| 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) |
| |
| 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) |
| |
| 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 |
| |
| 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 |
|
|
