From 1f9f96577927c59fd8ae0f5ae57795e9117f0a79 Mon Sep 17 00:00:00 2001
From: Fabian Pedregosa <fabian.pedregosa@inria.fr>
Date: Wed, 23 Feb 2011 11:14:55 +0100
Subject: [PATCH] More cosmetic changes in GMM.
---
scikits/learn/mixture.py | 17 ++++++++++-------
scikits/learn/tests/test_mixture.py | 14 +++++++++-----
2 files changed, 19 insertions(+), 12 deletions(-)
diff --git a/scikits/learn/mixture.py b/scikits/learn/mixture.py
index 2f66c780f9..dcd78d6d7e 100644
--- a/scikits/learn/mixture.py
+++ b/scikits/learn/mixture.py
@@ -79,23 +79,26 @@ def lmvnpdf(obs, means, covars, cvtype='diag'):
return lmvnpdf_dict[cvtype](obs, means, covars)
-def sample_gaussian(mean, covar, cvtype='diag', n=1):
+def sample_gaussian(mean, covar, cvtype='diag', n_samples=1):
"""Generate random samples from a Gaussian distribution.
Parameters
----------
mean : array_like, shape (n_features,)
Mean of the distribution.
- covars : array_like
+
+ covars : array_like, optional
Covariance of the distribution. The shape depends on `cvtype`:
scalar if 'spherical',
(D) if 'diag',
(D, D) if 'tied', or 'full'
- cvtype : string
+
+ cvtype : string, optional
Type of the covariance parameters. Must be one of
'spherical', 'tied', 'diag', 'full'. Defaults to 'diag'.
- n : int
- Number of samples to generate.
+
+ n_samples : int, optional
+ Number of samples to generate. Defaults to 1.
Returns
-------
@@ -103,8 +106,8 @@ def sample_gaussian(mean, covar, cvtype='diag', n=1):
Randomly generated sample
"""
ndim = len(mean)
- rand = np.random.randn(ndim, n)
- if n == 1:
+ rand = np.random.randn(ndim, n_samples)
+ if n_samples == 1:
rand.shape = (ndim,)
if cvtype == 'spherical':
diff --git a/scikits/learn/tests/test_mixture.py b/scikits/learn/tests/test_mixture.py
index 6a02eee9e3..6135452c13 100644
--- a/scikits/learn/tests/test_mixture.py
+++ b/scikits/learn/tests/test_mixture.py
@@ -62,22 +62,26 @@ def test_sample_gaussian():
mu = np.random.randint(10) * np.random.rand(n_features)
cv = (np.random.rand(n_features) + 1.0) ** 2
- samples = mixture.sample_gaussian(mu, cv, cvtype='diag', n=n_samples)
+ samples = mixture.sample_gaussian(
+ mu, cv, cvtype='diag', n_samples=n_samples)
assert np.allclose(samples.mean(axis), mu, atol=0.3)
assert np.allclose(samples.var(axis), cv, atol=0.5)
# the same for spherical covariances
cv = (np.random.rand() + 1.0) ** 2
- samples = mixture.sample_gaussian(mu, cv, cvtype='spherical', n=n_samples)
+ samples = mixture.sample_gaussian(
+ mu, cv, cvtype='spherical', n_samples=n_samples)
assert np.allclose(samples.mean(axis), mu, atol=0.3)
- assert np.allclose(samples.var(axis), np.repeat(cv, n_features), atol=0.5)
+ assert np.allclose(
+ samples.var(axis), np.repeat(cv, n_features), atol=0.5)
# and for full covariances
A = np.random.randn(n_features, n_features)
cv = np.dot(A.T, A) + np.eye(n_features)
- samples = mixture.sample_gaussian(mu, cv, cvtype='full', n=n_samples)
+ samples = mixture.sample_gaussian(
+ mu, cv, cvtype='full', n_samples=n_samples)
assert np.allclose(samples.mean(axis), mu, atol=0.3)
assert np.allclose(np.cov(samples), cv, atol=1.)
@@ -219,7 +223,7 @@ class GMMTester():
g._covars = 20 * self.covars[self.cvtype]
# Create a training set by sampling from the predefined distribution.
- train_obs = g.rvs(n=100)
+ train_obs = g.rvs(n_samples=100)
g.fit(train_obs, n_iter=0, init_params=params)
--
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