-
Notifications
You must be signed in to change notification settings - Fork 0
/
eval1.py
74 lines (63 loc) · 1.63 KB
/
eval1.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
from bayesiano import Bayesiano
from euclideo import Euclideo
import numpy as np
import sys
data=0
ident=0
dataset=0
evaluacion=0
tipo=0
def distance(c1,c2):
result=0
for i in range(0,len(c1)):
result=result+(c1[i]-c2[i])**2
return result
if(len(sys.argv)>1):
tipo=sys.argv[1]
if(len(sys.argv)>2):
dataset=sys.argv[2]
if(len(sys.argv)>3):
evaluacion=sys.argv[3]
if dataset=='ocr':
raw=np.load('datos_procesados.npy')
ident=[i[9] for i in raw]
data=[i[0:9] for i in raw]
elif(dataset=='wine'):
data=np.genfromtxt('wine.data',delimiter=',',usecols=(1,2,3,4,5,6,7,8,9,10,11,12,13))
ident=np.genfromtxt('wine.data',delimiter=',',usecols=(0))
elif dataset=='cancer':
raw=np.genfromtxt('wdbc.data',delimiter=',')
ident=np.genfromtxt('wdbc.data',dtype=None,delimiter=',',usecols=(1))
data=[i[2:32] for i in raw]
elif dataset=='iris':
data=np.genfromtxt("iris.data",delimiter=',',usecols=(0,1,2,3))
ident=np.genfromtxt("iris.data",dtype=None,delimiter=',',usecols=(4))
else:
print "Dataset no valido"
entren=data
idtr=ident
data=[i.tolist() for i in data]
entren=[i.tolist() for i in entren]
if(evaluacion=='-d1'):
entren=data[0:500]
idtr=ident[0:500]
ident=ident[0:500]
data=data[0:500]
if(evaluacion=='-d2'):
entren=data[0:500]
idtr=ident[0:500]
ident=ident[500:]
data=data[500:]
clasif=0
if tipo=='b':
clasif=Bayesiano(entren,idtr)
else:
clasif=Euclideo(entren,idtr)
"""medias=clasif.getCentroides()
distancias=np.zeros((10,10))
for i in range(len(medias)):
for j in range(len(medias)):
distancias[i][j]=distance(medias[i],medias[j])
print clasif.getClases()
print np.around(distancias,decimals=1)"""
clasif.clasificar(data,ident)