SVM代码和数据集；/

import numpy as np from matplotlib import pyplot as plt import scipy.io as scio from sklearn import svm from sklearn import preprocessing from sklearn.metrics import accuracy_score path= '../其他方法/chapter_WineClass.mat' data = scio.loadmat(path) winedata = np.transpose(data['wine']) labeldata = np.transpose(data['wine_labels']) # 选定训练集和测试集 # 将第一类的1-30,第二类的60-95,第三类的131-153做为训练集 train_wine=np.hstack((winedata[:,0:29],winedata[:,59:94],winedata[:,130:152])) train_label = np.hstack((labeldata[:,0:29],labeldata[:,59:94],labeldata[:,130:152])) # 将第一类的31-59,第二类的96-130,第三类的154-178做为测试集 test_wine = np.hstack((winedata[:,30:58],winedata[:,95:129],winedata[:,153:177])); test_label = np.hstack((labeldata[:,30:58],labeldata[:,95:129],labeldata[:,153:177])) # 数据归一化 min_max_scaler = preprocessing.MinMaxScaler() train_wine = min_max_scaler.fit_transform(train_wine) test_wine = min_max_scaler.fit_transform(test_wine) #转换为行向量 train_wine = train_wine.T test_wine = test_wine.T train_label = train_label.T test_label = test_label.T #3.训练svm分类器 classifier=svm.SVC(C=2,kernel='rbf',gamma=10) # ovr:一对多策略 classifier.fit(train_wine,train_label.ravel()) #ravel函数在降维时默认是行序优先 #4.计算svc分类器的准确率 tra_label=classifier.predict(train_wine) #训练集的预测标签 tes_label=classifier.predict(test_wine) #测试集的预测标签 print("训练集准确率：", accuracy_score(train_label,tra_label) ) print("测试集准确率：", accuracy_score(test_label,tes_label) ) plt.plot(train_label,'*',label = "True") plt.plot(tra_label,'o',label = "predict") plt.grid(True) plt.legend(bbox_to_anchor=(1.0, 1), loc=1, borderaxespad=0.) plt.show()