joel

#!//usr/bin/env python

max_digit=12
max_big=30

def get_add_operation():
    import random
    a=random.randint(1,max_big)
    b=random.randint(1,a)
    if random.random()<0.5:
        return ('%d+%d?'%(a-b,b),a)
    else:
        return ('%d+%d?'%(b,a-b),a)

def get_mult_operation():
    import random
    a=random.randint(1,max_digit)
    b=random.randint(1,max_digit)
    return ('%dx%d?'%(a,b),a*b)

def get_minus_operation():
    import random
    a=random.randint(1,max_big)
    b=random.randint(1,a)
    return ('%d-%d?'%(a,b),a-b)

def get_div_operation():
    import random
    a=random.randint(1,max_digit)
    b=random.randint(1,max_digit)
    return ('%d/%d?'%(a*b,b),a)

op_funcs = [ get_add_operation, get_mult_operation, get_minus_operation, get_div_operation ]

def get_random_operation():
    return random.choice(op_funcs)()

if __name__ == '__main__':
    import time
    import random
    import cPickle
    import sys
    if len(sys.argv) < 2: raise "Usage : compute.py login"
    ops_per_serie=15
    try:
        f=open("compute_stats_%s.pck" % sys.argv[1],"rb") #read stats on disk
        operations=cPickle.load(f)
        f.close()
        op_list = operations.items()
        op_list.sort(key=lambda i:sum(i[1][0])/len(i[1][0])) #sort by accuracy score
        bad_ops = [a[0] for a in op_list if sum(a[1][0])/len(a[1][0]) < 0.9] #ops the user doesn't master
        op_list.sort(key=lambda e:sum(e[1][1])/len(e[1][1]),reverse=True) #sort by answer time
        bad_ops.extend([a[0] for a in op_list[:10]]) #ops the user is slow to answer
        bad_ops=list(set(bad_ops)) #remove duplicates
    except Exception, e:
        operations={} #operations["5*2"] = [a,b,c,d,e] with a=1. if good, a=0. if wrong storing the 5 last results
    bad_ops=[]
    before = time.time()
    errors = 0
    for i in range(ops_per_serie):
        op_string,expected=get_random_operation()
        if random.random() < len(bad_ops)/(1.5*ops_per_serie):#train user specifically on stuff he doesn't master
            #print "training on bad op"
            op_string,expected = random.choice(bad_ops)
        #print len(bad_ops)/(1.5*ops_per_serie) #badop training probability
        scores,answer_times=operations.get((op_string,expected),([],[]))
        print op_string
        before_op=time.time()
        try:
            answer = int(raw_input())
        except:
            answer = expected-1 # this should be always wrong
        answer_time=time.time()-before_op
        if len(scores) > 5 : scores.pop(0) # keep only 5 scores
        if len(answer_times) > 5 : answer_times.pop(0) # keep only 5 timings
        answer_times.append(answer_time)
        if answer != expected:
            errors += 1
            scores.append(0.)
            operations[(op_string,expected)] = (scores,answer_times)
            print "WRONG!"
        else:
            scores.append(1.)
            operations[(op_string,expected)] = (scores,answer_times)
            print "good."
    total_time = time.time() - before
    print "Time: %.2fs, Errors : %d, Total points : %d" % (total_time,errors,100*ops_per_serie*(5.-errors)/total_time)
    cPickle.dump(operations,open("compute_stats_%s.pck"%sys.argv[1],"wb"))