From 7c53b1ec87f20b081235934bca872d0d5cf34de2 Mon Sep 17 00:00:00 2001
From: Fabian Pedregosa <fabian.pedregosa@inria.fr>
Date: Wed, 23 Feb 2011 10:59:47 +0100
Subject: [PATCH] Second patch by f0k.
---
scikits/learn/mixture.py | 20 ++++++++++----------
1 file changed, 10 insertions(+), 10 deletions(-)
diff --git a/scikits/learn/mixture.py b/scikits/learn/mixture.py
index af843a06b0..b5482d8973 100644
--- a/scikits/learn/mixture.py
+++ b/scikits/learn/mixture.py
@@ -405,17 +405,17 @@ class GMM(BaseEstimator):
obs = np.empty((n, self.n_features))
rand = np.random.rand(n)
# decide which component to use for each sample
- c = weight_cdf.searchsorted(rand)
+ comps = weight_cdf.searchsorted(rand)
# for each component, generate all needed samples
- for cc in xrange(self._n_states):
- ccc = (c==cc) # occurences of current component in obs
- nccc = ccc.sum() # number of those occurences
- if nccc > 0:
- if self._cvtype == 'tied':
- cv = self._covars
- else:
- cv = self._covars[cc]
- obs[ccc] = sample_gaussian(self._means[cc], cv, self._cvtype, nccc).T
+ for comp in xrange(self._n_states):
+ comp_in_obs = (comp==comps) # occurrences of current component in obs
+ num_comp_in_obs = comp_in_obs.sum() # number of those occurrences
+ if num_comp_in_obs > 0:
+ if self._cvtype == 'tied':
+ cv = self._covars
+ else:
+ cv = self._covars[comp]
+ obs[comp_in_obs] = sample_gaussian(self._means[comp], cv, self._cvtype, num_comp_in_obs).T
return obs
def fit(self, X, n_iter=10, min_covar=1e-3, thresh=1e-2, params='wmc',
--
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