def compute_pvalues_for_processes(self,U_matrix,chane_prob, num_bootstrapped_stats=100):
N = U_matrix.shape[0]
bootsraped_stats = np.zeros(num_bootstrapped_stats)
# orsetinW = simulate(N,num_bootstrapped_stats,corr)
for proc in range(num_bootstrapped_stats):
# W = np.sign(orsetinW[:,proc])
W = simulatepm(N,chane_prob)
WW = np.outer(W, W)
st = np.mean(U_matrix * WW)
bootsraped_stats[proc] = N * st
stat = N*np.mean(U_matrix)
return float(np.sum(bootsraped_stats > stat)) / num_bootstrapped_stats
def compute_stats_for_processes(self,samples,chane_prob):
U,stat = self.get_statistic_multiple(samples)
N = samples.shape[0]
W = simulatepm(N,chane_prob)
WW = np.outer(W, W)
st = np.mean(U * WW)
bootsraped_stat = N * st
stat = N*np.mean(U)
return stat,bootsraped_stat
