randomgen.generator.Generator.randn¶
- Generator.randn(d0, d1, ..., dn, dtype='d')¶
Return a sample (or samples) from the “standard normal” distribution.
Note
This is a convenience function for users porting code from Matlab, and wraps randomgen.generator.standard_normal. That function takes a tuple to specify the size of the output, which is consistent with other NumPy functions like numpy.zeros and numpy.ones.
If positive int_like arguments are provided, randn generates an array of shape
(d0, d1, ..., dn)
, filled with random floats sampled from a univariate “normal” (Gaussian) distribution of mean 0 and variance 1. A single float randomly sampled from the distribution is returned if no argument is provided.- Parameters
- d0, d1, …, dnint, optional
The dimensions of the returned array, must be non-negative. If no argument is given a single Python float is returned.
- dtype{str, dtype}, optional
Desired dtype of the result, either ‘d’ (or ‘float64’) or ‘f’ (or ‘float32’). All dtypes are determined by their name. The default value is ‘d’.
- Returns
- Zndarray or float
A
(d0, d1, ..., dn)
-shaped array of floating-point samples from the standard normal distribution, or a single such float if no parameters were supplied.
See also
standard_normal
Similar, but takes a tuple as its argument.
normal
Also accepts mu and sigma arguments.
Notes
For random samples from \(N(\mu, \sigma^2)\), use:
sigma * randomgen.generator.randn(...) + mu
Examples
>>> randomgen.generator.randn() 2.1923875335537315 # random
Two-by-four array of samples from N(3, 6.25):
>>> 3 + 2.5 * randomgen.generator.randn(2, 4) array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], # random [ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) # random