在cwru数据集划分的测试集上进行测试的代码

以下是在CWRU数据集上进行测试的代码示例：

import numpy as np
from scipy import signal
from sklearn.metrics import confusion_matrix
import glob
import os

# Load test data
data_path = "your_data_path"
test_files = glob.glob(os.path.join(data_path, "test", "*.npy"))

test_data = []
test_labels = []

for file in test_files:
    data = np.load(file)
    label = int(file.split("/")[-1].split("_")[0][1:])
    test_data.append(data)
    test_labels.append(label)

test_data = np.array(test_data)
test_labels = np.array(test_labels)

# Define filter coefficients for high-pass filter
fs = 12e3
f1 = 10
f2 = 50
wp = [f1, f2]
ws = [f1-5, f2+5]
gpass = 1
gstop = 20
N, Wn = signal.buttord(wp, ws, gpass, gstop, fs=fs)
b, a = signal.butter(N, Wn, btype='bandpass', output='ba', fs=fs)

# Apply high-pass filter to test data
test_data_filtered = signal.filtfilt(b, a, test_data)

# Load trained model
model_path = "your_model_path"
model = load_model(model_path)

# Predict labels for test data
test_pred = model.predict(test_data_filtered)

# Convert predictions to class labels
test_pred_labels = np.argmax(test_pred, axis=1)

# Compute confusion matrix
cm = confusion_matrix(test_labels, test_pred_labels)
print(cm)

请注意，这只是一个简单的代码示例，您需要根据您的模型和数据集进行适当的修改。