Python showSVM Examples

Example #1

    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)

Example #2

File: svm_test.py Project: sunbeansoft/LearnPython

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)

Example #3

File: test_svm.py Project: bmexue/start

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 = 1
#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)