基于机器学习的连续血压估计.zip

# -*-coding: utf-8-*- import numpy as np import matplotlib.pyplot as plt import pandas as pd import scipy.io as sio from sklearn.decomposition import PCA from sklearn.cross_validation import train_test_split from sklearn.preprocessing import StandardScaler import pca_vector_number # 导入matlab文件 matfn=u'/home/wangbo/特征数据/realdata3.mat' rawdata=sio.loadmat(matfn) # 得到的是字典类型 # 导出所有特征 ppgfeatures=rawdata['PPG_Features'] #这是一个 long×31 的 ecgfeatures=rawdata['ECG_Features'] #这是一个 long×5 的 alingfeatures=rawdata['PPG_ECG_alingFeatures'] #这是一个 long×3 的 sys_bp=rawdata['SYS_BP'].transpose() # 这里的数据的二维ndarray数据 1*long,再转置 dia_bp=rawdata['DIA_BP'].transpose() # 变ndarray为DataFrame a=pd.DataFrame(ppgfeatures,columns=['PH','DNH','DNHr','DPH','DPHr','RBW','RBW10','RBW25','RBW33','RBW50','RBW75','DBW10','DBW25','DBW33','DBW50','DBW75','PDNT','DNDPT','KVAL','PWA','RBAr','DBAr','DiaAr','SLP1','SLP2','SLP3','AmBE','E','KTEMIU','KTEDELTA','ENTROP']) b=pd.DataFrame(ecgfeatures,columns=['T_QR','T_RS','T_RR','H_QR','H_RS']) c=pd.DataFrame(alingfeatures,columns=['PWTT_RP','PWTT_RO','PWTT_RhalfP']) d=pd.DataFrame(sys_bp,columns=['SYS_BP']) e=pd.DataFrame(dia_bp,columns=['DIA_BP']) data=pd.concat([a,b,c,d,e],axis=1) # 将数据划分为训练数据集和测试集 x=data.iloc[:,0:39] y=data.iloc[:,[39,40]] x_train, x_test, y_train, y_test=train_test_split(x,y,test_size=0.1,random_state=0) #70%训练 # 特征数据标准化 stdsc = StandardScaler() x_train_std = stdsc.fit_transform(x_train) x_test_std = stdsc.transform(x_test) # 构造协方差矩阵 cov_mat = np.cov(x_train_std,rowvar=0) eigen_vals, engen_vecs = np.linalg.eig(cov_mat) print eigen_vals number=pca_vector_number.percentage2n(eigen_vals,0.95) print number # 要几个特征向量 tot=sum(eigen_vals) var_exp=[(i/tot) for i in sorted(eigen_vals,reverse=True)] cum_var_exp=np.cumsum(var_exp) plt.bar(range(1,40),var_exp,alpha=0.5,align='center') plt.step(range(1,40),cum_var_exp,where='mid') plt.show()