def PerformIca(X,Y,num_components,random_state):
result = {}
algo = FastICA(random_state=random_state,max_iter=800)
algo.fit(X)
full_mixing_matrix = algo.mixing_
full_unmixing_matrix = algo.components_
_x = algo.transform(X)
kt_value = np.abs(kt(_x))
largest_kt_values_idx = np.argsort(kt_value)[::-1]
result["ica_kt_all"] = kt_value
for n in num_components:
prefix = "ica_" + str(n) + "_"
component_idx_to_select = largest_kt_values_idx[0:n]
mixing_matrix = full_mixing_matrix.T[component_idx_to_select,:].T
unmixing_matrix = full_unmixing_matrix[component_idx_to_select,:]
algo.components_ = unmixing_matrix
algo.mixing_ = mixing_matrix
result[prefix+"mm"] = mixing_matrix
result[prefix+"umm"] = unmixing_matrix
_x = algo.transform(X)
result[prefix+"data"] = _x
X_recons = algo.inverse_transform(_x)
result[prefix+"reconstruction_error"] = ComputeReconstructionSSE(X,X_recons)
n_kt_value = kt_value[component_idx_to_select]
avg_kt = n_kt_value.mean()
#print("ICA num dim {0} : reconstruction error {1} avg kt {2}".format(str(n),str(result[prefix+"reconstruction_error"]),str(avg_kt)))
#print(np.sort(n_kt_value))
return result