def compute_sparse_weights(origin,
post,
transform,
fan_in,
noise=0.1,
num_samples=100):
encoder = post.encoders
radius = post.radii[0]
if hasattr(transform, 'tolist'): transform = transform.tolist()
approx = origin.node.getDecodingApproximator('AXON')
# create X matrix
X = approx.evalPoints
X = MU.transpose([f.multiMap(X) for f in origin.functions])
# create A matrix
A = approx.values
S = fan_in
N_A = len(A)
samples = len(A[0])
N_B = len(encoder)
w_sparse = np.zeros((N_B, N_A), 'f')
noise_sd = MU.max(A) * noise
decoder_list = [None for _ in range(num_samples)]
for i in range(num_samples):
indices = random.sample(range(N_A), S)
activity = [A[j] for j in indices]
n = [[random.gauss(0, noise_sd) for _ in range(samples)]
for j in range(S)]
activity = MU.sum(activity, n)
activityT = MU.transpose(activity)
gamma = MU.prod(activity, activityT)
upsilon = MU.prod(activity, X)
gamma_inv = pinv(gamma, noise_sd * noise_sd)
decoder_list[i] = MU.prod([[x for x in row] for row in gamma_inv],
upsilon)
for i in range(N_B):
ww = MU.prod(random.choice(decoder_list),
MU.prod(MU.transpose(transform), encoder[i]))
for j, k in enumerate(indices):
w_sparse[i, k] = float(ww[j]) / radius
return list(w_sparse)
