第八次作业

from sklearn.datasets import load_sample_image
china=load_sample_image('china.jpg')
print(china.shape)
china

(427, 640, 3)

array([[[174, 201, 231], [174, 201, 231], [174, 201, 231], ..., [250, 251, 255], [250, 251, 255], [250, 251, 255]], [[172, 199, 229], [173, 200, 230], [173, 200, 230], ..., [251, 252, 255], [251, 252, 255], [251, 252, 255]], [[174, 201, 231], [174, 201, 231], [174, 201, 231], ..., [252, 253, 255], [252, 253, 255], [252, 253, 255]], ..., [[ 88, 80, 7], [147, 138, 69], [122, 116, 38], ..., [ 39, 42, 33], [ 8, 14, 2], [ 6, 12, 0]], [[122, 112, 41], [129, 120, 53], [118, 112, 36], ..., [ 9, 12, 3], [ 9, 15, 3], [ 16, 24, 9]], [[116, 103, 35], [104, 93, 31], [108, 102, 28], ..., [ 43, 49, 39], [ 13, 21, 6], [ 15, 24, 7]]], dtype=uint8)

import matplotlib.pyplot as plt
plt.imshow(china)
plt.show()

plt.imshow(china[:,:,0],plt.cm.gray)
plt.show()

plt.imshow(china[:,:,1])
plt.show()

from sklearn.datasets import load_sample_image
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import numpy as np

china=load_sample_image("china.jpg")
plt.imshow(china)
plt.show()

image=china[::3,::3]
image.shape

plt.imshow(image)
plt.show()

x=image.reshape(-1,3)
model=KMeans(n_clusters=64)#64位颜色
model.fit_predict(x)
model.cluster_centers_#查看每一个的聚集中心

array([[235.17340363, 243.92677211, 253.86467487], [103.92811839, 102.02959831, 90.38477801], [ 34.62162162, 12.97788698, 9.86486486], [213.15746421, 219.2801636 , 223.78323108], [ 83.66666667, 84.93574297, 22.30522088], [145.05853659, 161.06829268, 166.27804878], [194.15384615, 130.77622378, 95.23776224], [ 68.04281346, 66.52293578, 30.30275229], [185.99453552, 190.55464481, 178.62568306], [208.28674482, 228.5915239 , 250.49504058], [141.125 , 135.94318182, 95.01136364], [ 50.82552504, 51.22294023, 42.67528271], [118.13333333, 118.70333333, 51.54666667], [186.53343239, 209.71471025, 236.37518574], [123.21590909, 54.64204545, 32.46590909], [ 32.46153846, 32.54509284, 20.26923077], [115.71774194, 132.74193548, 127.61290323], [ 74.86909871, 74.34549356, 66.44849785], [179.20960699, 173.57641921, 154.96943231], [ 64.27631579, 37.95065789, 31.81578947], [228.29756637, 230.18252212, 234.29756637], [ 5.7141339 , 5.7151966 , 2.15090329], [102.29608939, 100.65363128, 62.07821229], [138.05533597, 135.82213439, 65.81818182], [235.70114943, 187.79310345, 153.33333333], [179.07119205, 192.25993377, 194.25331126], [ 91.82738095, 117.57738095, 115.73809524], [161.01083032, 154.77617329, 140.66787004], [ 49.5234375 , 23.2109375 , 20.47916667], [242.38129496, 171.04316547, 112.15107914], [191.65740741, 102.53703704, 70.30555556], [ 69.07065217, 95.4076087 , 94.95652174], [103.93574297, 65.93172691, 53.93574297], [ 88.26132404, 45.59930314, 35.92682927], [243.97297297, 225.85135135, 197.21621622], [ 21.96846847, 44.67567568, 47.76576577], [ 53.8247012 , 56.11155378, 13.96414343], [ 21.55420219, 24.13032887, 17.09013398], [247.13185109, 248.43174767, 253.35367115], [101.64942529, 102.31609195, 36.22701149], [221.81357254, 236.95319813, 252.57566303], [201.2388974 , 211.36140888, 225.99234303], [206.62915601, 208.35421995, 212.17902813], [122.99516908, 118.41062802, 104.22222222], [142.17847769, 139.61154856, 124.8687664 ], [210.22619048, 151.94047619, 121.63690476], [ 84.35431235, 82.75990676, 46.55944056], [120.57948718, 114.4974359 , 77.88461538], [159.70846395, 177.04702194, 182.13793103], [157.4125 , 105.825 , 88.075 ], [ 65.25225225, 62.49009009, 52.19459459], [238.41261634, 238.84281282, 242.04446743], [191.56005398, 203.08636977, 205.02564103], [ 41.46387283, 40.4132948 , 31.06069364], [194.66636114, 217.97708524, 243.89550871], [127.06666667, 152.73846154, 148.45641026], [ 14.03688525, 15.13729508, 7.32172131], [ 91.35610766, 87.09730849, 74.61697723], [165.12931034, 78.32758621, 51.46551724], [225.42647059, 135.05882353, 79.16176471], [ 41.86956522, 67.57004831, 69.36231884], [ 88.15267176, 23.61832061, 10.44274809], [166.34782609, 154.63586957, 107.63586957], [130.53974895, 83.9748954 , 62.79079498]])

image1=china[::10,::10]
image1.shape
plt.imshow(image1)
plt.show()

import sys
sys.getsizeof(china)

819968

sys.getsizeof(image)

128

import matplotlib.image as img
img.imsave("E://01.jpg",china)
img.imsave("E://02.jpg",image)
img.imsave("E://03.jpg",image1

贝叶斯

• M桶：7红3黄
• N桶：1红9黄
• 现在：拿出了一个红球
• 试问：这个红球是M、N桶拿出来的概率分别是多少

解:   设从M桶拿出一个红球为事件P(A)，

从M桶拿出此球为事件P(M)，从N桶拿出此球为时间P(N)。

1) P(M|A) = P(A|M) · P(M) / P(A)  = (7/10 · 1/2)/ (8/20) =  7/8

2) P(N|A) = 1 - P(M|A) = 1/8