Ejemplo n.º 1
0
def makelabel():
	label=[]
	j=1
	final_time="-1"
	for i in range(1,noofrow):
		for j in range(i,noofrow+1):
			if csvlog[i][0]!=csvlog[j][0]:
				j=j-1
				break

		# considering only those traces which ends with "Send for Credit Collection"
		if csvlog[j][3]!="Send for Credit Collection":
			continue

		# finding last event of hte trace
		for j in range(i,noofrow):
			if(j==noofrow-1 or csvlog[j+1][0]!=csvlog[i][0]):
				break

		# taking time diffrence between the two timestamp
		timediff=mydatetime.time_diff(csvlog[i][2],csvlog[j][2])
		label.append(timediff)
		j+=1
	maxtime=1

	# maxtime stores the the maximum time among all the remaining time 
	for i in label:
		maxtime=max(i,maxtime)
	j=0

	# normalising the label data
	for i in label:
		label[j]=i/maxtime
		j+=1
	return label,maxtime
Ejemplo n.º 2
0
        curr_state.append(csvlog[ii][3])
    next_state = curr_state.copy()
    next_state.append(csvlog[i][3])

    event = csvlog[i][3]
    transition = [curr_state, event, next_state]

    # seperate feature and label set on the basis of transition
    for ii in range(len(transitions)):
        if transitions[ii] == transition:
            feature_data[ii].append(encodedlist[count])
            for j in range(i, noofrow):
                if (csvlog[j][0] != csvlog[i][0]):
                    j = j - 1
                    break
            timediff = mydatetime.time_diff(csvlog[i][2], csvlog[j][2])
            label_data[ii].append(timediff)

    # NB[ii][iii] contains number of transition between ii and iii
    for ii in range(len(states)):
        if states[ii] == curr_state:
            for iii in range(len(states)):
                if states[iii] == next_state:
                    NB[ii][iii] += 1
    count += 1

# converting the number into their possiblity
for i in range(len(NB)):
    sumi = 0
    for j in range(len(NB[i])):
        sumi += NB[i][j]