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比PCA降维更高级——（R/Python）t-SNE聚类算法实践指南

，看到了代码运行后的图片显示效果，因为好奇是如何做到

patch

块显示无重叠，就想研究下

python

代码，但是里面的代码是没缩进的，运行是指定没法运行的，本来是懒得改缩进，想搜下看有没格式正确的，很遗憾，没搜到，就自己锊下逻辑还原了下缩进，贴在此处。

（至于取这个名字，是为了方便那些想搜到带缩进代码的。。。）

# importing the required packages
from time import time
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import offsetbox
from sklearn import (manifold, datasets, decomposition, ensemble,
                     discriminant_analysis, random_projection)

# Loading and curating the data
digits = datasets.load_digits(n_class=10)
X = digits.data
y = digits.target
n_samples, n_features = X.shape
n_neighbors = 30


# Function to Scale and visualize the embedding vectors
def plot_embedding(X, title=None):

    x_min, x_max = np.min(X, 0), np.max(X, 0)
    X = (X - x_min) / (x_max - x_min)
    plt.figure()
    ax = plt.subplot(111)
    for i in range(X.shape[0]):
        plt.text(X[i, 0], X[i, 1], str(digits.target[i]),
                 color=plt.cm.Set1(y[i] / 10.),
                 fontdict={'weight': 'bold', 'size': 9})
    if hasattr(offsetbox, 'AnnotationBbox'):
        # only print thumbnails with matplotlib > 1.0
        shown_images = np.array([[1., 1.]])  # just something big
        for i in range(digits.data.shape[0]):
            dist = np.sum((X[i] - shown_images) ** 2, 1)
            #谁能告诉我这个阈值是咋选的，我换了下，显示效果就重叠了，如果谁知道请留言告知，感谢！
            if np.min(dist) < 4e-3:  #why this number?为啥捏 
                # don't show points that are too close
                continue
            shown_images = np.r_[shown_images, [X[i]]]
            imagebox = offsetbox.AnnotationBbox(
                offsetbox.OffsetImage(digits.images[i], cmap=plt.cm.gray_r),
                X[i])
            ax.add_artist(imagebox)
            plt.xticks([]), plt.yticks([])
    if title is not None:
        plt.title(title)


if __name__ == "__main__":
    #‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐
    # Plot images of the digits
    n_img_per_row = 20
    img = np.zeros((10 * n_img_per_row, 10 * n_img_per_row))
    for i in range(n_img_per_row):
        ix = 10 * i + 1
        for j in range(n_img_per_row):
            iy = 10 * j + 1
            img[ix:ix + 8, iy:iy + 8] = X[i * n_img_per_row + j].reshape((8, 8))
            plt.imshow(img, cmap=plt.cm.binary)
            plt.xticks([])
            plt.yticks([])
            plt.title('A selection from the 64‐dimensional digits dataset')
    # Computing PCA
    print("Computing PCA projection")
    t0 = time()
    X_pca = decomposition.TruncatedSVD(n_components=2).fit_transform(X)
    plot_embedding(X_pca,
                   "Principal Components projection of the digits (time %.2fs)" %
                   (time() - t0))
    # Computing t‐SNE
    print("Computing t‐SNE embedding")
    tsne = manifold.TSNE(n_components=2, init='pca', random_state=0)
    t0 = time()
    X_tsne = tsne.fit_transform(X)
    plot_embedding(X_tsne,
                   "t‐SNE embedding of the digits (time %.2fs)" %
                   (time() - t0))
    plt.show()

这个

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