def setUp(self):
# enable, disable visual inspection of graph
self.visual = False
# Create And pattern of depth 2 /!\ Do not change those parameter for the test /!\
self.n, self.ni, self.no, self.w0, self.t_mask = 100, 10, 2, 10, 5
self.ap2 = ap2(self.ni, self.no, w=self.w0, seed=1234)
self.ap2_fg = self.ap2.build_graph_pattern_init()
# Create And pattern of depth 3
self.ni, self.no, self.n_selected = 15, 2, 3
self.ap3 = ap3(self.ni,
self.no,
n_selected=self.n_selected,
w=self.w0,
seed=1234)
self.ap3_fg = self.ap3.build_graph_pattern_init()
# Create simple pattern to test multi output
self.sax_forward = vstack([csc_matrix(np.eye(4)) for _ in range(10)])
self.sax_backward = lil_matrix(self.sax_forward.shape)
self.sax_backward[:, :2] = 1
self.p, self.r = np.array([1, 1, 2, 1]), np.array([1, 2, 1, 1])
self.weight = 100
# Create patterns to test functionality of forward and backward patterns in server
d_matrices = create_empty_matrices(self.sax_forward.shape[1],
self.sax_backward.shape[1], 4)
for i in range(4):
d_matrices['Iw'][i, i], d_matrices['Ow'][
i, i], d_matrices['Im'][:, :] = self.weight, 1, True
self.fg = FiringGraph('test_server', np.ones(4), d_matrices, depth=2)
def setUp(self):
# Create input and output signal
self.sax_forward = csc_matrix([
[1, 0, 0, 0, 0, 1],
[1, 0, 1, 1, 1, 1],
[0, 1, 0, 1, 1, 0],
[0, 1, 0, 0, 1, 1],
[1, 1, 1, 0, 0, 1],
[1, 1, 0, 0, 1, 0],
[1, 1, 0, 1, 0, 1],
[1, 1, 0, 1, 1, 1],
])
self.sax_backward = csc_matrix([[0], [0], [0], [0], [1], [1], [1],
[1]])
self.sax_test = csc_matrix([[1], [0], [1], [0], [1], [1], [1], [0]])
# Create patterns to test functionality of forward and backward patterns in server
d_matrices = create_empty_matrices(self.sax_forward.shape[1],
self.sax_backward.shape[1], 1)
d_matrices['Iw'][3, 0], d_matrices['Ow'][0, 0] = 1, 1
self.pattern = FiringGraph('test_server',
np.ones(1),
d_matrices,
depth=2)
# Create server
self.server = ArrayServer(self.sax_forward,
self.sax_backward,
dtype_forward=int,
dtype_backward=int,
pattern_mask=self.pattern.copy())
self.sax_expected = np.array([-1, 0, -1, 0, 1, 1, 1, 0])
self.sax_expected_mask = np.array([-1, 0, 0, 0, 1, 1, 0, 0])