assert SAMPLE_X_LST.shape[0] == SAMPLE_Y_LST.shape[0]
assert type(SAMPLE_X_LST) == type(SAMPLE_Y_LST)
assert type(SAMPLE_X_LST) == np.ndarray
SAMPLE_NUM = SAMPLE_X_LST.shape[0]
SAMPLE_TRAIN_NUM = int(SAMPLE_NUM*0.8)
def GET_SAMPLE(batch_num):
idx_lst = np.random.permutation(SAMPLE_TRAIN_NUM)
count = int(SAMPLE_TRAIN_NUM/batch_num)+1
for i in range(count):
start = i*batch_num
end = (i+1)*batch_num
if end > SAMPLE_TRAIN_NUM:
end = SAMPLE_TRAIN_NUM
yield SAMPLE_X_LST[idx_lst[start:end]], SAMPLE_Y_LST[idx_lst[start:end]]
def GET_TRAIN():
idx_lst = np.random.permutation(SAMPLE_TRAIN_NUM)
return SAMPLE_X_LST[idx_lst[:SAMPLE_NUM-SAMPLE_TRAIN_NUM]], SAMPLE_Y_LST[idx_lst[:SAMPLE_NUM-SAMPLE_TRAIN_NUM]]
def GET_VAL():
return SAMPLE_X_LST[SAMPLE_TRAIN_NUM:], SAMPLE_Y_LST[SAMPLE_TRAIN_NUM:]
