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The numpy.random module defines the following 4 functions that all seem to return a float betweeb [0, 1.0) from the continuous uniform distribution. What (if any) is the difference between these functions?
random_sample([size]) Return random floats in the half-open interval [0.0, 1.0).
random([size]) Return random floats in the half-open interval [0.0, 1.0).
ranf([size]) Return random floats in the half-open interval [0.0, 1.0).
sample([size]) Return random floats in the half-open interval [0.0, 1.0).
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I found the following in numpy.random source code that supports @askewchan's answer:
# Some aliases: ranf = random = sample = random_sample __all__.extend(['ranf','random','sample']) 
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randn generates samples from the normal distribution, while numpy. random. rand from a uniform distribution (in the range [0,1)).
For this reason, neither numpy. random nor random. random is suitable for any serious cryptographic uses. But because the sequence is so very very long, both are fine for generating random numbers in cases where you aren't worried about people trying to reverse-engineer your data.
random() 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). Syntax : numpy.random.random(size=None)
NumPy random for generating an array of random numbers ndarray of 1000 random numbers. The reason why NumPy is fast when used right is that its arrays are extremely efficient. They are like C arrays instead of Python lists.
Nothing.
They're just aliases to random_sample:
In [660]: np.random.random Out[660]: <function random_sample> In [661]: np.random.ranf Out[661]: <function random_sample> In [662]: np.random.sample Out[662]: <function random_sample> In [663]: np.random.random_sample is np.random.random Out[663]: True In [664]: np.random.random_sample is np.random.ranf Out[664]: True In [665]: np.random.random_sample is np.random.sample Out[665]: True If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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