def test_FGDA_transform():
"""Test transform of FGDA"""
covset = generate_cov(10,3)
labels = np.array([0,1]).repeat(5)
ts = FGDA(metric='riemann')
ts.fit(covset,labels)
ts.transform(covset)
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 test_FGDA_init(tsupdate, metric, get_covmats, get_labels):
n_classes, n_trials, n_channels = 2, 6, 3
labels = get_labels(n_trials, n_classes)
covmats = get_covmats(n_trials, n_channels)
ts = FGDA(metric=metric, tsupdate=tsupdate)
ts.fit(covmats, labels)
Xtr = ts.transform(covmats)
assert Xtr.shape == (n_trials, n_channels, n_channels)
def learn_ts_fgda(X_train, y_train, T=0, NT=1):
classes = [T, NT]
train_fgda = FGDA()
train_fgda.fit(X=X_train, y=y_train)
X_train_fgda = train_fgda.transform(X=X_train)
centroids_train_fgda = [
mean_covariance(X_train_fgda[y_train == l, :, :], metric='riemann')
for l in classes
]
return X_train_fgda, centroids_train_fgda, train_fgda
def test_FGDA_transform():
"""Test transform of FGDA."""
covset = generate_cov(10, 3)
labels = np.array([0, 1]).repeat(5)
ts = FGDA(metric='riemann')
ts.fit(covset, labels)
ts.transform(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)
def test_FGDA_fit():
"""Test Fit of FGDA."""
covset = generate_cov(10, 3)
labels = np.array([0, 1]).repeat(5)
ts = FGDA(metric='riemann')
ts.fit(covset, labels)
def test_FGDA_init():
"""Test init of FGDA."""
FGDA(metric='riemann')
