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)
