02-34 非线性支持向量机(鸢尾花分类)+自定义数据分类

目录

• 非线性支持向量机(鸢尾花分类)+自定义随机数据
• 一、导入模块
• 二、自定义数据分类
  • 2.1 自定义数据
  • 2.2 构建决策边界
  • 2.3 训练模型
  • 2.4 可视化
• 三、鸢尾花分类
  • 3.1 获取数据
  • 3.2 构建决策边界
  • 3.3 训练模型(gamma=1)
  • 3.4 可视化
  • 3.5 训练模型(gamma=100)
  • 3.6 可视化

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非线性支持向量机(鸢尾花分类)+自定义随机数据

一、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')

二、自定义数据分类

2.1 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)

2.2 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[:, <span class="hljs-number">0</span>].<span class="hljs-built_in">min</span>()<span class="hljs-number">-1</span>, X[:, <span class="hljs-number">0</span>].<span class="hljs-built_in">max</span>()+<span class="hljs-number">1</span>
x2_min, x2_max = X[:, <span class="hljs-number">1</span>].<span class="hljs-built_in">min</span>()<span class="hljs-number">-1</span>, X[:, <span class="hljs-number">1</span>].<span class="hljs-built_in">max</span>()+<span class="hljs-number">1</span>
t1 = np.linspace(x1_min, x1_max, <span class="hljs-number">666</span>)
t2 = np.linspace(x2_min, x2_max, <span class="hljs-number">666</span>)

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=<span class="hljs-number">0.2</span>, cmap=cmap)
plt.xlim(x1_min, x1_max)
plt.ylim(x2_min, x2_max)

<span class="hljs-keyword">for</span> ind, clas <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(np.unique(y)):
    plt.scatter(X[y == clas, <span class="hljs-number">0</span>], X[y == clas, <span class="hljs-number">1</span>], alpha=<span class="hljs-number">0.8</span>, s=<span class="hljs-number">50</span>,
                c=color_list[ind], marker=marker_list[ind], label=clas)

2.3 训练模型

# rbf为高斯核
svm = SVC(kernel='rbf', gamma='auto', C=1, random_state=1)
svm.fit(X_custom, y_custom)

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

2.4 可视化

plot_decision_regions(X_custom, y_custom, classifier=svm)
plt.title('非线性支持向量机(自定义数据分类)',fontproperties=font)
plt.legend()
plt.show()

三、鸢尾花分类

3.1 获取数据

iris_data = datasets.load_iris()
X = iris_data.data[:, [2, 3]]
y = iris_data.target
label_list = ['山鸢尾', '杂色鸢尾', '维吉尼亚鸢尾']

3.2 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[:, <span class="hljs-number">0</span>].<span class="hljs-built_in">min</span>()<span class="hljs-number">-1</span>, X[:, <span class="hljs-number">0</span>].<span class="hljs-built_in">max</span>()+<span class="hljs-number">1</span>
x2_min, x2_max = X[:, <span class="hljs-number">1</span>].<span class="hljs-built_in">min</span>()<span class="hljs-number">-1</span>, X[:, <span class="hljs-number">1</span>].<span class="hljs-built_in">max</span>()+<span class="hljs-number">1</span>
t1 = np.linspace(x1_min, x1_max, <span class="hljs-number">666</span>)
t2 = np.linspace(x2_min, x2_max, <span class="hljs-number">666</span>)

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=<span class="hljs-number">0.2</span>, cmap=cmap)
plt.xlim(x1_min, x1_max)
plt.ylim(x2_min, x2_max)

<span class="hljs-keyword">for</span> ind, clas <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(np.unique(y)):
    plt.scatter(X[y == clas, <span class="hljs-number">0</span>], X[y == clas, <span class="hljs-number">1</span>], alpha=<span class="hljs-number">0.8</span>, s=<span class="hljs-number">50</span>,
                c=color_list[ind], marker=marker_list[ind], label=label_list[clas])

3.3 训练模型(gamma=1)

# rbf为高斯核
svm = SVC(kernel='rbf', gamma=1, C=1, random_state=1)
svm.fit(X, y)

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

3.4 可视化

plot_decision_regions(X, y, classifier=svm)
plt.xlabel('花瓣长度（cm）', fontproperties=font)
plt.ylabel('花瓣宽度（cm）', fontproperties=font)
plt.title('非线性支持向量机代码(鸢尾花分类, gamma=1)', fontproperties=font, fontsize=20)
plt.legend(prop=font)
plt.show()

3.5 训练模型(gamma=100)

# rbf为高斯核
svm = SVC(kernel='rbf', gamma=100, C=1, random_state=1)
svm.fit(X, y)

SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape='ovr', degree=3, gamma=100, kernel='rbf',
  max_iter=-1, probability=False, random_state=1, shrinking=True,
  tol=0.001, verbose=False)

3.6 可视化

plot_decision_regions(X, y, classifier=svm)
plt.xlabel('花瓣长度（cm）', fontproperties=font)
plt.ylabel('花瓣宽度（cm）', fontproperties=font)
plt.title('非线性支持向量机代码(鸢尾花分类, gamma=100)', fontproperties=font, fontsize=20)
plt.legend(prop=font)
plt.show()