def transform(self,X):
if self.tsupdate:
Cr = riemann.mean_covariance(X,metric=self.metric)
else:
Cr = self.Cr
return riemann.tangent_space(X,Cr)
def transform(self, X):
if self.tsupdate:
Cr = riemann.mean_covariance(X, metric=self.metric)
else:
Cr = self.Cr
return riemann.tangent_space(X, Cr)
def fit(self,X,y=None):
C1 = riemann.mean_covariance(X[y==1,...],self.metric)
C0 = riemann.mean_covariance(X[y==0,...],self.metric)
Ne,_ = C0.shape
self.subelec = range(0,Ne,1)
while (len(self.subelec)-2*self.nfilters)>self.nelec:
di = numpy.zeros((len(self.subelec),1))
for idx in range(2*self.nfilters,len(self.subelec)):
sub = self.subelec[:]
sub.pop(idx)
di[idx] = riemann.distance(C0[:,sub][sub,:],C1[:,sub][sub,:])
#print di
torm = di.argmax()
self.dist.append(di.max())
self.subelec.pop(torm)
def fit(self,X,y=None):
C1 = riemann.mean_covariance(X[y==1,...],self.metric)
C0 = riemann.mean_covariance(X[y==0,...],self.metric)
Ne,_ = C0.shape
self.subelec = list(range(0,Ne,1))
while (len(self.subelec)-2*self.nfilters)>self.nelec:
di = numpy.zeros((len(self.subelec),1))
for idx in range(2*self.nfilters,len(self.subelec)):
sub = self.subelec[:]
sub.pop(idx)
di[idx] = riemann.distance(C0[:,sub][sub,:],C1[:,sub][sub,:])
#print di
torm = di.argmax()
self.dist.append(di.max())
self.subelec.pop(torm)
def fit_transform(self,X,y=None):
# compute mean covariance
self.Cr = riemann.mean_covariance(X,metric=self.metric)
return riemann.tangent_space(X,self.Cr)
def fit(self,X,y=None):
# compute mean covariance
self.Cr = riemann.mean_covariance(X,metric=self.metric)
def fit_transform(self, X, y=None):
# compute mean covariance
self.Cr = riemann.mean_covariance(X, metric=self.metric)
return riemann.tangent_space(X, self.Cr)
def fit(self, X, y=None):
# compute mean covariance
self.Cr = riemann.mean_covariance(X, metric=self.metric)
