def testRbf(self, kTup=('rbf', 1.5)):
dataArr, labelArr = self.loadDataSet('data/svm/testSetRBF.txt')
svm = SVMLib()
datMat = mat(dataArr)
## 训练数据
RBF = svm.fit_RBF(dataArr, labelArr, 200, 0.0001, 10000, kTup)
print RBF.b, '----', RBF.alphas
print "支持向量数量为 %d " % shape(RBF.sVs)[0]
m, n = shape(datMat)
errorCount = 0
for i in range(m):
## 测试数据
predict = svm.predict_RBF(RBF, datMat[i, :], kTup)
if sign(predict) != sign(labelArr[i]): errorCount += 1
print "培训集错误率: %f" % (float(errorCount) / m)
#
dataArr, labelArr = self.loadDataSet('data/svm/testSetRBF2.txt')
errorCount = 0
datMat = mat(dataArr)
m, n = shape(datMat)
for i in range(m):
predict = svm.predict_RBF(RBF, datMat[i, :], kTup)
if sign(predict) != sign(labelArr[i]): errorCount += 1
print "训练集错误率: %f" % (float(errorCount) / m)
def testRbf(self, kTup=('rbf', 1.5)):
dataArr, labelArr = self.loadDataSet('data/dataset2svm/testSetRBF.txt')
svm = SVMLib()
datMat = mat(dataArr);
## 训练数据
RBF = svm.fit_RBF(dataArr, labelArr, 200, 0.0001, 10000, kTup)
print RBF.b, '----', RBF.alphas
print "支持向量数量为 %d " % shape(RBF.sVs)[0]
m, n = shape(datMat)
errorCount = 0
for i in range(m):
## 测试数据
predict = svm.predict_RBF(RBF, datMat[i, :], kTup)
if sign(predict) != sign(labelArr[i]): errorCount += 1
print "培训集错误率: %f" % (float(errorCount) / m)
#
dataArr, labelArr = self.loadDataSet('data/dataset2svm/testSetRBF2.txt')
errorCount = 0
datMat = mat(dataArr);
m, n = shape(datMat)
for i in range(m):
predict = svm.predict_RBF(RBF, datMat[i, :], kTup)
if sign(predict) != sign(labelArr[i]): errorCount += 1
print "训练集错误率: %f" % (float(errorCount) / m)
def testDigits(self, kTup=('rbf', 10)):
dataArr, labelArr = self.loadImages('data/svm/trainingDigits')
svm = SVMLib()
RBF = svm.fit_RBF(dataArr, labelArr, 200, 0.0001, 10000, kTup)
print RBF.b, '----'
datMat = mat(dataArr)
print "there are %d Support Vectors" % shape(RBF.sVs)[0]
m, n = shape(datMat)
errorCount = 0
for i in range(m):
predict = svm.predict_RBF(RBF, datMat[i, :], kTup)
if sign(predict) != sign(labelArr[i]): errorCount += 1
print "the training error rate is: %f" % (float(errorCount) / m)
dataArr, labelArr = self.loadImages('data/svm/testDigits')
errorCount = 0
datMat = mat(dataArr)
m, n = shape(datMat)
for i in range(m):
predict = svm.predict_RBF(RBF, datMat[i, :], kTup)
if sign(predict) != sign(labelArr[i]): errorCount += 1
print "the test error rate is: %f" % (float(errorCount) / m)
def testDigits(self, kTup=('rbf', 10)):
dataArr, labelArr = self.loadImages('data/dataset2svm/trainingDigits')
svm = SVMLib()
RBF = svm.fit_RBF(dataArr, labelArr, 200, 0.0001, 10000, kTup)
print RBF.b, '----'
datMat = mat(dataArr);
print "there are %d Support Vectors" % shape(RBF.sVs)[0]
m, n = shape(datMat)
errorCount = 0
for i in range(m):
predict = svm.predict_RBF(RBF, datMat[i, :], kTup)
if sign(predict) != sign(labelArr[i]): errorCount += 1
print "the training error rate is: %f" % (float(errorCount) / m)
dataArr, labelArr = self.loadImages('data/dataset2svm/testDigits')
errorCount = 0
datMat = mat(dataArr);
m, n = shape(datMat)
for i in range(m):
predict = svm.predict_RBF(RBF, datMat[i, :], kTup)
if sign(predict) != sign(labelArr[i]): errorCount += 1
print "the test error rate is: %f" % (float(errorCount) / m)