imporve test coverage

pyriemann/utils/mean.py

@@ -147,7 +147,7 @@ def mean_identity(covmats):
     :returns: the identity matrix of size Nchannels
     
     """
-    C = eye(covmats.shape[1])
+    C = numpy.eye(covmats.shape[1])
     return C
 
 def mean_covariance(covmats,metric='riemann',*args):

tests/test_classification.py

@@ -28,6 +28,13 @@ def test_MDM_predict():
     mdm.fit(covset,labels)
     mdm.predict(covset)
 
+def test_MDM_fit_predict():
+    """Test Fit & predict of MDM"""
+    covset = generate_cov(100,3)
+    labels = np.array([0,1]).repeat(50)
+    mdm = MDM(metric='riemann')
+    mdm.fit_predict(covset,labels)
+
 def test_MDM_transform():
     """Test transform of MDM"""
     covset = generate_cov(100,3)

tests/test_clustering.py

@@ -1,5 +1,5 @@
 import numpy as np
-from pyriemann.clustering import Kmeans
+from pyriemann.clustering import Kmeans,KmeansPerClassTransform
 
 def generate_cov(Nt,Ne):
     """Generate a set of cavariances matrices for test purpose"""
@@ -18,6 +18,25 @@ def test_Kmeans_fit():
     covset = generate_cov(20,3)
     km = Kmeans(2)
     km.fit(covset)
+
+def test_Kmeans_fit_with_init():
+    """Test Fit of Kmeans wit matric initialization"""
+    covset = generate_cov(20,3)
+    km = Kmeans(2,init=covset[0:2])
+    km.fit(covset)
+
+def test_Kmeans_fit_with_y():
+    """Test Fit of Kmeans with a given y"""
+    covset = generate_cov(20,3)
+    labels = np.array([0,1]).repeat(10)
+    km = Kmeans(2)
+    km.fit(covset,y=labels)
+
+def test_Kmeans_fit_parallel():
+    """Test Fit of Kmeans using paralell"""
+    covset = generate_cov(20,3)
+    km = Kmeans(2,n_jobs=2)
+    km.fit(covset)
 
 def test_Kmeans_predict():
     """Test prediction of Kmeans"""
@@ -31,4 +50,23 @@ def test_Kmeans_transform():
     covset = generate_cov(20,3)
     km = Kmeans(2)
     km.fit(covset)
+    km.transform(covset)
+
+def test_KmeansPCT_init():
+    """Test init of Kmeans PCT"""
+    km = KmeansPerClassTransform(2)
+
+def test_KmeansPCT_fit():
+    """Test Fit of Kmeans PCT"""
+    covset = generate_cov(20,3)
+    labels = np.array([0,1]).repeat(10)
+    km = KmeansPerClassTransform(2)
+    km.fit(covset,labels)
+
+def test_KmeansPCT_transform():
+    """Test Transform of Kmeans PCT"""
+    covset = generate_cov(20,3)
+    labels = np.array([0,1]).repeat(10)
+    km = KmeansPerClassTransform(2)
+    km.fit(covset,labels)
     km.transform(covset)

tests/test_tangentspace.py

@@ -27,6 +27,13 @@ def test_TangentSpace_transform():
     ts.fit(covset)
     ts.transform(covset)
 
+def test_TangentSpace_transform_with_ts_update():
+    """Test transform of Tangent Space with TSupdate"""
+    covset = generate_cov(100,3)
+    ts = TangentSpace(metric='riemann',tsupdate=True)
+    ts.fit(covset)
+    ts.transform(covset)
+
 def test_TangentSpace_inversetransform():
     """Test inverse transform of Tangent Space"""
     covset = generate_cov(100,3)

tests/test_utils_mean.py

@@ -1,7 +1,7 @@
 from numpy.testing import assert_array_almost_equal,assert_array_equal
 import numpy as np
 
-from pyriemann.utils.mean import (mean_riemann,mean_euclid,mean_logeuclid,mean_logdet,mean_ale,mean_covariance)
+from pyriemann.utils.mean import (mean_riemann,mean_euclid,mean_logeuclid,mean_logdet,mean_ale,mean_identity,mean_covariance)
 
 def generate_cov(Nt,Ne):
     """Generate a set of cavariances matrices for test purpose"""
@@ -36,7 +36,7 @@ def test_identity_mean():
     """Test the logdet mean"""
     covmats = generate_cov(100,3)
     C = mean_identity(covmats)
-    assert_array_equal(C,eye(3))
+    assert_array_equal(C,np.eye(3))
 
 def test_logdet_mean():
     """Test the logdet mean"""