diff --git a/examples/mixture/gmm_model_selection.py b/examples/mixture/gmm_model_selection.py
index 635add5e34a9ac1776f633a8f17a3b7534ec7a5d..8ea0ec61f2b5700b0b625a460602ce2f727376cd 100644
--- a/examples/mixture/gmm_model_selection.py
+++ b/examples/mixture/gmm_model_selection.py
@@ -64,7 +64,7 @@ spl.legend([b[0] for b in bars], cv_types)
# Plot the winner
splot = pl.subplot(2, 1, 2)
Y_ = clf.predict(X)
-for i, (mean, covar, color) in enumerate(zip(clf.means, clf.covars,
+for i, (mean, covar, color) in enumerate(zip(clf.means_, clf.covars_,
color_iter)):
v, w = linalg.eigh(covar)
if not np.any(Y_ == i):
diff --git a/examples/mixture/plot_gmm.py b/examples/mixture/plot_gmm.py
index eeebe2fa0835a386b567f88b28cd99732379ab35..2d97a3b1677415ed6c0ba29baa435b7e061a5964 100644
--- a/examples/mixture/plot_gmm.py
+++ b/examples/mixture/plot_gmm.py
@@ -52,8 +52,8 @@ for i, (clf, title) in enumerate([(gmm, 'GMM'),
(dpgmm, 'Dirichlet Process GMM')]):
splot = pl.subplot(2, 1, 1 + i)
Y_ = clf.predict(X)
- for i, (mean, covar, color) in enumerate(zip(clf.means_, clf.covars_,
- color_iter)):
+ for i, (mean, covar, color) in enumerate(zip(
+ clf._get_means(), clf._get_covars(), color_iter)):
v, w = linalg.eigh(covar)
u = w[0] / linalg.norm(w[0])
# as the DP will not use every component it has access to
diff --git a/examples/mixture/plot_gmm_classifier.py b/examples/mixture/plot_gmm_classifier.py
index f7aa1585d7ddd369d7c28994e03d23e3880e4eb1..835df829f69a99fa64e09d608053d255fb80aa31 100644
--- a/examples/mixture/plot_gmm_classifier.py
+++ b/examples/mixture/plot_gmm_classifier.py
@@ -37,13 +37,13 @@ from sklearn.mixture import GMM
def make_ellipses(gmm, ax):
for n, color in enumerate('rgb'):
- v, w = np.linalg.eigh(gmm.covars[n][:2, :2])
+ v, w = np.linalg.eigh(gmm._get_covars()[n][:2, :2])
u = w[0] / np.linalg.norm(w[0])
angle = np.arctan2(u[1], u[0])
angle = 180 * angle / np.pi # convert to degrees
v *= 9
- ell = mpl.patches.Ellipse(gmm.means[n, :2], v[0], v[1], 180 + angle,
- color=color)
+ ell = mpl.patches.Ellipse(gmm._get_means()[n, :2], v[0], v[1],
+ 180 + angle, color=color)
ell.set_clip_box(ax.bbox)
ell.set_alpha(0.5)
ax.add_artist(ell)
@@ -78,9 +78,9 @@ pl.subplots_adjust(bottom=.01, top=0.95, hspace=.15, wspace=.05,
for index, (name, classifier) in enumerate(classifiers.iteritems()):
# Since we have class labels for the training data, we can
# initialize the GMM parameters in a supervised manner.
- classifier.means = [X_train[y_train == i, :].mean(axis=0)
- for i in xrange(n_classes)]
-
+ classifier.means_ = np.array([X_train[y_train == i, :].mean(axis=0)
+ for i in xrange(n_classes)])
+
# Train the other parameters using the EM algorithm.
classifier.fit(X_train, init_params='wc', n_iter=20)
diff --git a/examples/mixture/plot_gmm_sin.py b/examples/mixture/plot_gmm_sin.py
index 31773855f261079ef9bf24432afb84af6cd84a2c..dc65dcadbb8643bd052fc0e3f9171c36ac0e187c 100644
--- a/examples/mixture/plot_gmm_sin.py
+++ b/examples/mixture/plot_gmm_sin.py
@@ -52,8 +52,8 @@ for i, (clf, title) in enumerate([
clf.fit(X, n_iter=100)
splot = pl.subplot(3, 1, 1 + i)
Y_ = clf.predict(X)
- for i, (mean, covar, color) in enumerate(zip(clf.means, clf.covars,
- color_iter)):
+ for i, (mean, covar, color) in enumerate(zip(
+ clf._get_means(), clf._get_covars(), color_iter)):
v, w = linalg.eigh(covar)
u = w[0] / linalg.norm(w[0])
# as the DP will not use every component it has access to
diff --git a/sklearn/mixture/dpgmm.py b/sklearn/mixture/dpgmm.py
index 12328b0e814a7c934ab53b550ee15630d5468420..23b132d6cc731e0db66d87b9c1ad2f9ddb0fedf5 100644
--- a/sklearn/mixture/dpgmm.py
+++ b/sklearn/mixture/dpgmm.py
@@ -57,8 +57,7 @@ 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 += (0.25 * (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
@@ -499,7 +498,7 @@ class DPGMM(GMM):
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 = - 0.5 * n_features * np.log(2 * np.pi)
self._initial_bound -= np.log(2 * np.pi * np.e)
if (init_params != '') or not hasattr(self, 'gamma_'):
@@ -510,7 +509,7 @@ class DPGMM(GMM):
k=self.n_components, random_state=self.random_state
).fit(X).cluster_centers_[::-1]
- if 'w' in init_params or not hasattr(self, 'log_weights_'): # fixme
+ if 'w' in init_params or not hasattr(self, 'weights_'):
self._set_weights(np.tile(1.0 / self.n_components,
self.n_components))
diff --git a/sklearn/mixture/gmm.py b/sklearn/mixture/gmm.py
index 5388976323f61efcc77c43d51c1ffad9539ed23e..87166d5a41c94076814b5f638e245e76defefdd1 100644
--- a/sklearn/mixture/gmm.py
+++ b/sklearn/mixture/gmm.py
@@ -139,8 +139,8 @@ class GMM(BaseEstimator):
covariance_type : string (read-only)
String describing the type of covariance parameters used by
the GMM. Must be one of 'spherical', 'tied', 'diag', 'full'.
- log_weights_ : array, shape (`n_components`,)
- log of mixing weights for each mixture component.
+ weights_ : array, shape (`n_components`,)
+ Mixing weights for each mixture component.
means_ : array, shape (`n_components`, `n_features`)
Mean parameters for each mixture component.
covars_ : array
@@ -212,8 +212,7 @@ class GMM(BaseEstimator):
if not covariance_type in ['spherical', 'tied', 'diag', 'full']:
raise ValueError('bad covariance_type: ' + str(covariance_type))
- self.log_weights_ = - np.ones(self.n_components) * \
- np.log(self.n_components)
+ self.weights_ = np.ones(self.n_components) / self.n_components
# flag to indicate exit status of fit() method: converged (True) or
# n_iter reached (False)
@@ -272,7 +271,7 @@ class GMM(BaseEstimator):
def _get_weights(self):
"""Mixing weights for each mixture component.
array, shape ``(n_states,)``"""
- return np.exp(self.log_weights_)
+ return self.weights_
def _set_weights(self, weights):
"""Provide value for micture weights"""
@@ -280,7 +279,7 @@ class GMM(BaseEstimator):
raise ValueError('weights must have length n_components')
if not np.allclose(np.sum(weights), 1.0):
raise ValueError('weights must sum to 1.0')
- self.log_weights_ = np.log(np.asarray(weights).copy())
+ self.weights_ = np.asarray(weights).copy()
def eval(self, X):
"""Evaluate the model on data
@@ -310,10 +309,10 @@ class GMM(BaseEstimator):
return np.array([]), np.empty((0, self.n_components))
if X.shape[1] != self.means_.shape[1]:
raise ValueError('the shape of X is not compatible with self')
-
+
lpr = (log_multivariate_normal_density(
X, self.means_, self.covars_, self._covariance_type)
- + self.log_weights_)
+ + np.log(self.weights_))
logprob = logsumexp(lpr, axis=1)
responsibilities = np.exp(lpr - logprob[:, np.newaxis])
return logprob, responsibilities
@@ -407,8 +406,7 @@ class GMM(BaseEstimator):
if random_state is None:
random_state = self.random_state
random_state = check_random_state(random_state)
- weight_pdf = np.exp(self.log_weights_)
- weight_cdf = np.cumsum(weight_pdf)
+ weight_cdf = np.cumsum(self.weights_)
X = np.empty((n_samples, self.means_.shape[1]))
rand = random_state.rand(n_samples)
@@ -484,8 +482,8 @@ class GMM(BaseEstimator):
k=self.n_components).fit(X).cluster_centers_
if 'w' in init_params or not hasattr(self, 'weights_'):
- self._set_weights(np.tile(1.0 / self.n_components,
- self.n_components))
+ self.weights_ = np.tile(1.0 / self.n_components,
+ self.n_components)
if 'c' in init_params or not hasattr(self, 'covars_'):
cv = np.cov(X.T) + self.min_covar * np.eye(X.shape[1])
@@ -517,13 +515,13 @@ class GMM(BaseEstimator):
if n_iter:
if log_likelihood[-1] > max_log_prob:
max_log_prob = log_likelihood[-1]
- best_params = {'weights': self._get_weights(),
+ best_params = {'weights': self.weights_,
'means': self.means_,
'covars': self.covars_}
if n_iter:
self.covars_ = best_params['covars']
self.means_ = best_params['means']
- self._set_weights(best_params['weights'])
+ self.weights_ = best_params['weights']
return self
def _do_mstep(self, X, responsibilities, params, min_covar=0):
@@ -534,8 +532,8 @@ class GMM(BaseEstimator):
inverse_weights = 1.0 / (weights[:, np.newaxis] + 10 * INF_EPS)
if 'w' in params:
- self.log_weights_ = np.log(
- weights / (weights.sum() + 10 * INF_EPS) + INF_EPS)
+ self.weights_ = (weights / (weights.sum() + 10 * INF_EPS) +
+ INF_EPS)
if 'm' in params:
self.means_ = weighted_X_sum * inverse_weights
if 'c' in params: