class_docstr: Class for the manifold of binomial distributions.
  
  This is the parameter space of binomial distributions
  i.e. the half-line of positive reals.
class_docstr_tok: ["Class","for","the","manifold","of","binomial","distributions",".","This","is","the","parameter","space","of","binomial","distributions","i.e",".","the","half-line","of","positive","reals","."]
docstr: Compute pmf associated to point.
  
  Compute the probability mass function of the binomial
  distribution with parameters provided by point.
  
  Parameters
  ----------
  point : array-like, shape=[..., 1]
      Point representing a binomial distribution (probability of success).
  
  Returns
  -------
  pmf : function
      Probability mass function of the binomial distribution with
      parameters provided by point.
docstr_tok: ["Compute","pmf","associated","to","point",".","Compute","the","probability","mass","function","of","the","binomial","distribution","with","parameters","provided","by","point",".","Parameters","--","--","--","--","--","point",":","array-like",",","shape=","[","...",",","1","]","Point","representing","a","binomial","distribution","(","probability","of","success",")",".","Returns","--","--","--","-","pmf",":","function","Probability","mass","function","of","the","binomial","distribution","with","parameters","provided","by","point","."]
code: def point_to_pdf(self, point):
      """Compute pmf associated to point.
  
      Compute the probability mass function of the binomial
      distribution with parameters provided by point.
  
      Parameters
      ----------
      point : array-like, shape=[..., 1]
          Point representing a binomial distribution (probability of success).
  
      Returns
      -------
      pmf : function
          Probability mass function of the binomial distribution with
          parameters provided by point.
      """
  
      def pmf(k):
          """Generate parameterized function for binomial pmf.
  
          Parameters
          ----------
          k : array-like, shape=[n_samples,]
              Integers in {0, ..., n_draws} at which to
              compute the probability mass function.
  
          Returns
          -------
          pmf_at_k : array-like, shape=[..., n_samples]
              Values of pdf at k for each value of the parameters provided
              by point.
          """
          k = gs.reshape(gs.array(k), (-1,))
          const = factorial(self.n_draws) / (
              factorial(k) * factorial(self.n_draws - k)
          )
          return (
              gs.from_numpy(const)
              * (point**k)
              * ((1 - point) ** (self.n_draws - k))
          )
  
      return pmf
  
imports: from scipy.special import factorial
  from scipy.stats import binom
  import geomstats.backend
  from geomstats.geometry.base import VectorSpaceOpenSet
  from geomstats.geometry.euclidean import Euclidean
  from geomstats.geometry.riemannian_metric import RiemannianMetric
  from geomstats.information_geometry.base import InformationManifoldMixin, ScipyUnivariateRandomVariable
label_desc: Use image node_id 7 for calling the BinomialDistributions obj's underlying member method code with example usage: obj.point_to_pdf(point) and returns: pmf, unknown