CODEWITHSUNDEEP
pythonrandomnormal-distributioninverse
SP00N
SP00N

Reputation: 25

Inverse normal random number generation in python?

I've used random.normal() in the past to generate a single number who, if called multiple times, in aggregate would create a bell curve distribution. What I'm trying to do now is to create the opposite / inverse, where the distribution is biased towards the extremes within a range? There are built in functions in excel that seem to do what I want. Is there a way to do it in python? Thank you

Upvotes: 0

Views: 1141

Answers (3)

Peter O.
Peter O.

Reputation: 32908

It appears you want a distribution with an "upside-down bell curve" compared to the normal distribution. If so, then the following method implements this distribution via rejection sampling and a modified version of the standard normal distribution. 'x0' and 'x1' are the ranges of numbers to generate.

def invertedNormal(x0, x1):
  # Get the ends of the PDF (the bounding
  # box will cover the PDF at the given range)
  x0pdf = 1-math.exp(-(x0*x0))
  x1pdf = 1-math.exp(-(x1*x1))
  ymax = max(x0pdf, x1pdf)
  while True:
    # Choose a random x-coordinate
    x=random.random()*(x1-x0)+x0
    # Choose a random y-coordinate
    y=random.random()*ymax
    # Return x if y falls within PDF
    if y < 1-math.exp(-(x*x)):
      return x

Upvotes: 1

Luke Storry
Luke Storry

Reputation: 6732

It sounds like what you are wanting is to shift the distribution the the edge of the range, and wrap around?

Something like this could do what you're looking for:

num = (random.normal() + 0.5) % 1

Upvotes: 0

wjakobw
wjakobw

Reputation: 535

You'd have to decide which probability distribution to use.

If you want to use pure Python, without external dependencies, check which distributions are available in the Random module: https://docs.python.org/3/library/random.html

For example you might use a Beta distribution with parameters (0.5, 0.5): https://docs.python.org/3/library/random.html#random.betavariate

See the Wikipedia page for beta distribution to understand the parameters: https://en.wikipedia.org/wiki/Beta_distribution

For advanced use, the external package scipy is the usual way to access probability distributions within Python: https://docs.scipy.org/doc/scipy/reference/stats.html

Upvotes: 0

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