rr_data47)
print(concat_data.shape)
file = open('concat_data.txt', "w")
numpy.savetxt('concat_data.txt', concat_data, fmt='%.18e')
################################### 4
num_train_examp = 0.9
x_train, x_test = make_train_and_test_concat_data(concat_data, num_train_examp)
################################### 5
critical_times_set = find_critical_times(rr_data39, rr_data40, rr_data41,
rr_data42, rr_data43, rr_data44,
rr_data45, rr_data46, rr_data47)
###################################### 6
num_iter = 100
alpha = 1.2
reduce_alpha_coef = 0.2
target_voxel_ind = 3122
n5 = x_train.shape[1]
t1 = x_train.shape[0]
c_back0 = concat_data
for i in range(my_theta_mean.shape[0]):
if i != target_voxel_ind:
if -0.002 < my_theta_mean[i] < 0.002:
c_back0[i, :] = 0
################################### 4
num_train_examp = 0.9
x_train, x_test = make_train_and_test_concat_data(c_back0, num_train_examp)
################################### 5
critical_times_set = find_critical_times(rr_data27, rr_data28, rr_data29,
rr_data30, rr_data31, rr_data32,
rr_data33, rr_data34, rr_data35)
###################################### 6
num_iter = 100
alpha = 1.2
reduce_alpha_coef = 0.2
target_voxel_ind = 3122
n5 = x_train.shape[1]
t1 = x_train.shape[0]