numpy.random.dirichlet(alpha, size=None)
Draw samples from the Dirichlet distribution.
Draw size
samples of dimension k from a Dirichlet distribution. A Dirichlet-distributed random variable can be seen as a multivariate generalization of a Beta distribution. Dirichlet pdf is the conjugate prior of a multinomial in Bayesian inference.
Parameters: |
alpha : array Parameter of the distribution (k dimension for sample of dimension k). size : int or tuple of ints, optional Output shape. If the given shape is, e.g., |
---|---|
Returns: |
samples : ndarray, The drawn samples, of shape (size, alpha.ndim). |
Raises: |
ValueError If any value in alpha is less than or equal to zero |
Uses the following property for computation: for each dimension, draw a random sample y_i from a standard gamma generator of shape alpha_i
, then is Dirichlet distributed.
[R429430] | David McKay, “Information Theory, Inference and Learning Algorithms,” chapter 23, http://www.inference.phy.cam.ac.uk/mackay/ |
[R430430] | Wikipedia, “Dirichlet distribution”, http://en.wikipedia.org/wiki/Dirichlet_distribution |
Taking an example cited in Wikipedia, this distribution can be used if one wanted to cut strings (each of initial length 1.0) into K pieces with different lengths, where each piece had, on average, a designated average length, but allowing some variation in the relative sizes of the pieces.
>>> s = np.random.dirichlet((10, 5, 3), 20).transpose()
>>> plt.barh(range(20), s[0]) >>> plt.barh(range(20), s[1], left=s[0], color='g') >>> plt.barh(range(20), s[2], left=s[0]+s[1], color='r') >>> plt.title("Lengths of Strings")
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https://docs.scipy.org/doc/numpy-1.14.2/reference/generated/numpy.random.dirichlet.html