randomstate.prng.mt19937.randn

randomstate.prng.mt19937.randn(d0, d1, ..., dn, method='bm', dtype='d')

Return a sample (or samples) from the “standard normal” distribution.

If positive, int_like or int-convertible 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 (if any of the \(d_i\) are floats, they are first converted to integers by truncation). A single float randomly sampled from the distribution is returned if no argument is provided.

This is a convenience function. If you want an interface that takes a tuple as the first argument, use numpy.random.standard_normal instead.

Parameters:

• d1, .., dn (d0,) – The dimensions of the returned array, should be all positive. If no argument is given a single Python float is returned.
• method (str, optional) – Either ‘bm’ or ‘zig’. ‘bm’ uses the default Box-Muller transformations method. ‘zig’ uses the much faster Ziggurat method of Marsaglia and Tsang.
• 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:

Z – 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.

Return type:

ndarray or float

See also

random.standard_normal()

Similar, but takes a tuple as its argument.

Notes

For random samples from \(N(\mu, \sigma^2)\), use:

sigma * np.random.randn(...) + mu

Examples

>>> np.random.randn()
2.1923875335537315 #random

Two-by-four array of samples from N(3, 6.25):

>>> 2.5 * np.random.randn(2, 4) + 3
array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],  #random
       [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]]) #random