本文整理汇总了Python中Classifier.Classifier.predictForTag方法的典型用法代码示例。如果您正苦于以下问题：Python Classifier.predictForTag方法的具体用法？Python Classifier.predictForTag怎么用？Python Classifier.predictForTag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.predictForTag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: run
# 需要导入模块: from Classifier import Classifier [as 别名]
# 或者: from Classifier.Classifier import predictForTag [as 别名]
def run(procId, procCount):
connection = PgSQL.connect(user = "postgres", database = DatabaseName);
memDb = redis.Redis( host='localhost', port=6379 );
TrainDbConfig = DbBuildConfig['train'];
TestDbConfig = DbBuildConfig['test'];
trainDocDb = DocumentsDatabase(connection,
TrainDbConfig['DocTagsTable'],
TrainDbConfig['RawDocTable'],
TrainDbConfig['TagsTable'],
TrainDbConfig['DocumentsTable'] );
testDocDb = DocumentsDatabase(connection,
TestDbConfig['DocTagsTable'],
TestDbConfig['RawDocTable'],
TestDbConfig['TagsTable'],
TestDbConfig['DocumentsTable'] );
trainFeatureDb = FeatureDatabase(connection,
memDb,
trainDocDb,
TrainDbConfig['FeaturesTable'],
TrainDbConfig['DocFeaturesTable'],
TrainDbConfig['TagSpecificFeatureTable']);
testFeatureDb = FeatureDatabase(connection,
memDb,
testDocDb,
TestDbConfig['FeaturesTable'],
TestDbConfig['DocFeaturesTable'],
TestDbConfig['TagSpecificFeatureTable']);
classifier = Classifier(connection, trainFeatureDb, testFeatureDb,
ClassifierTableConfig['predictedTrain'],
ClassifierTableConfig['predictedTest'], trainDocDb);
# if procId == 0:
# classifier.createTables();
# classifier.createTagPredictTables();
# classifier.cleanClassificationTables();
tags = trainDocDb.getTagsList();
count = 0;
for tag in tags:
count = count + 1;
if count % procCount != procId:
continue;
if count < 9000:
continue;
print "Processing ", tag, " ", count;
c1 = trainDocDb.getTagCount(tag);
if c1 <= 23:
continue;
classifier.predictForTag( tag );示例2: run
# 需要导入模块: from Classifier import Classifier [as 别名]
# 或者: from Classifier.Classifier import predictForTag [as 别名]
def run():
connection = PgSQL.connect(user = "postgres", database = DatabaseName);
memDb = redis.Redis( host='localhost', port=6379 );
TrainDbConfig = DbBuildConfig['train'];
TestDbConfig = DbBuildConfig['test'];
trainDocDb = DocumentsDatabase(connection,
TrainDbConfig['DocTagsTable'],
TrainDbConfig['RawDocTable'],
TrainDbConfig['TagsTable'],
TrainDbConfig['DocumentsTable'] );
trainFeatureDb = FeatureDatabase(connection,
memDb,
trainDocDb,
TrainDbConfig['FeaturesTable'],
TrainDbConfig['DocFeaturesTable'],
TrainDbConfig['TagSpecificFeatureTable']);
testFeatureDb = FeatureDatabase(connection,
memDb,
None,
TestDbConfig['FeaturesTable'],
TestDbConfig['DocFeaturesTable'],
TestDbConfig['TagSpecificFeatureTable']);
classifier = Classifier(connection, trainFeatureDb, testFeatureDb,
ClassifierTableConfig['predictedTrain'],
ClassifierTableConfig['predictedTest'], trainDocDb);
# classifier.createTables();
classifier.createTagPredictTables();
classifier.cleanClassificationTables();
tags = trainDocDb.getTagsList();
s1 = 0;
s2 = 0;
for tag in tags:
features = trainFeatureDb.getTagSpecificFeatures( tag );
testTag = tag;
hashes = trainFeatureDb.getTagSpecificFeatures(testTag);
if not hashes:
continue;
c1 = trainDocDb.getTagCount(testTag);
if c1 <= 25:
continue;
s1 += c1;
print classifier.predictForTag( tag );
classifier.saveClassificationResults();