In [1]:

    

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

%load_ext autoreload
%autoreload 2

%matplotlib inline

%precision 2
pd.set_option('display.precision', 3)

import ndl, sim
from zt import ztnbinom


    

In [2]:

    

ns = [ztnbinom.rvs(3,.6) for i in xrange(10000)]
data = np.zeros((max(ns)))
for i in ns:
    data[i-1] += 1
data


    

    
Out[2]:





array([  3.26e+03,   2.65e+03,   1.83e+03,   1.05e+03,   6.02e+02,
         3.06e+02,   1.56e+02,   7.80e+01,   4.00e+01,   8.00e+00,
         1.30e+01,   2.00e+00,   2.00e+00,   1.00e+00])

In [3]:

    

data = pd.DataFrame(data,columns=['Frequency'],index=range(1,len(data)+1))
data['Cues'] = [range(1,i+1) + ['exactly%d'%i] for i in data.index]
data['Number'] = data.index
data


    

    
Out[3]:






  

    

      

      
Frequency
      
Cues
      
Number
    
  
  

    

      
1 
      
 3265
      
                                     [1, exactly1]
      
  1
    
    

      
2 
      
 2648
      
                                  [1, 2, exactly2]
      
  2
    
    

      
3 
      
 1832
      
                               [1, 2, 3, exactly3]
      
  3
    
    

      
4 
      
 1047
      
                            [1, 2, 3, 4, exactly4]
      
  4
    
    

      
5 
      
  602
      
                         [1, 2, 3, 4, 5, exactly5]
      
  5
    
    

      
6 
      
  306
      
                      [1, 2, 3, 4, 5, 6, exactly6]
      
  6
    
    

      
7 
      
  156
      
                   [1, 2, 3, 4, 5, 6, 7, exactly7]
      
  7
    
    

      
8 
      
   78
      
                [1, 2, 3, 4, 5, 6, 7, 8, exactly8]
      
  8
    
    

      
9 
      
   40
      
             [1, 2, 3, 4, 5, 6, 7, 8, 9, exactly9]
      
  9
    
    

      
10
      
    8
      
        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, exactly10]
      
 10
    
    

      
11
      
   13
      
    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, exactly11]
      
 11
    
    

      
12
      
    2
      
 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, exactl...
      
 12
    
    

      
13
      
    2
      
 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, ex...
      
 13
    
    

      
14
      
    1
      
 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...
      
 14
    
  

14 rows × 3 columns

In [4]:

    

r = sim.experiment(data, P=200, M=100)
sim.all_results(r)


    

In [5]:

    

def sampler(p):
    def ztnb():
        return ztnbinom(*p).rvs()
    return ztnb

number_sampler = sampler((3,.6))


    

In [6]:

    

del(data['Frequency'])
data


    

    
Out[6]:






  

    

      

      
Cues
      
Number
      
Outcomes
    
  
  

    

      
1 
      
                                     [1, exactly1]
      
  1
      
 notdu
    
    

      
2 
      
                                  [1, 2, exactly2]
      
  2
      
    du
    
    

      
3 
      
                               [1, 2, 3, exactly3]
      
  3
      
 notdu
    
    

      
4 
      
                            [1, 2, 3, 4, exactly4]
      
  4
      
 notdu
    
    

      
5 
      
                         [1, 2, 3, 4, 5, exactly5]
      
  5
      
 notdu
    
    

      
6 
      
                      [1, 2, 3, 4, 5, 6, exactly6]
      
  6
      
 notdu
    
    

      
7 
      
                   [1, 2, 3, 4, 5, 6, 7, exactly7]
      
  7
      
 notdu
    
    

      
8 
      
                [1, 2, 3, 4, 5, 6, 7, 8, exactly8]
      
  8
      
 notdu
    
    

      
9 
      
             [1, 2, 3, 4, 5, 6, 7, 8, 9, exactly9]
      
  9
      
 notdu
    
    

      
10
      
        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, exactly10]
      
 10
      
 notdu
    
    

      
11
      
    [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, exactly11]
      
 11
      
 notdu
    
    

      
12
      
 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, exactl...
      
 12
      
 notdu
    
    

      
13
      
 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, ex...
      
 13
      
 notdu
    
    

      
14
      
 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...
      
 14
      
 notdu
    
  

14 rows × 3 columns

In [7]:

    

r2 = sim.experiment(data, P=200, M=100, distribution=number_sampler)
sim.all_results(r2)


    

In [7]: