How do you produce a random 0 or 1 with random.rand
Question:
I’m trying to produce a 0 or 1 with numpy’s random.rand.
np.random.rand() produces a random float between 0 and 1 but not just a 0 or a 1.
Thank you.
Answers:
You can use np.random.choice with a list of [0,1], or use np.random.radint with a range of 0,2
In [1]: import numpy as np
In [2]: np.random.choice([0,1])
Out[2]: 0
In [5]: np.random.choice([0,1])
Out[5]: 1
In [8]: np.random.randint(2)
Out[8]: 0
In [9]: np.random.randint(2)
Out[9]: 1
You can also use the random module for the equivalent of these functions
You can use numpy.random.random_integers
random_int= np.random.random_integers(0,1)
print (random_int)
You can use np.random.randint(low, high=None, size=None).
>>> np.random.randint(0,2,10)
array([0, 1, 1, 0, 1, 1, 0, 0, 1, 0])
>>> np.random.randint(2)
0
>>> np.random.randint(2)
1
Fore more details, you can refer to https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.randint.html
You should consider using np.random.randint() This function takes a range as an input.
For example,
>>> np.random.randint(2)
This will give you an output of either 0 or 1
To add to the other answers, it is possible to simply do
p_True = 0.5 # 50% probability that you get 1
your_bool = p_True >= np.random.rand() # >= because rand returns a float between 0 and 1, excluding 1.
You can have a biased sample by changing p_true.
Is there a reason to specifically use np.random.rand? This function outputs a float as noted in the question and previous answers, and you would need thresholding to obtain an int.
scipy.stats.bernoulli(p) directly outputs a 1 with probability p and 0 with probability 1-p.
You can use np.random.choice with a list of [0,1] and a size to get a random choice matrix like this:
In [1]: import numpy as np
In [2]: np.random.choice([0,1], size=(3,4))
Out[2]: array([[1, 0, 0, 0],
[0, 1, 1, 0],
[1, 1, 1, 1]])
I’m trying to produce a 0 or 1 with numpy’s random.rand.
np.random.rand() produces a random float between 0 and 1 but not just a 0 or a 1.
Thank you.
You can use np.random.choice with a list of [0,1], or use np.random.radint with a range of 0,2
In [1]: import numpy as np
In [2]: np.random.choice([0,1])
Out[2]: 0
In [5]: np.random.choice([0,1])
Out[5]: 1
In [8]: np.random.randint(2)
Out[8]: 0
In [9]: np.random.randint(2)
Out[9]: 1
You can also use the random module for the equivalent of these functions
You can use numpy.random.random_integers
random_int= np.random.random_integers(0,1)
print (random_int)
You can use np.random.randint(low, high=None, size=None).
>>> np.random.randint(0,2,10)
array([0, 1, 1, 0, 1, 1, 0, 0, 1, 0])
>>> np.random.randint(2)
0
>>> np.random.randint(2)
1
Fore more details, you can refer to https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.randint.html
You should consider using np.random.randint() This function takes a range as an input.
For example,
>>> np.random.randint(2)
This will give you an output of either 0 or 1
To add to the other answers, it is possible to simply do
p_True = 0.5 # 50% probability that you get 1
your_bool = p_True >= np.random.rand() # >= because rand returns a float between 0 and 1, excluding 1.
You can have a biased sample by changing p_true.
Is there a reason to specifically use np.random.rand? This function outputs a float as noted in the question and previous answers, and you would need thresholding to obtain an int.
scipy.stats.bernoulli(p) directly outputs a 1 with probability p and 0 with probability 1-p.
You can use np.random.choice with a list of [0,1] and a size to get a random choice matrix like this:
In [1]: import numpy as np
In [2]: np.random.choice([0,1], size=(3,4))
Out[2]: array([[1, 0, 0, 0],
[0, 1, 1, 0],
[1, 1, 1, 1]])