module datasets.dummies

Short summary

module papierstat.datasets.dummies

Jeux de données artificiels.

source on GitHub

Functions

function

truncated documentation

line2d

Simule un jeu de données y = ax + b + \epsilon. Notebooks associés à ce jeu de données :

Documentation

Jeux de données artificiels.

source on GitHub

papierstat.datasets.dummies.line2d(n, x0=0, x1=10, a=0.5, b=1, sigma=0.5)[source]

Simule un jeu de données y = ax + b + \epsilon. Notebooks associés à ce jeu de données :

Paramètres
  • n – nombre de points à simuler

  • x0 – dans l’intervalle [x0, x1]

  • x1 – dans l’intervalle [x0, x1]

  • aa

  • bb

  • sigma – écart type du bruit blanc

Renvoie

une matrice

source on GitHub

papierstat.datasets.dummies.rand(d0, d1, ..., dn)

Random values in a given shape.

Create an array of the given shape and populate it with random samples from a uniform distribution over [0, 1).

Paramètres

d1, .., dn (d0,) – The dimensions of the returned array, should all be positive. If no argument is given a single Python float is returned.

Renvoie

out – Random values.

Type renvoyé

ndarray, shape (d0, d1, ..., dn)

Voir aussi

random()

Notes

This is a convenience function. If you want an interface that takes a shape-tuple as the first argument, refer to np.random.random_sample .

Exemples

>>> np.random.rand(3,2)
array([[ 0.14022471,  0.96360618],  #random
       [ 0.37601032,  0.25528411],  #random
       [ 0.49313049,  0.94909878]]) #random
papierstat.datasets.dummies.randn(d0, d1, ..., dn)

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.

Paramètres

d1, .., dn (d0,) – The dimensions of the returned array, should be all positive. If no argument is given a single Python float is returned.

Renvoie

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.

Type renvoyé

ndarray or float

Voir aussi

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

Exemples

>>> 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