def oneItPerceptron(data_generator, weights, labels): errors = 0. num_insts = 0. # your code here for sample in data_generator: feature, label = sample pred = predict(feature, weights, labels)[0] num_insts += 1 if pred != label: errors += 1 for w in feature: weights[(label, w)] += feature[w] weights[(pred, w)] -= feature[w] return weights, errors, num_insts
def oneItPerceptron(data_generator,weights,labels): errors = 0. num_insts = 0. # your code here for sample in data_generator: feature, label = sample pred = predict(feature, weights, labels)[0] num_insts += 1 if pred != label: errors += 1 for w in feature: weights[(label, w)] += feature[w] weights[(pred, w)] -= feature[w] return weights, errors, num_insts
def oneItPerceptron(data_generator, weights, labels): errors = 0. num_insts = 0. for instance, label in data_generator: num_insts += 1 label_pred, scores = predict(instance, weights, labels) if label_pred != label: errors += 1 for word, value in instance.iteritems(): weights[(label, word)] += value weights[(label_pred, word)] -= value return weights, errors, num_insts
def oneItAvgPerceptron(inst_generator, weights, wsum, labels, Tinit=0): errors = 0.0 num_insts = 0.0 t = Tinit for sample in inst_generator: feature, label = sample pred = predict(feature, weights, labels)[0] num_insts += 1 if pred != label: errors += 1 for w in feature: weights[(label, w)] += feature[w] weights[(pred, w)] -= feature[w] wsum[(label, w)] += t * feature[w] wsum[(pred, w)] -= t * feature[w] t += 1 return weights, wsum, errors, num_insts
def oneItAvgPerceptron(inst_generator, weights, wsum, labels, Tinit=0): errors = 0. num_insts = 0. t = Tinit for sample in inst_generator: feature, label = sample pred = predict(feature, weights, labels)[0] num_insts += 1 if pred != label: errors += 1 for w in feature: weights[(label, w)] += feature[w] weights[(pred, w)] -= feature[w] wsum[(label, w)] += t * feature[w] wsum[(pred, w)] -= t * feature[w] t += 1 return weights, wsum, errors, num_insts