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Dirichlet Process Gaussian Mixture Models"""
from __future__ import print_function
# Author: Alexandre Passos (alexandre.tp@gmail.com)
# Bertrand Thirion <bertrand.thirion@inria.fr>
#
# Based on mixture.py by:
# Ron Weiss <ronweiss@gmail.com>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
#
import numpy as np
from scipy.special import digamma as _digamma, gammaln as _gammaln
from scipy import linalg
from scipy.spatial.distance import cdist
from ..externals.six.moves import xrange
from ..utils import check_random_state, deprecated
from ..utils.extmath import norm, logsumexp, pinvh
from .. import cluster
from .gmm import GMM
def sqnorm(v):
return norm(v) ** 2
def digamma(x):
return _digamma(x + np.finfo(np.float32).eps)
def gammaln(x):
return _gammaln(x + np.finfo(np.float32).eps)
def log_normalize(v, axis=0):
"""Normalized probabilities from unnormalized log-probabilites"""
v = np.rollaxis(v, axis)
v = v.copy()
v -= v.max(axis=0)
out = logsumexp(v)
v = np.exp(v - out)
v += np.finfo(np.float32).eps
v /= np.sum(v, axis=0)
return np.swapaxes(v, 0, axis)
def wishart_log_det(a, b, detB, n_features):
"""Expected value of the log of the determinant of a Wishart
The expected value of the logarithm of the determinant of a
wishart-distributed random variable with the specified parameters."""
l = np.sum(digamma(0.5 * (a - np.arange(-1, n_features - 1))))
l += n_features * np.log(2)
return l + detB
def wishart_logz(v, s, dets, n_features):
"The logarithm of the normalization constant for the wishart distribution"
z = 0.
z += 0.5 * v * n_features * np.log(2)
z += (0.25 * (n_features * (n_features - 1)) * np.log(np.pi))
z += 0.5 * v * np.log(dets)
z += np.sum(gammaln(0.5 * (v - np.arange(n_features) + 1)))
return z
def _bound_wishart(a, B, detB):
"""Returns a function of the dof, scale matrix and its determinant
used as an upper bound in variational approcimation of the evidence"""
n_features = B.shape[0]
logprior = wishart_logz(a, B, detB, n_features)
logprior -= wishart_logz(n_features,
np.identity(n_features),
1, n_features)
logprior += 0.5 * (a - 1) * wishart_log_det(a, B, detB, n_features)
logprior += 0.5 * a * np.trace(B)
return logprior
##############################################################################
# Variational bound on the log likelihood of each class
##############################################################################
def _sym_quad_form(x, mu, A):
"""helper function to calculate symmetric quadratic form x.T * A * x"""
q = (cdist(x, mu[np.newaxis], "mahalanobis", VI=A) ** 2).reshape(-1)
return q
def _bound_state_log_lik(X, initial_bound, precs, means, covariance_type):
"""Update the bound with likelihood terms, for standard covariance types"""
n_components, n_features = means.shape
n_samples = X.shape[0]
bound = np.empty((n_samples, n_components))
bound[:] = initial_bound
if covariance_type in ['diag', 'spherical']:
for k in range(n_components):
d = X - means[k]
bound[:, k] -= 0.5 * np.sum(d * d * precs[k], axis=1)
elif covariance_type == 'tied':
for k in range(n_components):
bound[:, k] -= 0.5 * _sym_quad_form(X, means[k], precs)
elif covariance_type == 'full':
for k in range(n_components):
bound[:, k] -= 0.5 * _sym_quad_form(X, means[k], precs[k])
return bound
class DPGMM(GMM):
"""Variational Inference for the Infinite Gaussian Mixture Model.
DPGMM stands for Dirichlet Process Gaussian Mixture Model, and it
is an infinite mixture model with the Dirichlet Process as a prior
distribution on the number of clusters. In practice the
approximate inference algorithm uses a truncated distribution with
a fixed maximum number of components, but almost always the number
of components actually used depends on the data.
Stick-breaking Representation of a Gaussian mixture model
probability distribution. This class allows for easy and efficient
inference of an approximate posterior distribution over the
parameters of a Gaussian mixture model with a variable number of
components (smaller than the truncation parameter n_components).
Initialization is with normally-distributed means and identity
covariance, for proper convergence.
Parameters
----------
n_components: int, optional
Number of mixture components. Defaults to 1.
covariance_type: string, optional
String describing the type of covariance parameters to
use. Must be one of 'spherical', 'tied', 'diag', 'full'.
Defaults to 'diag'.
alpha: float, optional
Real number representing the concentration parameter of
the dirichlet process. Intuitively, the Dirichlet Process
is as likely to start a new cluster for a point as it is
to add that point to a cluster with alpha elements. A
higher alpha means more clusters, as the expected number
of clusters is ``alpha*log(N)``. Defaults to 1.
thresh : float, optional
Convergence threshold.
n_iter : int, optional
Maximum number of iterations to perform before convergence.
params : string, optional
Controls which parameters are updated in the training
process. Can contain any combination of 'w' for weights,
'm' for means, and 'c' for covars. Defaults to 'wmc'.
init_params : string, optional
Controls which parameters are updated in the initialization
process. Can contain any combination of 'w' for weights,
'm' for means, and 'c' for covars. Defaults to 'wmc'.
Attributes
----------
covariance_type : string
String describing the type of covariance parameters used by
the DP-GMM. Must be one of 'spherical', 'tied', 'diag', 'full'.
n_components : int
Number of mixture components.
`weights_` : array, shape (`n_components`,)
Mixing weights for each mixture component.
`means_` : array, shape (`n_components`, `n_features`)
Mean parameters for each mixture component.
`precs_` : array
Precision (inverse covariance) parameters for each mixture
component. The shape depends on `covariance_type`::
(`n_components`, 'n_features') if 'spherical',
(`n_features`, `n_features`) if 'tied',
(`n_components`, `n_features`) if 'diag',
(`n_components`, `n_features`, `n_features`) if 'full'
`converged_` : bool
True when convergence was reached in fit(), False otherwise.
See Also
--------
GMM : Finite Gaussian mixture model fit with EM
VBGMM : Finite Gaussian mixture model fit with a variational
algorithm, better for situations where there might be too little
data to get a good estimate of the covariance matrix.
"""
def __init__(self, n_components=1, covariance_type='diag', alpha=1.0,
random_state=None, thresh=1e-2, verbose=False,
min_covar=None, n_iter=10, params='wmc', init_params='wmc'):
self.alpha = alpha
self.verbose = verbose
super(DPGMM, self).__init__(n_components, covariance_type,
random_state=random_state,
thresh=thresh, min_covar=min_covar,
n_iter=n_iter, params=params,
init_params=init_params)
def _get_precisions(self):
"""Return precisions as a full matrix."""
if self.covariance_type == 'full':
return self.precs_
elif self.covariance_type in ['diag', 'spherical']:
return [np.diag(cov) for cov in self.precs_]
elif self.covariance_type == 'tied':
return [self.precs_] * self.n_components
def _get_covars(self):
return [pinvh(c) for c in self._get_precisions()]
def _set_covars(self, covars):
raise NotImplementedError("""The variational algorithm does
not support setting the covariance parameters.""")
@deprecated("DPGMM.eval was renamed to DPGMM.score_samples in 0.14 and "
"will be removed in 0.16.")
def eval(self, X):
return self.score_samples(X)
def score_samples(self, X):
"""Return the likelihood of the data under the model.
Compute the bound on log probability of X under the model
and return the posterior distribution (responsibilities) of
each mixture component for each element of X.
This is done by computing the parameters for the mean-field of
z for each observation.
Parameters
----------
X : array_like, shape (n_samples, n_features)
List of n_features-dimensional data points. Each row
corresponds to a single data point.
Returns
-------
logprob : array_like, shape (n_samples,)
Log probabilities of each data point in X
responsibilities: array_like, shape (n_samples, n_components)
Posterior probabilities of each mixture component for each
observation
"""
X = np.asarray(X)
if X.ndim == 1:
X = X[:, np.newaxis]
z = np.zeros((X.shape[0], self.n_components))
sd = digamma(self.gamma_.T[1] + self.gamma_.T[2])
dgamma1 = digamma(self.gamma_.T[1]) - sd
dgamma2 = np.zeros(self.n_components)
dgamma2[0] = digamma(self.gamma_[0, 2]) - digamma(self.gamma_[0, 1] +
self.gamma_[0, 2])
for j in range(1, self.n_components):
dgamma2[j] = dgamma2[j - 1] + digamma(self.gamma_[j - 1, 2])
dgamma2[j] -= sd[j - 1]
dgamma = dgamma1 + dgamma2
# Free memory and developers cognitive load:
del dgamma1, dgamma2, sd
if self.covariance_type not in ['full', 'tied', 'diag', 'spherical']:
raise NotImplementedError("This ctype is not implemented: %s"
% self.covariance_type)
p = _bound_state_log_lik(X, self._initial_bound + self.bound_prec_,
self.precs_, self.means_,
self.covariance_type)
z = p + dgamma
z = log_normalize(z, axis=-1)
bound = np.sum(z * p, axis=-1)
return bound, z
def _update_concentration(self, z):
"""Update the concentration parameters for each cluster"""
sz = np.sum(z, axis=0)
self.gamma_.T[1] = 1. + sz
self.gamma_.T[2].fill(0)
for i in range(self.n_components - 2, -1, -1):
self.gamma_[i, 2] = self.gamma_[i + 1, 2] + sz[i]
self.gamma_.T[2] += self.alpha
def _update_means(self, X, z):
"""Update the variational distributions for the means"""
n_features = X.shape[1]
for k in range(self.n_components):
if self.covariance_type in ['spherical', 'diag']:
num = np.sum(z.T[k].reshape((-1, 1)) * X, axis=0)
num *= self.precs_[k]
den = 1. + self.precs_[k] * np.sum(z.T[k])
self.means_[k] = num / den
elif self.covariance_type in ['tied', 'full']:
if self.covariance_type == 'tied':
cov = self.precs_
else:
cov = self.precs_[k]
den = np.identity(n_features) + cov * np.sum(z.T[k])
num = np.sum(z.T[k].reshape((-1, 1)) * X, axis=0)
num = np.dot(cov, num)
self.means_[k] = linalg.lstsq(den, num)[0]
def _update_precisions(self, X, z):
"""Update the variational distributions for the precisions"""
n_features = X.shape[1]
if self.covariance_type == 'spherical':
self.dof_ = 0.5 * n_features * np.sum(z, axis=0)
for k in range(self.n_components):
# could be more memory efficient ?
sq_diff = np.sum((X - self.means_[k]) ** 2, axis=1)
self.scale_[k] = 1.
self.scale_[k] += 0.5 * np.sum(z.T[k] * (sq_diff + n_features))
self.bound_prec_[k] = (
0.5 * n_features * (
digamma(self.dof_[k]) - np.log(self.scale_[k])))
self.precs_ = np.tile(self.dof_ / self.scale_, [n_features, 1]).T
elif self.covariance_type == 'diag':
for k in range(self.n_components):
self.dof_[k].fill(1. + 0.5 * np.sum(z.T[k], axis=0))
sq_diff = (X - self.means_[k]) ** 2 # see comment above
self.scale_[k] = np.ones(n_features) + 0.5 * np.dot(
z.T[k], (sq_diff + 1))
self.precs_[k] = self.dof_[k] / self.scale_[k]
self.bound_prec_[k] = 0.5 * np.sum(digamma(self.dof_[k])
- np.log(self.scale_[k]))
self.bound_prec_[k] -= 0.5 * np.sum(self.precs_[k])
elif self.covariance_type == 'tied':
self.dof_ = 2 + X.shape[0] + n_features
self.scale_ = (X.shape[0] + 1) * np.identity(n_features)
for k in range(self.n_components):
diff = X - self.means_[k]
self.scale_ += np.dot(diff.T, z[:, k:k + 1] * diff)
self.scale_ = pinvh(self.scale_)
self.precs_ = self.dof_ * self.scale_
self.det_scale_ = linalg.det(self.scale_)
self.bound_prec_ = 0.5 * wishart_log_det(
self.dof_, self.scale_, self.det_scale_, n_features)
self.bound_prec_ -= 0.5 * self.dof_ * np.trace(self.scale_)
elif self.covariance_type == 'full':
for k in range(self.n_components):
sum_resp = np.sum(z.T[k])
self.dof_[k] = 2 + sum_resp + n_features
self.scale_[k] = (sum_resp + 1) * np.identity(n_features)
diff = X - self.means_[k]
self.scale_[k] += np.dot(diff.T, z[:, k:k + 1] * diff)
self.scale_[k] = pinvh(self.scale_[k])
self.precs_[k] = self.dof_[k] * self.scale_[k]
self.det_scale_[k] = linalg.det(self.scale_[k])
self.bound_prec_[k] = 0.5 * wishart_log_det(
self.dof_[k], self.scale_[k], self.det_scale_[k],
n_features)
self.bound_prec_[k] -= 0.5 * self.dof_[k] * np.trace(
self.scale_[k])
def _monitor(self, X, z, n, end=False):
"""Monitor the lower bound during iteration
Debug method to help see exactly when it is failing to converge as
expected.
Note: this is very expensive and should not be used by default."""
if self.verbose:
print("Bound after updating %8s: %f" % (n, self.lower_bound(X, z)))
if end:
print("Cluster proportions:", self.gamma_.T[1])
print("covariance_type:", self.covariance_type)
def _do_mstep(self, X, z, params):
"""Maximize the variational lower bound
Update each of the parameters to maximize the lower bound."""
self._monitor(X, z, "z")
self._update_concentration(z)
self._monitor(X, z, "gamma")
if 'm' in params:
self._update_means(X, z)
self._monitor(X, z, "mu")
if 'c' in params:
self._update_precisions(X, z)
self._monitor(X, z, "a and b", end=True)
def _initialize_gamma(self):
"Initializes the concentration parameters"
self.gamma_ = self.alpha * np.ones((self.n_components, 3))
def _bound_concentration(self):
"""The variational lower bound for the concentration parameter."""
logprior = gammaln(self.alpha) * self.n_components
logprior += np.sum((self.alpha - 1) * (
digamma(self.gamma_.T[2]) - digamma(self.gamma_.T[1] +
self.gamma_.T[2])))
logprior += np.sum(- gammaln(self.gamma_.T[1] + self.gamma_.T[2]))
logprior += np.sum(gammaln(self.gamma_.T[1]) +
gammaln(self.gamma_.T[2]))
logprior -= np.sum((self.gamma_.T[1] - 1) * (
digamma(self.gamma_.T[1]) - digamma(self.gamma_.T[1] +
self.gamma_.T[2])))
logprior -= np.sum((self.gamma_.T[2] - 1) * (
digamma(self.gamma_.T[2]) - digamma(self.gamma_.T[1] +
self.gamma_.T[2])))
return logprior
def _bound_means(self):
"The variational lower bound for the mean parameters"
logprior = 0.
logprior -= 0.5 * sqnorm(self.means_)
logprior -= 0.5 * self.means_.shape[1] * self.n_components
return logprior
def _bound_precisions(self):
"""Returns the bound term related to precisions"""
logprior = 0.
if self.covariance_type == 'spherical':
logprior += np.sum(gammaln(self.dof_))
logprior -= np.sum(
(self.dof_ - 1) * digamma(np.maximum(0.5, self.dof_)))
logprior += np.sum(- np.log(self.scale_) + self.dof_
- self.precs_[:, 0])
elif self.covariance_type == 'diag':
logprior += np.sum(gammaln(self.dof_))
logprior -= np.sum(
(self.dof_ - 1) * digamma(np.maximum(0.5, self.dof_)))
logprior += np.sum(- np.log(self.scale_) + self.dof_ - self.precs_)
elif self.covariance_type == 'tied':
logprior += _bound_wishart(self.dof_, self.scale_, self.det_scale_)
elif self.covariance_type == 'full':
for k in range(self.n_components):
logprior += _bound_wishart(self.dof_[k],
self.scale_[k],
self.det_scale_[k])
return logprior
def _bound_proportions(self, z):
"""Returns the bound term related to proportions"""
dg12 = digamma(self.gamma_.T[1] + self.gamma_.T[2])
dg1 = digamma(self.gamma_.T[1]) - dg12
dg2 = digamma(self.gamma_.T[2]) - dg12
cz = np.cumsum(z[:, ::-1], axis=-1)[:, -2::-1]
logprior = np.sum(cz * dg2[:-1]) + np.sum(z * dg1)
del cz # Save memory
z_non_zeros = z[z > np.finfo(np.float32).eps]
logprior -= np.sum(z_non_zeros * np.log(z_non_zeros))
return logprior
def _logprior(self, z):
logprior = self._bound_concentration()
logprior += self._bound_means()
logprior += self._bound_precisions()
logprior += self._bound_proportions(z)
return logprior
def lower_bound(self, X, z):
"""returns a lower bound on model evidence based on X and membership"""
if self.covariance_type not in ['full', 'tied', 'diag', 'spherical']:
raise NotImplementedError("This ctype is not implemented: %s"
% self.covariance_type)
X = np.asarray(X)
if X.ndim == 1:
X = X[:, np.newaxis]
c = np.sum(z * _bound_state_log_lik(X, self._initial_bound +
self.bound_prec_, self.precs_,
self.means_, self.covariance_type))
return c + self._logprior(z)
def _set_weights(self):
for i in xrange(self.n_components):
self.weights_[i] = self.gamma_[i, 1] / (self.gamma_[i, 1]
+ self.gamma_[i, 2])
self.weights_ /= np.sum(self.weights_)
def fit(self, X):
"""Estimate model parameters with the variational
algorithm.
For a full derivation and description of the algorithm see
doc/dp-derivation/dp-derivation.tex
A initialization step is performed before entering the em
algorithm. If you want to avoid this step, set the keyword
argument init_params to the empty string '' when when creating
the object. Likewise, if you would like just to do an
initialization, set n_iter=0.
Parameters
----------
X : array_like, shape (n, n_features)
List of n_features-dimensional data points. Each row
corresponds to a single data point.
"""
self.random_state = check_random_state(self.random_state)
## initialization step
X = np.asarray(X)
if X.ndim == 1:
X = X[:, np.newaxis]
n_features = X.shape[1]
z = np.ones((X.shape[0], self.n_components))
z /= self.n_components
self._initial_bound = - 0.5 * n_features * np.log(2 * np.pi)
self._initial_bound -= np.log(2 * np.pi * np.e)
if (self.init_params != '') or not hasattr(self, 'gamma_'):
self._initialize_gamma()
if 'm' in self.init_params or not hasattr(self, 'means_'):
self.means_ = cluster.KMeans(
n_clusters=self.n_components,
random_state=self.random_state).fit(X).cluster_centers_[::-1]
if 'w' in self.init_params or not hasattr(self, 'weights_'):
self.weights_ = np.tile(1.0 / self.n_components, self.n_components)
if 'c' in self.init_params or not hasattr(self, 'precs_'):
if self.covariance_type == 'spherical':
self.dof_ = np.ones(self.n_components)
self.scale_ = np.ones(self.n_components)
self.precs_ = np.ones((self.n_components, n_features))
self.bound_prec_ = 0.5 * n_features * (
digamma(self.dof_) - np.log(self.scale_))
elif self.covariance_type == 'diag':
self.dof_ = 1 + 0.5 * n_features
self.dof_ *= np.ones((self.n_components, n_features))
self.scale_ = np.ones((self.n_components, n_features))
self.precs_ = np.ones((self.n_components, n_features))
self.bound_prec_ = 0.5 * (np.sum(digamma(self.dof_) -
np.log(self.scale_), 1))
self.bound_prec_ -= 0.5 * np.sum(self.precs_, 1)
elif self.covariance_type == 'tied':
self.dof_ = 1.
self.scale_ = np.identity(n_features)
self.precs_ = np.identity(n_features)
self.det_scale_ = 1.
self.bound_prec_ = 0.5 * wishart_log_det(
self.dof_, self.scale_, self.det_scale_, n_features)
self.bound_prec_ -= 0.5 * self.dof_ * np.trace(self.scale_)
elif self.covariance_type == 'full':
self.dof_ = (1 + self.n_components + X.shape[0])
self.dof_ *= np.ones(self.n_components)
self.scale_ = [2 * np.identity(n_features)
for _ in range(self.n_components)]
self.precs_ = [np.identity(n_features)
for _ in range(self.n_components)]
self.det_scale_ = np.ones(self.n_components)
self.bound_prec_ = np.zeros(self.n_components)
for k in range(self.n_components):
self.bound_prec_[k] = wishart_log_det(
self.dof_[k], self.scale_[k], self.det_scale_[k],
n_features)
self.bound_prec_[k] -= (self.dof_[k] *
np.trace(self.scale_[k]))
self.bound_prec_ *= 0.5
logprob = []
# reset self.converged_ to False
self.converged_ = False
for i in range(self.n_iter):
# Expectation step
curr_logprob, z = self.score_samples(X)
logprob.append(curr_logprob.sum() + self._logprior(z))
# Check for convergence.
if i > 0 and abs(logprob[-1] - logprob[-2]) < self.thresh:
self.converged_ = True
break
# Maximization step
self._do_mstep(X, z, self.params)
self._set_weights()
return self
class VBGMM(DPGMM):
"""Variational Inference for the Gaussian Mixture Model
Variational inference for a Gaussian mixture model probability
distribution. This class allows for easy and efficient inference
of an approximate posterior distribution over the parameters of a
Gaussian mixture model with a fixed number of components.
Initialization is with normally-distributed means and identity
covariance, for proper convergence.
Parameters
----------
n_components: int, optional
Number of mixture components. Defaults to 1.
covariance_type: string, optional
String describing the type of covariance parameters to
use. Must be one of 'spherical', 'tied', 'diag', 'full'.
Defaults to 'diag'.
alpha: float, optional
Real number representing the concentration parameter of
the dirichlet distribution. Intuitively, the higher the
value of alpha the more likely the variational mixture of
Gaussians model will use all components it can. Defaults
to 1.
Attributes
----------
covariance_type : string
String describing the type of covariance parameters used by
the DP-GMM. Must be one of 'spherical', 'tied', 'diag', 'full'.
n_features : int
Dimensionality of the Gaussians.
n_components : int (read-only)
Number of mixture components.
`weights_` : array, shape (`n_components`,)
Mixing weights for each mixture component.
`means_` : array, shape (`n_components`, `n_features`)
Mean parameters for each mixture component.
`precs_` : array
Precision (inverse covariance) parameters for each mixture
component. The shape depends on `covariance_type`::
(`n_components`, 'n_features') if 'spherical',
(`n_features`, `n_features`) if 'tied',
(`n_components`, `n_features`) if 'diag',
(`n_components`, `n_features`, `n_features`) if 'full'
`converged_` : bool
True when convergence was reached in fit(), False
otherwise.
See Also
--------
GMM : Finite Gaussian mixture model fit with EM
DPGMM : Ininite Gaussian mixture model, using the dirichlet
process, fit with a variational algorithm
"""
def __init__(self, n_components=1, covariance_type='diag', alpha=1.0,
random_state=None, thresh=1e-2, verbose=False,
min_covar=None, n_iter=10, params='wmc', init_params='wmc'):
super(VBGMM, self).__init__(
n_components, covariance_type, random_state=random_state,
thresh=thresh, verbose=verbose, min_covar=min_covar,
n_iter=n_iter, params=params, init_params=init_params)
self.alpha = float(alpha) / n_components
@deprecated("VBGMM.eval was renamed to VBGMM.score_samples in 0.14 and"
" will be removed in 0.16.")
def eval(self, X):
return self.score_samples(X)
def score_samples(self, X):
"""Return the likelihood of the data under the model.
Compute the bound on log probability of X under the model
and return the posterior distribution (responsibilities) of
each mixture component for each element of X.
This is done by computing the parameters for the mean-field of
z for each observation.
Parameters
----------
X : array_like, shape (n_samples, n_features)
List of n_features-dimensional data points. Each row
corresponds to a single data point.
Returns
-------
logprob : array_like, shape (n_samples,)
Log probabilities of each data point in X
responsibilities: array_like, shape (n_samples, n_components)
Posterior probabilities of each mixture component for each
observation
"""
X = np.asarray(X)
if X.ndim == 1:
X = X[:, np.newaxis]
z = np.zeros((X.shape[0], self.n_components))
p = np.zeros(self.n_components)
bound = np.zeros(X.shape[0])
dg = digamma(self.gamma_) - digamma(np.sum(self.gamma_))
if self.covariance_type not in ['full', 'tied', 'diag', 'spherical']:
raise NotImplementedError("This ctype is not implemented: %s"
% self.covariance_type)
p = _bound_state_log_lik(X, self._initial_bound + self.bound_prec_,
self.precs_, self.means_,
self.covariance_type)
z = p + dg
z = log_normalize(z, axis=-1)
bound = np.sum(z * p, axis=-1)
return bound, z
def _update_concentration(self, z):
for i in range(self.n_components):
self.gamma_[i] = self.alpha + np.sum(z.T[i])
def _initialize_gamma(self):
self.gamma_ = self.alpha * np.ones(self.n_components)
def _bound_proportions(self, z):
logprior = 0.
dg = digamma(self.gamma_)
dg -= digamma(np.sum(self.gamma_))
logprior += np.sum(dg.reshape((-1, 1)) * z.T)
z_non_zeros = z[z > np.finfo(np.float32).eps]
logprior -= np.sum(z_non_zeros * np.log(z_non_zeros))
return logprior
def _bound_concentration(self):
logprior = 0.
logprior = gammaln(np.sum(self.gamma_)) - gammaln(self.n_components
* self.alpha)
logprior -= np.sum(gammaln(self.gamma_) - gammaln(self.alpha))
sg = digamma(np.sum(self.gamma_))
logprior += np.sum((self.gamma_ - self.alpha)
* (digamma(self.gamma_) - sg))
return logprior
def _monitor(self, X, z, n, end=False):
"""Monitor the lower bound during iteration
Debug method to help see exactly when it is failing to converge as
expected.
Note: this is very expensive and should not be used by default."""
if self.verbose:
print("Bound after updating %8s: %f" % (n, self.lower_bound(X, z)))
if end:
print("Cluster proportions:", self.gamma_)
print("covariance_type:", self.covariance_type)
def _set_weights(self):
self.weights_[:] = self.gamma_
self.weights_ /= np.sum(self.weights_)
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