ys1 = [] ys2 = [] tally = [] for r in range(r_start, r_end + 1): total = 0 maximum = 0 count = 0 worker = Worker(str(uuid.uuid1()), 0, p, r, 1, 1) worker.addNoise(0, 0.1) for i in range(0, runs): ts = [] cs = [] for j in range(0, horizon): task = tasks[j] answer = worker.doTask(task, outcomes) if answer == task: count += 1 ts.append(j + 1) cs.append(count) learning = learn.learnCurve(cs, ts) err = learning['e'] #print err #print learning['r'] if math.isnan(err): continue if err < threshold: total += j + 1 tally.append(j+1) if j + 1 > maximum: maximum = j + 1
cqs = [] #cumulative quality qs = [] #quality aqs = [] #average quality ecqs = [] #estimated by linear regression eqs = [] #estimated by linear regression fqs = [] ###for learning cs = [] ts = [] errs = [] count = 0 for i in range(0, len(tasks)): task = tasks[i] answer = worker.doTask(task) if answer == task: count += 1 cqs.append(worker.getCumulativeQuality(i + 1)) qs.append(worker.getQuality()) aqs.append(float(count) / float(i + 1)) #learn ts.append(i + 1) cs.append(count) learning = learn.learnCurve(cs, ts) fake = Worker(str(uuid.uuid1()), i+1, learning['p'], learning['r'], 1, 1) ecqs.append(fake.getCumulativeQuality(i+1)) eqs.append(fake.getQuality()) errs.append(learning['e']) if i == 0: fqs.append(float(count) / float(i+1))
