def funProjL12(self, w):
normedW, alpha = self.getGroupNorm(w)
alpha_proj = randomProject(alpha, self.lbda)
wOut = normedW.copy()
for i in range(self.nGroups):
groupInd = self.groupPtr[self.groupStart[i]:self.groupStart[i + 1]].astype(int)
wOut[groupInd] *= alpha_proj[i]
return wOut
def fun_project(self, w):
d1, d2 = self.X.shape
v = np.zeros((d2, ))
v1 = np.zeros(d2)
for i in range(d2):
v[i] = abs(w[i])
if v[i] != 0:
v1[i] = w[i] / v[i]
p = randomProject(v, self.alpha_lasso)
test = v1 * p
return test
def fun_project(self, w):
markers, repeats = np.unique(self.marker_groups, return_counts=True)
marker_groups_ravel = np.repeat(markers, repeats*(self.n_classes-1))
v = np.zeros((markers.shape[0],))
v1 = np.zeros(w.shape)
for i in markers:
indices1 = np.where(marker_groups_ravel == i)[0]
w_group = w[indices1]
v[i] = LA.norm(w_group, 2)
if v[i] != 0:
v1[indices1] = w_group / v[i]
p = randomProject(v, self.lbda)
test = v1 * np.repeat(p, repeats*(self.n_classes-1))
return test
def funProj(self, w):
if self.isDeep:
return np.hstack( (np.sign(w[0:self.d*self.h1]) * randomProject(np.fabs(w[:self.d*self.h1]), self.tau), w[self.d*self.h1:]))
else:
return np.hstack( (np.sign(w[:-1]) * randomProject(np.fabs(w[:-1]), self.tau), w[-1]))