From bbb60337c26403b946fb41ca3ecd804ccd916855 Mon Sep 17 00:00:00 2001
From: Fabian Pedregosa <fabian.pedregosa@inria.fr>
Date: Mon, 21 Feb 2011 10:29:05 +0100
Subject: [PATCH] Improve performance of GMM sampling
Patch by f0k.
---
scikits/learn/mixture.py | 23 ++++++++++++++---------
1 file changed, 14 insertions(+), 9 deletions(-)
diff --git a/scikits/learn/mixture.py b/scikits/learn/mixture.py
index 9798f8552b..af843a06b0 100644
--- a/scikits/learn/mixture.py
+++ b/scikits/learn/mixture.py
@@ -99,7 +99,7 @@ def sample_gaussian(mean, covar, cvtype='diag', n=1):
Returns
-------
- obs : array, shape (n, n_features)
+ obs : array, shape (n_features, n)
Randomly generated sample
"""
ndim = len(mean)
@@ -403,14 +403,19 @@ class GMM(BaseEstimator):
weight_cdf = np.cumsum(weight_pdf)
obs = np.empty((n, self.n_features))
- for x in xrange(n):
- rand = np.random.rand()
- c = (weight_cdf > rand).argmax()
- if self._cvtype == 'tied':
- cv = self._covars
- else:
- cv = self._covars[c]
- obs[x] = sample_gaussian(self._means[c], cv, self._cvtype)
+ rand = np.random.rand(n)
+ # decide which component to use for each sample
+ c = 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
return obs
def fit(self, X, n_iter=10, min_covar=1e-3, thresh=1e-2, params='wmc',
--
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