本文整理汇总了Python中hmm.HMM.classify_instance方法的典型用法代码示例。如果您正苦于以下问题：Python HMM.classify_instance方法的具体用法？Python HMM.classify_instance怎么用？Python HMM.classify_instance使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类hmm.HMM的用法示例。
在下文中一共展示了HMM.classify_instance方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: testSupervisedTraining
# 需要导入模块: from hmm import HMM [as 别名]
# 或者: from hmm.HMM import classify_instance [as 别名]
def testSupervisedTraining(self):
"""Test parameter fitting
Come up with a good non-trivial way to test your training function
You can use the given icecream dataset or make up your own
Add your implementation
"""
seq1 = Instance(label = ['odd', 'even', 'odd', 'even', 'odd'], data = [3, 2, 1, 4, 1])
seq2 = Instance(label = ['even', 'even', 'odd', 'odd', 'even'], data = [2, 4, 1, 3, 2])
seq3 = Instance(label = ['even', 'even', 'odd', 'odd', 'odd'], data = [1, 2, 3, 4, 3])
seq4 = Instance(label = ['odd', 'odd', 'even', 'even', 'even'], data = [4, 3, 4, 1, 2])
instances = [seq1, seq2, seq3, seq4]
hmm = HMM()
hmm.train(instances)
mystery = Instance(data = [2, 1, 3, 4, 2, 2, 1, 3])
labels = hmm.classify_instance(mystery)
self.assertEqual(labels, ['even', 'odd', 'odd', 'even', 'even', 'even', 'odd', 'odd'], 'NOOO')示例2: compute_PER
# 需要导入模块: from hmm import HMM [as 别名]
# 或者: from hmm.HMM import classify_instance [as 别名]
print compute_PER(references, hypotheses)
hmm.print_parameters()
"""
#unsupervised training
#for mode in ['uniform', 'random', 'sneaky']:
for mode in ['sneaky']:
hmm = HMM()
hmm._be_prepared_for_baum_welch(training_set, 'sneaky')
hmm.baum_welch_train(training_set)
#Experiment x.2
hypotheses2 = [hmm.classify_instance(x) for x in training_set]
references2 = [x.old_label for x in training_set]
print "Over Training Set:"
print compute_PER(references2, hypotheses2)
#Experiment x.3
hypotheses = [hmm.classify_instance(x) for x in test_set]
references = [x.label for x in test_set]
print "Over Test Set:"
print compute_PER(references, hypotheses)
hmm.print_parameters()