def __init__(self, params):
super(%CLASS%, self).__init__(params)
tmp = NuSVR()
params = tmp.get_params()
for key in params:
self.create_new_input(type_="data", label=key, widget_name="std line edit m", widget_pos="besides", pos=-1)
del tmp
def __init__(self, params):
super(NuSVRGetParams_NodeInstance, self).__init__(params)
tmp = NuSVR()
params = tmp.get_params()
for key in params:
self.create_new_output(type_="data", label=key, pos=-1)
del tmp
self.create_new_output(type_="data", label="param dict", pos=-1)
print 'LinearSVR precision test: {}'.format(lsvr_score_test)
print ''
nusvc = NuSVC()
print 'NuSVC config:'
print nusvc.get_params()
nusvc.fit(smr_train.feature_matrix, smr_train.labels)
nusvc_score_train = nusvc.score(smr_train.feature_matrix, smr_train.labels)
print 'NuSVC precision train: {}'.format(nusvc_score_train)
nusvc_score_test = nusvc.score(smr_test.feature_matrix, smr_test.labels)
print 'NuSVC precision test: {}'.format(nusvc_score_test)
print ''
nusvr = NuSVR()
print 'NuSVR config:'
print nusvr.get_params()
nusvr.fit(smr_train.feature_matrix, smr_train.labels)
nusvr_score_train = svc.score(smr_train.feature_matrix, smr_train.labels)
print 'NuSVR precision train: {}'.format(nusvr_score_train)
nusvr_score_test = nusvr.score(smr_test.feature_matrix, smr_test.labels)
print 'NuSVR precision test: {}'.format(nusvr_score_test)
print ''
dtc = DecisionTreeClassifier()
print 'DecisionTreeClassifier config:'
print dtc.get_params()
dtc.fit(smr_train.feature_matrix, smr_train.labels)
dtc_score_train = dtc.score(smr_train.feature_matrix, smr_train.labels)
print 'DecisionTreeClassifier precision train: {}'.format(dtc_score_train)
dtc_score_test = dtc.score(smr_test.feature_matrix, smr_test.labels)
