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  • 鸢尾花主成分分析 jupyter实现

    import matplotlib.pyplot as plt
    from sklearn.decomposition import PCA
    from sklearn.datasets import load_iris

    data = load_iris() #字典的形式
    #print(data,'!!!!!!')
    y = data.target
    #print(y)
    X = data.data
    #print(X)
    pca = PCA(n_components=2) #n_components:指定主成分的个数,即降维后数据的维度
    #print(pca)#PCA(copy=True, iterated_power='auto', n_components=2, random_state=None,
    #特征值分解 svd_solver='auto', tol=0.0, whiten=False)
    reduced_X = pca.fit_transform(X)#用PCA处理的数据是X
    #print(reduced_X,'!!!!') #此时的X是2维了
    #print(reduced_X.shape) #输出矩阵维度
    #建立三类数据集
    red_x, red_y = [], []
    blue_x, blue_y = [], []
    green_x, green_y = [], []

    for i in range(len(reduced_X)): #len(reduce_x)有150个
    if y[i] == 0:# 以字典的方式检索,y = data.target,target 就是0,1,2
    red_x.append(reduced_X[i][0])#不断地添加红色的样本点的个数
    red_y.append(reduced_X[i][1])
    #print(len(red_x),'!!!') #一个一个添加,一直有50个
    elif y[i] == 1:
    blue_x.append(reduced_X[i][0])
    blue_y.append(reduced_X[i][1])
    else:
    green_x.append(reduced_X[i][0])
    green_y.append(reduced_X[i][1])

    plt.scatter(red_x, red_y, c='r', marker='x')#c 是color
    #print(plt.scatter)
    plt.scatter(blue_x, blue_y, c='b', marker='D')
    plt.scatter(green_x, green_y, c='g', marker='+') #marker是标记点的形状
    plt.show()

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  • 原文地址:https://www.cnblogs.com/china520/p/11570102.html
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