算法更新更新

TensorFlow_eaxmple/Model_train_test/RUL/FFTUtils.py

@@ -0,0 +1,54 @@
+# -*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/6/13 21:32
+@Usage : 
+@Desc :  FFT的工具类
+'''
+import numpy as np
+import tensorflow as tf
+
+'''
+freq_value: 可以是时域通过np.fft.fft转换而来
+'''
+def ifft(freq_value):
+    # 幅值
+    amp = np.abs(freq_value / len(freq_value))
+    # 相角
+    angle = np.angle(freq_value)
+    length = len(amp)
+
+    result = []
+    for t in range(length):
+        wk = 2 * np.pi * np.arange(length) / length
+        cur = np.sum(amp * np.cos(wk * t + angle))
+        result.append(cur)
+
+    return result
+
+def fft(time_value):
+
+    pass
+
+if __name__ == '__main__':
+    array = np.array([-0.029078494757,
+                      -0.33095228672,
+                      -0.12124221772,
+                      0.553512275219,
+                      -0.158036053181,
+                      0.268739402294,
+                      -0.638222515583,
+                      0.233140587807,
+                      -0.173265621066,
+                      0.467218101025,
+                      -0.372010827065,
+                      -0.136630430818,
+                      0.343256533146,
+                      0.008932195604])
+
+    print(tf.signal.fft(array))
+    array_fft = np.fft.fft(array)
+
+    # print(ifft(array))
+    print(array_fft)

TensorFlow_eaxmple/Model_train_test/RUL/__init__.py

@@ -0,0 +1,8 @@
+#-*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/6/13 19:42
+@Usage : 
+@Desc :
+'''

TensorFlow_eaxmple/Model_train_test/RUL/test/FFTTest.py

@@ -0,0 +1,59 @@
+# -*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/6/13 19:42
+@Usage : 
+@Desc :测试一下FFT和IFFT公式计算
+'''
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+array = np.array([-0.029078494757,
+                  -0.33095228672,
+                  -0.12124221772,
+                  0.553512275219,
+                  -0.158036053181,
+                  0.268739402294,
+                  -0.638222515583,
+                  0.233140587807,
+                  -0.173265621066,
+                  0.467218101025,
+                  -0.372010827065,
+                  -0.136630430818,
+                  0.343256533146,
+                  0.008932195604])
+
+array_fft = np.fft.rfft(array)
+
+# 幅值
+amp = np.abs(np.fft.fft(array) / len(array))
+# 相角
+angle = np.angle(np.fft.fft(array))
+# 时部
+real = np.real(np.fft.rfft(array) * 2 / len(array))
+# 虚部
+imag = np.imag(np.fft.rfft(array) * 2 / len(array))
+#
+angle1 = np.arctan(imag / real)
+
+print(angle)
+print(angle1)
+# result = []
+# (num,) = amp.shape
+# (total,) = array.shape
+# for j in range(total):
+#     wk = 2 * np.pi * np.arange(num) / num
+#     cur = np.sum(amp * np.cos(wk * j + angle))
+#     result.append(cur)
+#
+# print(result)
+# print(array)
+# # print(array_fft)
+#
+# plt.subplot(2, 1, 1)
+# plt.plot(result)
+# plt.subplot(2, 1, 2)
+# plt.plot(array)
+# plt.show()

TensorFlow_eaxmple/Model_train_test/RUL/test/__init__.py

@@ -0,0 +1,8 @@
+#-*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/6/13 19:42
+@Usage : 
+@Desc :
+'''

TensorFlow_eaxmple/Model_train_test/model/LossFunction/FTMSE.py

@@ -0,0 +1,35 @@
+# -*- encoding:utf-8 -*-
+
+'''
+@Author : dingjiawen
+@Date : 2023/6/13 21:15
+@Usage : 
+@Desc :  时频域损失
+'''
+
+import tensorflow as tf
+import tensorflow.keras.backend as K
+
+
+
+class FTMSE(tf.keras.losses.Loss):
+    def call(self, y_true, y_pred):
+        y_true = tf.cast(y_true, tf.float64)
+        y_pred = tf.cast(y_pred, tf.float64)
+
+        # 需要转为复数形式
+        yt_fft = tf.signal.fft(tf.cast(y_true,tf.complex64))
+        yp_fft = tf.signal.fft(tf.cast(y_pred,tf.complex64))
+
+        # 幅值
+        amp = tf.abs(yt_fft / len(yt_fft))
+        # 相角
+        angle = tf.angle(freq_value)
+
+
+        time_loss = tf.reduce_mean(tf.abs(y_true - y_pred))
+
+        freq_loss = tf.reduce_mean(tf.abs(yt_fft - yp_fft))
+
+        ftLoss = time_loss+freq_loss
+        return ftLoss