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# numpy.random.dirichlet

`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 : array Number of samples to draw.
  Returns: | samples : ndarray, The drawn samples, of shape (alpha.ndim, size).

  Notes

  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.

  References

  [R189] | David McKay, “Information Theory, Inference and Learning Algorithms,” chapter 23, http://www.inference.phy.cam.ac.uk/mackay/

  [R190] | Wikipedia, “Dirichlet distribution”, http://en.wikipedia.org/wiki/Dirichlet_distribution

  Examples

  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")