def divide_row_by_sum(e):
e = gutils.scipy_to_np(e)
e = e / np.sum(e + 1e-100, axis=1, keepdims=True)
return e
def __MR(self, X, W, Y, labeledIndexes, p, tuning_iter, hook=None):
Y = np.copy(Y)
if Y.ndim == 1:
Y[np.logical_not(labeledIndexes)] = 0
Y = gutils.init_matrix(Y, labeledIndexes)
Y[np.logical_not(labeledIndexes), :] = 0
if not W.shape[0] == Y.shape[0]:
raise ValueError("W,Y shape not compatible")
l = np.reshape(np.array(np.where(labeledIndexes)), (-1))
num_lab = l.shape[0]
if not isinstance(p, int):
p = int(p * num_lab)
if p > Y.shape[0]:
p = Y.shape[0]
LOG.warn("Warning: p greater than the number of labeled indexes",
LOG.ll.FILTER)
W = scipy_to_np(W)
L = gutils.lap_matrix(W, is_normalized=False)
D = gutils.deg_matrix(W)
def check_symmetric(a, tol=1e-8):
return np.allclose(a, a.T, atol=tol)
if check_symmetric(L):
E = sp.eigh(L, D, eigvals=(1, p))[1]
else:
LOG.warn("Warning: Laplacian not symmetric", LOG.ll.FILTER)
eigenValues, eigenVectors = sp.eig(L, D)
idx = eigenValues.argsort()
eigenValues = eigenValues[idx]
assert eigenValues[0] <= eigenValues[eigenValues.shape[0] - 1]
eigenVectors = eigenVectors[:, idx]
E = eigenVectors[:, 1:(p + 1)]
e_lab = E[labeledIndexes, :]
""" TIKHONOV REGULARIZATION. Currently set to 0."""
TIK = np.zeros(shape=e_lab.shape)
try:
A = np.linalg.inv(e_lab.T @ e_lab + TIK.T @ TIK) @ e_lab.T
except:
A = np.linalg.pinv(e_lab.T @ e_lab + TIK.T @ TIK) @ e_lab.T
F = np.zeros(shape=Y.shape)
y_m = np.argmax(Y, axis=1)[labeledIndexes]
for i in range(Y.shape[1]):
c = np.ones(num_lab)
c[y_m != i] = -1
a = A @ np.transpose(c)
LOG.debug(a, LOG.ll.FILTER)
for j in np.arange(F.shape[0]):
F[j, i] = np.dot(a, E[j, :])
ERmat = -1 * np.ones((Y.shape[0], ))
Y_amax = np.argmax(Y, axis=1)
for i in np.where(labeledIndexes):
ERmat[i] = np.square(Y[i, Y_amax[i]] - F[i, Y_amax[i]])
removed_Lids = np.argsort(ERmat)
removed_Lids = removed_Lids[::-1]
labeledIndexes = np.array(labeledIndexes)
Y = np.copy(Y)
for i in range(tuning_iter):
labeledIndexes[removed_Lids[i]] = False
if not hook is None:
hook._step(step=i,
X=X,
W=W,
Y=Y,
labeledIndexes=labeledIndexes)
return Y, labeledIndexes
def __MR(self,X,W,Y,labeledIndexes,p,hook=None):
Y = np.copy(Y)
if Y.ndim == 1:
Y = gutils.init_matrix(Y,labeledIndexes)
Y[np.logical_not(labeledIndexes),:] = 0
if not W.shape[0] == Y.shape[0]:
raise ValueError("W,Y shape not compatible")
l = np.reshape(np.array(np.where(labeledIndexes)),(-1))
num_lab = l.shape[0]
if not isinstance(p, int):
p = int(p * num_lab)
if p > Y.shape[0]:
p = Y.shape[0]
LOG.warn("Warning: p greater than the number of labeled indexes",LOG.ll.CLASSIFIER)
W = gutils.scipy_to_np(W)
W = 0.5* (W + W.T)
L = gutils.lap_matrix(W, is_normalized=False)
D = gutils.deg_matrix(W)
def check_symmetric(a, tol=1e-8):
return np.allclose(a, a.T, atol=tol)
def is_pos_sdef(x):
return np.all(np.linalg.eigvals(x) >= -1e-06)
if check_symmetric(L):
eigenVectors, E = sp.eigh(L,D,eigvals=(1,p))
else:
LOG.warn("Warning: Laplacian not symmetric",LOG.ll.CLASSIFIER)
eigenValues, eigenVectors = sp.eig(L,D)
idx = eigenValues.argsort()
eigenValues = eigenValues[idx]
assert eigenValues[0] <= eigenValues[eigenValues.shape[0]-1]
eigenVectors = eigenVectors[:,idx]
E = eigenVectors[:,1:(p+1)]
e_lab = E[labeledIndexes,:]
#TIK = np.ones(shape=e_lab.shape)
TIK = np.zeros(shape=e_lab.shape)
try:
A = np.linalg.inv(e_lab.T @ e_lab + TIK.T@TIK) @ e_lab.T
except:
A = np.linalg.pinv(e_lab.T @ e_lab + TIK.T@TIK) @ e_lab.T
F = np.zeros(shape=Y.shape)
y_m = np.argmax(Y, axis=1)[labeledIndexes]
for i in range(p):
if not hook is None:
hook._step(step=i,X=X,W=W,Y=E[:,i])
for i in range(Y.shape[1]):
c = np.ones(num_lab)
c[y_m != i] = -1
a = A @ np.transpose(c)
LOG.debug(a,LOG.ll.CLASSIFIER)
for j in np.arange(F.shape[0]):
F[j,i] = np.dot(a,E[j,:])
F[j,i] = max(F[j,i],0)
return (F)