reconnect moved files to git repo

venv/lib/python3.11/site-packages/statsmodels/distributions/copula/extreme_value.py

@@ -0,0 +1,163 @@
+""" Extreme Value Copulas
+Created on Fri Jan 29 19:19:45 2021
+
+Author: Josef Perktold
+License: BSD-3
+
+"""
+
+import numpy as np
+from .copulas import Copula
+
+
+def copula_bv_ev(u, transform, args=()):
+    '''generic bivariate extreme value copula
+    '''
+    u, v = u
+    return np.exp(np.log(u * v) * (transform(np.log(u)/np.log(u*v), *args)))
+
+
+class ExtremeValueCopula(Copula):
+    """Extreme value copula constructed from Pickand's dependence function.
+
+    Currently only bivariate copulas are available.
+
+    Parameters
+    ----------
+    transform: instance of transformation class
+        Pickand's dependence function with required methods including first
+        and second derivatives
+    args : tuple
+        Optional copula parameters. Copula parameters can be either provided
+        when creating the instance or as arguments when calling methods.
+    k_dim : int
+        Currently only bivariate extreme value copulas are supported.
+
+    Notes
+    -----
+    currently the following dependence function and copulas are available
+
+    - AsymLogistic
+    - AsymNegLogistic
+    - AsymMixed
+    - HR
+
+    TEV and AsymBiLogistic currently do not have required derivatives for pdf.
+
+    See Also
+    --------
+    dep_func_ev
+
+    """
+
+    def __init__(self, transform, args=(), k_dim=2):
+        super().__init__(k_dim=k_dim)
+        self.transform = transform
+        self.k_args = transform.k_args
+        self.args = args
+        if k_dim != 2:
+            raise ValueError("Only bivariate EV copulas are available.")
+
+    def _handle_args(self, args):
+        # TODO: how to we handle non-tuple args? two we allow single values?
+        # Model fit might give an args that can be empty
+        if isinstance(args, np.ndarray):
+            args = tuple(args)  # handles empty arrays, unpacks otherwise
+        if args == () or args is None:
+            args = self.args
+        if not isinstance(args, tuple):
+            args = (args,)
+
+        return args
+
+    def cdf(self, u, args=()):
+        """Evaluate cdf of bivariate extreme value copula.
+
+        Parameters
+        ----------
+        u : array_like
+            Values of random bivariate random variable, each defined on [0, 1],
+            for which cdf is computed.
+            Can be two dimensional with multivariate components in columns and
+            observation in rows.
+        args : tuple
+            Required parameters for the copula. The meaning and number of
+            parameters in the tuple depends on the specific copula.
+
+        Returns
+        -------
+        CDF values at evaluation points.
+        """
+        # currently only Bivariate
+        u, v = np.asarray(u).T
+        args = self._handle_args(args)
+        cdfv = np.exp(np.log(u * v) *
+                      self.transform(np.log(u)/np.log(u*v), *args))
+        return cdfv
+
+    def pdf(self, u, args=()):
+        """Evaluate pdf of bivariate extreme value copula.
+
+        Parameters
+        ----------
+        u : array_like
+            Values of random bivariate random variable, each defined on [0, 1],
+            for which cdf is computed.
+            Can be two dimensional with multivariate components in columns and
+            observation in rows.
+        args : tuple
+            Required parameters for the copula. The meaning and number of
+            parameters in the tuple depends on the specific copula.
+
+        Returns
+        -------
+        PDF values at evaluation points.
+        """
+        tr = self.transform
+        u1, u2 = np.asarray(u).T
+        args = self._handle_args(args)
+
+        log_u12 = np.log(u1 * u2)
+        t = np.log(u1) / log_u12
+        cdf = self.cdf(u, args)
+        dep = tr(t, *args)
+        d1 = tr.deriv(t, *args)
+        d2 = tr.deriv2(t, *args)
+        pdf_ = cdf / (u1 * u2) * ((dep + (1 - t) * d1) * (dep - t * d1) -
+                                  d2 * (1 - t) * t / log_u12)
+
+        return pdf_
+
+    def logpdf(self, u, args=()):
+        """Evaluate log-pdf of bivariate extreme value copula.
+
+        Parameters
+        ----------
+        u : array_like
+            Values of random bivariate random variable, each defined on [0, 1],
+            for which cdf is computed.
+            Can be two dimensional with multivariate components in columns and
+            observation in rows.
+        args : tuple
+            Required parameters for the copula. The meaning and number of
+            parameters in the tuple depends on the specific copula.
+
+        Returns
+        -------
+        Log-pdf values at evaluation points.
+        """
+        return np.log(self.pdf(u, args=args))
+
+    def conditional_2g1(self, u, args=()):
+        """conditional distribution
+
+        not yet implemented
+
+        C2|1(u2|u1) := ∂C(u1, u2) / ∂u1 = C(u1, u2) / u1 * (A(t) − t A'(t))
+
+        where t = np.log(v)/np.log(u*v)
+        """
+        raise NotImplementedError
+
+    def fit_corr_param(self, data):
+        raise NotImplementedError