def extract_features(time_series, window):
"""
Extracts three types of features from the time series.
:param time_series: the time series to extract the feature of
:type time_series: pandas.Series
:param window: the length of window
:type window: int
:return: the value of features
:return type: list with float
"""
if not tsd_common.is_standard_time_series(time_series, window):
# add your report of this error here...
return []
# spilt time_series
split_time_series = tsd_common.split_time_series(time_series, window)
# nomalize time_series
normalized_split_time_series = tsd_common.normalize_time_series(
split_time_series)
max_min_normalized_time_series = tsd_common.normalize_time_series_by_max_min(
split_time_series)
s_features = statistical_features.get_statistical_features(
normalized_split_time_series[4])
f_features = fitting_features.get_fitting_features(
normalized_split_time_series)
c_features = classification_features.get_classification_features(
max_min_normalized_time_series)
# combine features with types
features = s_features + f_features + c_features
return features
def extract_features(time_series, window):
"""
Extracts three types of features from the time series.
:param time_series: the time series to extract the feature of
:type time_series: pandas.Series
:param window: the length of window
:type window: int
:return: the value of features
:return type: list with float
"""
if not tsd_common.is_standard_time_series(time_series, window):
# add your report of this error here...
return []
# spilt time_series
split_time_series = tsd_common.split_time_series(time_series, window)
# nomalize time_series
normalized_split_time_series = tsd_common.normalize_time_series(split_time_series)
max_min_normalized_time_series = tsd_common.normalize_time_series_by_max_min(split_time_series)
s_features = statistical_features.get_statistical_features(normalized_split_time_series[4])
f_features = fitting_features.get_fitting_features(normalized_split_time_series)
c_features = classification_features.get_classification_features(max_min_normalized_time_series)
# combine features with types
features = s_features + f_features + c_features
return features