卒業論文

In [1]:

    

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


    

In [2]:

    

prop_num = 4
resp_num = 4
prop_prefs = [
    [3, 1, 0, 2],
    [1, 3, 2, 0],
    [2, 3, 0, 1],
    [3, 1, 2, 0]
]
resp_prefs = [
    [3, 0, 2, 1],
    [3, 0, 2, 1],
    [3, 0, 2, 1],
    [3, 0, 2, 1]
]
prop_caps = [1, 1, 2, 2]
resp_caps = [1, 1, 2, 2]
list_length = 2


    

In [3]:

    

prop_matched, resp_matched, prop_indptr, resp_indptr = mf.BOS(prop_prefs, resp_prefs, resp_caps, prop_caps, list_length)
print('prop_matched = ' + str(prop_matched))
print('resp_matched = ' + str(resp_matched))
print('prop_indptr = ' + str(prop_indptr))
print('resp_indptr = ' + str(resp_indptr))
mf.Graph(prop_matched, resp_matched, prop_indptr, resp_indptr, prop_name=["1", "2", "3", "4"], resp_name=["A", "B", "C", "D"])
plt.savefig('bos.png', dpi=150)


    

    




prop_matched = [3 1 2 0 3 2]
resp_matched = [2 1 2 3 0 3]
prop_indptr = [0 1 2 4 6]
resp_indptr = [0 1 2 4 6]






    

In [5]:

    

prop_matched, resp_matched, prop_indptr, resp_indptr = mf.DA(prop_prefs, resp_prefs, resp_caps, prop_caps, list_length)
print('prop_matched = ' + str(prop_matched))
print('resp_matched = ' + str(resp_matched))
print('prop_indptr = ' + str(prop_indptr))
print('resp_indptr = ' + str(resp_indptr))
mf.Graph(prop_matched, resp_matched, prop_indptr, resp_indptr, prop_name=["1", "2", "3", "4"], resp_name=["A", "B", "C", "D"])
plt.savefig('da.png', dpi=150)


    

    




prop_matched = [3 4 2 4 3 1]
resp_matched = [4 3 2 4 0 3]
prop_indptr = [0 1 2 4 6]
resp_indptr = [0 1 2 4 6]






    

In [6]:

    

mf.Comp('BOS', prop_prefs, prop_prefs, resp_prefs, resp_caps, prop_caps, list_length)


    

    
Out[6]:





(1.0, 4.0, 1.0, 0.5, 0.0, 0.0, 6.0)

In [7]:

    

mf.Comp('DA', prop_prefs, prop_prefs, resp_prefs, resp_caps, prop_caps, list_length)


    

    
Out[7]:





(2.0, 4.0, 0.5, 0.5, 1.0, 2.0, 8.0)

In [7]:

    

data1 = np.loadtxt("data/grad/data1.csv",delimiter=",").astype(int)


    

In [8]:

    

data2 = np.loadtxt("data/grad/data2.csv",delimiter=",").astype(int)


    

In [9]:

    

list_length = 2
values1 = [0, 0, 0, 0, 0, 0, 0]
for i in range(5):
    prefs = data1[4*i:4*i+4].tolist()
    values1 += np.array(mf.Comp('BOS', prefs, prop_prefs, resp_prefs, resp_caps, prop_caps, list_length))
values1 /= 5
print(values1)


    

    




[ 1.8  1.6  1.3  0.8  0.   1.   6. ]

In [10]:

    

list_length = 2
values2 = [0, 0, 0, 0, 0, 0, 0]
for i in range(5):
    prefs = data2[4*i:4*i+4].tolist()
    values2 += np.array(mf.Comp('DA', prefs, prop_prefs, resp_prefs, resp_caps, prop_caps, list_length))
values2 /= 5
print(values2)


    

    




[ 2.8  0.6  1.3  0.2  0.6  0.8  8. ]