def main():
row1 = 0
count = 0
accuracy = 0
orig_op = []
print "Training Naive-Bayes ..."
tic = time.clock()
file1 = open('CreditTraining.csv')
finattr = "class"
base1 = [[]]
baseT1 = [[]]
basedata = [[]]
basedata1 = [[]]
basedata2 = [[]]
for line in file1:
line = line.strip("\r\n")
basedata.append(line.split(','))
basedata.remove([])
#print " base main "+str(basedata)
parameters = basedata[0]
basedata.remove(parameters)
#import pudb
#pudb.set_trace()
#base1, A2 = DecisionTree.getContinuous(basedata, parameters, parameters[1])
#base1, A3 = DecisionTree.getContinuous(base1, parameters, parameters[2])
#base1, A8 = DecisionTree.getContinuous(base1, parameters, parameters[7])
#base1, A11 = DecisionTree.getContinuous(base1, parameters, parameters[10])
#base1, A14 = DecisionTree.getContinuous(base1, parameters, parameters[13])
#base1, A15 = DecisionTree.getContinuous(base1, parameters, parameters[14])
#print " base final " + str(base1)
#print " A2 " + str(A2)
#print " A3" + str(A3)
#print " A8 " + str(A8)
#print " A11 " + str(A11)
#print " A14 " + str(A14)
#Run ID3'''
tree = DecisionTree.ID(basedata, parameters, finattr)
#print "generated decision tree"+ str(tree)
f = open('Credit.csv')
for line in f:
line = line.strip("\r\n")
basedata1.append(line.split(','))
basedata1.remove([])
#import pudb
#pudb.set_trace()
'''baseT1= DecisionTree.getContinuousTest(basedata1, parameters, parameters[1],A2)
#print "based="+str(basedata1)
#print "baset="+str(baseT1)
baseT1= DecisionTree.getContinuousTest(baseT1, parameters, parameters[2],A3)
baseT1= DecisionTree.getContinuousTest(baseT1, parameters, parameters[7],A8)
#import pudb
#pudb.set_trace()
baseT1= DecisionTree.getContinuousTest(baseT1, parameters, parameters[10],A11)
baseT1= DecisionTree.getContinuousTest(baseT1, parameters, parameters[13],A14)
baseT1= DecisionTree.getContinuousTest(baseT1, parameters, parameters[14],A15)'''
#print " baseT1 " + str(baseT1)
#import pudb
#pudb.set_trace()
for entry in basedata1:
row1 += 1
train_data = tree.copy()
output = ""
#import pudb
#pudb.set_trace()
while(isinstance(train_data, dict)):
root = Node.Node(train_data.keys()[0], train_data[train_data.keys()[0]])
train_data = train_data[train_data.keys()[0]]
index = parameters.index(root.X)
value = entry[index]
if(value in train_data.keys()):
Node.Node(value, train_data[value])
output = train_data[value]
train_data = train_data[value]
else:
#print " value break at " + str(value)
#print "can't process input %s" % count
output = DecisionTree.freq_check(parameters, basedata, parameters[15])
break
orig_op.append(output)
#print ("row%s = %s" % (row1, output))
#print "written program"
f1 = open('classcredit.csv')
for line in f1:
line = line.strip("\r\n")
basedata2.append(line)
basedata2.remove([])
i = 0
for ent in basedata2:
#print "orig_op[i] " + str(orig_op[i]) + "ent = " + ent
if (ent == orig_op[i]):
count += 1
i += 1
accuracy = (int)(count/100)
print "Calculated accuracy for the testing data = "+ str(count)
