def cal_three_mse(r_list, ap_list, seed_list, iteration, use_data, program_num, c_method):
result = np.zeros([len(seed_list), len(ap_list), 3]) # [0]:ori-nmf [1]:ori-snmf [2]:nmf-snmf
v_original, im_var, im_hol = ipf.read_pgm("/home/ionishi/mnt/workspace/sketchingNMF/face_data/" + use_data)
v_list = np.zeros([v_original.shape[0], v_original.shape[1], 3]) # [0]:original [1]:snmf [2]:nmf
v_list[:, :, 0] = v_original
for ap_i, ap in enumerate(ap_list):
for seed_i, seed in enumerate(seed_list):
path, file_name = ff.path_and_file_name(r_list[0], ap, iteration, seed, use_data, program_num, c_method,
"r,k", directory="matrix")
v_list[:, :, 1] = ipf.calculate_v(path, file_name, snmf=True)
path, file_name = ff.path_and_file_name(r_list[0], ap_list[-1], iteration, seed, use_data, program_num, c_method,
"r,k", directory="matrix")
v_list[:, :, 2] = ipf.calculate_v(path, file_name, snmf=False)
for i in range(3):
result[seed_i, ap_i, (i + 1) % 3] = ipf.mse_calculate(v_list[:, :, i], v_list[:, :, (i + 1) % 3])
return result
r_list, r_list_size, ap_list, ap_list_size, seed_list, seed_list_size = ff.parallel_make_list(tmp[2:])
# r_list_size = len(r_list)
# ap_list_size = len(ap_list)
# seed_list_size = len(seed_list)
os.makedirs(w_path, exist_ok=True)
# last 1% of iteration error average -----------------------------------------------------------------------------
NMF_e_result = np.zeros([r_list_size, ap_list_size, seed_list_size])
SNMF_e_result = np.zeros([r_list_size, ap_list_size, seed_list_size])
for r_i, r in enumerate(r_list):
for ap_i, ap in enumerate(ap_list):
for seed_i, seed in enumerate(seed_list):
pr_path, pr_file_name = ff.path_and_file_name(r, ap, ite, seed, use_data, program_num, c_method, "r,k", directory="error")
error_M = pd.read_csv(pr_path + pr_file_name + "_error.csv")
e_result = error_M.iloc[int(-0.01 * ite):].mean()
NMF_e_result[r_i, ap_i, seed_i] = e_result["NMF error"]
SNMF_e_result[r_i, ap_i, seed_i] = e_result["SNMF error"]
NMF_e_result = np.mean(NMF_e_result, axis=2)
SNMF_e_result = np.mean(SNMF_e_result, axis=2)
NMF_e_result = np.mean(NMF_e_result, axis=1)
df_e_result = pd.DataFrame({"k=" + str(ap_list[0]): SNMF_e_result[0, 0]}, index=["r=" + str(r_list[0])])
for r_i, r in enumerate(r_list[1:]):
df_e_result.loc["r=" + str(r)] = SNMF_e_result[r_i + 1, 0]
for ap_i, k in enumerate(ap_list[1:]):
df_e_result["k=" + str(k)] = SNMF_e_result[:, ap_i + 1]
df_e_result["NMF"] = NMF_e_result
use_data, c_method, seed_list))
for wh_seed in seed_list:
original_v, im_var, im_hol = ipf.read_pgm(
"/home/ionishi/mnt/workspace/sketchingNMF/face_data/" + use_data)
error_map = np.zeros([im_var, im_hol, len(image_num_list), 3])
for r in plot_r_list:
for ap in plot_ap_list:
for im_i, im_num in enumerate(image_num_list):
read_path, read_file_name = ff.path_and_file_name(
r,
ap,
ite,
wh_seed,
use_data,
program_num,
c_method,
"r,k",
real_or_not=real_or_not,
directory="matrix",
space=space)
write_path = "/home/ionishi/mnt/workspace/sketchingNMF/{}/{}/{}/{}/Emap_wh_seed={}/" \
.format(use_data, w_program_num, c_method, "error_map", wh_seed)
os.makedirs(write_path, exist_ok=True)
Emap_nmf_v = ipf.calculate_v(read_path,
read_file_name,
snmf=False)
Emap_snmf_v = ipf.calculate_v(read_path,
read_file_name,
snmf=True)
os.makedirs(w_path, exist_ok=True)
print("start {} {} {} >>>>>".format(w_program_num, use_data, c_method))
# time measurement--------------------------------------------------------------------------------------------------
nmf_t_result = np.zeros([r_list_size, seed_list_size])
snmf_t_result = np.zeros([r_list_size, ap_list_size, seed_list_size])
for r_i, r in enumerate(r_list):
for ap_i, ap in enumerate(ap_list):
for seed_i, seed in enumerate(seed_list):
pr_path, pr_file_name = ff.path_and_file_name(r,
ap,
ite,
seed,
use_data,
program_num,
c_method,
"r,k",
directory="time",
space=space)
t_result = pd.read_csv(pr_path + pr_file_name + ".csv")
snmf_t_result[r_i, ap_i, seed_i] = t_result["SNMF time"]
if ap == ap_list[-1]:
nmf_t_result[r_i, seed_i] = t_result["NMF time"]
nmf_t_result = np.mean(nmf_t_result, axis=1)
snmf_t_result = np.mean(snmf_t_result, axis=2)
df_t_result = pd.DataFrame({"k=" + str(ap_list[0]): snmf_t_result[0, 0]},
index=["r=" + str(r_list[0])])
for r_i, r in enumerate(r_list[1:]):