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。解释SVM算法的优点

叁歲伎倆 2023-02-09 14:19 52阅读 0赞

# 1.SVM算法的优点 #

(1）使用核函数可以向高维空间进行映射  
（2）使用核函数可以解决非线性的分类  
（3）分类思想很简单，即样本与决策面的间隔最大化  
（4）分类效果较好

# 2MNIST数据集的可视化LDA算法分析 #

## 2.1线性LDA算法对月亮数据集进行分类 ##

#基于线性LDA算法对月亮数据集进行分类
    import numpy as np
    import matplotlib.pyplot as plt
    from sklearn.datasets import make_moons
    from mpl_toolkits.mplot3d import Axes3D
    def LDA(X, y):
        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])
        len1 = len(X1)
        len2 = len(X2)
        mju1 = np.mean(X1, axis=0)#求中心点
        mju2 = np.mean(X2, axis=0)
        cov1 = np.dot((X1 - mju1).T, (X1 - mju1))
        cov2 = np.dot((X2 - mju2).T, (X2 - mju2))
        Sw = cov1 + cov2
        w = np.dot(np.mat(Sw).I,(mju1 - mju2).reshape((len(mju1),1)))# 计算w
        X1_new = func(X1, w)
        X2_new = func(X2, w)
        y1_new = [1 for i in range(len1)]
        y2_new = [2 for i in range(len2)]
        return X1_new, X2_new, y1_new, y2_new
    def func(x, w):
        return np.dot((x), w)
    if '__main__' == __name__:
        X, y = make_moons(n_samples=100, noise=0.15, random_state=42)
        X1_new, X2_new, y1_new, y2_new = LDA(X, y)
        plt.scatter(X[:, 0], X[:, 1], marker='o', c=y)
        plt.show()
        plt.plot(X1_new, y1_new, 'b*')
        plt.plot(X2_new, y2_new, 'ro')
        plt.show()

运行结果：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI_size_16_color_FFFFFF_t_70]

## 2.2线性LDA算法LDA辅助Logistic回归用于对鸢尾花数据集进行分类 ##

#基于线性LDA算法对鸢尾花数据集进行分类
    import numpy as np
    import matplotlib.pyplot as plt
    import pandas as pd
    from sklearn import preprocessing
    dataset = pd.read_csv('iris.data')
    X = dataset.values[:, :-1]
    y = dataset.values[:, -1]
    le = preprocessing.LabelEncoder()
    le.fit(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'])
    y = le.transform(y)
    X = X[:, :2] 
    # Splitting the dataset into the Training set and Test set
    from sklearn.model_selection import train_test_split
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
      
    # Feature Scaling
    from sklearn.preprocessing import StandardScaler
    sc = StandardScaler()
    X_train = sc.fit_transform(X_train)
    X_test = sc.transform(X_test)
      
    # Applying LDA
    from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
    lda = LDA(n_components = 2)
    X_train = lda.fit_transform(X_train, y_train)
    X_test = lda.transform(X_test)
      
    # Fitting Logistic Regression to the Training set
    from sklearn.linear_model import LogisticRegression
    classifier = LogisticRegression(random_state = 0)
    classifier.fit(X_train, y_train)
      
    # Predicting the Test set results
    y_pred = classifier.predict(X_test)
      
    # Making the Confusion Matrix
    from sklearn.metrics import confusion_matrix
    cm = confusion_matrix(y_test, y_pred)
      
    # Visualising the Training set results
    from matplotlib.colors import ListedColormap
    X_set, y_set = X_train, y_train
    X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
                         np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
    plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
                 alpha = 0.75, cmap = ListedColormap(('red', 'green', 'blue')))
    plt.xlim(X1.min(), X1.max())
    plt.ylim(X2.min(), X2.max())
    for i, j in enumerate(np.unique(y_set)):
        plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
                    c = ListedColormap(('red', 'green', 'blue'))(i), label = j)
    plt.title('Logistic Regression (Training set)')
    plt.xlabel('LD1')
    plt.ylabel('LD2')
    plt.legend()
    plt.show()

运行结果：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI_size_16_color_FFFFFF_t_70 1]

# 3.MNIST数据集的可视化SVM算法分析 #

## 3.1SVM算法对月亮数据集进行分类 ##

在from sklearn.datasets import make_moons
    from sklearn.pipeline import Pipeline
    from sklearn.preprocessing import PolynomialFeatures
    import numpy as np
    from sklearn import datasets
    from sklearn.preprocessing import StandardScaler
    from sklearn.svm import LinearSVC
    X, y = make_moons(n_samples=100, noise=0.15, random_state=42)
    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_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)
    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()
    这里插入代码片

运行结果：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI_size_16_color_FFFFFF_t_70 2]

## 3.2SVM算法对鸢尾花数据集进行分类 ##

在这里插入代码片from sklearn.svm import SVC
    from sklearn.datasets import load_iris
    import matplotlib.pyplot as plt
    import numpy as np
    from sklearn.model_selection import train_test_split
    from sklearn.model_selection import GridSearchCV
    def plot_point2(dataArr, labelArr, Support_vector_index):
        for i in range(np.shape(dataArr)[0]):
            if labelArr[i] == 0:
                plt.scatter(dataArr[i][0], dataArr[i][1], c='b', s=20)
            elif labelArr[i] == 1:
                plt.scatter(dataArr[i][0], dataArr[i][1], c='y', s=20)
            else:
                plt.scatter(dataArr[i][0], dataArr[i][1], c='g', s=20)
        
        for j in Support_vector_index:
            plt.scatter(dataArr[j][0], dataArr[j][1], s=100, c='', alpha=0.5, linewidth=1.5, edgecolor='red')
        plt.show()
    if __name__ == "__main__":
        iris = load_iris()
        x, y = iris.data, iris.target
        x = x[:, :2]
        X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0)
        clf = SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,
                    decision_function_shape='ovr', degree=3, gamma=0.1,
                    kernel='linear', max_iter=-1, probability=False, random_state=None,
                    shrinking=True, tol=0.001, verbose=False)
        # 调参选取最优参数
        # clf = GridSearchCV(SVC(), param_grid={"kernel": ['rbf', 'linear', 'poly', 'sigmoid'],
        #                                       "C": [0.1, 1, 10], "gamma": [1, 0.1, 0.01]}, cv=3)
        clf.fit(X_train, y_train)
     
        # print("The best parameters are %s with a score of %0.2f" % (clf.best_params_, clf.best_score_))
     
        predict_list = clf.predict(X_test)
     
        precition = clf.score(X_test, y_test)
        print("preciton is : ", precition * 100, "%")
     
        n_Support_vector = clf.n_support_
        print("vector num is : ", n_Support_vector)
        Support_vector_index = clf.support_
     
        x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
        y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
        h = 0.02
        xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
        Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
        Z = Z.reshape(xx.shape)
        plt.contourf(xx, yy, Z, cmap=plt.cm.Paired, alpha=0.8)
        plot_point2(x, y, Support_vector_index)

运行结果：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI_size_16_color_FFFFFF_t_70 3]

# 4MNIST数据集的可视化k-means算法分析 #

## 4.1k-means算法对月亮数据集进行分类 ##

#基于k-means算法对月亮数据集进行分类
    import matplotlib.pyplot as plt
    from sklearn.cluster import KMeans
    from sklearn.datasets import make_moons
    from sklearn.pipeline import Pipeline
    import numpy as np
    X,y=make_moons(n_samples=100,shuffle=True,noise=0.15,random_state=42)
    clf = KMeans()
    clf.fit(X,y)
    predicted = clf.predict(X)   
    plt.scatter(X[:,0], X[:,1], c=predicted, marker='s',s=100,cmap=plt.cm.Paired)    
    plt.title("KMeans")    
    plt.show()

运行结果：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI_size_16_color_FFFFFF_t_70 4]

## 4.2k-means算法对鸢尾花数据集进行分类 ##

#基于k-means算法对鸢尾花数据集进行分类
    from sklearn.cluster import KMeans
    import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
    from pylab import *
    mpl.rcParams['font.sans-serif'] = ['SimHei']    #用于画图时显示中文
    from sklearn.datasets import load_iris #导入数据集iris  
    iris = load_iris() #载入数据集
    url = "iris.data"  
    names = ['花萼-length', '花萼-width', '花瓣-length', '花瓣-width', 'class']  
    dataset = pd.read_csv(url, names=names)
    clf = KMeans()
    clf.fit(iris.data,iris.target)
    predicted = clf.predict(iris.data)
    pos = pd.DataFrame(dataset)    
    L1 = pos['花萼-length'].values   
    L2 = pos['花萼-width'].values     
    plt.scatter(L1, L2, c=predicted, marker='s',s=100,cmap=plt.cm.Paired)    
    plt.title("KMeans聚类分析")    
    plt.show()

运行结果：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI_size_16_color_FFFFFF_t_70 5]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI_size_16_color_FFFFFF_t_70]: https://img-blog.csdnimg.cn/20200519163530852.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI=,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI_size_16_color_FFFFFF_t_70 1]: https://img-blog.csdnimg.cn/20200519163721590.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI=,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI_size_16_color_FFFFFF_t_70 2]: https://img-blog.csdnimg.cn/20200519164115620.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI=,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI_size_16_color_FFFFFF_t_70 3]: https://img-blog.csdnimg.cn/20200519164322142.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI=,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI_size_16_color_FFFFFF_t_70 4]: https://img-blog.csdnimg.cn/20200519164841211.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI=,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI_size_16_color_FFFFFF_t_70 5]: https://img-blog.csdnimg.cn/20200519165158128.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI=,size_16,color_FFFFFF,t_70