基于KNN实现使用脑电波预测眼睛睁闭项目源码+数据+超详细注释.zip

from pandas import read_csv from sklearn.metrics import accuracy_score from sklearn.model_selection import KFold from sklearn.neighbors import KNeighborsClassifier from numpy import mean # load the dataset data = read_csv('EEG_Eye_State_no_outliers.csv', header=None) values = data.values scores = list() # 对数据进行K折交叉验证，K=10，因此会产生10组数据 kfold = KFold(10, shuffle=True, random_state=1) # 对K折交叉验证构建的10组数据分别建模，并求均值 for train_ix, test_ix in kfold.split(values): # 构建测试集和训练集 trainX, trainy = values[train_ix, :-1], values[train_ix, -1] testX, testy = values[test_ix, :-1], values[test_ix, -1] # 定义KNN模型，K=3 model = KNeighborsClassifier(n_neighbors=3) # 训练模型并预测 model.fit(trainX, trainy) yhat = model.predict(testX) # 评估记录预测值，求均值 score = accuracy_score(testy, yhat) scores.append(score) print('>%.3f' % score) # calculate mean score across each run print('Final Score: %.3f' % (mean(scores)))