trainingData=[['slashdot','USA','yes',18,'None'],
        ['google','France','yes',23,'Premium'],
        ['google','France','yes',23,'Basic'],
        ['google','France','yes',23,'Basic'],
        ['digg','USA','yes',24,'Basic'],
        ['kiwitobes','France','yes',23,'Basic'],
        ['google','UK','no',21,'Premium'],
        ['(direct)','New Zealand','no',12,'None'],
        ['(direct)','UK','no',21,'Basic'],
        ['google','USA','no',24,'Premium'],
        ['slashdot','France','yes',19,'None'],
        ['digg','USA','no',18,'None'],
        ['google','UK','no',18,'None'],
        ['kiwitobes','UK','no',19,'None'],
        ['digg','New Zealand','yes',12,'Basic'],
        ['slashdot','UK','no',21,'None'],
        ['google','UK','yes',18,'Basic'],
        ['kiwitobes','France','yes',19,'Basic']]


class decisionnode:
    def __init__(self, col=-1, value=None, results=None, tb=None, fb=None):
        self.col = col
        self.value = value
        self.results = results
        self.tb = tb
        self.fb = fb



def sporedi_broj(value1, value2):
    return value1 >= value2


def sporedi_string(value1, value2):
    return value1 == value2


# In[43]:


def get_compare_func(value):
    if isinstance(value, int) or isinstance(value, float):
        comparer = sporedi_broj
    else:
        comparer = sporedi_string
    return comparer


def compare_values(v1, v2):
    sporedi = get_compare_func(v1)
    return sporedi(v1, v2)


def divideset(rows, column, value):
    sporedi = get_compare_func(value)
    #     print(split_function)
    # Divide the rows into two sets and return them
    set_false = []
    set_true = []
    for row in rows:
        uslov = sporedi(row[column], value)
        #         print(column, value, row[column], uslov, row)
        if uslov:
            set_true.append(row)
        else:
            set_false.append(row)
    #     print(len(set_true), len(set_false))
    #     set_true = [row for row in rows if
    #             split_function(row, column, value)]  # za sekoj row od rows za koj split_function vrakja true
    #     set_false = [row for row in rows if
    #             not split_function(row, column, value)]  # za sekoj row od rows za koj split_function vrakja false
    return (set_true, set_false)


def uniquecounts(rows):
    d = {}
    for r in rows:
        #         print(r[-1])
        d.setdefault(r[-1], 0)
        d[r[-1]] += 1
    return d


from math import log

log2 = lambda x: log(x) / log(2)


def entropy(rows):
    results = uniquecounts(rows)
    # Now calculate the entropy
    ent = 0.0
    n = len(rows)
    for label, cnt in results.items():
        #         print(r)
        p = float(cnt) / n
        #         print(label, cnt, p)
        ent -= p * log2(p)
    return ent


def info_gain(current_score, sets, scoref=entropy):
    m = sum([len(s) for s in sets])
    gain = current_score
    for s in sets:
        n = len(s)
        p = 1. * n / m
        gain -= p * scoref(s)
    return gain


def buildtree(rows, scoref=entropy):
    if len(rows) == 0: return decisionnode()
    current_score = scoref(rows)

    # Set up some variables to track the best criteria
    best_gain = 0.0
    best_criteria = None
    best_sets = None

    column_count = len(rows[0]) - 1
    for col in range(0, column_count):
        # Generate the list of different values in
        # this column
        column_values = {}
        for row in rows:
            column_values[row[col]] = 1

        # Now try dividing the rows up for each value
        # in this column
        for value in column_values.keys():
            (set1, set2) = divideset(rows, col, value)

            # Information gain
            p = float(len(set1)) / len(rows)
            gain = current_score - p * scoref(set1) - (1 - p) * scoref(set2)
            if gain > best_gain and len(set1) > 0 and len(set2) > 0:
                best_gain = gain
                best_criteria = (col, value)
                best_sets = (set1, set2)

    # Create the subbranches
    if best_gain > 0:
        trueBranch = buildtree(best_sets[0])
        falseBranch = buildtree(best_sets[1])
        return decisionnode(col=best_criteria[0], value=best_criteria[1], tb=trueBranch, fb=falseBranch)
    else:
        return decisionnode(results=uniquecounts(rows))


def printtree(tree, indent='',level=0):
    # Is this a leaf node?
    if tree.results != None:
        print(tree.results)
    else:
        # Print the criteria
        print(str(tree.col) + ':' + str(tree.value) + '? ' +'Level=%d' % (level))
        # Print the branches
        print(indent + 'T->'),
        printtree(tree.tb,indent +'  ',level+1)
        print(indent+'F->'),
        printtree(tree.fb,indent+'  ',level+1)


def classify(observation, tree):
    if tree.results != None:
        #         return tree.results
        if len(tree.results) == 1:
            return list(tree.results.keys())[0], 1.0
        else:
            inv = sorted([(cnt, label) for label, cnt in tree.results.items()], reverse=True)
            label = inv[0][1]
            cnt = inv[0][0]
            total_count = sum(tree.results.values())
            return label, cnt / total_count
    #         return tree.results
    else:
        vrednost = observation[tree.col]
        if compare_values(vrednost, tree.value):
            branch = tree.tb
        else:
            branch = tree.fb
        return classify(observation, branch)


if __name__ == "__main__":
    # referrer='slashdot'
    # location='US'
    # readFAQ='no'
    # pagesVisited=19
    # serviceChosen='None'

    referrer=input()
    location=input()
    readFAQ=input()
    pagesVisited=input()
    serviceChosen=input()

    testCase=[referrer, location, readFAQ, pagesVisited, serviceChosen]
    trainingData.append(testCase)
    t=buildtree(trainingData)
    printtree(t)