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HASTIEDecisionTree_C4-5_parametersNode2.py
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HASTIEDecisionTree_C4-5_parametersNode2.py
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Campo=9
NodoRaiz=8
Nodo1=8
ValorDecision=150
ValorDecision1=151
import numpy as np
import math
NumCampos=11
Start = 0
End = 9600
Max=[float(-9.9999E+16)]
Min=[float(9.9999E+16)]
for i in range(NumCampos):
Max.append(-9.9999E+16)
Min.append(9.9999E+16)
f=open("C:\Hastie10_2Corrected.txt","r")
Conta=0
for linea in f:
lineadelTrain =linea.split(";")
Conta = Conta + 1
if Conta < Start:
continue
if Conta > End:
break
linea_x =[""]
z=-1
for x in lineadelTrain:
z=z+1
if z==0: continue
if z==NumCampos: break
if float(lineadelTrain[z]) > Max[z]:
Max[z]=float(lineadelTrain[z])
if float(lineadelTrain[z]) < Min[z]:
Min[z]=float(lineadelTrain[z])
#for i in range(NumCampos):
# print(" i ="+ str(i) + " MAX="+ str(Max[i]) + " MIN=" + str(Min[i]))
""" CLASIFIER FUNCTION ==========================================================="""
def Hastie_C4_5( W):
NumClases=2
NumCampos =11
TopeMemoria = 304
# Got frm https://stackoverflow.com/questions/15448594/how-to-add-elements-to-3-dimensional-array-in-python
TabVotos = np.zeros((NumCampos,TopeMemoria+1,NumClases))
Maximo=0.0
Conta=0.0
Cont=-1
ContClase=[float(0.0)]
for j in range(NumClases):
ContClase.append(float(0.0))
Start=0
End = 9600
f=open("C:\Hastie10_2Corrected.txt","r")
for linea in f:
lineadelTrain =linea.split(";")
Conta = Conta + 1
if Conta < Start:
continue
if Conta > End:
break
ValorTrain =float(lineadelTrain[NodoRaiz])
indice=CalcIndex(Max, Min, NodoRaiz, ValorTrain, TopeMemoria)
if indice < ValorDecision: continue
ValorTrain =float(lineadelTrain[Nodo1])
indice=CalcIndex(Max, Min, Nodo1, ValorTrain, TopeMemoria)
if (indice >= ValorDecision1):continue
Cont = Cont +1
if len(W) == 1:
FactorPri=1.0
else:
FactorPri=W[Cont]
ClaseLeida=float(lineadelTrain[0])
if ClaseLeida==-1.0:
Clase=0
else:
Clase=int(ClaseLeida)
ContClase[Clase]=ContClase[Clase] + 1
# Acumulacion valores
z=-1
for x in lineadelTrain:
z=z+1
if z==NumCampos:
break
if z==0: continue
ValorTrain =float(lineadelTrain[z])
ValorTrain =ValorTrain - Min[z]
Maximo = Max[z]
Maximo = Maximo - Min[z]
indice =(int) (((TopeMemoria - 2.0) * ValorTrain)/ Maximo)
if ( (indice > (TopeMemoria-2)) or (indice < 0)):
print("index overflowed=" + str( indice) + " in the field ="+ str(z-1) )
Wvalor=0.0
Wvalor= TabVotos[z,TopeMemoria-1,Clase]
Wvalor= Wvalor + 1
TabVotos[z,TopeMemoria-1,Clase]=Wvalor
Wvalor= TabVotos[z,indice,Clase]
Wvalor=Wvalor+FactorPri
TabVotos[z,indice,Clase]=Wvalor
# print("Acumula en el indice = " + str(indice) + " Clase =" + str(Clase) + " Wvalor =" + str(Wvalor))
f.close()
print("Total Clase 0 = " + str(ContClase[0]) +" Total Clase 1 = " +str(ContClase[1]))
# for Campo in range(NumCampos):
ContaPanta=0
HClase_PreguntaMin=999999999.0
indiceMin=999999
i=0
for i in range (TopeMemoria-2):
# ClaseMenos y ClaseMas computan computan las clases
# que existen con valores del campo menos o mas del
# campo considerado
ClasesMenos = np.zeros(NumClases)
ClasesMas = np.zeros(NumClases)
TotClaseMenos=0
TotClaseMas=0
EntroCampoMenos=0
EntroCampoMas=0
for y in range(i):
for w in range (NumClases):
# print("Recupera indice = "+str(i) + "Clase= " + str(w) + "Valor = " +str(TabVotos[Campo,i,w]))
TotClaseMenos= TotClaseMenos + TabVotos[Campo,y,w]
ClasesMenos[w]=ClasesMenos[w]+ TabVotos[Campo,y,w]
for j in range (NumClases):
if (ClasesMenos[j] != 0.0) :
P=ClasesMenos[j]/TotClaseMenos
# print("Indice =" + str(i) + " Clase= " + str(j) + " Probabilidad Campo "+ str(P) + "Total Clases = "+ str(TotClases))
EntroCampoMenos= EntroCampoMenos + P*math.log(1.0/P,2)
#print("Entropy < indiex =" +str(i) + " field "+ str(Campo)+ " = " + str(EntroCampoMenos) )
for y in range(i,TopeMemoria-2):
for w in range (NumClases):
# print("Recupera indice = "+str(i) + "Clase= " + str(w) + "Valor = " +str(TabVotos[Campo,i,w]))
TotClaseMas= TotClaseMas + TabVotos[Campo,y,w]
ClasesMas[w]=ClasesMas[w]+ TabVotos[Campo,y,w]
for j in range (NumClases):
if (ClasesMas[j] != 0.0) :
P=ClasesMas[j]/TotClaseMas
# print("Indice =" + str(i) + " Clase= " + str(j) + " Probabilidad Campo "+ str(P) + "Total Clases = "+ str(TotClases))
EntroCampoMas= EntroCampoMas + P*math.log(1.0/P,2)
#print("Entropy > index=" +str(i) + " field "+ str(Campo)+ " = " + str(EntroCampoMas) )
HClase_Pregunta =(TotClaseMenos/(TotClaseMenos+TotClaseMas) )*EntroCampoMenos + (TotClaseMas/(TotClaseMenos+TotClaseMas) )*EntroCampoMas
#print("HClass_Question index =" +str(i) + " field "+ str(Campo)+ " = " + str(HClase_Pregunta) )
if (HClase_Pregunta < HClase_PreguntaMin):
indiceMin=i
HClase_PreguntaMin=HClase_Pregunta
ContaPanta=ContaPanta + 1
if ContaPanta > 1000:
print("Procesa 1000")
ContaPanta=0
print("Index minimum =" +str(indiceMin) + " field "+ str(Campo)+ " Class Question " + str(HClase_PreguntaMin) )
ClasesMenos = np.zeros(NumClases)
ClasesMas = np.zeros(NumClases)
TotClaseMenos=0
TotClaseMas=0
# ANTES for y in range(indiceMin+1,TopeMemoria-2): QUITAR + 1
for y in range(indiceMin,TopeMemoria-2):
for w in range (NumClases):
TotClaseMas= TotClaseMas + TabVotos[Campo,y,w]
ClasesMas[w]=ClasesMas[w]+ TabVotos[Campo,y,w]
for y in range(indiceMin):
for w in range (NumClases):
TotClaseMenos = TotClaseMenos + TabVotos[Campo,y,w]
ClasesMenos[w]=ClasesMenos[w]+ TabVotos[Campo,y,w]
print(" " )
print("Index Classes Values = " + str(indiceMin))
for w in range (NumClases):
print("Class = " + str(w) + " in branch > "+ str( ClasesMas[w]) + " in branch < "+ str( ClasesMenos[w]))
def CalcIndex(Max, Min, Nodo, ValorNodo, TopeMemoria):
ValorNodo =ValorNodo - Min[Nodo]
Maximo = Max[Nodo]
Maximo = Maximo - Min[Nodo]
indice =(int) (((TopeMemoria - 2.0) * ValorNodo)/ Maximo)
if ( (indice > (TopeMemoria-2)) or (indice < 0)):
print("index overflowed=" + str( indice) + " in the field ="+ str(Nodo) )
indice=TopeMemoria
return indice
############################################################################33
# MAIN
###########################################################################333
W=[float(1.0)]
Hastie_C4_5( W)