k折交叉验证法python实现_使用python+sklearn实现使用K均值进行颜色量化

注意：单击此处https://urlify.cn/A3EJ3a下载完整的示例代码，或通过Binder在浏览器中运行此示例

Fitting model on a small sub-sample of the datadone in 0.356s.Predicting color indices on the full image (k-means)done in 0.149s.Predicting color indices on the full image (random)done in 0.152s.

# 作者: Robert Layton #          Olivier Grisel #          Mathieu Blondel ## 许可证: BSD 3 clauseprint(__doc__)import numpy as npimport matplotlib.pyplot as pltfrom sklearn.cluster import KMeansfrom sklearn.metrics import pairwise_distances_argminfrom sklearn.datasets import load_sample_imagefrom sklearn.utils import shufflefrom time import timen_colors = 64# 加载颐和园照片china = load_sample_image(\"china.jpg\")# 转换为浮点数，而不是默认的8位整数编码。# 除以255很重要，这使得plt.imshow的显示在浮点数据上效果很好# (需要在[0-1]范围内)china = np.array(china, dtype=np.float64) / 255# 加载图片(Image)并将其转换为二维numpy数组。w, h, d = original_shape = tuple(china.shape)assert d == 3image_array = np.reshape(china, (w * h, d))print(\"Fitting model on a small sub-sample of the data\")t0 = time()image_array_sample = shuffle(image_array, random_state=0)[:1000]kmeans = KMeans(n_clusters=n_colors, random_state=0).fit(image_array_sample)print(\"done in %0.3fs.\" % (time() - t0))# 获取所有点的标签print(\"Predicting color indices on the full image (k-means)\")t0 = time()labels = kmeans.predict(image_array)print(\"done in %0.3fs.\" % (time() - t0))codebook_random = shuffle(image_array, random_state=0)[:n_colors]print(\"Predicting color indices on the full image (random)\")t0 = time()labels_random = pairwise_distances_argmin(codebook_random,                                          image_array,                                          axis=0)print(\"done in %0.3fs.\" % (time() - t0))def recreate_image(codebook, labels, w, h):    \"\"\"从密码本和标签重新创建(压缩的)图像\"\"\"    d = codebook.shape[1]    image = np.zeros((w, h, d))    label_idx = 0    for i in range(w):        for j in range(h):            image[i][j] = codebook[labels[label_idx]]            label_idx += 1    return image# 显示所有结果以及原始图像plt.figure(1)plt.clf()plt.axis(\'off\')plt.title(\'Original image (96,615 colors)\')plt.imshow(china)plt.figure(2)plt.clf()plt.axis(\'off\')plt.title(\'Quantized image (64 colors, K-Means)\')plt.imshow(recreate_image(kmeans.cluster_centers_, labels, w, h))plt.figure(3)plt.clf()plt.axis(\'off\')plt.title(\'Quantized image (64 colors, Random)\')plt.imshow(recreate_image(codebook_random, labels_random, w, h))plt.show()

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