对鸢尾花数据集和月亮数据集，分别采用线性LDA、k-means和SVM算法进行二分类可视化分析(python编程)

一、简述算法线性LDA、k-means和SVM算法

算法	基本思想
LDA	LDA 是一种可作为特征抽取的技术，其目标是向最大化类间差异，最小化类内差异的方向投影，以利于分类等任务即将不同类的样本有效的分开。LDA 可以提高数据分析过程中的计算效率，对于未能正则化的模型，可以降低维度灾难带来的过拟合
k-means	在K-means算法中K是事先给定的，这个K值的选定是非常难以估计的；在K-means算法中，首先需要根据初始聚类中心来确定一个初始划分，然后对初始划分进行优化；从K-means算法框架可以看出，该算法需要不断地进行样本分类调整，不断地计算调整后的新的聚类中心，因此当数据量非常大时，算法的时间开销是非常大的。
SVM	SVM (支持向量机)是一种算法，它以以下方法做工，它是一种种非线性映射额，把原训练数据映射到较高的维上，在新的维上，它搜索最佳分离超平面。

二、线性判别分析LDA

1.鸢尾花数据集

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets.samples_generator import make_classification

class LDA():
    def Train(self, X, y):
        """X为训练数据集，y为训练label"""
        X1 = np.array([X[i] for i in range(len(X)) if y[i] == 0])
        X2 = np.array([X[i] for i in range(len(X)) if y[i] == 1])

        # 求中心点
        mju1 = np.mean(X1, axis=0)  # mju1是ndrray类型
        mju2 = np.mean(X2, axis=0)

        # dot(a, b, out=None) 计算矩阵乘法
        cov1 = np.dot((X1 - mju1).T, (X1 - mju1))
        cov2 = np.dot((X2 - mju2).T, (X2 - mju2))
        Sw = cov1 + cov2
        # 计算w
        w = np.dot(np.mat(Sw).I, (mju1 - mju2).reshape((len(mju1), 1)))
        # 记录训练结果
        self.mju1 = mju1  # 第1类的分类中心
        self.cov1 = cov1
        self.mju2 = mju2  # 第2类的分类中心
        self.cov2 = cov2
        self.Sw = Sw  # 类内散度矩阵
        self.w = w  # 判别权重矩阵
    def Test(self, X, y):
        """X为测试数据集，y为测试label"""
        # 分类结果
        y_new = np.dot((X), self.w)
        # 计算fisher线性判别式
        nums = len(y)
        c1 = np.dot((self.mju1 - self.mju2).reshape(1, (len(self.mju1))), np.mat(self.Sw).I)
        c2 = np.dot(c1, (self.mju1 + self.mju2).reshape((len(self.mju1), 1)))
        c = 1/2 * c2  # 2个分类的中心
        h = y_new - c
        # 判别
        y_hat = []
        for i in range(nums):
            if h[i] >= 0:
                y_hat.append(0)
            else:
                y_hat.append(1)
        # 计算分类精度
        count = 0
        for i in range(nums):
            if y_hat[i] == y[i]:
                count += 1
        precise = count / nums
        # 显示信息
        print("测试样本数量:", nums)
        print("预测正确样本的数量:", count)
        print("测试准确度:", precise)
        return precise
if '__main__' == __name__:
    # 产生分类数据
    n_samples = 500
    X, y = make_classification(n_samples=n_samples, n_features=2, n_redundant=0, n_classes=2,n_informative=1, n_clusters_per_class=1, class_sep=0.5, random_state=10)
    # LDA线性判别分析(二分类)
    lda = LDA()
    # 60% 用作训练，40%用作测试
    Xtrain = X[:299, :]
    Ytrain = y[:299]
    Xtest = X[300:, :]
    Ytest = y[300:]
    lda.Train(Xtrain, Ytrain)
    precise = lda.Test(Xtest, Ytest)
    # 原始数据
    plt.scatter(X[:, 0], X[:, 1], marker='o', c=y)
    plt.xlabel("x1")
    plt.ylabel("x2")
    plt.title("Test precise:" + str(precise))
    plt.show()

2.月亮数据集

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_moons
class LDA():
    def Train(self, X, y):
        """X为训练数据集，y为训练label"""
        X1 = np.array([X[i] for i in range(len(X)) if y[i] == 0])
        X2 = np.array([X[i] for i in range(len(X)) if y[i] == 1])
        # 求中心点
        mju1 = np.mean(X1, axis=0)  # mju1是ndrray类型
        mju2 = np.mean(X2, axis=0)
        # dot(a, b, out=None) 计算矩阵乘法
        cov1 = np.dot((X1 - mju1).T, (X1 - mju1))
        cov2 = np.dot((X2 - mju2).T, (X2 - mju2))
        Sw = cov1 + cov2
        # 计算w
        w = np.dot(np.mat(Sw).I, (mju1 - mju2).reshape((len(mju1), 1)))
        # 记录训练结果
        self.mju1 = mju1  # 第1类的分类中心
        self.cov1 = cov1
        self.mju2 = mju2  # 第1类的分类中心
        self.cov2 = cov2
        self.Sw = Sw  # 类内散度矩阵
        self.w = w  # 判别权重矩阵
    def Test(self, X, y):
        """X为测试数据集，y为测试label"""
        # 分类结果
        y_new = np.dot((X), self.w)
        # 计算fisher线性判别式
        nums = len(y)
        c1 = np.dot((self.mju1 - self.mju2).reshape(1, (len(self.mju1))), np.mat(self.Sw).I)
        c2 = np.dot(c1, (self.mju1 + self.mju2).reshape((len(self.mju1), 1)))
        c = 1/2 * c2  # 2个分类的中心
        h = y_new - c
        # 判别
        y_hat = []
        for i in range(nums):
            if h[i] >= 0:
                y_hat.append(0)
            else:
                y_hat.append(1)
        # 计算分类精度
        count = 0
        for i in range(nums):
            if y_hat[i] == y[i]:
                count += 1
        precise = count / (nums+0.000001)
        # 显示信息
        print("测试样本数量:", nums)
        print("预测正确样本的数量:", count)
        print("测试准确度:", precise)
        return precise
if '__main__' == __name__:
    # 产生分类数据
    X, y = make_moons(n_samples=100, noise=0.15, random_state=42)
    # LDA线性判别分析(二分类)
    lda = LDA()
    # 60% 用作训练，40%用作测试
    Xtrain = X[:60, :]
    Ytrain = y[:60]
    Xtest = X[40:, :]
    Ytest = y[40:]
    lda.Train(Xtrain, Ytrain)
    precise = lda.Test(Xtest, Ytest)
    # 原始数据
    plt.scatter(X[:, 0], X[:, 1], marker='o', c=y)
    plt.xlabel("x1")
    plt.ylabel("x2")
    plt.title("Test precise:" + str(precise))
    plt.show()

三、k-means聚类分析

1.鸢尾花数据集

import matplotlib.pyplot as plt
import numpy as np
from sklearn.cluster import KMeans
from sklearn import datasets
from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data[:] ##表示我们只取特征空间中的后两个维度
estimator = KMeans(n_clusters=5)#构造聚类器
estimator.fit(X)#聚类
label_pred = estimator.labels_ #获取聚类标签
#绘制k-means结果
x0 = X[label_pred == 0]
x1 = X[label_pred == 1]
x2 = X[label_pred == 2]
x3 = X[label_pred == 3]
plt.scatter(x0[:, 0], x0[:, 1], c = "red", marker='o', label='label0')
plt.scatter(x1[:, 0], x1[:, 1], c = "green", marker='*', label='label1')
#plt.scatter(x2[:, 0], x2[:, 1], c = "blue", marker='+', label='label2')
#plt.scatter(x3[:, 0], x3[:, 1], c = "yellow", marker='o', label='label3')
plt.xlabel('petal length')
plt.ylabel('petal width')
plt.legend(loc=2)
plt.show()

2.月亮数据集

import matplotlib.pyplot as plt
import numpy as np
from sklearn.cluster import KMeans
from sklearn.datasets import make_moons
X, y = make_moons(n_samples=100, noise=0.15, random_state=42)
estimator = KMeans(n_clusters=5)#构造聚类器
estimator.fit(X)#聚类
label_pred = estimator.labels_ #获取聚类标签
#绘制k-means结果
x0 = X[label_pred == 0]
x1 = X[label_pred == 1]
x2 = X[label_pred == 2]
x3 = X[label_pred == 3]
plt.scatter(x0[:, 0], x0[:, 1], c = "red", marker='o', label='label0')
plt.scatter(x1[:, 0], x1[:, 1], c = "green", marker='*', label='label1')
#plt.scatter(x2[:, 0], x2[:, 1], c = "blue", marker='+', label='label2')
#plt.scatter(x3[:, 0], x3[:, 1], c = "yellow", marker='o', label='label3')
plt.xlabel('petal length')
plt.ylabel('petal width')
plt.legend(loc=2)
plt.show()

四、SVM（支持向量机）算法

1.鸢尾花数据集

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets, svm
import pandas as pd
from pylab import *
mpl.rcParams['font.sans-serif'] = ['SimHei']

iris = datasets.load_iris()
iris = datasets.load_iris()
X = iris.data                  
y = iris.target                
X = X[y != 0, :2]             # 选择X的前两个特性
y = y[y != 0]
n_sample = len(X)              
np.random.seed(0)
order = np.random.permutation(n_sample)  # 排列，置换
X = X[order]
y = y[order].astype(np.float)
X_train = X[:int(.9 * n_sample)]
y_train = y[:int(.9 * n_sample)]
X_test = X[int(.9 * n_sample):]
y_test = y[int(.9 * n_sample):]

#合适的模型
for fig_num, kernel in enumerate(('linear', 'rbf','poly')):  # 径向基函数 (Radial Basis Function 简称 RBF),常用的是高斯基函数
    clf = svm.SVC(kernel=kernel, gamma=10)   # gamma是“rbf”、“poly”和“sigmoid”的核系数。
    clf.fit(X_train, y_train)
    plt.figure(str(kernel))
    plt.xlabel('x1')
    plt.ylabel('x2')
    plt.scatter(X[:, 0], X[:, 1], c=y, zorder=10, cmap=plt.cm.Paired, edgecolor='k', s=20)
    # zorder: z方向上排列顺序，数值越大，在上方显示
    # paired两个色彩相近输出(paired)
    # 圈出测试数据
    plt.scatter(X_test[:, 0], X_test[:, 1], s=80, facecolors='none',zorder=10, edgecolor='k')
    plt.axis('tight')  #更改 x 和 y 轴限制，以便显示所有数据
    x_min = X[:, 0].min()
    x_max = X[:, 0].max()
    y_min = X[:, 1].min()
    y_max = X[:, 1].max()
    XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j]   
    Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()])  # 样本X到分离超平面的距离
    Z = Z.reshape(XX.shape)
    plt.contourf(XX,YY,Z>0,cmap=plt.cm.Paired)  
    plt.contour(XX, YY, Z, colors=['r', 'k', 'b'],
                linestyles=['--', '-', '--'], levels=[-0.5, 0, 0.5])   # 范围
    plt.title(kernel)
plt.show()

2.月亮数据集

import matplotlib.pyplot as plt
from sklearn.pipeline import Pipeline
import numpy as np
import matplotlib as mpl
from sklearn.datasets import make_moons
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import StandardScaler
from sklearn.svm import LinearSVC
# 为了显示中文
mpl.rcParams['font.sans-serif'] = [u'SimHei']
mpl.rcParams['axes.unicode_minus'] = False
X, y = make_moons(n_samples=100, noise=0.15, random_state=42)
def plot_dataset(X, y, axes):
    plt.plot(X[:, 0][y==0], X[:, 1][y==0], "bs")
    plt.plot(X[:, 0][y==1], X[:, 1][y==1], "g^")
    plt.axis(axes)
    plt.grid(True, which='both')
    plt.xlabel(r"$x_1$", fontsize=20)
    plt.ylabel(r"$x_2$", fontsize=20, rotation=0)
    plt.title("月亮数据",fontsize=20)
plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])
plt.show()

polynomial_svm_clf = Pipeline([
        # 将源数据 映射到 3阶多项式
        ("poly_features", PolynomialFeatures(degree=3)),
        # 标准化
        ("scaler", StandardScaler()),
        # SVC线性分类器
        ("svm_clf", LinearSVC(C=10, loss="hinge", random_state=42))
    ])
polynomial_svm_clf.fit(X, y)
def plot_predictions(clf, axes):
    # 打表
    x0s = np.linspace(axes[0], axes[1], 100)
    x1s = np.linspace(axes[2], axes[3], 100)
    x0, x1 = np.meshgrid(x0s, x1s)
    X = np.c_[x0.ravel(), x1.ravel()]
    y_pred = clf.predict(X).reshape(x0.shape)
    y_decision = clf.decision_function(X).reshape(x0.shape)
#     print(y_pred)
#     print(y_decision)  
    plt.contourf(x0, x1, y_pred, cmap=plt.cm.brg, alpha=0.2)
    plt.contourf(x0, x1, y_decision, cmap=plt.cm.brg, alpha=0.1)
plot_predictions(polynomial_svm_clf, [-1.5, 2.5, -1, 1.5])
plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])
plt.show()

from sklearn.svm import SVC
gamma1, gamma2 = 0.1, 5
C1, C2 = 0.001, 1000
hyperparams = (gamma1, C1), (gamma1, C2)
svm_clfs = []
for gamma, C in hyperparams:
    rbf_kernel_svm_clf = Pipeline([
            ("scaler", StandardScaler()),
            ("svm_clf", SVC(kernel="rbf", gamma=gamma, C=C))
        ])
    rbf_kernel_svm_clf.fit(X, y)
    svm_clfs.append(rbf_kernel_svm_clf)
plt.figure(figsize=(11, 7))
for i, svm_clf in enumerate(svm_clfs):
    plt.subplot(221 + i)
    plot_predictions(svm_clf, [-1.5, 2.5, -1, 1.5])
    plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])
    gamma, C = hyperparams[i]
    plt.title(r"$\gamma = {}, C = {}$".format(gamma, C), fontsize=16)
plt.tight_layout()
plt.show()

五、SVM算法的优缺点

优点	①SVM算法既可以解决线性问题，又可以解决非线性问题②非线性映射是SVM方法的理论基础,SVM利用内积核函数代替向高维空间的非线性映射③对特征空间划分的最优超平面是SVM的目标,最大化分类边际的思想是SVM方法的核心④支持向量是SVM的训练结果,在SVM分类决策中起决定作用的是支持向量
优点	①对参数调节和核函数的选择敏感②不易处理多分类问题③对大规模训练样本难以实施④SVM的可解释性较差，无法给出决策树那样的规则

• 参考文献

周志华《机器学习》LDA深入补充推导和python实现（二分类问题）