def min_max(x, axis=None):
min = test.min(axis=axis, keepdims=True)
max = test.max(axis=axis, keepdims=True)
result = (x - min) / (max - min)
return result
from Utilities import *
from modeling import *
from test import test
import numpy as np
from keras.optimizers import SGD, nadam, Adam, Adamax, Adagrad, Adadelta, RMSprop
from keras import metrics
from keras.utils import np_utils
from keras.callbacks import EarlyStopping, TensorBoard, ModelCheckpoint
from keras.backend import tensorflow_backend as KTF
import tensorflow as tf
# test = np.loadtxt('CBF_TEST.txt',dtype=np.float,delimiter=',')
test = np.loadtxt('Earthquakes_TEST.txt', dtype=np.float, delimiter=',')
# test = np.loadtxt('Cricket_Y_TEST.txt',dtype=np.float,delimiter=',')
print(test.shape)
min = test.min(axis=None, keepdims=True)
max = test.max(axis=None, keepdims=True)
# train = np.loadtxt('CBF_TRAIN.txt',dtype=np.float,delimiter=',')
train = np.loadtxt('Earthquakes_TRAIN.txt', dtype=np.float, delimiter=',')
# train = np.loadtxt('Cricket_Y_TRAIN.txt',dtype=np.float,delimiter=',')
print(train.shape)
min1 = train.min(axis=None, keepdims=True)
max1 = train.max(axis=None, keepdims=True)
print(min)
print(max)
print(min1)
print(max1)