randomNormal

Generates pseudorandom numbers from a standard normal distribution using an inverse CDF method.

Synopsis

randomNormal (nRandom)

Required Arguments

int nRandom (Input)

Number of random numbers to generate.

Return Value

A vector of length nRandom containing the random standard normal deviates.

Description

Function randomNormal generates pseudorandom numbers from a standard normal (Gaussian) distribution using an inverse CDF technique. In this method, a uniform (0, 1) random deviate is generated. Then, the inverse of the normal distribution function is evaluated at that point, using the function normalInverseCdf (See Chapter 11 of the PyIMSL Stat Numerical Library user guide.)

Deviates from the normal distribution with mean mean and standard deviation std_dev can be obtained by scaling the output from randomNormal.

Example

In this example, randomNormal is used to generate five pseudorandom deviates from a standard normal distribution.

from __future__ import print_function
from numpy import *
from pyimsl.math.randomNormal import randomNormal
from pyimsl.math.randomSeedSet import randomSeedSet

randomSeedSet(123457)
r = randomNormal(5)
print("Standard normal random deviates: %8.4f%8.4f%8.4f%8.4f%8.4f"
      % (r[0], r[1], r[2], r[3], r[4]))

Output

Standard normal random deviates:   1.8279 -0.6412  0.7266  0.1747  1.0145

Remark

The function randomSeedSet can be used to initialize the seed of the random number generator. The function randomOption can be used to select the form of the generator.