def test_knn_state(self):
train = pure_multivariate_signal( 40, 3 )
test = pure_multivariate_signal( 20, 3 )
clf = kNN(k=10)
clf.train(train)
clf.ca.enable(['estimates', 'predictions', 'distances'])
p = clf.predict(test.samples)
self.failUnless(p == clf.ca.predictions)
self.failUnless(np.array(clf.ca.estimates).shape == (80,2))
self.failUnless(clf.ca.distances.shape == (80,160))
self.failUnless(not clf.ca.distances.fa is train.sa)
# Those are deep-copied now by default so they should not be the same
self.failUnless(not (clf.ca.distances.fa['chunks'] is train.sa['chunks']))
self.failUnless(not (clf.ca.distances.fa.chunks is train.sa.chunks))
def test_knn_state(self):
train = pure_multivariate_signal( 40, 3 )
test = pure_multivariate_signal( 20, 3 )
clf = kNN(k=10)
clf.train(train)
clf.ca.enable(['estimates', 'predictions', 'distances'])
p = clf.predict(test.samples)
self.failUnless(p == clf.ca.predictions)
self.failUnless(np.array(clf.ca.estimates).shape == (80,2))
self.failUnless(clf.ca.distances.shape == (80,160))
self.failUnless(not clf.ca.distances.fa is train.sa)
# XXX if we revert to deepcopy for that state following test
# should fail
self.failUnless(clf.ca.distances.fa['chunks'] is train.sa['chunks'])
self.failUnless(clf.ca.distances.fa.chunks is train.sa.chunks)
def test_multivariate(self):
mv_perf = []
uv_perf = []
clf = kNN(k=10)
for i in xrange(20):
train = pure_multivariate_signal( 20, 3 )
test = pure_multivariate_signal( 20, 3 )
clf.train(train)
p_mv = clf.predict( test.samples )
mv_perf.append( np.mean(p_mv==test.targets) )
clf.train(train[:, 0])
p_uv = clf.predict(test[:, 0].samples)
uv_perf.append( np.mean(p_uv==test.targets) )
mean_mv_perf = np.mean(mv_perf)
mean_uv_perf = np.mean(uv_perf)
self.failUnless( mean_mv_perf > 0.9 )
self.failUnless( mean_uv_perf < mean_mv_perf )
def test_multivariate(self):
mv_perf = []
uv_perf = []
clf = kNN(k=10)
for i in xrange(20):
train = pure_multivariate_signal( 20, 3 )
test = pure_multivariate_signal( 20, 3 )
clf.train(train)
p_mv = clf.predict( test.samples )
mv_perf.append( np.mean(p_mv==test.targets) )
clf.train(train[:, 0])
p_uv = clf.predict(test[:, 0].samples)
uv_perf.append( np.mean(p_uv==test.targets) )
mean_mv_perf = np.mean(mv_perf)
mean_uv_perf = np.mean(uv_perf)
self.failUnless( mean_mv_perf > 0.9 )
self.failUnless( mean_uv_perf < mean_mv_perf )