cs231n notes:Python Numpy Tutorial

##cs231n notes:Python Numpy Tutorial#idctionaryd = {'cat':'cute','dog':'furry'}#三种遍历方式for k,v in d.items():    print(k,v)for i in d:    print(i,d[i])

cat cutedog furrycat cutedog furry

#get(key[, default])¶# Return the value for key if key is in the dictionary, else default. # If default is not given, it defaults to None, so that this method never raises a KeyError.d['monkey'] = 'wet'print(d.get('fish'))             # Noneprint(d)   #get 并不会让字典里面的key-value增加print(d.get('monkey', 'N/A'))    # wetdel d['monkey']                       print(d.get('monkey', 'N/A'))    # N/Aprint(d)

None{'cat': 'cute', 'dog': 'furry', 'monkey': 'wet'}wetN/A{'cat': 'cute', 'dog': 'furry'}

# sorted(iterable,key,reverse)# 其中iterable表示可以迭代的对象，例如可以是dict.items()、dict.keys()等，key是一个函数，用来选取参与比较的元素，# reverse则是用来指定排序是倒序还是顺序，reverse=true则是倒序，reverse=false时则是顺序，默认时reverse=false。# 根据value排序a = [9,4,9,9,3,4,3,6,7,6,9]d = {}for i in a:    d[i] = d.get(i,1)+1  #如果i第一次放进来，即不存在，则d.get()=1sorted(d.items(),key=lambda item:item[1],reverse=False)

[(7, 2), (4, 3), (3, 3), (6, 3), (9, 5)]

# lambda 对简单函数的简洁表示# map()函数接受两个参数，一个是函数，一个是iterablea = [1,2,3]b = map(lambda a:a+10,li)# print(list(b))print(map(str, [1, 2, 3, 4, 5, 6, 7, 8, 9]))

<map object at 0x7f299876ff28>

# reduce()函数# 这个函数必须接收两个参数，reduce把结果继续和序列的下一个元素做累积计算from functools import reducedef add(x, y):    return 10 * x + yreduce(add, [1, 3, 5, 7, 9])

13579

# enumerateanimals = {'cat', 'dog', 'fish'}for idx, animal in enumerate(animals):    print('#%d: %s' % (idx + 1, animal))

#1: dog#2: fish#3: cat

# strings = 'hello'print(s.capitalize())print(s.upper())           #转换为大写print(s.rjust(7))print(s.replace('l','(ell)'))print(s.rjust(10).strip())    #Strip leading and trailing whitespace 去掉开头和结尾的空格

HelloHELLO  hellohe(ell)(ell)ohello

# numpyimport numpy as npa = np.array([[1,2],[3,4],[5,6]])print(a[[0,1,2],[0,1,1]])  ## array([1, 4, 6]) 相当于a[0,0],a[1,1],a[2,1]print([a[0,0],a[1,1],a[2,1]])print(np.array([a[0,0],a[1,1],a[2,1]]))

[1 4 6][1, 4, 6][1 4 6]

#和MATLAB不同，*是元素逐个相乘，而不是矩阵乘法。#在Numpy中使用dot来进行矩阵乘法x = np.array([[1,2],[3,4]], dtype=np.float64)y = np.array([[5,6],[7,8]], dtype=np.float64)print(x * y)print(np.multiply(x,y))print(np.dot(x,y))# 求和 sumprint(np.sum(x))  # print(np.sum(x, axis=0))  # 列print(np.sum(x, axis=1))  # 行

[[  5.  12.] [ 21.  32.]][[  5.  12.] [ 21.  32.]][[ 19.  22.] [ 43.  50.]]10.0[ 4.  6.][ 3.  7.]

# broadcastingx = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])v = np.array([1, 0, 1])y = x + v  # Add v to each row of x using broadcastingprint(y)  # Prints "[[ 2  2  4]         #          [ 5  5  7]         #          [ 8  8 10]         #          [11 11 13]]"x = np.arange(4)        # [0 1 2 3]xx = x.reshape(4,1)     # np.array([[0],[1],[2],[3]])y = np.ones(5)          # [ 1.  1.  1.  1.  1.]yy = np.reshape(y,(1,5)) # [[ 1.  1.  1.  1.  1.]]print(yy)print(xx+y)    #boardcasting (4,1)+(5,)= (4,5)print(xx+yy)   #boardcasting (4,1)+(1,5)= (4,5) 这两个结果是一样的

[[ 2  2  4] [ 5  5  7] [ 8  8 10] [11 11 13]][[ 1.  1.  1.  1.  1.]][[ 1.  1.  1.  1.  1.] [ 2.  2.  2.  2.  2.] [ 3.  3.  3.  3.  3.] [ 4.  4.  4.  4.  4.]][[ 1.  1.  1.  1.  1.] [ 2.  2.  2.  2.  2.] [ 3.  3.  3.  3.  3.] [ 4.  4.  4.  4.  4.]]

# SciPyfrom scipy.misc import imread, imsave, imresizeimg = imread('/home/panxie/图片/aaa.jpg')print(img.dtype,img.shape) # uint8 (1009, 1428, 3)                            # 像素：宽度1428,高度1009,通道：red，green and blue channelsimg_tinted = img * [1,0.5,0.9]img_tinted = imresize(img_tinted,(500,500))imsave('/home/panxie/图片/bbb.jpg',img_tinted)plt.subplot(1,2,1)plt.imshow(img)plt.subplot(1,2,2)plt.imshow(np.uint8(img_tinted))plt.show()

uint8 (1009, 1428, 3)

# distancesimport numpy as npfrom scipy.spatial.distance import pdist,squareformx = np.array([[0,1],[1,0],[2,0]])print(x)d = pdist(x,'euclidean') #计算（2范数）欧几里德距离#Computes the distance between m points using Euclidean distance (2-norm) as the distance metric between the points. The points are arranged as m n-dimensional row vectors in the matrix X.print(d)

[[0 1] [1 0] [2 0]][ 1.41421356  2.23606798  1.        ]

# plottingimport  matplotlib.pyplot as pltx = np.arange(0, 8, 0.1)y = np.sin(x)plt.plot(x,y)plt.xlabel('x axis label')plt.ylabel('y axis label')plt.title('Sine')plt.legend(['sine'])plt.show()