机器学习sklearn（8）支持向量机svm——线性

import pandas as pd
import numpy as np
from sklearn import svm
import matplotlib.pyplot as plt
%matplotlib inline

读取数据

path = r"C:\Users\Machine Learning\svm_testset.txt"
data_svm = pd.read_csv(path, header=None, sep='\t', encoding='ANSI')
print(data_svm.head())

          0         1  2
0  3.542485  1.977398 -1
1  3.018896  2.556416 -1
2  7.551510 -1.580030  1
3  2.114999 -0.004466 -1
4  8.127113  1.274372  1

分出特征向量和标签

data_svm = np.array(data_svm)
svm_data = data_svm[:,:-1]
svm_label = data_svm[:,-1]

数据集可视化

plt.scatter(svm_data[svm_label==1,0], svm_data[svm_label==1,1], s=30, alpha=.7)
plt.scatter(svm_data[svm_label==-1,0], svm_data[svm_label==-1,1], s=30, alpha=.7)
plt.show()

训练模型

clf = svm.SVC(kernel='linear', gamma='scale')
clf.fit(svm_data, svm_label)

SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape='ovr', degree=3, gamma='scale', kernel='linear',
  max_iter=-1, probability=False, random_state=None, shrinking=True,
  tol=0.001, verbose=False)

clf.support_vectors_ #支持向量

array([[ 4.658191,  3.507396],
       [ 3.457096, -0.082216],
       [ 6.080573,  0.418886]])

clf.support_ #支持向量索引

array([17, 29, 55])

clf.n_support_ #下方、上方支持向量个数

array([2, 1])

clf.coef_ #系数

array([[ 0.81444269, -0.27274371]])

clf.intercept_ #截距

array([-3.83775658])

#模型训练后得到的划分直线：clf.coef_[0,0]*x1 + clf.coef_[0,1]*x2 + clf.intercept_ = 0
a = -clf.coef_[0,0]/clf.coef_[0,1] #直线方程系数
b = -clf.intercept_/clf.coef_[0,1] #直线方程截距
#根据刚才所得到的支持向量来分别求得上下方直线截距
#三条直线平行，可得y_down=a*x+b_down, y_up= a*x+b_up,然后将这两条直线上的支持向量点带入方程即可求得b_down和b_up
X_down = clf.support_vectors_[0] #直线y_down上的支持向量
x_down = X_down[0]
y_down = X_down[1]
b_down = y_down - a*x_down #直线y_down截距

X_up = clf.support_vectors_[-1] #直线y_up上的支持向量
x_up = X_up[0]
y_up = X_up[1]
b_up = y_up - a*x_up #直线y_up截距

x_max = max(svm_data[:,0])
x_min = min(svm_data[:,0])

X = [x_min,x_max]
X = np.array(X)

array([-0.743036,  9.854303])

Y = a*X + b
Y_up = a*X + b_up
Y_down = a*X + b_down

plt.scatter(svm_data[svm_label==1,0], svm_data[svm_label==1,1], s=30, alpha=.7)
plt.scatter(svm_data[svm_label==-1,0], svm_data[svm_label==-1,1], s=30, alpha=.7)
plt.scatter(clf.support_vectors_[:,0], clf.support_vectors_[:,1], s=150, c='none', alpha=0.7, linewidth=1.2, edgecolor='red')
plt.plot(X,Y,c='red')
plt.plot(X,Y_down,c='blue',linestyle='--')
plt.plot(X,Y_up,c='blue',linestyle='--')
plt.show()

机器学习sklearn（8）支持向量机svm——线性

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