def grow(self, bmu):
# type: (object) -> object
p = self.grid[bmu]
up = p + np.array([0, +1])
right = p + np.array([+1, 0])
down = p + np.array([0, -1])
left = p + np.array([-1, 0])
neighbors = np.array([up, right, down, left])
direction = 0
for nei in neighbors:
try:
self.errors[str(list(nei))] += self.errors[bmu] * self.fd
except KeyError:
try:
w1, w2, b1, b2 = self.type_b(nei, direction)
if np.isnan(w1).any():
print 'shit'
except:
try:
w1, w2, b1, b2 = self.type_a(nei, direction)
if np.isnan(w1).any():
print 'shit'
except:
try:
w1, w2, b1, b2 = self.type_c(nei, direction)
if np.isnan(w1).any():
print 'shit'
except:
w1 = None
w2 = None#np.ones((self.hid, self.dims))
b1 = None#np.ones(self.hid)
b2 = None#np.ones(self.dims)
# if new_a.any():
# if newf_c.any():
# # w.fill(0.5)
# else:
# w = new_c
# else:
# w = new_a
# else:
# w = new_b
AE = AutoEncoder(self.dims, self.hid, self.s1, self.m1)
# if np.isnan(w1).any():
# print 'shit'
AE.set_params(w1, w2, b1, b2)
self.learners[str(list(nei))] = AE
self.grid[str(list(nei))] = list(nei)
self.errors[str(list(nei))] = self.GT/2
self.gen[str(list(nei))] = self.current_gen
self.hits[str(list(nei))] = 0
direction += 1
self.errors[bmu] = self.GT / 2
def grow(self, bmu):
# type: (object) -> object
p = self.grid[bmu]
up = p + np.array([0, +1])
right = p + np.array([+1, 0])
down = p + np.array([0, -1])
left = p + np.array([-1, 0])
neighbors = np.array([up, right, down, left])
for nei in neighbors:
try:
self.errors[str(list(nei))] += self.errors[bmu] * self.fd
except KeyError:
w1, w2, b1, b2 = self.get_new_weight(bmu, nei)
AE = AutoEncoder(self.dims, self.hid, self.s1, self.m1, gaussian=self.gaussian)
AE.set_params(w1, w2, b1, b2)
self.learners[str(list(nei))] = AE
self.grid[str(list(nei))] = list(nei)
self.errors[str(list(nei))] = self.GT/2
self.gen[str(list(nei))] = self.current_gen
self.hits[str(list(nei))] = 0
self.errors[bmu] = self.GT / 2
