Beau Cronin beaucronin

## example1.py
pr = analysis.predict({'petal_length': 1.5, 'petal_width': None})
interval = pr.credible_values('petal_width')
# => (0.06619570898596525, 0.45519138428493605)
interval[1] - interval[0]
# => 0.38899567529897083

pr = analysis.predict({'petal_length': 5.0, 'petal_width': None})
interval = pr.credible_values('petal_width')
# => (1.3341578189754613, 2.4761532421771784)
interval[1] - interval[0]

## iris.csv

          
            sepal_length
            sepal_width
            petal_length
            petal_width
            class

            
              5.1
              3.5
              1.4
              0.2
              Iris-setosa

            
              4.9
              3.0
              1.4
              0.2
              Iris-setosa

            
              4.7
              3.2
              1.3
              0.2
              Iris-setosa

            
              4.6
              3.1
              1.5
              0.2
              Iris-setosa

            
              5.0
              3.6
              1.4
              0.2
              Iris-setosa

            
              5.4
              3.9
              1.7
              0.4
              Iris-setosa

            
              4.6
              3.4
              1.4
              0.3
              Iris-setosa

            
              5.0
              3.4
              1.5
              0.2
              Iris-setosa

            
              4.4
              2.9
              1.4
              0.2
              Iris-setosa

## crp_generator.py
from random import random

def crpgen(N = None, alpha = 1.0):
  """
  A generator that implements the Chinese Restaurant Process
  """
  counts = []
  n = 0
  while N == None or n < N:
    # Compute the (unnormalized) probabilities of assigning the new object

## gist:1258210
x = initial()
prob = target_dist(x)
for i in range(steps):
  x_star = propose(x)
  prob_star = target_dist(x)
  if prob_star > prob or random() < prob_star / prob:
    x = x_star
    prob = prob_star
	pr = analysis.predict({'petal_length': 1.5, 'petal_width': None})
	interval = pr.credible_values('petal_width')
	# => (0.06619570898596525, 0.45519138428493605)
	interval[1] - interval[0]
	# => 0.38899567529897083

	pr = analysis.predict({'petal_length': 5.0, 'petal_width': None})
	interval = pr.credible_values('petal_width')
	# => (1.3341578189754613, 2.4761532421771784)
	interval[1] - interval[0]
sepal_length	sepal_width	petal_length	petal_width	class
5.1	3.5	1.4	0.2	Iris-setosa
4.9	3.0	1.4	0.2	Iris-setosa
4.7	3.2	1.3	0.2	Iris-setosa
4.6	3.1	1.5	0.2	Iris-setosa
5.0	3.6	1.4	0.2	Iris-setosa
5.4	3.9	1.7	0.4	Iris-setosa
4.6	3.4	1.4	0.3	Iris-setosa
5.0	3.4	1.5	0.2	Iris-setosa
4.4	2.9	1.4	0.2	Iris-setosa
	from random import random

	def crpgen(N = None, alpha = 1.0):
	"""
	A generator that implements the Chinese Restaurant Process
	"""
	counts = []
	n = 0
	while N == None or n < N:
	# Compute the (unnormalized) probabilities of assigning the new object
	x = initial()
	prob = target_dist(x)
	for i in range(steps):
	x_star = propose(x)
	prob_star = target_dist(x)
	if prob_star > prob or random() < prob_star / prob:
	x = x_star
	prob = prob_star