卒業論文

In [1]:

    

# coding: UTF-8
%matplotlib inline
import itertools
import numpy as np
import matchfuncs as mf


    

In [2]:

    

prop_num = 345
resp_num = 38
alpha = 0.5
beta = 1
prop_caps = [1 for i in range(230)] + [2 for i in range(115)]
resp_caps = [12 for i in range(25)] + [6 for i in range(13)]
list_length = 2


    

In [3]:

    

bos_data = np.zeros(7)
daadd_data = np.zeros(7)
da_data = np.zeros(7)


    

In [4]:

    

t = 10000
for i in range(t):
    prop_prefs, resp_prefs, popularity, grade = mf.MakeCVprefs(prop_num, resp_num, alpha, beta)
    bos_data += np.asarray(mf.Comp('BOS', prop_prefs, prop_prefs, resp_prefs, resp_caps, prop_caps, list_length))
    daadd_data += np.asarray(mf.Comp('DAAdd', prop_prefs, prop_prefs, resp_prefs, resp_caps, prop_caps, list_length))
    da_data += np.asarray(mf.Comp('DA', prop_prefs, prop_prefs, resp_prefs, resp_caps, prop_caps))
bos_data /= t
daadd_data /= t
da_data /= t


    

In [5]:

    

print(bos_data)
print(daadd_data)
print(da_data)


    

    




[ 240.3507     345.           4.2881     121.3594517  116.8141     106.4156
  825.7327   ]
[ 216.0581     345.           2.7225     110.3444166  152.6249     153.0434
  934.6192   ]
[  0.00000000e+00   3.45000000e+02   9.24105000e+00   1.27819164e+02
   6.08445000e+01   0.00000000e+00   1.31100000e+04]