"CS231N Study Notes" 2. Python NumPy's NumPy

NumPy Creation of arrays

 import   NumPy as NPA  = Np.full ((3, 3), 1< Span style= "COLOR: #000000" >)  print   (a) a  = Np.random.random ((3, 3 print  Span style= "COLOR: #000000" > (a) a  = Np.eye (3 print    (a) a  = Np.array ([[1, 2, 3, 4], [ 5, 6, 7, 8 9, ten, one, 1213, +, 16 print   (a)  print  (A.shape) 

output: [[1 1 1] [1 1 1] [1 1 10.09670856  0.44868154  0.43326738  0.57400445  0.47124464  0.763103750.72557452  0.98591433  0.971471271.  0.  0.] [0.  1.  0.] [0.  0.  11  2  3  45  6  7  89 Ten[ (4, 4)

Access Methods for arrays

ImportNumPy as NPA= Np.array ([[1, 2, 3, 4],              [5, 6, 7, 8],              [9, 10, 11, 12],              [13, 14, 15, 16]])Print(a)Print(A.shape)Print(A[1:3])Print(A[1:-1, 1:-1])Print(A[0, 1])Print(A[1:3, 2])Print(A[2, 1:3])Print(A[[0, 1, 3, 3], [2, 3, 2, 2]])#Print a[0,2],a[1,3],a[3,2],a[3,2]

1  2  3  45  6  7  89 [all][](4, 4  5  6  7  89 Ten6 7  ] [ten]]27 each] [3  8 15 15]

Honey Juice Usage

 import   NumPy as NPA  = Np.array ([[1, 2, 3 , 4 ", [ 5, 6, 7, 8 9, Ten, one, 12, +, 16]])  print  (Np.arange (4 print  (Np.full ([1, 4], 1 print  (A[np.arange (4), 1 4), [2, 3, 2, 3]] + = 100 print  (a) 

[0 1 2 3][[1 1 1 12  6][[  1 2   103   4]  [5   6   7 108
   
    ] [  9  111  
      15 116]]
   

Boolean

Import= Np.array ([[1, 2, 3, 4],              [5, 6, 7, 8],              [9, ten, one, and],
    [, a > 5 # and this kind of operation???     print(b)print(A[a > 6])

[[False false false] [false true True] [true True True   ] [true  true]  True   7  8  9 10 11 12 13 14 15 16]

Array calculations

 import   NumPy as NPA  = Np.array ([1, 2< Span style= "COLOR: #000000" >]) b  = Np.array ([3, 4 print  (a + b)  print  (a- b)  print  (a * b)  print  (a/ b)  print  (A * 2 print  (A + 3 print  (A * * 0.5) 

[4 6][-2-2] [38 0.33333333  0.5       ][2 4][4 5 1.          1.41421356]

Matrix Multiplication & Transpose

 import   NumPy as NPA  = Np.array ([1, 2< Span style= "COLOR: #000000" >]) b  = Np.array ([3, 4 print  (A.dot (b)) #   a  = Np.array ([[1, 2, 34, 5, 6]]) b  = Np.array ([[1, 2, 3], [4, 5, 6 print  (b.t) #   transpose  print  (A.dot (b . T)) #   matrix multiplication  

One[[1 4] [2 5] [3 6]][[] [32 77]]

Sum

Import= Np.array ([[1, 2, 3],              [4, 5, 6]])print(A.sum ())  #  sum

21st

Various functions http://link.zhihu.com/?target=http%3A//docs.scipy.org/doc/numpy/reference/routines.array-manipulation.html

Broadcasting

A matrix with different rank can be calculated together

 import   NumPy as NPA  = Np.array ([[1, 2, 3 ], [1, 2, 3], [1, 2, 3 = Np.array ([1, 1, 0])  print  (A + b) v  = Np.array ([1, 2, 3 = Np.array ([4, 5]) V.reshape ([ 3, 1])  print  (V.reshape (3, 1) + W)  print  (W + v.reshape (3, 1)) 

[[2 3 3][2 3 3] [2 3 3]][[5 6] [6 7] [7 8]][[5 6] [6 7  ] [7 8]]

"CS231N Study Notes" 2. Python NumPy's NumPy