File size: 2,165 Bytes
d4c7aae | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 | import numpy as np
def find_max_min(input_array):
input_np_arr = np.array(input_array)
x, y, z = input_np_arr.shape
if z == 0:
return None, None
input_np_arr = input_np_arr.reshape(-y, z)
# Find Max
max_array = np.max(input_np_arr, axis=0)
min_array = np.min(input_np_arr, axis=0)
data_amount = max_array - min_array
data_margin = np.multiply(data_amount, 0.15)
max_with_added = np.add(max_array, data_margin)
# Find Min
min_with_added = np.subtract(min_array, data_margin)
min_with_added[min_with_added < 0] = 0
return max_with_added, min_with_added
def normalized(input_array, max_arr, min_arr):
input_np_arr = np.array(input_array, dtype=np.float32)
x, y, z = input_np_arr.shape
if z == 0:
return input_np_arr
input_np_arr = input_np_arr.reshape(-y, z)
normalized_array = input_np_arr.copy()
for i in range(0, len(input_np_arr)):
for j in range(0, len(input_np_arr[i])):
max = max_arr[j]
min = min_arr[j]
if max == min:
new_data = 1
else:
new_data = (input_np_arr[i][j] - min) / (max - min)
normalized_array[i][j] = new_data
normalized_array = normalized_array.reshape(x, y, z)
return normalized_array
def normalized_2d(validate_payload, train_payload, max_arr, min_arr):
x, y, z = train_payload.shape
if z == 0:
return validate_payload
print('----')
print("Validate Payload size", validate_payload.shape)
print("Train Payload size", train_payload.shape)
x, y = np.array(validate_payload).shape
validate_payload = validate_payload.reshape(x, 1, z)
result = normalized(validate_payload, max_arr, min_arr)
return result
def denormalize(value, max, min):
data_amount = max - min
data_margin = np.multiply(data_amount, 0.15)
max_with_added = max + data_margin
# Find Min
min_with_added = min - data_margin
if min_with_added < 0:
min_with_added = 0
denormalized_vaule = (
value * (max_with_added - min_with_added)) + min_with_added
return denormalized_vaule
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