reconnect moved files to git repo

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

@@ -0,0 +1,122 @@
+"""
+Created on Fri Jan 29 19:19:45 2021
+
+Author: Josef Perktold
+License: BSD-3
+
+"""
+import numpy as np
+from scipy import stats
+
+from statsmodels.tools.rng_qrng import check_random_state
+from statsmodels.distributions.copula.copulas import Copula
+
+
+class IndependenceCopula(Copula):
+    """Independence copula.
+
+    Copula with independent random variables.
+
+    .. math::
+
+        C_\theta(u,v) = uv
+
+    Parameters
+    ----------
+    k_dim : int
+        Dimension, number of components in the multivariate random variable.
+
+    Notes
+    -----
+    IndependenceCopula does not have copula parameters.
+    If non-empty ``args`` are provided in methods, then a ValueError is raised.
+    The ``args`` keyword is provided for a consistent interface across
+    copulas.
+
+    """
+    def __init__(self, k_dim=2):
+        super().__init__(k_dim=k_dim)
+
+    def _handle_args(self, args):
+        if args != () and args is not None:
+            msg = ("Independence copula does not use copula parameters.")
+            raise ValueError(msg)
+        else:
+            return args
+
+    def rvs(self, nobs=1, args=(), random_state=None):
+        self._handle_args(args)
+        rng = check_random_state(random_state)
+        x = rng.random((nobs, self.k_dim))
+        return x
+
+    def pdf(self, u, args=()):
+        u = np.asarray(u)
+        return np.ones(u.shape[:-1])
+
+    def cdf(self, u, args=()):
+        return np.prod(u, axis=-1)
+
+    def tau(self):
+        return 0
+
+    def plot_pdf(self, *args):
+        raise NotImplementedError("PDF is constant over the domain.")
+
+
+def rvs_kernel(sample, size, bw=1, k_func=None, return_extras=False):
+    """Random sampling from empirical copula using Beta distribution
+
+    Parameters
+    ----------
+    sample : ndarray
+        Sample of multivariate observations in (o, 1) interval.
+    size : int
+        Number of observations to simulate.
+    bw : float
+        Bandwidth for Beta sampling. The beta copula corresponds to a kernel
+        estimate of the distribution. bw=1 corresponds to the empirical beta
+        copula. A small bandwidth like bw=0.001 corresponds to small noise
+        added to the empirical distribution. Larger bw, e.g. bw=10 corresponds
+        to kernel estimate with more smoothing.
+    k_func : None or callable
+        The default kernel function is currently a beta function with 1 added
+        to the first beta parameter.
+    return_extras : bool
+        If this is False, then only the random sample will be returned.
+        If true, then extra information is returned that is mainly of interest
+        for verification.
+
+    Returns
+    -------
+    rvs : ndarray
+        Multivariate sample with ``size`` observations drawn from the Beta
+        Copula.
+
+    Notes
+    -----
+    Status: experimental, API will change.
+    """
+    # vectorized for observations
+    n = sample.shape[0]
+    if k_func is None:
+        kfunc = _kernel_rvs_beta1
+    idx = np.random.randint(0, n, size=size)
+    xi = sample[idx]
+    krvs = np.column_stack([kfunc(xii, bw) for xii in xi.T])
+
+    if return_extras:
+        return krvs, idx, xi
+    else:
+        return krvs
+
+
+def _kernel_rvs_beta(x, bw):
+    # Beta kernel for density, pdf, estimation
+    return stats.beta.rvs(x / bw + 1, (1 - x) / bw + 1, size=x.shape)
+
+
+def _kernel_rvs_beta1(x, bw):
+    # Beta kernel for density, pdf, estimation
+    # Kiriliouk, Segers, Tsukuhara 2020 arxiv, using bandwith 1/nobs sample
+    return stats.beta.rvs(x / bw, (1 - x) / bw + 1)