本文整理汇总了Python中weather.Weather.getWeather方法的典型用法代码示例。如果您正苦于以下问题：Python Weather.getWeather方法的具体用法？Python Weather.getWeather怎么用？Python Weather.getWeather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weather.Weather的用法示例。
在下文中一共展示了Weather.getWeather方法的1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: FeatureExtractor
# 需要导入模块: from weather import Weather [as 别名]
# 或者: from weather.Weather import getWeather [as 别名]
class FeatureExtractor(object):
def __init__(self, use_sparse):
self.config = ConfigParser.RawConfigParser()
self.config.read(util.FEATURES_FILE)
self.vectorizer = DictVectorizer(sparse=use_sparse)
if self.config.getboolean(FEATURE_SELECTION, 'Cluster'):
# Vectorizer without cluster features.
self.precluster_vectorizer = DictVectorizer(sparse=use_sparse)
self.clusterer = MiniBatchKMeans(n_clusters=15, init='k-means++')
if self.config.getboolean(FEATURE_SELECTION, 'DailyWeather') or \
self.config.getboolean(FEATURE_SELECTION, 'HourlyWeather') or \
self.config.getboolean(FEATURE_SELECTION, 'Zone_DayOfWeek_HourlyWeather'):
self.weather_data = Weather()
if util.VERBOSE:
feature_list = self.getFeatureList()
print 'Feature Template List:'
for feature in feature_list:
print '\t%s' % feature
def getFeatureList(self):
feature_list = []
for feature in self.config.options(FEATURE_SELECTION):
if self.config.getboolean(FEATURE_SELECTION, feature):
feature_list.append(feature)
return feature_list
def getFeatureVectors(self, X, is_test=False):
'''
Transform input list of training examples from a list of dicts to a
numpy array or scipy sparse matrix for input into an sklearn model.
:param X: a list of training examples, represented as a list of dicts
where each dict maps column names to column values.
:param use_sparse: boolean for whether the return value should be
represented as a sparse matrix.
:return: scipy matrix representing the training data.
'''
feature_dicts = [self.getFeatureDict(x) for x in X]
# If clustering is enabled, compute the centroids, then
# append the nearest centroid ID to each feature vector.
if self.config.getboolean(FEATURE_SELECTION, 'Cluster'):
self._appendClusterFeatures(feature_dicts, is_test)
transformed = self.vectorizer.transform(feature_dicts) \
if is_test \
else self.vectorizer.fit_transform(feature_dicts)
return transformed
def getFeatureNameIndices(self):
"""
:return: dict mapping feature names to indices.
"""
return self.vectorizer.vocabulary_
def _extractZone(self, x, feature_dict):
feature_dict['Zone'] = str(x['zone_id'])
def _extractHourOfDay(self, x, feature_dict):
# Pad hours < 10 with a leading zero.
feature_dict['HourOfDay'] = '%02d' % x['start_datetime'].hour
def _extractDayOfWeek(self, x, feature_dict):
# Pad day of week with a leading zero.
feature_dict['DayOfWeek'] = '%02d' % x['start_datetime'].weekday()
def _extractZoneHourOfDay(self, x, feature_dict):
feature_dict['Zone_HourOfDay'] = '%d_%02d' % (x['zone_id'], x['start_datetime'].hour)
# Concatenate the zone, day of week, and hour of day.
def _extractZoneDayHour(self, x, feature_dict):
# Pad day of week with a leading zero.
# Pad hours < 10 with a leading zero.
feature_dict['Zone_DayOfWeek_Hour'] = '%d_%02d_%02d' % \
(x['zone_id'], x['start_datetime'].weekday(), x['start_datetime'].hour)
def _extractZoneDayOfWeekHourlyWeather(self, x, feature_dict):
hourly_weather = self.weather_data.getHourlyWeather(x['start_datetime'])
rainfallValue = self._getHourlyRainfallValue(hourly_weather['PRCP'], True)
feature_dict['Zone_DayOfWeek_HourlyRainfall'] = '%d_%02d_%s' % \
(x['zone_id'], x['start_datetime'].weekday(), rainfallValue)
def _extractCluster(self, x, feature_dict):
feature_dict['Cluster'] = str(x['cluster_id'])
def _extractDailyWeather(self, x, feature_dict):
daily_weather = self.weather_data.getWeather(x['start_datetime'])
feature_dict['DailyRainfall'] = self._getDailyRainfallValue(daily_weather['PRCP'])
def _getDailyRainfallValue(self, rainfall):
if rainfall == 0:
return 'No_rainfall'
elif rainfall < 100:
return 'Less_than_1_inch'
else:
#.........这里部分代码省略.........