import randommax_num = 0min_num = 20for i in range(10): num = random.randint(1, 20) if num > max_num: max_num = num if num < min_num: min_num = numprint("最大数:", max_num)print("最小数:", min_num)
时间: 2023-11-22 08:56:37 浏览: 39
这是一个随机生成10个1到20之间的数,并找出其中的最大值和最小值的程序。
代码有一处错误,变量 `max_num` 在第1行声明时没有赋初值,应该改为 `max_num = 0`。
以下是修正后的代码:
```python
import random
max_num = 0
min_num = 20
for i in range(10):
num = random.randint(1, 20)
if num > max_num:
max_num = num
if num < min_num:
min_num = num
print("最大数:", max_num)
print("最小数:", min_num)
```
运行结果可能如下:
```
最大数: 19
最小数: 1
```
相关问题
image=np.array(grayImage,dtype=float) percent=0.001 num=int(percent*image.shape[0]*image.shape[1]) for i in range(num): temp1=np.random.randint(image.shape[0]) temp2=np.random.randint(image.shape[1]) scale=150 noise=np.random.poisson(scale,1) image[temp1][temp2]+=noise out=image if out.min()<0: low_clip=-1. else: low_clip=0. out=np.clip(out,low_clip,255) expon_image=np.uint8(out) print(expon_image.shape) cv2.imshow("expon_image",expon_image) k=cv2.waitKey(0)优化这段代码的for循环
可以使用numpy的随机函数生成随机的坐标,然后使用numpy的切片和花式索引方式对图像进行操作,可以避免使用循环。
以下是优化后的代码:
```python
import numpy as np
import cv2
grayImage = cv2.imread('test.jpg', 0)
# 转换为浮点型
image = grayImage.astype(float)
percent = 0.001
num = int(percent * image.shape[0] * image.shape[1])
# 生成随机坐标
rand_x = np.random.randint(0, image.shape[0], num)
rand_y = np.random.randint(0, image.shape[1], num)
scale = 150
noise = np.random.poisson(scale, num)
# 对图像进行操作
image[rand_x, rand_y] += noise
# 调整像素值范围
out = np.clip(image, 0, 255)
# 转换为8位整型
expon_image = out.astype(np.uint8)
print(expon_image.shape)
cv2.imshow("expon_image", expon_image)
k = cv2.waitKey(0)
```
使用numpy的切片和花式索引方式,可以避免使用循环,提高代码的效率。同时,也需要注意对图像进行操作时,需要保证图像的类型一致,避免出现类型错误的问题。
代码改进:import numpy as np import pandas as pd import matplotlib as mpl import matplotlib.pyplot as plt from sklearn.datasets import make_blobs def distEclud(arrA,arrB): #欧氏距离 d = arrA - arrB dist = np.sum(np.power(d,2),axis=1) #差的平方的和 return dist def randCent(dataSet,k): #寻找质心 n = dataSet.shape[1] #列数 data_min = dataSet.min() data_max = dataSet.max() #生成k行n列处于data_min到data_max的质心 data_cent = np.random.uniform(data_min,data_max,(k,n)) return data_cent def kMeans(dataSet,k,distMeans = distEclud, createCent = randCent): x,y = make_blobs(centers=100)#生成k质心的数据 x = pd.DataFrame(x) m,n = dataSet.shape centroids = createCent(dataSet,k) #初始化质心,k即为初始化质心的总个数 clusterAssment = np.zeros((m,3)) #初始化容器 clusterAssment[:,0] = np.inf #第一列设置为无穷大 clusterAssment[:,1:3] = -1 #第二列放本次迭代点的簇编号,第三列存放上次迭代点的簇编号 result_set = pd.concat([pd.DataFrame(dataSet), pd.DataFrame(clusterAssment)],axis = 1,ignore_index = True) #将数据进行拼接,横向拼接,即将该容器放在数据集后面 clusterChanged = True while clusterChanged: clusterChanged = False for i in range(m): dist = distMeans(dataSet.iloc[i,:n].values,centroids) #计算点到质心的距离(即每个值到质心的差的平方和) result_set.iloc[i,n] = dist.min() #放入距离的最小值 result_set.iloc[i,n+1] = np.where(dist == dist.min())[0] #放入距离最小值的质心标号 clusterChanged = not (result_set.iloc[:,-1] == result_set.iloc[:,-2]).all() if clusterChanged: cent_df = result_set.groupby(n+1).mean() #按照当前迭代的数据集的分类,进行计算每一类中各个属性的平均值 centroids = cent_df.iloc[:,:n].values #当前质心 result_set.iloc[:,-1] = result_set.iloc[:,-2] #本次质心放到最后一列里 return centroids, result_set x = np.random.randint(0,100,size=100) y = np.random.randint(0,100,size=100) randintnum=pd.concat([pd.DataFrame(x), pd.DataFrame(y)],axis = 1,ignore_index = True) #randintnum_test, randintnum_test = kMeans(randintnum,3) #plt.scatter(randintnum_test.iloc[:,0],randintnum_test.iloc[:,1],c=randintnum_test.iloc[:,-1]) #result_test,cent_test = kMeans(data, 4) cent_test,result_test = kMeans(randintnum, 3) plt.scatter(result_test.iloc[:,0],result_test.iloc[:,1],c=result_test.iloc[:,-1]) plt.scatter(cent_test[:,0],cent_test[:,1],color = 'red',marker = 'x',s=100)
这段代码实现的是k-means聚类算法,但是有一些地方可以改进:
1. 函数kMeans中的createCent参数可以通过修改为直接传入质心的方式来更加灵活。
2. 函数kMeans中的distMeans参数可以修改为scipy库中的距离函数,如scipy.spatial.distance.cdist,来提高计算效率。
3. 函数kMeans中的clusterAssment可以使用pd.DataFrame来进行初始化,这样可以避免使用np.zeros和np.inf来进行初始化。
改进后的代码如下:
```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs
from scipy.spatial.distance import cdist
def randCent(dataSet, k):
"""
随机生成k个质心
"""
n = dataSet.shape[1] # 列数
data_min = dataSet.min()
data_max = dataSet.max()
# 生成k行n列处于data_min到data_max的质心
data_cent = np.random.uniform(data_min, data_max, (k, n))
return data_cent
def kMeans(dataSet, k, createCent=randCent, distMeans=cdist):
"""
k-means聚类算法
"""
centroids = createCent(dataSet, k) # 初始化质心,k即为初始化质心的总个数
result_set = pd.concat([pd.DataFrame(dataSet), pd.DataFrame()], axis=1, ignore_index=True)
# 将数据进行拼接,横向拼接,即将该容器放在数据集后面
clusterChanged = True
while clusterChanged:
clusterChanged = False
dist = distMeans(dataSet, centroids, metric='euclidean')
clusterAssment = np.argmin(dist, axis=1)
result_set.iloc[:, -1] = pd.Series(clusterAssment)
for i in range(k):
cent_df = result_set[result_set.iloc[:, -1] == i].mean() # 按照当前迭代的数据集的分类,进行计算每一类中各个属性的平均值
if not cent_df.empty:
centroids[i] = cent_df.iloc[:-1].values # 当前质心
clusterChanged = True
return centroids, result_set
x = np.random.randint(0, 100, size=100)
y = np.random.randint(0, 100, size=100)
randintnum = pd.concat([pd.DataFrame(x), pd.DataFrame(y)], axis=1, ignore_index=True)
cent_test, result_test = kMeans(randintnum, 3)
plt.scatter(result_test.iloc[:, 0], result_test.iloc[:, 1], c=result_test.iloc[:, -1])
plt.scatter(cent_test[:, 0], cent_test[:, 1], color='red', marker='x', s=100)
```