def test_MDM_predict():
"""Test prediction of MDM"""
covset = generate_cov(100, 3)
labels = np.array([0, 1]).repeat(50)
mdm = MDM(metric='riemann')
mdm.fit(covset, labels)
mdm.predict(covset)
# test fit_predict
mdm = MDM(metric='riemann')
mdm.fit_predict(covset, labels)
# test transform
mdm.transform(covset)
# predict proba
mdm.predict_proba(covset)
# test n_jobs
mdm = MDM(metric='riemann', n_jobs=2)
mdm.fit(covset, labels)
mdm.predict(covset)
def test_MDM_predict():
"""Test prediction of MDM"""
covset = generate_cov(100, 3)
labels = np.array([0, 1]).repeat(50)
mdm = MDM(metric='riemann')
mdm.fit(covset, labels)
mdm.predict(covset)
# test fit_predict
mdm = MDM(metric='riemann')
mdm.fit_predict(covset, labels)
# test transform
mdm.transform(covset)
# predict proba
mdm.predict_proba(covset)
# test n_jobs
mdm = MDM(metric='riemann', n_jobs=2)
mdm.fit(covset, labels)
mdm.predict(covset)
class FgMDM2(BaseEstimator, ClassifierMixin, TransformerMixin):
def __init__(self, metric='riemann', tsupdate=False, n_jobs=1):
"""Init."""
self.metric = metric
self.n_jobs = n_jobs
self.tsupdate = tsupdate
if isinstance(metric, str):
self.metric_mean = metric
elif isinstance(metric, dict):
# check keys
for key in ['mean', 'distance']:
if key not in metric.keys():
raise KeyError('metric must contain "mean" and "distance"')
self.metric_mean = metric['mean']
else:
raise TypeError('metric must be dict or str')
def fit(self, X, y):
self.classes_ = unique_labels(y)
self._mdm = MDM(metric=self.metric, n_jobs=self.n_jobs)
self._fgda = FGDA(metric=self.metric_mean, tsupdate=self.tsupdate)
cov = self._fgda.fit_transform(X, y)
self._mdm.fit(cov, y)
return self
def predict(self, X):
cov = self._fgda.transform(X)
return self._mdm.predict(cov)
def predict_proba(self, X):
cov = self._fgda.transform(X)
return self._mdm.predict_proba(cov)
def transform(self, X):
cov = self._fgda.transform(X)
return self._mdm.transform(cov)
