def window_matrices(rsp, movs, n_lag):
def movie_window_matrix(mov, n_lag):
LY, LX, T = mov.shape
p_mov = np.pad(mov, ((0, 0), (0, 0), (n_lag - 1, 0)), mode='constant')
mat = np.empty((T, LY * LX * n_lag))
for t in range(T):
mat[t, :] = p_mov[:, :, t:t + n_lag].flatten()
return mat
def response_window_matrix(rsp, n_lag):
S, N, T, R = rsp.data.shape
mat = np.empty((T * R * S, N))
i = 0
for s in range(S):
for r in range(R):
for t in range(T):
mat[i, :] = rsp.data[s, :, t, r]
i += 1
return mat
mov_mats = [movie_window_matrix(m, n_lag) for m in movs]
rsp_mat = response_window_matrix(rsp, n_lag)
n_trials = rsp.data.shape[3]
mov_mat = np.vstack(
[np.vstack([m for i in range(n_trials)]) for m in mov_mats])
return shared_dataset((mov_mat, rsp_mat))
def window_matrices(rsp, movs, n_lag):
def movie_window_matrix(mov, n_lag):
LY, LX, T = mov.shape
p_mov = np.pad(mov, ((0,0), (0,0), (n_lag-1,0)), mode='constant')
mat = np.empty((T, LY*LX*n_lag))
for t in range(T):
mat[t,:] = p_mov[:,:,t:t+n_lag].flatten()
return mat
def response_window_matrix(rsp, n_lag):
S,N,T,R = rsp.data.shape
mat = np.empty((T*R*S,N))
i = 0
for s in range(S):
for r in range(R):
for t in range(T):
mat[i,:] = rsp.data[s,:,t,r]
i += 1
return mat
mov_mats = [movie_window_matrix(m, n_lag) for m in movs]
rsp_mat = response_window_matrix(rsp, n_lag)
n_trials = rsp.data.shape[3]
mov_mat = np.vstack([np.vstack([m for i in range(n_trials)])
for m in mov_mats])
return shared_dataset((mov_mat, rsp_mat))
def load_data(n_ex, n_features_in, n_features_out):
print("Creating data...")
data_input = np.random.random((n_ex, n_features_in))
weights = (1.0 / n_features_in) * np.ones((n_features_in, n_features_out))
print("*\n")
print(weights)
print("\n*")
data_output = np.dot(data_input, weights) + 0.1 * np.random.random((n_ex, n_features_out)) + 1.0
t1, t2 = int(0.6 * n_ex), int(0.8 * n_ex)
train_input, train_output = data_input[:t1, :], data_output[:t1, :]
val_input, val_output = data_input[t1:t2, :], data_output[t1:t2, :]
test_input, test_output = data_input[t2:, :], data_output[t2:, :]
print("Data created...")
test_x, test_y = shared_dataset((test_input, test_output))
train_x, train_y = shared_dataset((train_input, train_output))
val_x, val_y = shared_dataset((val_input, val_output))
rval = [(train_x, train_y), (val_x, val_y), (test_x, test_y)]
return rval
def load_data(n_ex, n_features_in, n_features_out):
print('Creating data...')
data_input = np.random.random((n_ex, n_features_in))
weights = (1.0 / n_features_in) * np.ones((n_features_in, n_features_out))
print('*\n')
print(weights)
print('\n*')
data_output = np.dot(data_input, weights)\
+ 0.1 * np.random.random((n_ex, n_features_out)) + 1.0
t1, t2 = int(0.6 * n_ex), int(0.8 * n_ex)
train_input, train_output = data_input[:t1, :], data_output[:t1, :]
val_input, val_output = data_input[t1:t2, :], data_output[t1:t2, :]
test_input, test_output = data_input[t2:, :], data_output[t2:, :]
print('Data created...')
test_x, test_y = shared_dataset((test_input, test_output))
train_x, train_y = shared_dataset((train_input, train_output))
val_x, val_y = shared_dataset((val_input, val_output))
rval = [(train_x, train_y), (val_x, val_y), (test_x, test_y)]
return rval
