human
I am working on the file "2015-08-03-nonparametric-latent-dirichlet-allocation.ipynb".
The first task is:
Recall that a sample from a Dirichlet process is a distribution that approximates (but varies from) the base distribution. In this case, a sample from the Dirichlet process will be a distribution over topics that varies from the uniform distribution we provided as a base. If we use the stick-breaking metaphor, we are effectively breaking a stick one time and the size of each portion corresponds to the proportion of a topic in the document.
To construct a sample from the DP, we need to again define our DP class:
Can you write Python code for it?
 ... What is a the Python code you requested?
assistant
#Here is the Python code you requested.

from scipy.stats import beta
from numpy.random import choice

class DirichletProcessSample():
    def __init__(self, base_measure, alpha):
        self.base_measure = base_measure
        self.alpha = alpha
        
        self.cache = []
        self.weights = []
        self.total_stick_used = 0.

    def __call__(self):
        remaining = 1.0 - self.total_stick_used
        i = DirichletProcessSample.roll_die(self.weights + [remaining])
        if i is not None and i < len(self.weights) :
            return self.cache[i]
        else:
            stick_piece = beta(1, self.alpha).rvs() * remaining
            self.total_stick_used += stick_piece
            self.weights.append(stick_piece)
            new_value = self.base_measure()
            self.cache.append(new_value)
            return new_value
      
    @staticmethod 
    def roll_die(weights):
        if weights:
            return choice(range(len(weights)), p=weights)
        else:
            return None