非线性支持向量机(鸢尾花分类)+自定义随机数据

导入模块

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from matplotlib.font_manager import FontProperties
from sklearn import datasets
from sklearn.svm import SVC
%matplotlib inline
font = FontProperties(fname='/Library/Fonts/Heiti.ttc')

自定义数据分类

自定义数据

# 保证随机数不重复
np.random.seed(1)
# 创建100个二维数组，即100个2个特征的样本
X_custom = np.random.randn(100, 2)
# np.logical_xor(bool1, bool2)，异或逻辑运算，如果bool1和bool2的结果相同则为False，否则为True
# ++和--为一三象限，+-和-+为二四象限，如此做则100个样本必定线性不可分
y_custom = np.logical_xor(X_custom[:, 0] > 0, X_custom[:, 1] > 0)
# 二四象限为True，即为1类；一三象限为False，即为-1类
y_custom = np.where(y_custom, 1, -1)

构建决策边界

def plot_decision_regions(X, y, classifier=None):
    marker_list = ['o', 'x', 's']
    color_list = ['r', 'b', 'g']
    cmap = ListedColormap(color_list[:len(np.unique(y))])

    x1_min, x1_max = X[:, 0].min()-1, X[:, 0].max()+1
    x2_min, x2_max = X[:, 1].min()-1, X[:, 1].max()+1
    t1 = np.linspace(x1_min, x1_max, 666)
    t2 = np.linspace(x2_min, x2_max, 666)

    x1, x2 = np.meshgrid(t1, t2)
    y_hat = classifier.predict(np.array([x1.ravel(), x2.ravel()]).T)
    y_hat = y_hat.reshape(x1.shape)
    plt.contourf(x1, x2, y_hat, alpha=0.2, cmap=cmap)
    plt.xlim(x1_min, x1_max)
    plt.ylim(x2_min, x2_max)

    for ind, clas in enumerate(np.unique(y)):
        plt.scatter(X[y == clas, 0], X[y == clas, 1], alpha=0.8, s=50,
                    c=color_list[ind], marker=marker_list[ind], label=clas)

训练模型