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| import numpy as np | |
| from scipy.stats import skew, kurtosis | |
| class StatisticalMeasures: | |
| """ | |
| A class dedicated to computing various statistical measures on a given feature array. | |
| This utility class provides a method to compute a predefined set of statistical measures | |
| which are commonly used in data analysis and feature extraction. | |
| Methods: | |
| -------- | |
| compute_statistical_measures(feature_array, measures=None): | |
| Computes selected statistical measures from a provided numerical array. | |
| """ | |
| def compute_statistical_measures(feature_array, measures=None): | |
| if measures is None: | |
| measures = ['mean', 'std', 'var', 'min', 'max', 'range', '25th_percentile', '50th_percentile', '75th_percentile', 'skew', 'kurtosis'] | |
| stats = {} | |
| if 'mean' in measures: | |
| stats['mean'] = np.mean(feature_array) | |
| if 'std' in measures: | |
| stats['std'] = np.std(feature_array) | |
| if 'var' in measures: | |
| stats['var'] = np.var(feature_array) | |
| if 'min' in measures: | |
| stats['min'] = np.min(feature_array) | |
| if 'max' in measures: | |
| stats['max'] = np.max(feature_array) | |
| if 'range' in measures: | |
| stats['range'] = np.ptp(feature_array) | |
| if '25th_percentile' in measures: | |
| stats['25th_percentile'] = np.percentile(feature_array, 25) | |
| if '50th_percentile' in measures: | |
| stats['50th_percentile'] = np.percentile(feature_array, 50) | |
| if '75th_percentile' in measures: | |
| stats['75th_percentile'] = np.percentile(feature_array, 75) | |
| if 'skew' in measures and len(np.unique(feature_array)) > 1: | |
| stats['skew'] = skew(feature_array) | |
| else: | |
| stats['skew'] = np.nan | |
| if 'kurtosis' in measures and len(np.unique(feature_array)) > 1: | |
| stats['kurtosis'] = kurtosis(feature_array) | |
| else: | |
| stats['kurtosis'] = np.nan | |
| return stats | |