Cosmetic fixes in GMM.

Fabian Pedregosa · Fabian Pedregosa · commit e76eec53b2d4 · 2011-02-27T13:04:12.000+01:00
diff --git a/scikits/learn/mixture.py b/scikits/learn/mixture.py
@@ -134,9 +134,10 @@ class GMM(BaseEstimator):
 
     Parameters
     ----------
-    n_states : int
-        Number of mixture components.
-    cvtype : string (read-only)
+    n_states : int, optional
+        Number of mixture components. Defaults to 1.
+
+    cvtype : string (read-only), optional
         String describing the type of covariance parameters to
         use.  Must be one of 'spherical', 'tied', 'diag', 'full'.
         Defaults to 'diag'.
@@ -386,36 +387,39 @@ def predict_proba(self, X):
         logprob, posteriors = self.eval(X)
         return posteriors
 
-    def rvs(self, n=1):
+    def rvs(self, n_samples=1):
         """Generate random samples from the model.
 
         Parameters
         ----------
-        n : int
-            Number of samples to generate.
+        n_samples : int, optional
+            Number of samples to generate. Defaults to 1.
 
         Returns
         -------
-        obs : array_like, shape (n, n_features)
+        obs : array_like, shape (n_samples, n_features)
             List of samples
         """
         weight_pdf = self.weights
         weight_cdf = np.cumsum(weight_pdf)
 
-        obs = np.empty((n, self.n_features))
-        rand = np.random.rand(n)
+        obs = np.empty((n_samples, self.n_features))
+        rand = np.random.rand(n_samples)
         # decide which component to use for each sample
         comps = weight_cdf.searchsorted(rand)
         # for each component, generate all needed samples
         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
+            # occurrences of current component in obs
+            comp_in_obs = (comp==comps)
+            # number of those occurrences
+            num_comp_in_obs = comp_in_obs.sum() 
             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
+                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',
