal Mixture | Normal-Inverse Gaussian (NIG) | Multivariate | mvt_nig | +| Normal Mixture | Symmetric Normal-Inverse Gaussian | Multivariate | mvt_snig | +| Normal Mixture | Marginal Hyperbolic | Multivariate | mvt_mh | +| Normal Mixture | Symmetric Marginal Hyperbolic | Multivariate | mvt_smh | +| Numerical | Gaussian KDE | Multivariate | mvt_gaussian_kde | ### Implementation Status - [x] Normal Mixture diff --git a/sklarpy/multivariate/__init__.py b/sklarpy/multivariate/__init__.py index d0525d2..fda3627 100644 --- a/sklarpy/multivariate/__init__.py +++ b/sklarpy/multivariate/__init__.py @@ -1,8 +1,5 @@ -from sklarpy.multivariate.distributions import multivariate_gaussian_kde, \ - multivariate_gen_hyperbolic, multivariate_marginal_hyperbolic, \ - multivariate_hyperbolic, multivariate_nig, multivariate_normal, \ - multivariate_skewed_t, multivariate_student_t, \ - multivariate_sym_gen_hyperbolic, multivariate_sym_marginal_hyperbolic, \ - multivariate_sym_hyperbolic, multivariate_sym_nig +from sklarpy.multivariate.distributions import ( + mvt_gaussian_kde, mvt_gh, mvt_mh, mvt_hyperbolic, mvt_nig, mvt_normal, + mvt_skewed_t, mvt_student_t, mvt_sgh, mvt_smh, mvt_shyperbolic, mvt_snig) from sklarpy.multivariate.distributions_map import distributions_map diff --git a/sklarpy/multivariate/_params/_archimedean.py b/sklarpy/multivariate/_params/_archimedean.py index fc2b8a5..d4a6012 100644 --- a/sklarpy/multivariate/_params/_archimedean.py +++ b/sklarpy/multivariate/_params/_archimedean.py @@ -1,11 +1,11 @@ # Contains code for holding Archimedean copula parameters from sklarpy._utils import Params -__all__ = ['MultivariateClaytonParams', 'MultivariateGumbelParams', - 'BivariateFrankParams'] +__all__ = ['MvtClaytonParams', 'MvtGumbelParams', + 'BvtFrankParams'] -class MultivariateArchimedeanParamsBase(Params): +class MvtArchimedeanParamsBase(Params): """Base class containing the fitted parameters of an Archimedean Copula.""" @property def theta(self) -> float: @@ -32,13 +32,13 @@ def d(self) -> int: return self.to_dict['d'] -class MultivariateClaytonParams(MultivariateArchimedeanParamsBase): +class MvtClaytonParams(MvtArchimedeanParamsBase): """Contains the fitted parameters of a Clayton Copula.""" -class MultivariateGumbelParams(MultivariateArchimedeanParamsBase): +class MvtGumbelParams(MvtArchimedeanParamsBase): """Contains the fitted parameters of a Gumbel Copula.""" -class BivariateFrankParams(MultivariateArchimedeanParamsBase): +class BvtFrankParams(MvtArchimedeanParamsBase): """Contains the fitted parameters of a Frank Copula.""" diff --git a/sklarpy/multivariate/_params/_gaussian_kde.py b/sklarpy/multivariate/_params/_gaussian_kde.py index 4dd7a62..3f038b1 100644 --- a/sklarpy/multivariate/_params/_gaussian_kde.py +++ b/sklarpy/multivariate/_params/_gaussian_kde.py @@ -1,10 +1,10 @@ # Contains code for holding Gaussian KDE parameters. from sklarpy._utils import Params -__all__ = ['MultivariateGaussianKDEParams'] +__all__ = ['MvtGaussianKDEParams'] -class MultivariateGaussianKDEParams(Params): +class MvtGaussianKDEParams(Params): """Contains the fitted parameters of a Multivariate Gaussian KDE distribution.""" @property diff --git a/sklarpy/multivariate/_params/_generalized_hyperbolic.py b/sklarpy/multivariate/_params/_generalized_hyperbolic.py index 41cff13..40c2113 100644 --- a/sklarpy/multivariate/_params/_generalized_hyperbolic.py +++ b/sklarpy/multivariate/_params/_generalized_hyperbolic.py @@ -2,14 +2,14 @@ import numpy as np from sklarpy.multivariate._params._symmetric_generalized_hyperbolic import \ - MultivariateSymGenHyperbolicParams + MvtSGHParams from sklarpy.multivariate._distributions._generalized_hyperbolic import \ multivariate_gen_hyperbolic_gen -__all__ = ['MultivariateGenHyperbolicParams'] +__all__ = ['MvtGHParams'] -class MultivariateGenHyperbolicParams(MultivariateSymGenHyperbolicParams): +class MvtGHParams(MvtSGHParams): """Contains the fitted parameters of a Multivariate Generalized Hyperbolic distribution.""" _DIST_GENERATOR = multivariate_gen_hyperbolic_gen diff --git a/sklarpy/multivariate/_params/_hyperbolics.py b/sklarpy/multivariate/_params/_hyperbolics.py index 9802076..a8b2e46 100644 --- a/sklarpy/multivariate/_params/_hyperbolics.py +++ b/sklarpy/multivariate/_params/_hyperbolics.py @@ -1,16 +1,14 @@ # Contains code for holding the parameters of Hyperbolic models -from sklarpy.multivariate._params._generalized_hyperbolic import \ - MultivariateGenHyperbolicParams +from sklarpy.multivariate._params._generalized_hyperbolic import MvtGHParams from sklarpy.multivariate._distributions._hyperbolics import \ multivariate_nig_gen, multivariate_hyperbolic_gen,\ multivariate_marginal_hyperbolic_gen -__all__ = ['MultivariateMarginalHyperbolicParams', - 'MultivariateHyperbolicParams', 'MultivariateNIGParams'] +__all__ = ['MvtMHParams', 'MvtHyperbolicParams', 'MvtNIGParams'] -class MultivariateMarginalHyperbolicParams(MultivariateGenHyperbolicParams): +class MvtMHParams(MvtGHParams): """Contains the fitted parameters of a Multivariate Marginal Hyperbolic distribution.""" _DIST_GENERATOR = multivariate_marginal_hyperbolic_gen @@ -20,7 +18,7 @@ def lamb(self) -> float: return 1.0 -class MultivariateHyperbolicParams(MultivariateGenHyperbolicParams): +class MvtHyperbolicParams(MvtGHParams): """Contains the fitted parameters of a Multivariate Hyperbolic distribution.""" _DIST_GENERATOR = multivariate_hyperbolic_gen @@ -30,7 +28,7 @@ def lamb(self) -> float: return 0.5 * (self.loc.size + 1) -class MultivariateNIGParams(MultivariateGenHyperbolicParams): +class MvtNIGParams(MvtGHParams): """Contains the fitted parameters of a Multivariate Normal-Inverse Gaussian (NIG) distribution.""" _DIST_GENERATOR = multivariate_nig_gen diff --git a/sklarpy/multivariate/_params/_normal.py b/sklarpy/multivariate/_params/_normal.py index 3fd82fc..467d3ee 100644 --- a/sklarpy/multivariate/_params/_normal.py +++ b/sklarpy/multivariate/_params/_normal.py @@ -3,10 +3,10 @@ from sklarpy._utils import Params -__all__ = ['MultivariateNormalParams'] +__all__ = ['MvtNormalParams'] -class MultivariateNormalParams(Params): +class MvtNormalParams(Params): """Contains the fitted parameters of a Multivariate Gaussian / Normal distribution.""" diff --git a/sklarpy/multivariate/_params/_skewed_t.py b/sklarpy/multivariate/_params/_skewed_t.py index 8555f69..7c3a4f2 100644 --- a/sklarpy/multivariate/_params/_skewed_t.py +++ b/sklarpy/multivariate/_params/_skewed_t.py @@ -1,15 +1,13 @@ # Contains code for holding Skewed-T parameters -from sklarpy.multivariate._params._generalized_hyperbolic import \ - MultivariateGenHyperbolicParams -from sklarpy.multivariate._params._student_t import MultivariateStudentTParams +from sklarpy.multivariate._params._generalized_hyperbolic import MvtGHParams +from sklarpy.multivariate._params._student_t import MvtStudentTParams from sklarpy.multivariate._distributions._skewed_t import \ multivariate_skewed_t_gen -__all__ = ['MultivariateSkewedTParams'] +__all__ = ['MvtSkewedTParams'] -class MultivariateSkewedTParams(MultivariateGenHyperbolicParams, - MultivariateStudentTParams): +class MvtSkewedTParams(MvtGHParams, MvtStudentTParams): """Contains the fitted parameters of a Multivariate Skewed-T distribution. """ _DIST_GENERATOR = multivariate_skewed_t_gen diff --git a/sklarpy/multivariate/_params/_student_t.py b/sklarpy/multivariate/_params/_student_t.py index ec82aaf..6c4843c 100644 --- a/sklarpy/multivariate/_params/_student_t.py +++ b/sklarpy/multivariate/_params/_student_t.py @@ -1,12 +1,12 @@ # Contains code for holding Student-T parameters import numpy as np -from sklarpy.multivariate._params._normal import MultivariateNormalParams +from sklarpy.multivariate._params._normal import MvtNormalParams -__all__ = ['MultivariateStudentTParams'] +__all__ = ['MvtStudentTParams'] -class MultivariateStudentTParams(MultivariateNormalParams): +class MvtStudentTParams(MvtNormalParams): """Contains the fitted parameters of a Multivariate Student-T distribution.""" diff --git a/sklarpy/multivariate/_params/_symmetric_generalized_hyperbolic.py b/sklarpy/multivariate/_params/_symmetric_generalized_hyperbolic.py index f2bf576..202923f 100644 --- a/sklarpy/multivariate/_params/_symmetric_generalized_hyperbolic.py +++ b/sklarpy/multivariate/_params/_symmetric_generalized_hyperbolic.py @@ -1,14 +1,14 @@ # Contains code for holding Symmetric Generalized Hyperbolic parameters import numpy as np -from sklarpy.multivariate._params._normal import MultivariateNormalParams +from sklarpy.multivariate._params._normal import MvtNormalParams from sklarpy.multivariate._distributions._symmetric_generalized_hyperbolic \ import multivariate_sym_gen_hyperbolic_gen -__all__ = ['MultivariateSymGenHyperbolicParams'] +__all__ = ['MvtSGHParams'] -class MultivariateSymGenHyperbolicParams(MultivariateNormalParams): +class MvtSGHParams(MvtNormalParams): """Contains the fitted parameters of a Multivariate Symmetric Generalized Hyperbolic distribution.""" _DIST_GENERATOR = multivariate_sym_gen_hyperbolic_gen diff --git a/sklarpy/multivariate/_params/_symmetric_hyperbolics.py b/sklarpy/multivariate/_params/_symmetric_hyperbolics.py index 0915df7..001f9e5 100644 --- a/sklarpy/multivariate/_params/_symmetric_hyperbolics.py +++ b/sklarpy/multivariate/_params/_symmetric_hyperbolics.py @@ -1,18 +1,16 @@ # Contains code for holding the parameters of Symmetric Hyperbolic models import numpy as np -from sklarpy.multivariate._params._hyperbolics import \ - MultivariateMarginalHyperbolicParams, MultivariateHyperbolicParams, \ - MultivariateNIGParams +from sklarpy.multivariate._params._hyperbolics import ( + MvtMHParams, MvtHyperbolicParams, MvtNIGParams) from sklarpy.multivariate._distributions._symmetric_hyperbolics import \ multivariate_sym_hyperbolic_gen, multivariate_sym_marginal_hyperbolic_gen,\ multivariate_sym_nig_gen -__all__ = ['MultivariateSymMarginalHyperbolicParams', - 'MultivariateSymHyperbolicParams', 'MultivariateSymNIGParams'] +__all__ = ['MvtSMHParams', 'MvtSHyperbolicParams', 'MvtSNIGParams'] -class MultivariateSymHyperbolicParamsBase: +class MvtSHyperbolicParamsBase: """Base Class containing the fitted parameters of a Multivariate Symmetric Hyperbolic distribution.""" @@ -21,22 +19,19 @@ def gamma(self) -> np.ndarray: return np.zeros(self.loc.shape, dtype=float) -class MultivariateSymMarginalHyperbolicParams( - MultivariateSymHyperbolicParamsBase, MultivariateMarginalHyperbolicParams): +class MvtSMHParams(MvtSHyperbolicParamsBase, MvtMHParams): """Contains the fitted parameters of a Multivariate Symmetric Marginal Hyperbolic distribution.""" _DIST_GENERATOR = multivariate_sym_marginal_hyperbolic_gen -class MultivariateSymHyperbolicParams(MultivariateSymHyperbolicParamsBase, - MultivariateHyperbolicParams): +class MvtSHyperbolicParams(MvtSHyperbolicParamsBase, MvtHyperbolicParams): """Contains the fitted parameters of a Multivariate Symmetric Hyperbolic distribution.""" _DIST_GENERATOR = multivariate_sym_hyperbolic_gen -class MultivariateSymNIGParams(MultivariateSymHyperbolicParamsBase, - MultivariateNIGParams): +class MvtSNIGParams(MvtSHyperbolicParamsBase, MvtNIGParams): """Contains the fitted parameters of a Multivariate Symmetric Normal-Inverse Gaussian (NIG) distribution.""" _DIST_GENERATOR = multivariate_sym_nig_gen diff --git a/sklarpy/multivariate/distributions.py b/sklarpy/multivariate/distributions.py index 2dd4909..61b4a5a 100644 --- a/sklarpy/multivariate/distributions.py +++ b/sklarpy/multivariate/distributions.py @@ -23,136 +23,129 @@ multivariate_clayton_gen, multivariate_gumbel_gen, bivariate_frank_gen from sklarpy.multivariate._params._gaussian_kde import \ - MultivariateGaussianKDEParams + MvtGaussianKDEParams from sklarpy.multivariate._params._generalized_hyperbolic import \ - MultivariateGenHyperbolicParams + MvtGHParams from sklarpy.multivariate._params._hyperbolics import \ - MultivariateMarginalHyperbolicParams, MultivariateHyperbolicParams, \ - MultivariateNIGParams -from sklarpy.multivariate._params._normal import MultivariateNormalParams -from sklarpy.multivariate._params._skewed_t import MultivariateSkewedTParams -from sklarpy.multivariate._params._student_t import MultivariateStudentTParams + MvtMHParams, MvtHyperbolicParams, \ + MvtNIGParams +from sklarpy.multivariate._params._normal import MvtNormalParams +from sklarpy.multivariate._params._skewed_t import MvtSkewedTParams +from sklarpy.multivariate._params._student_t import MvtStudentTParams from sklarpy.multivariate._params._symmetric_generalized_hyperbolic import \ - MultivariateSymGenHyperbolicParams + MvtSGHParams from sklarpy.multivariate._params._symmetric_hyperbolics import \ - MultivariateSymMarginalHyperbolicParams, MultivariateSymHyperbolicParams, \ - MultivariateSymNIGParams -from sklarpy.multivariate._params._archimedean import \ - MultivariateClaytonParams, MultivariateGumbelParams, BivariateFrankParams + MvtSMHParams, MvtSHyperbolicParams, \ + MvtSNIGParams +from sklarpy.multivariate._params._archimedean import ( + MvtClaytonParams, MvtGumbelParams, BvtFrankParams) __all__ = [ - 'multivariate_gaussian_kde', - 'multivariate_gen_hyperbolic', - 'multivariate_marginal_hyperbolic', - 'multivariate_hyperbolic', - 'multivariate_nig', - 'multivariate_normal', - 'multivariate_skewed_t', - 'multivariate_student_t', - 'multivariate_sym_gen_hyperbolic', - 'multivariate_sym_marginal_hyperbolic', - 'multivariate_sym_hyperbolic', - 'multivariate_sym_nig' + 'mvt_gaussian_kde', + 'mvt_gh', + 'mvt_mh', + 'mvt_hyperbolic', + 'mvt_nig', + 'mvt_normal', + 'mvt_skewed_t', + 'mvt_student_t', + 'mvt_sgh', + 'mvt_smh', + 'mvt_shyperbolic', + 'mvt_snig' ] ############################################################################### # Numerical/Non-Parametric ############################################################################### -multivariate_gaussian_kde: multivariate_gaussian_kde_gen = \ +mvt_gaussian_kde: multivariate_gaussian_kde_gen = \ multivariate_gaussian_kde_gen( - name='multivariate_gaussian_kde', - params_obj=MultivariateGaussianKDEParams, + name='mvt_gaussian_kde', + params_obj=MvtGaussianKDEParams, num_params=1, max_num_variables=np.inf ) ############################################################################### # Continuous (Parametric) ############################################################################### -multivariate_gen_hyperbolic: multivariate_gen_hyperbolic_gen = \ - multivariate_gen_hyperbolic_gen( - name="multivariate_gen_hyperbolic", - params_obj=MultivariateGenHyperbolicParams, - num_params=6, max_num_variables=np.inf +mvt_gh: multivariate_gen_hyperbolic_gen = multivariate_gen_hyperbolic_gen( + name="mvt_gh", + params_obj=MvtGHParams, + num_params=6, max_num_variables=np.inf ) -multivariate_marginal_hyperbolic: multivariate_marginal_hyperbolic_gen = \ +mvt_mh: multivariate_marginal_hyperbolic_gen = ( multivariate_marginal_hyperbolic_gen( - name='multivariate_marginal_hyperbolic', - params_obj=MultivariateMarginalHyperbolicParams, + name='mvt_mh', + params_obj=MvtMHParams, num_params=5, max_num_variables=np.inf - ) + )) -multivariate_hyperbolic: multivariate_hyperbolic_gen = \ +mvt_hyperbolic: multivariate_hyperbolic_gen = \ multivariate_hyperbolic_gen( - name='multivariate_hyperbolic', - params_obj=MultivariateHyperbolicParams, num_params=5, + name='mvt_hyperbolic', + params_obj=MvtHyperbolicParams, num_params=5, max_num_variables=np.inf ) -multivariate_nig: multivariate_nig_gen = multivariate_nig_gen( - name='multivariate_nig', params_obj=MultivariateNIGParams, +mvt_nig: multivariate_nig_gen = multivariate_nig_gen( + name='mvt_nig', params_obj=MvtNIGParams, num_params=5, max_num_variables=np.inf ) -multivariate_normal: multivariate_normal_gen = multivariate_normal_gen( - name="multivariate_normal", params_obj=MultivariateNormalParams, +mvt_normal: multivariate_normal_gen = multivariate_normal_gen( + name="mvt_normal", params_obj=MvtNormalParams, num_params=2, max_num_variables=np.inf ) -multivariate_student_t: multivariate_student_t_gen = \ - multivariate_student_t_gen( - name="multivariate_student_t", params_obj=MultivariateStudentTParams, - num_params=3, max_num_variables=np.inf +mvt_student_t: multivariate_student_t_gen = multivariate_student_t_gen( + name="mvt_student_t", params_obj=MvtStudentTParams, + num_params=3, max_num_variables=np.inf ) -multivariate_skewed_t: multivariate_skewed_t_gen = multivariate_skewed_t_gen( - name='multivariate_skewed_t', params_obj=MultivariateSkewedTParams, - num_params=4, max_num_variables=np.inf, mvt_t=multivariate_student_t +mvt_skewed_t: multivariate_skewed_t_gen = multivariate_skewed_t_gen( + name='mvt_skewed_t', params_obj=MvtSkewedTParams, + num_params=4, max_num_variables=np.inf, mvt_t=mvt_student_t ) - -multivariate_sym_gen_hyperbolic: multivariate_sym_gen_hyperbolic_gen = \ +mvt_sgh: multivariate_sym_gen_hyperbolic_gen = ( multivariate_sym_gen_hyperbolic_gen( - name="multivariate_sym_gen_hyperbolic", - params_obj=MultivariateSymGenHyperbolicParams, num_params=5, - max_num_variables=np.inf - ) + name="mvt_sgh", params_obj=MvtSGHParams, + num_params=5, max_num_variables=np.inf + )) -multivariate_sym_marginal_hyperbolic: \ - multivariate_sym_marginal_hyperbolic_gen = \ +mvt_smh: multivariate_sym_marginal_hyperbolic_gen = ( multivariate_sym_marginal_hyperbolic_gen( - name='multivariate_sym_marginal_hyperbolic', - params_obj=MultivariateSymMarginalHyperbolicParams, num_params=4, - max_num_variables=np.inf - ) + name='mvt_smh', params_obj=MvtSMHParams, + num_params=4, max_num_variables=np.inf + )) -multivariate_sym_hyperbolic: multivariate_sym_hyperbolic_gen = \ +mvt_shyperbolic: multivariate_sym_hyperbolic_gen = ( multivariate_sym_hyperbolic_gen( - name='multivariate_sym_hyperbolic', - params_obj=MultivariateSymHyperbolicParams, num_params=4, - max_num_variables=np.inf - ) + name='mvt_shyperbolic', params_obj=MvtSHyperbolicParams, + num_params=4, max_num_variables=np.inf + )) -multivariate_sym_nig: multivariate_sym_nig_gen = multivariate_sym_nig_gen( - name='multivariate_sym_nig', params_obj=MultivariateSymNIGParams, +mvt_snig: multivariate_sym_nig_gen = multivariate_sym_nig_gen( + name='mvt_snig', params_obj=MvtSNIGParams, num_params=4, max_num_variables=np.inf ) ############################################################################### # Copula Only ############################################################################### -multivariate_clayton: multivariate_clayton_gen = multivariate_clayton_gen( - name='multivariate_clayton', params_obj=MultivariateClaytonParams, +mvt_clayton: multivariate_clayton_gen = multivariate_clayton_gen( + name='mvt_clayton', params_obj=MvtClaytonParams, num_params=2, max_num_variables=np.inf ) -multivariate_gumbel: multivariate_gumbel_gen = multivariate_gumbel_gen( - name='multivariate_gumbel', params_obj=MultivariateGumbelParams, +mvt_gumbel: multivariate_gumbel_gen = multivariate_gumbel_gen( + name='mvt_gumbel', params_obj=MvtGumbelParams, num_params=2, max_num_variables=sys.getrecursionlimit() ) -bivariate_frank: bivariate_frank_gen = bivariate_frank_gen( - name='bivariate_frank', params_obj=BivariateFrankParams, +bvt_frank: bivariate_frank_gen = bivariate_frank_gen( + name='bvt_frank', params_obj=BvtFrankParams, num_params=2, max_num_variables=2 ) diff --git a/sklarpy/multivariate/distributions_map.py b/sklarpy/multivariate/distributions_map.py index 07fb824..80af8bb 100644 --- a/sklarpy/multivariate/distributions_map.py +++ b/sklarpy/multivariate/distributions_map.py @@ -5,17 +5,17 @@ # Continuous (Parametric) ############################################################################### continuous_parametric_names: tuple = ( - 'multivariate_normal', - 'multivariate_student_t', - 'multivariate_gen_hyperbolic', - 'multivariate_marginal_hyperbolic', - 'multivariate_hyperbolic', - 'multivariate_nig', - 'multivariate_skewed_t', - 'multivariate_sym_gen_hyperbolic', - 'multivariate_sym_marginal_hyperbolic', - 'multivariate_sym_hyperbolic', - 'multivariate_sym_nig') + 'mvt_normal', + 'mvt_student_t', + 'mvt_gh', + 'mvt_mh', + 'mvt_hyperbolic', + 'mvt_nig', + 'mvt_skewed_t', + 'mvt_sgh', + 'mvt_smh', + 'mvt_shyperbolic', + 'mvt_snig') ############################################################################### # Discrete (Parametric) @@ -25,7 +25,7 @@ ############################################################################### # Numerical/Non-Parametric ############################################################################### -continuous_numerical_names: tuple = ('multivariate_gaussian_kde',) +continuous_numerical_names: tuple = ('mvt_gaussian_kde',) discrete_numerical_names: tuple = tuple() ############################################################################### diff --git a/sklarpy/tests/copulas/conftest.py b/sklarpy/tests/copulas/conftest.py index c2ac3af..5d851ea 100644 --- a/sklarpy/tests/copulas/conftest.py +++ b/sklarpy/tests/copulas/conftest.py @@ -90,14 +90,14 @@ def copula_params_2d(): np.array([[1.00000000e+00, 1.01465364e-17], [1.01465364e-17, 1.00000000e+00]])), - 'gen_hyperbolic_copula': ( + 'gh_copula': ( -10.0, 5.494053523793585, 10.0, np.array([[0.], [0.]]), np.array([[1., 0.12029435], [0.12029435, 1.]]), np.array([[0.99431474], [0.98828561]])), - 'marginal_hyperbolic_copula': ( + 'mh_copula': ( 0.3301436843587767, 10.0, np.array([[0.], [0.]]), np.array([[1., 0.12029435], @@ -131,25 +131,25 @@ def copula_params_2d(): np.array([[1., 0.12029435], [0.12029435, 1.]])), - 'sym_gen_hyperbolic_copula': ( + 'sgh_copula': ( -10.0, 6.994876984856893, 10.0, np.array([[0.], [0.]]), np.array([[1., 0.12029435], [0.12029435, 1.]])), - 'sym_marginal_hyperbolic_copula': ( + 'smh_copula': ( 0.6164857476885669, 10.0, np.array([[0.], [0.]]), np.array([[1., 0.12029435], [0.12029435, 1.]])), - 'sym_hyperbolic_copula': ( + 'shyperbolic_copula': ( 0.38355188192345885, 10.0, np.array([[0.], [0.]]), np.array([[1., 0.12029435], [0.12029435, 1.]])), - 'sym_nig_copula': ( + 'snig_copula': ( 1.391754560716251, 10.0, np.array([[0.], [0.]]), np.array([[1., 0.12029435], @@ -170,7 +170,7 @@ def copula_params_3d(): [2.04732829e-16, 1.00000000e+00, -2.90580716e-16], [-3.62175368e-17, -3.69147970e-17, 1.00000000e+00]])), - 'gen_hyperbolic_copula': ( + 'gh_copula': ( -10.0, 5.779700036883725, 10.0, np.array([[0.], [0.], [0.]]), np.array([[1., 0.00317332, 0.02411494], @@ -178,7 +178,7 @@ def copula_params_3d(): [0.02411494, 0.03426521, 1.]]), np.array([[0.99072914], [0.91655511], [0.99732581]])), - 'marginal_hyperbolic_copula': ( + 'mh_copula': ( 0.40831049405571085, 10.0, np.array([[0.], [0.], [0.]]), np.array([[1., 0.00317332, 0.02411494], @@ -217,28 +217,28 @@ def copula_params_3d(): [0.00317332, 1., 0.03426521], [0.02411494, 0.03426521, 1.]])), - 'sym_gen_hyperbolic_copula': ( + 'sgh_copula': ( -10.0, 7.46609734158415, 10.0, np.array([[0.], [0.], [0.]]), np.array([[1., 0.00317332, 0.02411494], [0.00317332, 1., 0.03426521], [0.02411494, 0.03426521, 1.]])), - 'sym_marginal_hyperbolic_copula': ( + 'smh_copula': ( 0.9105081789228913, 10.0, np.array([[0.], [0.], [0.]]), np.array([[1., -0.0329966, 0.00253866], [-0.0329966, 1., -0.04758192], [0.00253866, -0.04758192, 1.]])), - 'sym_hyperbolic_copula': ( + 'shyperbolic_copula': ( 0.3697932213423236, 10.0, np.array([[0.], [0.], [0.]]), np.array([[1., -0.0329966, 0.00253866], [-0.0329966, 1., -0.04758192], [0.00253866, -0.04758192, 1.]])), - 'sym_nig_copula': ( + 'snig_copula': ( 1.5752917143604088, 10.0, np.array([[0.], [0.], [0.]]), np.array([[1., 0.00317332, 0.02411494], diff --git a/sklarpy/tests/multivariate/conftest.py b/sklarpy/tests/multivariate/conftest.py index ea0e7b6..312d7e9 100644 --- a/sklarpy/tests/multivariate/conftest.py +++ b/sklarpy/tests/multivariate/conftest.py @@ -46,71 +46,71 @@ def mv_dists_to_test(): @pytest.fixture(scope="session", autouse=True) def params_2d(): return { - 'multivariate_normal': ( + 'mvt_normal': ( np.array([[-0.09739442], [0.06748115]]), np.array([[1.22604687e+00, 9.60076079e-18], [9.60076079e-18, 7.30245004e-01]])), - 'multivariate_student_t': ( + 'mvt_student_t': ( 100.0, np.array([[-0.09739442], [0.06748115]]), np.array([[1.20152593, 0.19624622], [0.19624622, 0.7156401]])), - 'multivariate_gen_hyperbolic': ( + 'mvt_gh': ( -1.1788364839820724, 10.0, 10.0, np.array([[0.84415713], [-0.45785498]]), np.array([[1.2253664, 0.26909617], [0.26909617, 0.75701993]]), np.array([[-1.00453046], [0.56047505]])), - 'multivariate_marginal_hyperbolic': ( + 'mvt_mh': ( 6.430016040119274, 10.0, np.array([[0.69052415], [-0.39804144]]), np.array([[1.22869356, 0.2616279], [0.2616279, 0.75174415]]), np.array([[-0.82414258], [0.48692467]])), - 'multivariate_hyperbolic': ( + 'mvt_hyperbolic': ( 5.584106488470228, 10.0, np.array([[0.65995643], [-0.3874908]]), np.array([[1.2296575, 0.26089854], [0.26089854, 0.75084492]]), np.array([[-0.78967095], [0.47438798]])), - 'multivariate_nig': ( + 'mvt_nig': ( 8.986208938433768, 10.0, np.array([[0.78169119], [-0.43129769]]), np.array([[1.22628047, 0.26411374], [0.26411374, 0.7542892]]), np.array([[-0.92734838], [0.52616235]])), - 'multivariate_skewed_t': ( + 'mvt_skewed_t': ( 10.0, np.array([[0.15711164], [-0.10459758]]), np.array([[1.22892318, 0.21921408], [0.21921408, 0.7350084]]), np.array([[-0.20360485], [0.13766299]])), - 'multivariate_sym_gen_hyperbolic': ( + 'mvt_sgh': ( -0.4268651272656109, 10.0, 10.0, np.array([[-0.09739442], [0.06748115]]), np.array([[1.22604687, 0.20025125], [0.20025125, 0.730245]])), - 'multivariate_sym_marginal_hyperbolic': ( + 'mvt_smh': ( 7.523270258556909, 10.0, np.array([[-0.09739442], [0.06748115]]), np.array([[1.22604687, 0.20025125], [0.20025125, 0.730245]])), - 'multivariate_sym_hyperbolic': ( + 'mvt_shyperbolic': ( 6.610028122530992, 10.0, np.array([[-0.09739442], [0.06748115]]), np.array([[1.22604687, 0.20025125], [0.20025125, 0.730245]])), - 'multivariate_sym_nig': ( + 'mvt_snig': ( 10.0, 10.0, np.array([[-0.09739442], [0.06748115]]), np.array([[1.22604687, 0.20025125], @@ -121,20 +121,20 @@ def params_2d(): @pytest.fixture(scope="session", autouse=True) def params_3d(): return { - 'multivariate_normal': ( + 'mvt_normal': ( np.array([[-0.06068104], [-0.10572418], [0.03489784]]), np.array([[1.21898949e+00, 9.34403992e-17, 8.06538495e-17], [3.91669234e-18, 9.03438954e-01, 7.39590407e-19], [5.18678009e-18, 3.39803556e-17, 1.05766110e+00]])), - 'multivariate_student_t': ( + 'mvt_student_t': ( 100.0, np.array([[-0.06068104], [-0.10572418], [0.03489784]]), np.array([[1.1946097, 0.09775865, -0.00776843], [0.09775865, 0.88537018, 0.08621716], [-0.00776843, 0.08621716, 1.03650788]])), - 'multivariate_gen_hyperbolic': ( + 'mvt_gh': ( -1.782382671254414, 10.0, 10.0, np.array([[-0.62581127], [-1.87660553], [0.13564066]]), np.array([[ 1.38090727e+00, -2.01241655e-04, -2.60836067e-03], @@ -142,7 +142,7 @@ def params_3d(): [-2.60836067e-03, 1.23061470e-01, 1.22915669e+00]]), np.array([[0.63853468], [2.00090016], [-0.11382825]])), - 'multivariate_marginal_hyperbolic': ( + 'mvt_mh': ( 6.205679755772674, 10.0, np.array([[-0.4760037], [-1.50649459], [0.0978438 ]]), np.array([[1.33664701, 0.03084231, -0.0052302], @@ -150,7 +150,7 @@ def params_3d(): [-0.0052302, 0.1103901, 1.17990614]]), np.array([[0.44089418], [1.4870162], [-0.06682156]])), - 'multivariate_hyperbolic': ( + 'mvt_hyperbolic': ( 4.669931146010268, 10.0, np.array([[-0.43035465], [-1.40305972], [0.08801923]]), np.array([[1.32964083, 0.03793152, -0.00580953], @@ -158,7 +158,7 @@ def params_3d(): [-0.00580953, 0.10788308, 1.17118297]]), np.array([[0.38622221], [1.35541132], [-0.05549939]])), - 'multivariate_nig': ( + 'mvt_nig': ( 8.517549891625043, 10.0, np.array([[-0.54136409], [-1.66194387], [0.11216012]]), np.array([[1.34739657, 0.02110339, -0.00440445], @@ -166,7 +166,7 @@ def params_3d(): [-0.00440445, 0.11398558, 1.19285811]]), np.array([[0.52083656], [1.68621739], [-0.08371633]])), - 'multivariate_skewed_t': ( + 'mvt_skewed_t': ( 10.0, np.array([[-0.1552794], [-0.58786565], [0.03910667]]), np.array([[ 1.25348961, 0.0871567, -0.00803451], @@ -174,28 +174,28 @@ def params_3d(): [-0.00803451, 0.09138582, 1.09025713]]), np.array([[0.07567869], [0.38571318], [-0.00336706]])), - 'multivariate_sym_gen_hyperbolic': ( + 'mvt_sgh': ( -0.33835936636262204, 10.0, 10.0, np.array([[-0.06068104], [-0.10572418], [0.03489784]]), np.array([[1.21898949, 0.09975372, -0.00792697], [0.09975372, 0.90343895, 0.08797669], [-0.00792697, 0.08797669, 1.0576611]])), - 'multivariate_sym_marginal_hyperbolic': ( + 'mvt_smh': ( 7.799728823974279, 10.0, np.array([[-0.06068104], [-0.10572418], [0.03489784]]), np.array([[1.21898949, 0.09975372, -0.00792697], [0.09975372, 0.90343895, 0.08797669], [-0.00792697, 0.08797669, 1.0576611]])), - 'multivariate_sym_hyperbolic': ( + 'mvt_shyperbolic': ( 5.992259894031568, 10.0, np.array([[-0.06068104], [-0.10572418], [0.03489784]]), np.array([[1.21898949, 0.09975372, -0.00792697], [0.09975372, 0.90343895, 0.08797669], [-0.00792697, 0.08797669, 1.0576611]])), - 'multivariate_sym_nig': ( + 'mvt_snig': ( 10.0, 9.96376429525435, np.array([[-0.06068104], [-0.10572418], [0.03489784]]), np.array([[1.21898949, 0.09975372, -0.00792697],