final double[] discretizedFeatures = new double[lp.features().size()]; for (int i = 0; i < lp.features().size(); ++i) { discretizedFeatures[i] = Math.floor(lp.features().apply(i) / 16); } return new LabeledPoint(lp.label(), Vectors.dense(discretizedFeatures)); } }); // C...
字符串 Documentation：tsfel.feature_extraction.calc_features.time_series_features_extractor ...返回：...
Different time series synthesis parameters are used to cover a variety of properties (e.g. constant values, modulation of amplitude offsets, periodicity and noise addition). The contribution guidelines to incorporate new features in TSFEL require the introduction of at least one unit test for the ...
time_series_features_extractor(cfg, df)Available featuresStatistical domainFeaturesComputational Cost ECDF 1 ECDF Percentile 1 ECDF Percentile Count 1 ECDF Slope 1 Histogram 1 Interquartile range 1 Kurtosis 1 Max 1 Mean 1 Mean absolute deviation 1 Median 1 Median absolute deviation 1 Min 1 Root ...
importtsfelimportpandasaspd# load datasetdf=pd.read_csv('Dataset.txt')# Retrieves a pre-defined feature configuration file to extract all available featurescfg=tsfel.get_features_by_domain()# Extract featuresX=tsfel.time_series_features_extractor(cfg,df) ...