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z2o&NqTpP*6sKV9=@u5?ljiVb1qM*pd`Tj#BLF0iBx4VU=-%bfiMdz z+F~nTapB*jF|VpwO8hj4(6T3X9_2+ncqcTIX_xb)TR(Jy0lJ7BpG~3VPB6{VJz{-n z8a)7i$%XvBxn5VeFM(*sUSKJHlKv>ybxQLe@o~F3{{Z6xXQaLY`Tqdp6-aN638$X_ z0NPQwNw=_rJOl;!t~ynZWxW;5;m3W)O#l%J^T@e!4KkbDpY)DNO9zuge4UOd1h={aM>X|%hFuzn@RE-ivQzU30#=zsy03Ao7p57eWz zPpPQT5bCpfhLpCTg5CYi6_Sm;`-}OFYYl*1EyBg5#~de5+}sbuZIl2k7)${wbpw~V zLJK!2YlY$#o_$U#6Wlt~S6SQ_5aYRTnB5Cz*n|owMMJLx>UN5B*m#%o8~H-YhZaQ +# print(im.dtype) # uint8 +# print(im.shape) # (597, 950 ,3) + +# Image.open('...').convert('L') to convert image to grey-image +# im_gray = np.array(Image.open('./image/1.jpg').convert('L')) +# print(im_gray.shape) + +# np.asarray() can get a read-only ndarray, while np.array() gets a read-write one +'''np.array()''' +# im = np.array(Image.open('./image/1.jpg')) +# print(im.flags.writeable) +# print(im[0, 0, 0]) +# im [0, 0, 0] = 0 +# print(im[0, 0, 0]) + + +''' 图片数据类型以 uint8 进行读取。 + 若想以 float 类型进行读取, + 可以使用 astype(), + 或指定 np.array() 的第二个参数 +''' +# im_f = im.astype(np.float64) +# print(im_f.dtype) +# print(im_f) +# im_f2 = np.array(Image.open('./image/1.jpg'), np.float64) +# print(im_f2.dtype) + + +''' 图片文件保存,ndarray转换成图片 + 使用 Image.fromarray() 函数读取 ndarray 数据时,可以得到一个 PIL.Image 类型的文件。 + 再使用 save() 函数即可将其保存下来。被保存的文件格式,将根据其指定的扩展名自动进行判别。 +''' +# pil_img = Image.fromarray(im) +# print(pil_img.mode) # RGB 和 L +# pil_img.save('./test.jpg') diff --git a/plot_cluster_iris.py b/plot_cluster_iris.py new file mode 100644 index 0000000..531a2b4 --- /dev/null +++ b/plot_cluster_iris.py @@ -0,0 +1,90 @@ +# -*- coding: utf-8 -*- +""" +========================================================= +K-means Clustering +========================================================= + +The plots display firstly what a K-means algorithm would yield +using three clusters. It is then shown what the effect of a bad +initialization is on the classification process: +By setting n_init to only 1 (default is 10), the amount of +times that the algorithm will be run with different centroid +seeds is reduced. +The next plot displays what using eight clusters would deliver +and finally the ground truth. + +""" + +# Code source: Gaël Varoquaux +# Modified for documentation by Jaques Grobler +# License: BSD 3 clause + +import numpy as np +import matplotlib.pyplot as plt + +# Though the following import is not directly being used, it is required +# for 3D projection to work +from mpl_toolkits.mplot3d import Axes3D + +from sklearn.cluster import KMeans +from sklearn import datasets + +np.random.seed(5) + +iris = datasets.load_iris() +X = iris.data +y = iris.target + +estimators = [ + ("k_means_iris_8", KMeans(n_clusters=8)), + ("k_means_iris_3", KMeans(n_clusters=3)), + ("k_means_iris_bad_init", KMeans(n_clusters=3, n_init=1, init="random")), +] + +fignum = 1 +titles = ["8 clusters", "3 clusters", "3 clusters, bad initialization"] +for name, est in estimators: + fig = plt.figure(fignum, figsize=(4, 3)) + ax = Axes3D(fig, rect=[0, 0, 0.95, 1], elev=48, azim=134) + est.fit(X) + labels = est.labels_ + + ax.scatter(X[:, 3], X[:, 0], X[:, 2], c=labels.astype(float), edgecolor="k") + + ax.w_xaxis.set_ticklabels([]) + ax.w_yaxis.set_ticklabels([]) + ax.w_zaxis.set_ticklabels([]) + ax.set_xlabel("Petal width") + ax.set_ylabel("Sepal length") + ax.set_zlabel("Petal length") + ax.set_title(titles[fignum - 1]) + ax.dist = 12 + fignum = fignum + 1 + +# Plot the ground truth +fig = plt.figure(fignum, figsize=(4, 3)) +ax = Axes3D(fig, rect=[0, 0, 0.95, 1], elev=48, azim=134) + +for name, label in [("Setosa", 0), ("Versicolour", 1), ("Virginica", 2)]: + ax.text3D( + X[y == label, 3].mean(), + X[y == label, 0].mean(), + X[y == label, 2].mean() + 2, + name, + horizontalalignment="center", + bbox=dict(alpha=0.2, edgecolor="w", facecolor="w"), + ) +# Reorder the labels to have colors matching the cluster results +y = np.choose(y, [1, 2, 0]).astype(float) +ax.scatter(X[:, 3], X[:, 0], X[:, 2], c=y, edgecolor="k") + +ax.w_xaxis.set_ticklabels([]) +ax.w_yaxis.set_ticklabels([]) +ax.w_zaxis.set_ticklabels([]) +ax.set_xlabel("Petal width") +ax.set_ylabel("Sepal length") +ax.set_zlabel("Petal length") +ax.set_title("Ground Truth") +ax.dist = 12 + +fig.show() -- Gitee