def cdf(self, x):
x = self._dist._process_quantiles(x, self.dim)
out = self._dist._cdf(x, self.mean, self.cov_info.U)
return _squeeze_output(out)
return self._dist.cdf(x)
x = self._dist._process_quantiles(x, self.dim)
out = self._dist._cdf(x, self.mean, self.cov, self.maxpts, self.abseps,
self.releps)
return _squeeze_output(out)
def logpdf(x,
mean=None,
cov=None,
allow_singular=False,
coef=1,
psd=None,
return_psd=False):
if mean is None:
mean = np.zeros(x.shape[-1], dtype=np.float64)
if cov is None:
cov = np.eye(x.shape[-1], dtype=np.float64)
if psd is None:
psd = _PSD(cov, allow_singular=allow_singular)
out = _logpdf(x, mean, psd.U, psd.log_pdet, psd.rank, coef)
return (_squeeze_output(out), psd) if return_psd else _squeeze_output(out)
def infer(self, *args, **kwargs):
sampler = self._get_sampler(kwargs.pop('sampler', None))
c_n, phi_c = sampler.infer(*args, **kwargs)
return _squeeze_output(c_n), phi_c
def pdf(self, x, location=None, scale=1, dof=None, allow_singular=False):
"""
Multivariate Student's t probability density function.
Parameters
----------
x : array_like
Quantiles, with the last axis of `x` denoting the components.
location : ndarray
Location of the distribution
scale : array_like
Scale matrix of the distribution
dof : scalar
Degrees-of-freedom of the distribution
Returns
-------
pdf : ndarray or scalar
Probability density function evaluated at `x`
"""
dim, location, scale, dof = self._process_parameters(
None, location, scale, dof)
x = self._process_quantiles(x, dim)
psd = _PSD(scale, allow_singular=allow_singular)
out = np.exp(
self._logpdf(x, location, psd.U, psd.log_pdet, psd.rank, dof))
return _squeeze_output(out)
def logpdf(self, x, df, scale):
"""
Log of the Wishart probability density function.
Parameters
----------
x : array_like
Quantiles, with the last axis of `x` denoting the components.
Each quantile must be a symmetric positive definite matrix.
%(_doc_default_callparams)s
Notes
-----
%(_doc_callparams_note)s
Returns
-------
pdf : ndarray
Log of the probability density function evaluated at `x`
"""
dim, df, scale = self._process_parameters(df, scale)
x = self._process_quantiles(x, dim)
# Cholesky decomposition of scale, get log(det(scale))
C = scipy.linalg.cholesky(scale, lower=True)
log_det_scale = 2 * np.sum(np.log(C.diagonal()))
out = self._logpdf(x, dim, df, scale, log_det_scale, C)
return _squeeze_output(out)
def rvs(self, location=None, scale=1, dof=None, size=1, random_state=None):
"""
Draw random samples from a multivariate Student's t distribution.
Parameters
location : ndarray
Location of the distribution
scale : array_like
Scale matrix of the distribution
dof : scalar
Degrees-of-freedom of the distribution
size : int
Number of samples to draw
random_state : np.random.RandomState, optional
Returns
-------
rvs : ndarray or scalar
Random variates of size (`size`, `N`), where `N` is the
dimension of the random variable.
"""
_, location, scale, dof = self._process_parameters(
None, location, scale, dof)
if dof == np.inf:
random_state = self._get_random_state(random_state)
out = random_state.multivariate_normal(location, scale, size)
return _squeeze_output(out)
else:
return _multivariate_t_random(location, scale, dof, size,
random_state)
def logpdf(self, x, df, scale):
"""
Log of the inverse Wishart probability density function.
Parameters
----------
x : array_like
Quantiles, with the last axis of `x` denoting the components.
Each quantile must be a symmetric positive definite matrix.
%(_doc_default_callparams)s
Notes
-----
%(_doc_callparams_note)s
Returns
-------
pdf : ndarray
Log of the probability density function evaluated at `x`
"""
dim, df, scale = self._process_parameters(df, scale)
x = self._process_quantiles(x, dim)
# TODO replace with np.linalg.slogdet when Numpy 1.5.x not necessary
log_det_scale = np.log(np.linalg.det(scale))
out = self._logpdf(x, dim, df, scale, log_det_scale)
return _squeeze_output(out)
def _cdf(self, x, df, mean, cov, maxpts, abseps, releps):
"""
Parameters
----------
x : ndarray
Points at which to evaluate the cumulative distribution function.
df : float
Degrees of freedom of the distribution
mean : ndarray
Mean of the distribution
cov : array_like
Covariance matrix of the distribution
maxpts: integer
The maximum number of points to use for integration
abseps: float
Absolute error tolerance
releps: float
Relative error tolerance
Notes
-----
As this function does no argument checking, it should not be
called directly; use 'cdf' instead.
.. versionadded:: 1.0.0
"""
raise NotImplementedError
lower = np.full(mean.shape, -np.inf)
# mvnun expects 1-d arguments, so process points sequentially
func1d = lambda x_slice: mvn.mvnun(lower, x_slice, mean, cov, maxpts,
abseps, releps)[0]
out = np.apply_along_axis(func1d, -1, x)
return _squeeze_output(out)
def rvs(self, df, scale, size=1):
"""
Draw random samples from a Wishart distribution.
Parameters
----------
%(_doc_default_callparams)s
size : integer or iterable of integers, optional
Number of samples to draw (default 1).
Notes
-----
%(_doc_callparams_note)s
Returns
-------
rvs : ndarray
Random variates of shape (`size`) + (`dim`, `dim), where `dim` is
the dimension of the scale matrix.
"""
n, shape = self._process_size(size)
dim, df, scale = self._process_parameters(df, scale)
# Cholesky decomposition of scale
C = scipy.linalg.cholesky(scale, lower=True)
out = self._rvs(n, shape, dim, df, C)
return _squeeze_output(out)
def infer(self, *args, **kwargs):
sampler = self._get_sampler(kwargs.pop('sampler', None))
c_n, phi_c = sampler.infer(*args, **kwargs)
return _squeeze_output(c_n), phi_c
def cdf(self, x, mean=None, cov=1, allow_singular=False):
"""
Multivariate laplace cumulative distribution function.
Parameters
----------
x : array_like
Quantiles, with the last axis of `x` denoting the components.
%(_mvl_doc_default_callparams)s
Returns
-------
cdf : ndarray or scalar
Cumulative distribution function evaluated at `x`
Notes
-----
%(_mvl_doc_callparams_note)s
.. versionadded:: 1.0.0
"""
dim, mean, cov = self._process_parameters(None, mean, cov)
x = self._process_quantiles(x, dim)
psd = _PSD(cov, allow_singular=allow_singular)
out = self._cdf(x, mean, psd.U)
return _squeeze_output(out)
def logpdf(self, x, df, scale):
"""
Log of the Wishart probability density function.
Parameters
----------
x : array_like
Quantiles, with the last axis of `x` denoting the components.
Each quantile must be a symmetric positive definite matrix.
%(_doc_default_callparams)s
Notes
-----
%(_doc_callparams_note)s
Returns
-------
pdf : ndarray
Log of the probability density function evaluated at `x`
"""
dim, df, scale = self._process_parameters(df, scale)
x = self._process_quantiles(x, dim)
# Cholesky decomposition of scale, get log(det(scale))
C = scipy.linalg.cholesky(scale, lower=True)
log_det_scale = 2 * np.sum(np.log(C.diagonal()))
out = self._logpdf(x, dim, df, scale, log_det_scale, C)
return _squeeze_output(out)
def rvs(self, df, scale, size=1):
"""
Draw random samples from a Wishart distribution.
Parameters
----------
%(_doc_default_callparams)s
size : integer or iterable of integers, optional
Number of samples to draw (default 1).
Notes
-----
%(_doc_callparams_note)s
Returns
-------
rvs : ndarray
Random variates of shape (`size`) + (`dim`, `dim), where `dim` is
the dimension of the scale matrix.
"""
n, shape = self._process_size(size)
dim, df, scale = self._process_parameters(df, scale)
# Cholesky decomposition of scale
C = scipy.linalg.cholesky(scale, lower=True)
out = self._rvs(n, shape, dim, df, C)
return _squeeze_output(out)
def pdf(self, x, mean=None, cov=1, allow_singular=False):
"""
Multivariate laplace probability density function.
Parameters
----------
x : array_like
Quantiles, with the last axis of `x` denoting the components.
%(_mvl_doc_default_callparams)s
Returns
-------
pdf : ndarray or scalar
Probability density function evaluated at `x`
Notes
-----
%(_mvl_doc_callparams_note)s
"""
dim, mean, cov = self._process_parameters(None, mean, cov)
x = self._process_quantiles(x, dim)
psd = _PSD(cov, allow_singular=allow_singular)
out = np.exp(self._logpdf(x, mean, psd.U, psd.log_pdet, psd.rank))
return _squeeze_output(out)
def logpdf(self, x, df, scale):
"""
Log of the inverse Wishart probability density function.
Parameters
----------
x : array_like
Quantiles, with the last axis of `x` denoting the components.
Each quantile must be a symmetric positive definite matrix.
%(_doc_default_callparams)s
Notes
-----
%(_doc_callparams_note)s
Returns
-------
pdf : ndarray
Log of the probability density function evaluated at `x`
"""
dim, df, scale = self._process_parameters(df, scale)
x = self._process_quantiles(x, dim)
# TODO replace with np.linalg.slogdet when Numpy 1.5.x not necessary
log_det_scale = np.log(np.linalg.det(scale))
out = self._logpdf(x, dim, df, scale, log_det_scale)
return _squeeze_output(out)
def var(self, df, scale):
"""
Variance of the Wishart distribution
Parameters
----------
%(_doc_default_callparams)s
Returns
-------
var : float
The variance of the distribution
"""
dim, df, scale = self._process_parameters(df, scale)
out = self._var(dim, df, scale)
return _squeeze_output(out)
def var(self, df, scale):
"""
Variance of the Wishart distribution
Parameters
----------
%(_doc_default_callparams)s
Returns
-------
var : float
The variance of the distribution
"""
dim, df, scale = self._process_parameters(df, scale)
out = self._var(dim, df, scale)
return _squeeze_output(out)
def mean(self, df, scale):
"""
Mean of the Wishart distribution
Parameters
----------
%(_doc_default_callparams)s
Returns
-------
mean : float
The mean of the distribution
"""
dim, df, scale = self._process_parameters(df, scale)
out = self._mean(dim, df, scale)
return _squeeze_output(out)
def mode(self, df, scale):
"""
Mode of the inverse Wishart distribution
Parameters
----------
%(_doc_default_callparams)s
Returns
-------
mode : float
The Mode of the distribution
"""
dim, df, scale = self._process_parameters(df, scale)
out = self._mode(dim, df, scale)
return _squeeze_output(out)
def mode(self, df, scale):
"""
Mode of the inverse Wishart distribution
Parameters
----------
%(_doc_default_callparams)s
Returns
-------
mode : float
The Mode of the distribution
"""
dim, df, scale = self._process_parameters(df, scale)
out = self._mode(dim, df, scale)
return _squeeze_output(out)
def mean(self, df, scale):
"""
Mean of the Wishart distribution
Parameters
----------
%(_doc_default_callparams)s
Returns
-------
mean : float
The mean of the distribution
"""
dim, df, scale = self._process_parameters(df, scale)
out = self._mean(dim, df, scale)
return _squeeze_output(out)
def var(self, df, scale):
"""
Variance of the inverse Wishart distribution
Only valid if the degrees of freedom are greater than the dimension of
the scale matrix plus three.
Parameters
----------
%(_doc_default_callparams)s
Returns
-------
var : float
The variance of the distribution
"""
dim, df, scale = self._process_parameters(df, scale)
out = self._var(dim, df, scale)
return _squeeze_output(out) if out is not None else out
def mean(self, df, scale):
"""
Mean of the inverse Wishart distribution
Only valid if the degrees of freedom are greater than the dimension of
the scale matrix plus one.
Parameters
----------
%(_doc_default_callparams)s
Returns
-------
mean : float or None
The mean of the distribution
"""
dim, df, scale = self._process_parameters(df, scale)
out = self._mean(dim, df, scale)
return _squeeze_output(out) if out is not None else out
def var(self, df, scale):
"""
Variance of the inverse Wishart distribution
Only valid if the degrees of freedom are greater than the dimension of
the scale matrix plus three.
Parameters
----------
%(_doc_default_callparams)s
Returns
-------
var : float
The variance of the distribution
"""
dim, df, scale = self._process_parameters(df, scale)
out = self._var(dim, df, scale)
return _squeeze_output(out) if out is not None else out
def mean(self, df, scale):
"""
Mean of the inverse Wishart distribution
Only valid if the degrees of freedom are greater than the dimension of
the scale matrix plus one.
Parameters
----------
%(_doc_default_callparams)s
Returns
-------
mean : float or None
The mean of the distribution
"""
dim, df, scale = self._process_parameters(df, scale)
out = self._mean(dim, df, scale)
return _squeeze_output(out) if out is not None else out
def rvs(self, df, mean=None, cov=1, size=1, random_state=None):
"""
Draw random samples from a multivariate Student's T distribution.
Parameters
----------
%(_mvt_doc_default_callparams)s
size : integer, optional
Number of samples to draw (default 1).
%(_doc_random_state)s
Returns
-------
rvs : ndarray or scalar
Random variates of size (`size`, `N`), where `N` is the
dimension of the random variable.
Notes
-----
%(_mvt_doc_callparams_note)s
Sampling is based on the observation that
.. math::
X = \mu + \frac{Y}{\sqrt{\frac{U}{\nu}}} = \mu + Y\sqrt{\frac{\nu}{U}}
has a math:`t_\nu({\boldsymbol {\mu}}, {\boldsymbol {\Sigma}})` distribution when
math:`Y` has a math:`N(0,{\boldsymbol {\Sigma}})` distribution and independently
math:`U` has a math:`\chi^2_{\boldsymbol {\nu}}` distribution.
"""
dim, df, mean, cov = self._process_parameters(None, df, mean, cov)
random_state = self._get_random_state(random_state)
# Y, shape (*size, dim)
norm_comp = random_state.multivariate_normal(np.zeros(dim), cov, size)
# U, shape size
chi2_comp = random_state.chisquare(df, size)
out = mean + norm_comp * np.sqrt(df / chi2_comp[..., np.newaxis])
return _squeeze_output(out)
def cdf(self, x):
x = self._dist._process_quantiles(x, self.dim)
out = self._dist._cdf(x, self.location, self.scale, self.dof,
self.maxpts, self.abseps, self.releps)
return _squeeze_output(out)
def logpdf(self, x):
x = self._dist._process_quantiles(x, self.dim)
out = self._dist._logpdf(x, self.mean, self.cov_info.U,
self.cov_info.log_pdet, self.cov_info.rank)
return _squeeze_output(out)
def mode(self):
out = self._invwishart._mode(self.dim, self.df, self.scale)
return _squeeze_output(out)
def var(self):
out = self._invwishart._var(self.dim, self.df, self.scale)
return _squeeze_output(out) if out is not None else out
def logpdf(self, x):
x = self._wishart._process_quantiles(x, self.dim)
out = self._wishart._logpdf(x, self.dim, self.df, self.scale,
self.log_det_scale, self.C)
return _squeeze_output(out)
def mode(self):
out = self._wishart._mode(self.dim, self.df, self.scale)
return _squeeze_output(out) if out is not None else out
def rvs(self, size=1):
n, shape = self._wishart._process_size(size)
out = self._wishart._rvs(n, shape,
self.dim, self.df, self.C)
return _squeeze_output(out)
def var(self):
out = self._wishart._var(self.dim, self.df, self.scale)
return _squeeze_output(out)
def mode(self):
out = self._wishart._mode(self.dim, self.df, self.scale)
return _squeeze_output(out) if out is not None else out
def logpdf(self, x):
x = self._wishart._process_quantiles(x, self.dim)
out = self._wishart._logpdf(x, self.dim, self.df, self.scale,
self.log_det_scale, self.C)
return _squeeze_output(out)
def var(self):
out = self._wishart._var(self.dim, self.df, self.scale)
return _squeeze_output(out)
def rvs(self, size=1):
n, shape = self._wishart._process_size(size)
out = self._wishart._rvs(n, shape, self.dim, self.df, self.C)
return _squeeze_output(out)
def rvs(self, w_0, V_0, a_0, b_0, size=1):
dim, w_0, V_0 = _process_parameters(None, w_0, V_0)
ig = self._invgamma.rvs(a=a_0, scale=b_0, size=size)
result = np.vstack([np.append(self._mnorm.rvs(mean=w_0, cov=var*V_0, size=1), var) for var in ig])
return _squeeze_output(result)
def draw(self, size, random_state=None):
"""
Draw from the Chinese restaurant process.
"""
mm = self._mixture_model
n, m, shape = self._process_size(size)
random_state = self._get_random_state(random_state)
# Array of vectors of component indicator variables. In the beginning,
# assign every example to the component with indicator value 0
c_n = np.empty(m*n, dtype=int).reshape((shape+(n,)))
# Maximum number of components is number of examples
c_max = n
# Array of examples
# TODO: Make this truly model-agnostic, i.e. get rid of mm.dim and
# dtype=float
x_n = np.empty((m*n*mm.dim), dtype=float).reshape((shape+(n,mm.dim)))
for index in np.ndindex(shape):
process_param = self.DrawParam(self, random_state)
n_c = np.zeros(c_max, dtype=int)
active_components = set()
inactive_components = set(range(c_max))
# Lazily instantiate the components
mixture_params = [mm.DrawParam(mm, random_state)
for _ in range(c_max)]
for i in range(n):
# Draw a component k for example i from the Chinese restaurant
# process
# Get a new component from the stack
prop_k = inactive_components.pop()
active_components.add(prop_k)
# Initialize and populate the log probability accumulator
log_dist = np.empty(c_max, dtype=float)
log_dist.fill(-np.inf)
for k in active_components:
# Calculate the process prior
log_dist[k] = process_param.log_prior(i+1, n_c[k])
# Sample from log_dist. Normalization is required
log_dist -= _logsumexp(c_max, log_dist)
next_k = random_state.choice(a=c_max, p=np.exp(log_dist))
# cdf = np.cumsum(np.exp(log_dist - log_dist.max()))
# r = random_state.uniform(size=1) * cdf[-1]
# [next_k] = cdf.searchsorted(r)
c_n[index+(i,)] = next_k
# Update component counter
n_c[next_k] += 1
# New components are instantiated automatically when needed
x_n[index+(i,)] = mixture_params[next_k].draw_x_n()
# Cleanup
if next_k != prop_k:
active_components.remove(prop_k)
inactive_components.add(prop_k)
# TODO: Make it possible to return the component parameters
return _squeeze_output(x_n), _squeeze_output(c_n)
def logpdf(self, x):
x = self._dist._process_quantiles(x, self.dim)
out = self._dist._logpdf(x, self.location, self.scale_info.U,
self.scale_info.log_pdet,
self.scale_info.rank, self.dof)
return _squeeze_output(out)
def mode(self):
out = self._invwishart._mode(self.dim, self.df, self.scale)
return _squeeze_output(out)
def var(self):
out = self._invwishart._var(self.dim, self.df, self.scale)
return _squeeze_output(out) if out is not None else out
def draw(self, size, random_state=None):
"""
Draw from the Chinese restaurant process.
"""
mm = self._mixture_model
n, m, shape = self._process_size(size)
random_state = self._get_random_state(random_state)
# Array of vectors of component indicator variables. In the beginning,
# assign every example to the component with indicator value 0
c_n = np.empty(m * n, dtype=int).reshape((shape + (n, )))
# Maximum number of components is number of examples
c_max = n
# Array of examples
# TODO: Make this truly model-agnostic, i.e. get rid of mm.dim and
# dtype=float
x_n = np.empty((m * n * mm.dim), dtype=float).reshape(
(shape + (n, mm.dim)))
for index in np.ndindex(shape):
process_param = self.DrawParam(self, random_state)
n_c = np.zeros(c_max, dtype=int)
active_components = set()
inactive_components = set(range(c_max))
# Lazily instantiate the components
mixture_params = [
mm.DrawParam(mm, random_state) for _ in range(c_max)
]
for i in range(n):
# Draw a component k for example i from the Chinese restaurant
# process
# Get a new component from the stack
prop_k = inactive_components.pop()
active_components.add(prop_k)
# Initialize and populate the log probability accumulator
log_dist = np.empty(c_max, dtype=float)
log_dist.fill(-np.inf)
for k in active_components:
# Calculate the process prior
log_dist[k] = process_param.log_prior(i + 1, n_c[k])
# Sample from log_dist. Normalization is required
log_dist -= _logsumexp(c_max, log_dist)
next_k = random_state.choice(a=c_max, p=np.exp(log_dist))
# cdf = np.cumsum(np.exp(log_dist - log_dist.max()))
# r = random_state.uniform(size=1) * cdf[-1]
# [next_k] = cdf.searchsorted(r)
c_n[index + (i, )] = next_k
# Update component counter
n_c[next_k] += 1
# New components are instantiated automatically when needed
x_n[index + (i, )] = mixture_params[next_k].draw_x_n()
# Cleanup
if next_k != prop_k:
active_components.remove(prop_k)
inactive_components.add(prop_k)
# TODO: Make it possible to return the component parameters
return _squeeze_output(x_n), _squeeze_output(c_n)
