diff --git a/scikits/learn/datasets/base.py b/scikits/learn/datasets/base.py
index 4dccd0c73ceff5b3bfd29bd6911094b180b360b8..290dc8dd2a6feb1d2c006322478fad16e6da96bf 100644
--- a/scikits/learn/datasets/base.py
+++ b/scikits/learn/datasets/base.py
@@ -40,11 +40,14 @@ def load_iris():
Let's say you are interested in the samples 10, 25, and 50, and want to
know their class name.
+ >>> from scikits.learn.datasets import load_iris
>>> data = load_iris()
- >>> print data.target[[10, 25, 50]]
- [0 0 1]
- >>> print data.target_names
- ['setosa' 'versicolor' 'virginica']
+ >>> data.target[[10, 25, 50]]
+ array([0, 0, 1])
+ >>> data.target_names
+ array(['setosa', 'versicolor', 'virginica'],
+ dtype='|S10')
+
"""
data_file = csv.reader(open(os.path.dirname(__file__)
diff --git a/scikits/learn/glm/ridge.py b/scikits/learn/glm/ridge.py
index 80127b08be7f79b16a6911c3ff02f675ddf6b3e3..6836759b791f017d6327b8062c972674109276cc 100644
--- a/scikits/learn/glm/ridge.py
+++ b/scikits/learn/glm/ridge.py
@@ -1,3 +1,7 @@
+"""
+Ridge regression
+"""
+
import numpy as np
from scipy import linalg
@@ -19,6 +23,7 @@ class Ridge(LinearModel):
Examples
--------
+ >>> from scikits.learn.glm import Ridge
>>> import numpy as np
>>> n_samples, n_features = 10, 5
>>> np.random.seed(0)
diff --git a/scikits/learn/gmm.py b/scikits/learn/gmm.py
index 12adc5118adffa37e6e42501db4bed348175fb45..9011387b2cc7968f85c31cb8ddb0408fd370c7d1 100644
--- a/scikits/learn/gmm.py
+++ b/scikits/learn/gmm.py
@@ -1,6 +1,7 @@
-#
-# Gaussian Mixture Models
-#
+"""
+Gaussian Mixture Models
+"""
+
# Author: Ron Weiss <ronweiss@gmail.com>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
#
@@ -184,43 +185,56 @@ class GMM(BaseEstimator):
Examples
--------
>>> import numpy as np
+ >>> from scikits.learn.gmm import GMM
>>> g = GMM(n_states=2, n_dim=1)
>>> # The initial parameters are fixed.
- >>> print np.round(g.weights, 2)
- [ 0.5 0.5]
- >>> print np.round(g.means, 2)
- [[ 0.]
- [ 0.]]
- >>> print np.round(g.covars, 2)
- [[[ 1.]]
+ >>> np.round(g.weights, 2)
+ array([ 0.5, 0.5])
+ >>> np.round(g.means, 2)
+ array([[ 0.],
+ [ 0.]])
+ >>> np.round(g.covars, 2)
+ array([[[ 1.]],
<BLANKLINE>
- [[ 1.]]]
+ [[ 1.]]])
>>> # Generate random observations with two modes centered on 0
>>> # and 10 to use for training.
>>> np.random.seed(0)
>>> obs = np.concatenate((np.random.randn(100, 1),
... 10 + np.random.randn(300, 1)))
- >>> _ = g.fit(obs)
- >>> print np.round(g.weights, 2)
- [ 0.75 0.25]
- >>> print np.round(g.means, 2)
- [[ 9.94]
- [ 0.06]]
- >>> print np.round(g.covars, 2)
- [[[ 0.96]]
+ >>> g.fit(obs)
+ GMM(n_dim=1, cvtype='diag',
+ means=array([[ 9.94199],
+ [ 0.05981]]),
+ covars=[array([[ 0.96081]]), array([[ 1.01683]])], n_states=2,
+ weights=array([ 0.75, 0.25]))
+
+ >>> np.round(g.weights, 2)
+ array([ 0.75, 0.25])
+ >>> np.round(g.means, 2)
+ array([[ 9.94],
+ [ 0.06]])
+ >>> np.round(g.covars, 2)
+ array([[[ 0.96]],
<BLANKLINE>
- [[ 1.02]]]
- >>> print g.predict([[0], [2], [9], [10]])
- [1 1 0 0]
- >>> print np.round(g.score([[0], [2], [9], [10]]), 2)
- [-2.32 -4.16 -1.65 -1.19]
+ [[ 1.02]]])
+ >>> g.predict([[0], [2], [9], [10]])
+ array([1, 1, 0, 0])
+ >>> np.round(g.score([[0], [2], [9], [10]]), 2)
+ array([-2.32, -4.16, -1.65, -1.19])
>>> # Refit the model on new data (initial parameters remain the
>>> #same), this time with an even split between the two modes.
- >>> _ = g.fit(20 * [[0]] + 20 * [[10]])
- >>> print np.round(g.weights, 2)
- [ 0.5 0.5]
+ >>> g.fit(20 * [[0]] + 20 * [[10]])
+ GMM(n_dim=1, cvtype='diag',
+ means=array([[ 10.],
+ [ 0.]]),
+ covars=[array([[ 0.001]]), array([[ 0.001]])], n_states=2,
+ weights=array([ 0.5, 0.5]))
+
+ >>> np.round(g.weights, 2)
+ array([ 0.5, 0.5])
"""
def __init__(self, n_states=1, n_dim=1, cvtype='diag', weights=None,
diff --git a/scikits/learn/grid_search.py b/scikits/learn/grid_search.py
index 6032ae6047c348d442499861dffc97b1197d5047..bed1bc5893c084c4355cbcd6fa5ae92ec5161fc2 100644
--- a/scikits/learn/grid_search.py
+++ b/scikits/learn/grid_search.py
@@ -41,9 +41,11 @@ def iter_grid(param_grid):
Examples
---------
+ >>> from scikits.learn.grid_search import iter_grid
>>> param_grid = {'a':[1, 2], 'b':[True, False]}
>>> list(iter_grid(param_grid))
[{'a': 1, 'b': True}, {'a': 1, 'b': False}, {'a': 2, 'b': True}, {'a': 2, 'b': False}]
+
"""
if hasattr(param_grid, 'has_key'):
param_grid = [param_grid]
@@ -79,6 +81,7 @@ def fit_grid_point(X, y, base_clf, clf_params, cv, loss_func, iid,
return clf, score
+################################################################################
class GridSearchCV(object):
"""
Grid search on the parameters of a classifier.
@@ -126,13 +129,14 @@ class GridSearchCV(object):
>>> import numpy as np
>>> from scikits.learn.cross_val import LeaveOneOut
>>> from scikits.learn.svm import SVR
+ >>> from scikits.learn.grid_search import GridSearchCV
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
>>> y = np.array([1, 1, 2, 2])
>>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
>>> svr = SVR()
>>> clf = GridSearchCV(svr, parameters, n_jobs=1)
- >>> print clf.fit(X, y).predict([[-0.8, -1]])
- [ 1.]
+ >>> clf.fit(X, y).predict([[-0.8, -1]])
+ array([ 1.])
"""
def __init__(self, estimator, param_grid, loss_func=None,
diff --git a/scikits/learn/hmm.py b/scikits/learn/hmm.py
index fb26abb714a2d97cd9548b35dc4de38c0f7714ae..ca5e60b03f57e01f1f375dc3b458a3909edaef1a 100644
--- a/scikits/learn/hmm.py
+++ b/scikits/learn/hmm.py
@@ -569,7 +569,18 @@ class GaussianHMM(_BaseHMM):
Examples
--------
- >>> ghmm = GaussianHMM(n_states=2, n_dim=1)
+ >>> from scikits.learn.hmm import GaussianHMM
+ >>> GaussianHMM(n_states=2, n_dim=1)
+ GaussianHMM(n_dim=1, cvtype='diag', n_states=2, means_weight=0,
+ means=array([[ 0.],
+ [ 0.]]),
+ covars=array([[ 1.],
+ [ 1.]]), labels=[None, None],
+ startprob=array([ 0.5, 0.5]),
+ transmat=array([[ 0.5, 0.5],
+ [ 0.5, 0.5]]),
+ startprob_prior=1.0, transmat_prior=1.0, means_prior=None,
+ covars_weight=1, covars_prior=0.01)
See Also
--------
@@ -808,8 +819,17 @@ class MultinomialHMM(_BaseHMM):
Examples
--------
- >>> mhmm = MultinomialHMM(n_states=2, nsymbols=3)
-
+ >>> from scikits.learn.hmm import MultinomialHMM
+ >>> MultinomialHMM(n_states=2, nsymbols=3)
+ MultinomialHMM(n_states=2,
+ emissionprob=array([[ 0.3663 , 0.12783, 0.50587],
+ [ 0.35851, 0.21559, 0.42589]]),
+ labels=[None, None], startprob_prior=1.0,
+ startprob=array([ 0.5, 0.5]),
+ transmat=array([[ 0.5, 0.5],
+ [ 0.5, 0.5]]), nsymbols=3,
+ transmat_prior=1.0)
+
See Also
--------
GaussianHMM : HMM with Gaussian emissions
@@ -931,7 +951,9 @@ class GMMHMM(_BaseHMM):
Examples
--------
- >>> hmm = GMMHMM(n_states=2, n_mix=10, n_dim=3)
+ >>> from scikits.learn.hmm import GMMHMM
+ >>> GMMHMM(n_states=2, n_mix=10, n_dim=3) # doctest: +ELLIPSIS
+ GMMHMM(n_dim=3, n_mix=10, n_states=2, cvtype=None, labels=[None, None], ...)
See Also
--------
@@ -957,6 +979,10 @@ class GMMHMM(_BaseHMM):
self._n_dim = n_dim
+ # XXX: Hotfit for n_mix that is incompatible with the scikit's
+ # BaseEstimator API
+ self.n_mix = n_mix
+ self.cvtype= cvtype
if gmms is None:
gmms = []
for x in xrange(self.n_states):
diff --git a/scikits/learn/naive_bayes.py b/scikits/learn/naive_bayes.py
index 50d0a595b64d6c1a317a5693502fa64fc35f537a..a4635827f87addbb43ef360e0174f8281440920c 100644
--- a/scikits/learn/naive_bayes.py
+++ b/scikits/learn/naive_bayes.py
@@ -1,3 +1,6 @@
+""" Naives Bayes classifiers.
+"""
+
# Author: Vincent Michel <vincent.michel@inria.fr>
# License: BSD Style.
import numpy as np
@@ -39,8 +42,10 @@ class GNB(BaseEstimator, ClassifierMixin):
Examples
--------
+ >>> import numpy as np
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
>>> Y = np.array([1, 1, 1, 2, 2, 2])
+ >>> from scikits.learn.naive_bayes import GNB
>>> clf = GNB()
>>> clf.fit(X, Y)
GNB()
diff --git a/scikits/learn/neighbors.py b/scikits/learn/neighbors.py
index 4fc9b451e1ab72ab4bc3803fb34caa1546d64ea1..06385b50ff324fab34e2aea5e2d86bf88f3097bd 100644
--- a/scikits/learn/neighbors.py
+++ b/scikits/learn/neighbors.py
@@ -31,6 +31,7 @@ class Neighbors(BaseEstimator, ClassifierMixin):
--------
>>> samples = [[0.,0.,1.], [1.,0.,0.], [2.,2.,2.], [2.,5.,4.]]
>>> labels = [0,0,1,1]
+ >>> from scikits.learn.neighbors import Neighbors
>>> neigh = Neighbors(k=3)
>>> neigh.fit(samples, labels)
Neighbors(k=3, window_size=1)
@@ -78,9 +79,9 @@ class Neighbors(BaseEstimator, ClassifierMixin):
array representing our data set and ask who's the closest point to
[1,1,1]
- >>> import numpy as np
>>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]
>>> labels = [0, 0, 1]
+ >>> from scikits.learn.neighbors import Neighbors
>>> neigh = Neighbors(k=1)
>>> neigh.fit(samples, labels)
Neighbors(k=1, window_size=1)
@@ -119,9 +120,9 @@ class Neighbors(BaseEstimator, ClassifierMixin):
Examples
--------
- >>> import numpy as np
>>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]
>>> labels = [0, 0, 1]
+ >>> from scikits.learn.neighbors import Neighbors
>>> neigh = Neighbors(k=1)
>>> neigh.fit(samples, labels)
Neighbors(k=1, window_size=1)
diff --git a/scikits/learn/pca.py b/scikits/learn/pca.py
index f111962da734731e380a98adf39300b26b264a5f..c7428bb0e6b63867ecfbe924ff3ce2db2562419a 100644
--- a/scikits/learn/pca.py
+++ b/scikits/learn/pca.py
@@ -113,7 +113,9 @@ class PCA(BaseEstimator):
Examples
--------
+ >>> import numpy as np
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
+ >>> from scikits.learn.pca import PCA
>>> pca = PCA(n_comp=2)
>>> pca.fit(X)
PCA(n_comp=2, copy=True)
diff --git a/scikits/learn/pipeline.py b/scikits/learn/pipeline.py
index c611adc2708988bb2ab8c845de9300397a8445e9..ea4ce2ea72a026cc96366868bd1c4c31dff9f0f7 100644
--- a/scikits/learn/pipeline.py
+++ b/scikits/learn/pipeline.py
@@ -62,7 +62,9 @@ class Pipeline(BaseEstimator):
>>> # You can set the parameters using the names issued
>>> # For instance, fit using a k of 10 in the SelectKBest
>>> # and a parameter 'C' of the svn
- >>> _ = anova_svm.fit(X, y, anova__k=10, svc__C=.1)
+ >>> anova_svm.fit(X, y, anova__k=10, svc__C=.1) #doctest: +ELLIPSIS
+ Pipeline(steps=[('anova', SelectKBest(k=10, score_func=<function f_regression at ...>)), ('svc', SVC(kernel='linear', C=0.1, probability=False, degree=3, coef0=0.0, eps=0.001,
+ cache_size=100.0, shrinking=True, gamma=0.01))])
>>> prediction = anova_svm.predict(X)
>>> score = anova_svm.score(X)
diff --git a/scikits/learn/svm.py b/scikits/learn/svm.py
index 32b16b9b33ffa26d6b698fb3a87732a1af600a18..a8226b7fa1887345aa58b1238a5bca46aabf5850 100644
--- a/scikits/learn/svm.py
+++ b/scikits/learn/svm.py
@@ -416,8 +416,10 @@ class SVC(_BaseLibSVM, ClassifierMixin):
Examples
--------
+ >>> import numpy as np
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
>>> Y = np.array([1, 1, 2, 2])
+ >>> from scikits.learn.svm import SVC
>>> clf = SVC()
>>> clf.fit(X, Y)
SVC(kernel='rbf', C=1.0, probability=False, degree=3, coef0=0.0, eps=0.001,
@@ -516,8 +518,10 @@ class NuSVC(_BaseLibSVM, ClassifierMixin):
Examples
--------
+ >>> import numpy as np
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
>>> Y = np.array([1, 1, 2, 2])
+ >>> from scikits.learn.svm import NuSVC
>>> clf = NuSVC()
>>> clf.fit(X, Y)
NuSVC(kernel='rbf', probability=False, degree=3, coef0=0.0, eps=0.001,
diff --git a/scikits/learn/utils/graph.py b/scikits/learn/utils/graph.py
index a25f0cc4512fea699751af16d20e9b727111311c..96dae729d58fcf8a3d373f00c9cb434e17626d54 100644
--- a/scikits/learn/utils/graph.py
+++ b/scikits/learn/utils/graph.py
@@ -33,6 +33,7 @@ def single_source_shortest_path_length(graph, source, cutoff=None):
Examples
--------
+ >>> from scikits.learn.utils.graph import single_source_shortest_path_length
>>> import numpy as np
>>> graph = np.array([[ 0, 1, 0, 0],
... [ 1, 0, 1, 0],
@@ -127,6 +128,8 @@ def _graph_laplacian_dense(graph, normed=False, return_diag=False):
def graph_laplacian(graph, normed=False, return_diag=False):
+ """ Return the Laplacian of the given graph.
+ """
if normed and (np.issubdtype(graph.dtype, np.int)
or np.issubdtype(graph.dtype, np.uint)):
graph = graph.astype(np.float)