reconnect moved files to git repo

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

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+import numpy as np
+
+def _make_index(prob,size):
+    """
+    Returns a boolean index for given probabilities.
+
+    Notes
+    -----
+    prob = [.75,.25] means that there is a 75% chance of the first column
+    being True and a 25% chance of the second column being True. The
+    columns are mutually exclusive.
+    """
+    rv = np.random.uniform(size=(size,1))
+    cumprob = np.cumsum(prob)
+    return np.logical_and(np.r_[0,cumprob[:-1]] <= rv, rv < cumprob)
+
+def mixture_rvs(prob, size, dist, kwargs=None):
+    """
+    Sample from a mixture of distributions.
+
+    Parameters
+    ----------
+    prob : array_like
+        Probability of sampling from each distribution in dist
+    size : int
+        The length of the returned sample.
+    dist : array_like
+        An iterable of distributions objects from scipy.stats.
+    kwargs : tuple of dicts, optional
+        A tuple of dicts.  Each dict in kwargs can have keys loc, scale, and
+        args to be passed to the respective distribution in dist.  If not
+        provided, the distribution defaults are used.
+
+    Examples
+    --------
+    Say we want 5000 random variables from mixture of normals with two
+    distributions norm(-1,.5) and norm(1,.5) and we want to sample from the
+    first with probability .75 and the second with probability .25.
+
+    >>> from scipy import stats
+    >>> prob = [.75,.25]
+    >>> Y = mixture_rvs(prob, 5000, dist=[stats.norm, stats.norm],
+    ...                 kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=.5)))
+    """
+    if len(prob) != len(dist):
+        raise ValueError("You must provide as many probabilities as distributions")
+    if not np.allclose(np.sum(prob), 1):
+        raise ValueError("prob does not sum to 1")
+
+    if kwargs is None:
+        kwargs = ({},)*len(prob)
+
+    idx = _make_index(prob,size)
+    sample = np.empty(size)
+    for i in range(len(prob)):
+        sample_idx = idx[...,i]
+        sample_size = sample_idx.sum()
+        loc = kwargs[i].get('loc',0)
+        scale = kwargs[i].get('scale',1)
+        args = kwargs[i].get('args',())
+        sample[sample_idx] = dist[i].rvs(*args, **dict(loc=loc,scale=scale,
+            size=sample_size))
+    return sample
+
+
+class MixtureDistribution:
+    '''univariate mixture distribution
+
+    for simple case for now (unbound support)
+    does not yet inherit from scipy.stats.distributions
+
+    adding pdf to mixture_rvs, some restrictions on broadcasting
+    Currently it does not hold any state, all arguments included in each method.
+    '''
+
+    #def __init__(self, prob, size, dist, kwargs=None):
+
+    def rvs(self, prob, size, dist, kwargs=None):
+        return mixture_rvs(prob, size, dist, kwargs=kwargs)
+
+
+    def pdf(self, x, prob, dist, kwargs=None):
+        """
+        pdf a mixture of distributions.
+
+        Parameters
+        ----------
+        x : array_like
+            Array containing locations where the PDF should be evaluated
+        prob : array_like
+            Probability of sampling from each distribution in dist
+        dist : array_like
+            An iterable of distributions objects from scipy.stats.
+        kwargs : tuple of dicts, optional
+            A tuple of dicts.  Each dict in kwargs can have keys loc, scale, and
+            args to be passed to the respective distribution in dist.  If not
+            provided, the distribution defaults are used.
+
+        Examples
+        --------
+        Say we want 5000 random variables from mixture of normals with two
+        distributions norm(-1,.5) and norm(1,.5) and we want to sample from the
+        first with probability .75 and the second with probability .25.
+
+        >>> import numpy as np
+        >>> from scipy import stats
+        >>> from statsmodels.distributions.mixture_rvs import MixtureDistribution
+        >>> x = np.arange(-4.0, 4.0, 0.01)
+        >>> prob = [.75,.25]
+        >>> mixture = MixtureDistribution()
+        >>> Y = mixture.pdf(x, prob, dist=[stats.norm, stats.norm],
+        ...                 kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=.5)))
+        """
+        if len(prob) != len(dist):
+            raise ValueError("You must provide as many probabilities as distributions")
+        if not np.allclose(np.sum(prob), 1):
+            raise ValueError("prob does not sum to 1")
+
+        if kwargs is None:
+            kwargs = ({},)*len(prob)
+
+        for i in range(len(prob)):
+            loc = kwargs[i].get('loc',0)
+            scale = kwargs[i].get('scale',1)
+            args = kwargs[i].get('args',())
+            if i == 0:  #assume all broadcast the same as the first dist
+                pdf_ = prob[i] * dist[i].pdf(x, *args, loc=loc, scale=scale)
+            else:
+                pdf_ += prob[i] * dist[i].pdf(x, *args, loc=loc, scale=scale)
+        return pdf_
+
+    def cdf(self, x, prob, dist, kwargs=None):
+        """
+        cdf of a mixture of distributions.
+
+        Parameters
+        ----------
+        x : array_like
+            Array containing locations where the CDF should be evaluated
+        prob : array_like
+            Probability of sampling from each distribution in dist
+        size : int
+            The length of the returned sample.
+        dist : array_like
+            An iterable of distributions objects from scipy.stats.
+        kwargs : tuple of dicts, optional
+            A tuple of dicts.  Each dict in kwargs can have keys loc, scale, and
+            args to be passed to the respective distribution in dist.  If not
+            provided, the distribution defaults are used.
+
+        Examples
+        --------
+        Say we want 5000 random variables from mixture of normals with two
+        distributions norm(-1,.5) and norm(1,.5) and we want to sample from the
+        first with probability .75 and the second with probability .25.
+
+        >>> import numpy as np
+        >>> from scipy import stats
+        >>> from statsmodels.distributions.mixture_rvs import MixtureDistribution
+        >>> x = np.arange(-4.0, 4.0, 0.01)
+        >>> prob = [.75,.25]
+        >>> mixture = MixtureDistribution()
+        >>> Y = mixture.pdf(x, prob, dist=[stats.norm, stats.norm],
+        ...                 kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=.5)))
+        """
+        if len(prob) != len(dist):
+            raise ValueError("You must provide as many probabilities as distributions")
+        if not np.allclose(np.sum(prob), 1):
+            raise ValueError("prob does not sum to 1")
+
+        if kwargs is None:
+            kwargs = ({},)*len(prob)
+
+        for i in range(len(prob)):
+            loc = kwargs[i].get('loc',0)
+            scale = kwargs[i].get('scale',1)
+            args = kwargs[i].get('args',())
+            if i == 0:  #assume all broadcast the same as the first dist
+                cdf_ = prob[i] * dist[i].cdf(x, *args, loc=loc, scale=scale)
+            else:
+                cdf_ += prob[i] * dist[i].cdf(x, *args, loc=loc, scale=scale)
+        return cdf_
+
+
+def mv_mixture_rvs(prob, size, dist, nvars, **kwargs):
+    """
+    Sample from a mixture of multivariate distributions.
+
+    Parameters
+    ----------
+    prob : array_like
+        Probability of sampling from each distribution in dist
+    size : int
+        The length of the returned sample.
+    dist : array_like
+        An iterable of distributions instances with callable method rvs.
+    nvargs : int
+        dimension of the multivariate distribution, could be inferred instead
+    kwargs : tuple of dicts, optional
+        ignored
+
+    Examples
+    --------
+    Say we want 2000 random variables from mixture of normals with two
+    multivariate normal distributions, and we want to sample from the
+    first with probability .4 and the second with probability .6.
+
+    import statsmodels.sandbox.distributions.mv_normal as mvd
+
+    cov3 = np.array([[ 1.  ,  0.5 ,  0.75],
+                       [ 0.5 ,  1.5 ,  0.6 ],
+                       [ 0.75,  0.6 ,  2.  ]])
+
+    mu = np.array([-1, 0.0, 2.0])
+    mu2 = np.array([4, 2.0, 2.0])
+    mvn3 = mvd.MVNormal(mu, cov3)
+    mvn32 = mvd.MVNormal(mu2, cov3/2., 4)
+    rvs = mix.mv_mixture_rvs([0.4, 0.6], 2000, [mvn3, mvn32], 3)
+    """
+    if len(prob) != len(dist):
+        raise ValueError("You must provide as many probabilities as distributions")
+    if not np.allclose(np.sum(prob), 1):
+        raise ValueError("prob does not sum to 1")
+
+    if kwargs is None:
+        kwargs = ({},)*len(prob)
+
+    idx = _make_index(prob,size)
+    sample = np.empty((size, nvars))
+    for i in range(len(prob)):
+        sample_idx = idx[...,i]
+        sample_size = sample_idx.sum()
+        #loc = kwargs[i].get('loc',0)
+        #scale = kwargs[i].get('scale',1)
+        #args = kwargs[i].get('args',())
+        # use int to avoid numpy bug with np.random.multivariate_normal
+        sample[sample_idx] = dist[i].rvs(size=int(sample_size))
+    return sample
+
+
+
+if __name__ == '__main__':
+
+    from scipy import stats
+
+    obs_dist = mixture_rvs([.25,.75], size=10000, dist=[stats.norm, stats.beta],
+                kwargs=(dict(loc=-1,scale=.5),dict(loc=1,scale=1,args=(1,.5))))
+
+
+
+    nobs = 10000
+    mix = MixtureDistribution()
+##    mrvs = mixture_rvs([1/3.,2/3.], size=nobs, dist=[stats.norm, stats.norm],
+##                   kwargs = (dict(loc=-1,scale=.5),dict(loc=1,scale=.75)))
+
+    mix_kwds = (dict(loc=-1,scale=.25),dict(loc=1,scale=.75))
+    mrvs = mix.rvs([1/3.,2/3.], size=nobs, dist=[stats.norm, stats.norm],
+                   kwargs=mix_kwds)
+
+    grid = np.linspace(-4,4, 100)
+    mpdf = mix.pdf(grid, [1/3.,2/3.], dist=[stats.norm, stats.norm],
+                   kwargs=mix_kwds)
+    mcdf = mix.cdf(grid, [1/3.,2/3.], dist=[stats.norm, stats.norm],
+                   kwargs=mix_kwds)
+
+    doplot = 1
+    if doplot:
+        import matplotlib.pyplot as plt
+        plt.figure()
+        plt.hist(mrvs, bins=50, normed=True, color='red')
+        plt.title('histogram of sample and pdf')
+        plt.plot(grid, mpdf, lw=2, color='black')
+
+        plt.figure()
+        plt.hist(mrvs, bins=50, normed=True, cumulative=True, color='red')
+        plt.title('histogram of sample and pdf')
+        plt.plot(grid, mcdf, lw=2, color='black')
+
+        plt.show()