labels = [] # fileIn = open('../data/testSet.txt') fileIn = open('../data/lr_data') for line in fileIn.readlines(): lineArr = line.strip().split('\t') dataSet.append([float(lineArr[0]), float(lineArr[1])]) labels.append(float(lineArr[2])) dataSet = mat(dataSet) labels = mat(labels).T train_x = dataSet[0:81, :] train_y = labels[0:81, :] test_x = dataSet[80:101, :] test_y = labels[80:101, :] ## step 2: training... print "step 2: training..." C = 0.6 toler = 0.001 maxIter = 50 svmClassifier = svm.trainSVM(train_x, train_y, C, toler, maxIter, kernelOption=('linear', 0)) ## step 3: testing print "step 3: testing..." accuracy = svm.testSVM(svmClassifier, test_x, test_y) ## step 4: show the result print "step 4: show the result..." print 'The classify accuracy is: %.3f%%' % (accuracy * 100) svm.showSVM(svmClassifier)
lineArr = line.strip().split('\t') dataSet.append([float(lineArr[0]), float(lineArr[1])]) labels.append(float(lineArr[2])) dataSet = mat(dataSet) labels = mat(labels).T train_x = dataSet[0:81, :] train_y = labels[0:81, :] test_x = dataSet[80:101, :] test_y = labels[80:101, :] ## step 2: training print("step 2: training...") C = 0.6 toler = 0.001 maxIter = 50 svmClassifier = svm.trainSVM(train_x, train_y, C, toler, maxIter, kernelOption=('linear', 0)) ## step 3: testing print("step 3: testing...") accuracy = svm.testSVM(svmClassifier, test_x, test_y) ## step 4: show the result print("step 4: show the result...") print('The classify accuracy is: %.3f%%' % (accuracy * 100)) svm.showSVM(svmClassifier)
from knn import knn_test from naiveBayes import testNaiveBayes from svm import testSVM if __name__ == "__main__": # Naive Bayes Test print("Running Naive Bayes.") print("Accuracy: ") testNaiveBayes() # KNN Test print("Running KNN. This will take some time.") print("Accuracy: ") knn_test() # SVM Test print("Running SVM.") print("Accuracy: ") testSVM()
# print(label.shape) # print(datas.shape) if __name__ == '__main__': # data,label = svm.loadDataSet("testSet.txt") print("step 1:load data...") train, test, train_label, test_label = loadDataSet("testSet.txt") print("step 2: training...") C = 0.6 toler = 0.001 maxIter = 40 svmClassifier, b, alpha = svm.trainSVM(train, train_label, C, toler, maxIter, kernelOption=('linear', 0)) # # print(b) # # print(alpha[alpha>0]) joblib.dump(svmClassifier, "train_model.m") # 保存模型 # svmClassifier = joblib.load("train_model.m")#加载模型 # # ## step 3: testing print("step 3: testing...") test_accuracy = svm.testSVM(svmClassifier, test, test_label) # print(accuracy) print('The classify test_accuracy is: %.3f%%' % (test_accuracy * 100)) ## step 4: show the result print("step 4: show the result...") svm.showSVM(svmClassifier)