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']]
def entropy(rows):
    from math import log
    log2=lambda x:log(x)/log(2)
    results=uniquecounts(rows)
    # Now calculate the entropy
    ent=0.0
    for r in results.keys():
        p=float(results[r])/len(rows)
        ent=ent-p*log2(p)
    return ent
def buildtree(rows,scoref=entropy):
    if len(rows)==0: return decisionnode()
    current_score=scoref(rows)
    # promenlivi so koi sledime koj kriterium e najdobar
    best_gain=0.0
    best_criteria=None
    best_sets=None
    column_count=len(rows[0])-1
    for col in range(0,column_count): # za sekoja kolona (col se dvizi vo intervalot 0 do column_count-1)
        # Sledniov ciklus e za generiranje na recnik od razlicni vrednosti vo ovaa kolona
        column_values={}
        for row in rows:
            column_values[row[col]]=1
            # za sekoja redica se zema vrednosta vo ovaa kolona i se postavuva kako kluc vo column_values
            # za sekoja razlicna vrednost na ovaa kolona se pravi podelba na mnozestvoto
        for value in column_values.keys():
            (set1,set2)=divideset(rows,col,value)
            # za konkretnata podelba se presmetuva informaciona dobivka (gain)
            p=float(len(set1))/len(rows) # verojatnost da se pogodi tekovnata vrednost na ovoj atribut
            gain=current_score-p*scoref(set1)-(1-p)*scoref(set2)
            if gain>best_gain and len(set1)>0 and len(set2)>0:
                # ovaa e podelba e najadobra do sega pa ke ja zacuvame
                best_gain=gain
                best_criteria=(col,value)
                best_sets=(set1,set2)
    # gradenje na podgrankite
    if best_gain>0:
        # bidejki dobivkata e >0 mozna e druga podelba
        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:
        #ne e mozna druga podelba bidejki site instanci vo ova mnozestvo ja imaat istata klasa
        #rezultato sto ke se vrati ke sodrzi recnik so kluc klasata koja se predviduva
        #vo ovoj list i vrednost kolku pati bila sretnata vo trening mnozestvoto
        return decisionnode(results=uniquecounts(rows))
def printtree(tree,indent=''):
    # Ako ova e list?
    if tree.results!=None:
        print str(tree.results)
    else:
        # se pecati uslovot (indeks na kolonata i vrednost)
        print str(tree.col)+':'+str(tree.value)+'? '
        # Se pecatat True grankite, pa potoa F grankite
        print indent+'T->',
        printtree(tree.tb,indent+' ')
        print indent+'F->',
        printtree(tree.fb,indent+' ')

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)