def testKNNState(self):
train = pureMultivariateSignal( 20, 3 )
test = pureMultivariateSignal( 20, 3 )
clf = kNN(k=10)
clf.train(train)
clf.states.enable('values')
clf.states.enable('predictions')
p = clf.predict(test.samples)
self.failUnless(p == clf.predictions)
self.failUnless(N.array(clf.values).shape == (80,2))
def testMultivariate(self):
mv_perf = []
uv_perf = []
clf = kNN(k=10)
for i in xrange(20):
train = pureMultivariateSignal( 20, 3 )
test = pureMultivariateSignal( 20, 3 )
clf.train(train)
p_mv = clf.predict( test.samples )
mv_perf.append( N.mean(p_mv==test.labels) )
clf.train(train.selectFeatures([0]))
p_uv = clf.predict( test.selectFeatures([0]).samples )
uv_perf.append( N.mean(p_uv==test.labels) )
mean_mv_perf = N.mean(mv_perf)
mean_uv_perf = N.mean(uv_perf)
self.failUnless( mean_mv_perf > 0.9 )
self.failUnless( mean_uv_perf < mean_mv_perf )
def testMultivariate(self):
mv_perf = []
mv_lin_perf = []
uv_perf = []
l_clf = clfswh['linear', 'svm'][0]
nl_clf = clfswh['non-linear', 'svm'][0]
#orig_keys = nl_clf.param._params.keys()
#nl_param_orig = nl_clf.param._params.copy()
# l_clf = LinearNuSVMC()
# XXX ??? not sure what below meant and it is obsolete if
# using SG... commenting out for now
# for some reason order is not preserved thus dictionaries are not
# the same any longer -- lets compare values
#self.failUnlessEqual([nl_clf.param._params[k] for k in orig_keys],
# [nl_param_orig[k] for k in orig_keys],
# msg="New instance mustn't override values in previously created")
## and keys separately
#self.failUnlessEqual(Set(nl_clf.param._params.keys()),
# Set(orig_keys),
# msg="New instance doesn't change set of parameters in original")
# We must be able to deepcopy not yet trained SVMs now
import mvpa.support.copy as copy
try:
nl_clf.untrain()
nl_clf_copy = copy.deepcopy(nl_clf)
except:
self.fail(msg="Failed to deepcopy not-yet trained SVM %s" % nl_clf)
for i in xrange(20):
train = pureMultivariateSignal( 20, 3 )
test = pureMultivariateSignal( 20, 3 )
# use non-linear CLF on 2d data
nl_clf.train(train)
p_mv = nl_clf.predict(test.samples)
mv_perf.append(N.mean(p_mv==test.labels))
# use linear CLF on 2d data
l_clf.train(train)
p_lin_mv = l_clf.predict(test.samples)
mv_lin_perf.append(N.mean(p_lin_mv==test.labels))
# use non-linear CLF on 1d data
nl_clf.train(train.selectFeatures([0]))
p_uv = nl_clf.predict(test.selectFeatures([0]).samples)
uv_perf.append(N.mean(p_uv==test.labels))
mean_mv_perf = N.mean(mv_perf)
mean_mv_lin_perf = N.mean(mv_lin_perf)
mean_uv_perf = N.mean(uv_perf)
# non-linear CLF has to be close to perfect
self.failUnless( mean_mv_perf > 0.9 )
# linear CLF cannot learn this problem!
self.failUnless( mean_mv_perf > mean_mv_lin_perf )
# univariate has insufficient information
self.failUnless( mean_uv_perf < mean_mv_perf )
