Generate random array of floats between a range

问题:

This might be a silly question but I haven't been able to find a function to generate an array of random floats of a given length between a certain range.

I've looked at Random sampling but no function seems to do what I need.

random.uniform comes close but it only returns a single element, not a specific number.

This is what I'm after:

ran_floats = some_function(low=0.5, high=13.3, size=50)

which would return an array of 50 random non-unique floats (ie: repetitions are allowed) uniformly distributed in the range [0.5, 13.3].

Is there such a function?

回答1:

np.random.uniform fits your use case: http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.uniform.html

sampl = np.random.uniform(low=0.5, high=13.3, size=(50,))

回答2:

Why not use a list comprehension?

ran_floats = [random.uniform(low,high) for _ in xrange(size)]

回答3:

Why not to combine random.uniform with a list comprehension?

>>> def random_floats(low, high, size):
...    return [random.uniform(low, high) for _ in xrange(size)]
... 
>>> random_floats(0.5, 2.8, 5)
[2.366910411506704, 1.878800401620107, 1.0145196974227986, 2.332600336488709, 1.945869474662082]

回答4:

There may already be a function to do what you're looking for, but I don't know about it (yet?). In the meantime, I would suggess using:

ran_floats = numpy.random.rand(50) * (13.3-0.5) + 0.5

This will produce an array of shape (50,) with a uniform distribution between 0.5 and 13.3.

You could also define a function:

def random_uniform_range(shape=[1,],low=0,high=1):
    """
    Random uniform range

    Produces a random uniform distribution of specified shape, with arbitrary max and
    min values. Default shape is [1], and default range is [0,1].
    """
    return numpy.random.rand(shape) * (high - min) + min

EDIT: Hmm, yeah, so I missed it, there is numpy.random.uniform() with the same exact call you want! Try import numpy; help(numpy.random.uniform) for more information.

回答5:

This is the simplest way

np.random.uniform(start,stop,(rows,columns))

回答6:

The for loop in list comprehension takes time and makes it slow. It is better to use numpy parameters (low, high, size, ..etc)

import numpy as np
import time
rang = 10000
tic = time.time()
for i in range(rang):
    sampl = np.random.uniform(low=0, high=2, size=(182))
print("it took: ", time.time() - tic)

tic = time.time()
for i in range(rang):
    ran_floats = [np.random.uniform(0,2) for _ in range(182)]
print("it took: ", time.time() - tic)

sample output:

('it took: ', 0.06406784057617188)

('it took: ', 1.7253198623657227)

回答7:

Alternatively you could use SciPy

from scipy import stats
stats.uniform(0.5, 13.3).rvs(50)

and for the record to sample integers it's

stats.randint(10, 20).rvs(50)