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90 lines
2.9 KiB
90 lines
2.9 KiB
2 weeks ago
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# -*- coding: utf-8 -*-
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"""
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@Time : 2020/6/14 19:35
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@Author : 杰森·家乐森
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@File : sae_main.py
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@Software: PyCharm
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"""
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import json
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import time
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import torch
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import itertools
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import numpy as np
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import pandas as pd
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import torch.nn as nn
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import torch.utils.data as Data
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from sae_diagnosis import diagnosis
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from torch.autograd import Variable
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from sklearn.metrics import r2_score
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from sklearn.preprocessing import MinMaxScaler
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def get_diagnosis(model, fault_data, true_fault, spe, name):
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"""
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诊断函数
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:param model: 模型信息
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:param fault_data: 故障数据
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:param true_fault: 真实故障
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:param spe: spe限值
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:param name: 诊出结果保存文件名
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:return:
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"""
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dection_array, amp_array = diagnosis(model, fault_data[:100, :], spe)
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dection_array = dection_array * 10
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fault_array = true_fault[:100, :]
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detection = fault_array - dection_array
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FDR = 1 - np.sum(detection > 0) / np.sum(fault_array > 0)
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FAR = np.sum(detection == -10) / np.sum(fault_array == 0)
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detection_cfg = {
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"FDR": FDR,
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"FAR": FAR
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}
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with open(name, 'w') as f:
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f.write(json.dumps(detection_cfg))
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def diag(hi, lo, name):
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# data = np.array(pd.read_excel('旧数据.xlsx'))
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# data1 = np.array(pd.read_excel('旧数据.xlsx'))
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with open('train_data.json', 'r') as f:
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data = np.array(json.load(f)['data'])
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with open('test_data.json', 'r') as f:
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data1 = np.array(json.load(f)['data'])[:500, :]
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mms = MinMaxScaler()
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mms.fit_transform(data)
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data = mms.transform(data[:100, :])
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fault_dir = np.zeros(data.shape)
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index_array = np.random.randint(0, 10, (data.shape[0], 1))
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index_array1 = np.random.randint(0, 10, (data.shape[0], 1))
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index_array2 = np.random.randint(0, 10, (data.shape[0], 1))
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for n in range(data.shape[1]):
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fault_dir[np.where(index_array[:, 0] == n), n] = 1
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fault_dir[np.where(index_array1[:, 0] == n), n] = 1
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fault_dir[np.where(index_array2[:, 0] == n), n] = 1
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amp_array = np.random.random_sample(data.shape) * 1
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fault_dir[np.where(amp_array == 0)] = 0
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fault_a = fault_dir * amp_array * (hi - lo) + fault_dir * lo
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fault_a = fault_dir * 0.5
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fault_data = data + fault_a
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data_fault = mms.inverse_transform(fault_data)
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bias_data = data_fault - data1[:100, :]
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true_fault = np.zeros(data.shape)
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true_fault[np.where(fault_a > 0)] = 10
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true_fault[np.where(fault_a == 0)] = 0
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with open('model.json', 'r') as f:
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model_cfg = json.load(f)
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model = model_cfg["model"]
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spe = model_cfg["spe"]
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# spe = 0.009
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get_diagnosis(model, fault_data, true_fault, spe, 'rbct%d.json' % name)
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print('OK')
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if __name__ == '__main__':
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# hi = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 2.0, 3.0, 5.0, 10.0]
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# lo = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 2.0, 3.0, 5.0]
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# for i in range(len(hi)):
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# diag(hi[i], lo[i], i)
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diag(1, 0, 4)
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print('OK')
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