original_docstring: 
      Monte-Carlo numerical integration.
      :param distr_rvs: random sampler (takes size as `size` parameter and returns numpy array of samples
      :param f: function to be integrated
      :param initial_chunk_size: initial chunk size
      :param chunk_size: chunk size
      :param eps: precision
      :return: integral value
      
docstring: Monte-Carlo numerical integration.
docstring_tokens: Monte - Carlo numerical integration .
code: def monte_carlo_integration(distr_rvs, f, initial_chunk_size=100, chunk_size=10, eps=0.1):
      """
      Monte-Carlo numerical integration.
      :param distr_rvs: random sampler (takes size as `size` parameter and returns numpy array of samples
      :param f: function to be integrated
      :param initial_chunk_size: initial chunk size
      :param chunk_size: chunk size
      :param eps: precision
      :return: integral value
      """
      current_size = initial_chunk_size
      prev = 0.
      current = sum(map(f, distr_rvs(size=current_size))) / current_size
      i = 0
      while abs(current - prev) > eps:
          i += 1
          prev = current
          current = current * current_size / (current_size + chunk_size)
          current_size += chunk_size
          current += sum(map(f, distr_rvs(size=chunk_size))) / current_size
      return current
code_tokens: def monte_carlo_integration ( distr_rvs , f , initial_chunk_size = 100 , chunk_size = 10 , eps = 0.1 ) : current_size = initial_chunk_size prev = 0. current = sum ( map ( f , distr_rvs ( size = current_size ) ) ) / current_size i = 0 while abs ( current - prev ) > eps : i += 1 prev = current current = current * current_size / ( current_size + chunk_size ) current_size += chunk_size current += sum ( map ( f , distr_rvs ( size = chunk_size ) ) ) / current_size return current
short_docstring: Monte-Carlo numerical integration.
short_docstring_tokens: Monte - Carlo numerical integration .
comment: """
      Monte-Carlo numerical integration.
      :param distr_rvs: random sampler (takes size as `size` parameter and returns numpy array of samples
      :param f: function to be integrated
      :param initial_chunk_size: initial chunk size
      :param chunk_size: chunk size
      :param eps: precision
      :return: integral value
      """