本文整理汇总了Python中classifier.Classifier.load方法的典型用法代码示例。如果您正苦于以下问题：Python Classifier.load方法的具体用法？Python Classifier.load怎么用？Python Classifier.load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classifier.Classifier的用法示例。
在下文中一共展示了Classifier.load方法的4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: test_save_load
# 需要导入模块: from classifier import Classifier [as 别名]
# 或者: from classifier.Classifier import load [as 别名]
def test_save_load(self):
sentences = split_sentences(self.text)
sentencesWithoutStemming = remove_stemming (sentences)
allBigrams = defaultdict(int)
for s in sentencesWithoutStemming:
newBigrams = make_bigrams(s)
merge_and_sum_bigrams(allBigrams, newBigrams)
self.classifier.update_joint_apriori(allBigrams)
for k,v in self.classifier.apriori.items():
print k,v
self.classifier.save('testC')
newClassifier = Classifier()
newClassifier.load('testC')
#self.assertDictEqual(self.classifier.apriori, newClassifier.apriori)
print '\nCOMPARE\n'
for k,v in self.classifier.apriori.items():
print k,v
for k,v in newClassifier.apriori.items():
print k,v
print '\nEND OF COMPARE\n'示例2: main
# 需要导入模块: from classifier import Classifier [as 别名]
# 或者: from classifier.Classifier import load [as 别名]
def main():
args = parser.parse_args()
data_json = read_dataset(args.data)
random.shuffle(data_json)
training_set_ratio = 0.7
training_set_size = int(training_set_ratio * len(data_json) + 0.5)
training_set = data_json[:training_set_size]
test_set = data_json[training_set_size:]
processor = TextProcessor()
classifier = Classifier(processor)
classifier.train(training_set)
print classifier.dump() == Classifier.load(classifier.dump(), processor).dump()示例3: usage
# 需要导入模块: from classifier import Classifier [as 别名]
# 或者: from classifier.Classifier import load [as 别名]
classifier = "classifier.pickle"
opts, args = getopt.getopt(sys.argv[1:], "hc:d:k:s:e:")
for o, a in opts:
if o == "-d":
db = a
elif o == "-c":
classifier = a
elif o == "-k":
keywords.append(a)
elif o == "-s":
start = datetime.strptime(a, "%Y-%M-%d")
elif o == "-e":
end = datetime.strptime(a, "%Y-%M-%d")
else:
usage()
sys.exit(0)
classifier = Classifier.load(classifier)
aggregator = RetweetWeightedAggregator()
ts = TweetStore(db)
for t in ts.get(keywords, start, end):
s = classifier.classify(t)
print("%s -- sentiment: %s" % (tweet.to_ascii(t)[tweet.TEXT], "positive" if (s == 1) else "negative"))
aggregator.add(t, s)
print("Aggregated sentiment: %f" % aggregator.get_sentiment())
print("ID of last tweet: %d" % aggregator.get_last_id())
print("Total number of tweets: %d" % aggregator.get_num())
示例4: Classifier
# 需要导入模块: from classifier import Classifier [as 别名]
# 或者: from classifier.Classifier import load [as 别名]
## - Comp 4710 - Data Mining
## - Prof: Dr. Carson K. Leung
## - Authors: Trevor Blanchard, Stefan Harris, Brett Small, Sam Peers
## - Sentiment Miner
## - December 10, 2015
## - An interactive classifier
import sys
from classifier import Classifier
print "\nPlease wait while the training data is loaded.."
myClassifier = Classifier()
myClassifier.load()
print "Ready for input"
filename = raw_input("Enter a file name or a directory (type \"quit\" to quit) > ")
while filename != "quit":
if ".txt" in filename:
with open(filename, 'r') as infile:
clsfy = myClassifier.classify(infile)
if clsfy > 0:
print "Positive! Weight = {0}".format(clsfy)
elif clsfy < 0:
print "Negative! Weight = {0}".format(clsfy)
elif clsfy == 0:
print "Undertermined"
else: