def raw_sim_one_disc(start, steps, cur_step, dy_data):
result = []
prev_affine_val = np.zeros(len(start))
for idx in range(cur_step, cur_step + steps, 1):
a_exp = mat_pow(dy_data.dense_a_matrix, idx - 1)
new_affine_val = np.array(
np.matmul(a_exp, dy_data.dense_b_vector).flatten().tolist()
[0]) + np.array(prev_affine_val)
prev_affine_val = new_affine_val
# abc = np.matmul(mat_pow(dy_data.dense_a_matrix, idx), np.array(start)).tolist()[0]
new_state = np.matmul(mat_pow(dy_data.dense_a_matrix, idx),
np.array(start)).tolist()[0] + prev_affine_val
result.append(new_state)
return result
def try_conjecture_1(n, m, cap=10000):
for _ in xrange(cap):
M_m = get_H(n)
M_p = get_H(m)
C = get_M(n, m)
Cp = np.transpose(C)
terms = []
for i in xrange(5):
M1 = mat_pow(M_m, 4 - i)
M2 = mat_pow(M_p, i)
MM = np.dot(M1, C)
MM2 = np.dot(M2, Cp)
MM3 = np.dot(MM, MM2)
terms.append(np.trace(MM3) * binom(4, i))
ans = sum(terms)
if (ans < -eps):
print "FAIL"
print ans
exit(0)
def simulate_origin_disc(self, start, steps, cur_step):
Timers.tic("simulation")
result = []
prev_val = start
for idx in range(cur_step, cur_step + steps, 1):
a_exp = mat_pow(self.dy_data.dense_a_matrix, idx - 1)
new_val = np.array(
np.matmul(a_exp, self.dy_data.dense_b_vector).flatten().tolist(
)[0]) + np.array(prev_val)
prev_val = new_val
result.append(new_val)
Timers.toc("simulation")
return result