#######################################################################################################
x_begin = min(data[:, 0]) #扫描分界面位置
x_end = max(data[:, 0])
y_begin = min(data[:, 1])
y_end = max(data[:, 1]) + 1
pointnumber = 1200
linex = np.linspace(x_begin, x_end, pointnumber)
liney = np.zeros(pointnumber)
linextest = np.ones(pointnumber)
tempdata = np.zeros((pointnumber, 2))
tempdata[:, 1] = np.linspace(y_begin, y_end, pointnumber)
def coutline():
for i in range(0, pointnumber):
tempdata[:, 0] = linex[i] * linextest
temp = svm_predict(np.zeros(pointnumber), tempdata, model, '-q')
for j in range(1, pointnumber):
if (temp[0][j] + temp[0][j - 1] == 1):
break
liney[i] = tempdata[j][1]
return liney
coutline()
#######################################################################################################
#输出数据
np.savez("SVM_Linear", linex, liney)
#np.savez("SVM_Gauss",linex,liney)
plotall(data, label, np.array(predict[0]), None, None, None,
train_size + test_size, 0, False, linex, liney)
x_end = max(data[:, 0])
y_begin = min(data[:, 1])
y_end = max(data[:, 1]) + 1
pointnumber = 1200
linex = np.linspace(x_begin, x_end, pointnumber)
liney = np.zeros(pointnumber)
linextest = np.ones(pointnumber)
tempdata = np.zeros((pointnumber, 2))
tempdata[:, 1] = np.linspace(y_begin, y_end, pointnumber)
def coutline():
for i in range(0, pointnumber):
tempdata[:, 0] = linex[i] * linextest
temp = model.predict(tempdata)
for j in range(1, pointnumber):
if (temp[j] + temp[j - 1] == 1):
break
liney[i] = tempdata[j][1]
return liney
coutline()
plotall(data, label, decide, None, None, None, train_size + test_size, 0,
False, linex, liney)
#np.savez("Perceptron",linex,liney)
#withoutnorm
#average accuracy=0.9628288209606433
#withnorm
#average accuracy=0.983187772925717