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  • Python之机器学习-sklearn生成随机数据

    sklearn-生成随机数据

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

    多标签分类数据

    X1, y1 = datasets.make_multilabel_classification(
        n_samples=1000, n_classes=4, n_features=2, random_state=1)
    datasets.make_multilabel_classification()
    plt.scatter(X1[:, 0], X1[:, 1], marker='*', c=y1)
    plt.show()
    

    png

    生成分类数据

    import matplotlib.pyplot as plt
    %matplotlib inline
    
    plt.figure(figsize=(10, 10))
    
    plt.subplot(221)
    plt.title("One informative feature, one cluster per class", fontsize=12)
    X1, y1 = datasets.make_classification(n_samples=1000, random_state=1, n_features=2, n_redundant=0, n_informative=1,
                                          n_clusters_per_class=1)
    plt.scatter(X1[:, 0], X1[:, 1], marker='*', c=y1)
    
    plt.subplot(222)
    plt.title("Two informative features, one cluster per class", fontsize=12)
    X1, y1 = datasets.make_classification(n_samples=1000, random_state=1, n_features=2, n_redundant=0, n_informative=2,
                                          n_clusters_per_class=1)
    plt.scatter(X1[:, 0], X1[:, 1], marker='*', c=y1)
    
    plt.subplot(223)
    plt.title("Two informative features, two clusters per class", fontsize=12)
    X1, y1 = datasets.make_classification(
        n_samples=1000, random_state=1, n_features=2, n_redundant=0, n_informative=2)
    plt.scatter(X1[:, 0], X1[:, 1], marker='*', c=y1)
    
    
    plt.subplot(224)
    plt.title("Multi-class, two informative features, one cluster",
              fontsize=12)
    X1, y1 = datasets.make_classification(n_samples=1000, random_state=1, n_features=2, n_redundant=0, n_informative=2,
                                          n_clusters_per_class=1, n_classes=4)
    plt.scatter(X1[:, 0], X1[:, 1], marker='*', c=y1)
    plt.show()
    

    png

    图像数据集

    # 图像数据集
    china = datasets.load_sample_image('china.jpg')
    plt.axis('off')
    plt.title('中国颐和园图像', fontproperties=font, fontsize=20)
    plt.imshow(china)
    plt.show()
    

    png

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  • 原文地址:https://www.cnblogs.com/nickchen121/p/10826023.html
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