python-numpy最全攻略十-random_sample, ranf, bitwise

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np.random_sample()

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 importing numpy 
import numpy as np
  
# output random value 
out_val = np.random.random_sample() 
print ("Output random float value : ", out_val)

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Output random float value :  0.2450768662139805

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import numpy as geek 
  
  
# output array 
out_arr = geek.random.random_sample(size =(1, 3)) 
print ("Output 2D Array filled with random floats : ", out_arr)

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Output 2D Array filled with random floats :  [[0.15468058 0.26536462 0.54954387]]

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import numpy as geek

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# output array 
out_arr = geek.random.random_sample((3, 2, 1)) 
print ("Output 3D Array filled with random floats : ", out_arr)

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Output 3D Array filled with random floats :  [[[0.6380224 ]
  [0.73029307]]

 [[0.51149673]
  [0.3413279 ]]

 [[0.20853883]
  [0.61981115]]]

random.ranf()

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# numpy.random.ranf() is one of the function for doing random sampling in numpy. It returns an array of specified shape 
# and fills it with random floats in the half-open interval [0.0, 1.0).
import numpy as np
  
  
# output random float value 
out_val = np.random.ranf() 
print ("Output random float value : ", out_val)

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Output random float value :  0.44112568416235265

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# importing numpy 
import numpy as np
  
  
# output array 
out_arr = np.random.ranf(size =(2, 1)) 
print ("Output 2D Array filled with random floats : ", out_arr)

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Output 2D Array filled with random floats :  [[0.69303583]
 [0.8020658 ]]

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import numpy as geek 
  
# output array 
out_arr = geek.random.ranf((3, 3, 2)) 
print ("Output 3D Array filled with random floats : ", out_arr)

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Output 3D Array filled with random floats :  [[[0.50709171 0.02493862]
  [0.51112692 0.8210353 ]
  [0.98668934 0.20536282]]

 [[0.0707417  0.38774696]
  [0.01399582 0.14022261]
  [0.47580447 0.70451949]]

 [[0.07844355 0.28663839]
  [0.84763223 0.98383207]
  [0.6413255  0.63548128]]]
numpy.random.random.randint()

np.bitwise-function

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# Python code to demonstrate bitwise-function 
import numpy as np 
  
# construct an array of even and odd numbers 
even = np.array([0, 2, 4, 6, 8, 16, 32]) 
odd = np.array([1, 3, 5, 7, 9, 17, 33]) 
  
# bitwise_and 
print('bitwise_and of two arrays: ') 
print(np.bitwise_and(even, odd)) 
  
# bitwise_or 
print('bitwise_or of two arrays: ') 
print(np.bitwise_or(even, odd)) 
  
# bitwise_xor 
print('bitwise_xor of two arrays: ') 
print(np.bitwise_xor(even, odd)) 
   
# invert or not 
print('inversion of even no. array: ') 
print(np.invert(even)) 
  
# left_shift  
print('left_shift of even no. array: ') 
print(np.left_shift(even, 1)) 
  
# right_shift  
print('right_shift of even no. array: ') 
print(np.right_shift(even, 1))

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bitwise_and of two arrays: 
[ 0  2  4  6  8 16 32]
bitwise_or of two arrays: 
[ 1  3  5  7  9 17 33]
bitwise_xor of two arrays: 
[1 1 1 1 1 1 1]
inversion of even no. array: 
[ -1  -3  -5  -7  -9 -17 -33]
left_shift of even no. array: 
[ 0  4  8 12 16 32 64]
right_shift of even no. array: 
[ 0  1  2  3  4  8 16]