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Feature_time.py
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Feature_time.py
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from scipy.stats import skew
import numpy as np
import pandas as pd
import os
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
from scipy.stats import kurtosis
from scipy.fft import fft
# conda install numpy -i https://pypi.tuna.tsinghua.edu.cn/simple
# 计算时域统计特征
def calculate_time_domain_features(signal):
mean = np.mean(signal) #均值
std = np.std(signal) #标准差
rms = np.sqrt(np.mean(signal**2)) #方差
skewness = skew(signal) #斜率
kurt = kurtosis(signal) #峰值
return mean, std, rms, skewness, kurt
# 计算频域统计特征
def calculate_frequency_domain_features(signal, freq_axis):
signal = signal[:len(freq_axis)]
mean_freq = np.mean(freq_axis)
std_freq = np.std(freq_axis)
rms_freq = np.sqrt(np.mean(freq_axis**2))
central_freq = np.sum(freq_axis * np.abs(signal)) / np.sum(np.abs(signal))
kurt_freq = kurtosis(signal)
return mean_freq, std_freq, rms_freq, central_freq, kurt_freq
dataset_dir = "E:/increment_classfy/dataset"
csv_files = [file for file in os.listdir(dataset_dir) if file.endswith('.csv')]
csv_files = sorted(csv_files, key=lambda x: int(x.split('S')[1].split('.')[0]))
hea_files = [file for file in os.listdir(dataset_dir) if file.endswith('.hea')]
hea_files = sorted(hea_files, key=lambda x: int(x.split('S')[1].split('.')[0]))
all_features = []
all_labels = []
feature_dir = 'E:/increment_classfy/time_features'
# 遍历做TET #？？？？？？？？？？？？？？？？
for csv_file, hea_file in zip(csv_files, hea_files):
# 读取.csv文件
data = pd.read_csv(os.path.join(dataset_dir, csv_file))
signal = data.iloc[:, 0] + 1j * data.iloc[:, 1] # 复数信号 #iloc：通过行、列的索引位置来寻找数据
print(len(signal))
# 读取.hea文件
with open(os.path.join(dataset_dir, hea_file), 'r') as f:
label = f.readline().strip() # 读取第一行作为标签
print(label)
# # TET变换
# tfr, Te, GD, TEO, freq_axis = TET_Y(signal.values, hlength=1024, fs=100000000) # 调整hlength的值以适应您的数据
# reconstructed_signal = reconstruct_signal(Te)
# reconstructed_signal = np.real(reconstructed_signal)
# 计算原始信号的统计特征
mean, std, rms, skewness, kurt = calculate_time_domain_features(signal)
# mean_freq, std_freq, rms_freq, central_freq, kurt_freq = calculate_frequency_domain_features(np.abs(fft(signal.values))
# # 计算瞬态分量的统计特征
# mean_te, std_te, rms_te, skewness_te, kurt_te = calculate_time_domain_features(reconstructed_signal)
# print(len(np.abs(fft(reconstructed_signal))))
# mean_freq_te, std_freq_te, rms_freq_te, central_freq_te, kurt_freq_te = calculate_frequency_domain_features(
# np.abs(fft(reconstructed_signal)), freq_axis)
# features = np.array([mean, std, rms, skewness, kurt,
# mean_freq, std_freq, rms_freq, central_freq, kurt_freq,
# mean_te, std_te, rms_te, skewness_te, kurt_te,
# mean_freq_te, std_freq_te, rms_freq_te, central_freq_te, kurt_freq_te])
# features = np.array([mean, std, rms, skewness, kurt,
# mean_freq, std_freq, rms_freq, central_freq, kurt_freq])
# 保存特征数组到.npy文件
np.save(feature_dir + '\\' + str(csv_file).split('.')[0], features) #只取文件名中除后缀之外的内容
if int(str(csv_file)[1:].split('.')[0]) >= 4500: ##########
break
for npy_file in sorted(os.listdir(feature_dir)):
if npy_file.endswith('.npy'):
features_array = np.load(os.path.join(feature_dir, npy_file)).astype(float)
all_features.append(features_array)
# 根据文件名获取标签信息，并添加到标签列表中
with open(os.path.join(dataset_dir, hea_files[int(npy_file.split('.')[0][1:]) - 1]), 'r') as f:
label = f.readline().strip() # 读取第一行作为标签
all_labels.extend([label] * features_array.shape[0])
# 将列表转换为numpy数组
all_features = np.concatenate(all_features, axis=0)
all_labels = np.array(all_labels)
print(all_labels)
# 划分数据集为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(all_features, all_labels, test_size=0.2, random_state=42)
# 初始化SVM分类器
svm_classifier = SVC(kernel='linear', C=1.0, random_state=42)
# 在训练集上训练模型
svm_classifier.fit(X_train, y_train)
# 在测试集上进行预测
predictions = svm_classifier.predict(X_test)
# 计算准确率
accuracy = accuracy_score(y_test, predictions)
print("准确率:", accuracy)