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# imports from jax import jit import jax.numpy as jnp # define the cube function def cube (x): return x * x * x # generate data x = jnp. ones ((10000, 10000)) # create the jit version of the cube function jit_cube = jit (cube) # apply the cube and jit_cube functions to the same data for speed comparison cube (x) jit_cube (x)
The Marsaglia polar method [1] is a pseudo-random number sampling method for generating a pair of independent standard normal random variables. [2] Standard normal random variables are frequently used in computer science, computational statistics, and in particular, in applications of the Monte Carlo method. The polar method works by choosing ...
CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them. [3] CuPy shares the same API set as NumPy and SciPy, allowing it to be a drop-in replacement to run NumPy/SciPy code on GPU.
Default generator in R and the Python language starting from version 2.3. Xorshift: 2003 G. Marsaglia [26] It is a very fast sub-type of LFSR generators. Marsaglia also suggested as an improvement the xorwow generator, in which the output of a xorshift generator is added with a Weyl sequence.
NumPy (pronounced / ˈ n ʌ m p aɪ / NUM-py) is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. [3]
The Ziggurat algorithm used to generate sample values with a normal distribution. (Only positive values are shown for simplicity.) The pink dots are initially uniform-distributed random numbers. The desired distribution function is first segmented into equal areas "A". One layer i is selected at random by the uniform source at the left.
It discards 1 − π /4 ≈ 21.46% of the total input uniformly distributed random number pairs generated, i.e. discards 4/ π − 1 ≈ 27.32% uniformly distributed random number pairs per Gaussian random number pair generated, requiring 4/ π ≈ 1.2732 input random numbers per output random number.
For more on simulating a draw from the truncated normal distribution, see Robert (1995), Lynch (2007, Section 8.1.3 (pages 200–206)), Devroye (1986). The MSM package in R has a function, rtnorm, that calculates draws from a truncated normal. The truncnorm package in R also has functions to draw from a truncated normal.