feature_transforms.py 文件源码

def first_order(feature, aggregates, verbose=False):
    if not type(aggregates) is list:
        aggregates = [aggregates]
    for aggregate in aggregates:
        if verbose:
            print('        first order computation: ' + aggregate)
        if aggregate == 'log':
            feature = np.log(feature)
        elif aggregate == 'sqrt':
            feature = np.sqrt(feature)
        elif aggregate == 'minlog':
            feature = np.log(1 - feature)
        elif aggregate == 'minsqrt':
            feature = np.sqrt(1 - feature)
        elif aggregate == 'mean':
            # feature = np.mean(feature, axis=0)
            feature = np.nanmean(feature, axis=0)
        elif aggregate == 'var':
            feature = np.var(feature, axis=0)
        elif aggregate == 'std':
            # feature = np.std(feature, axis=0)
            feature = np.nanstd(feature, axis=0)
        elif aggregate == 'stdmean':
            feature = np.hstack([np.mean(feature, axis=0), np.std(feature, axis=0)])
        elif aggregate == 'cov':
            feature = np.flatten(np.cov(feature, axis=0))
        elif aggregate == 'totvar':
            feature = np.array([np.mean(np.var(feature, axis=0))])
        elif aggregate == 'totstd':
            feature = np.array([np.mean(np.std(feature, axis=0))])
        elif aggregate == 'entropy':
            feature = feature.flatten()
            feature = np.array([stats.entropy(feature)])
        elif aggregate == 'normentropy':
            feature = feature.flatten()
            feature = np.array([stats.entropy(feature) / np.log(feature.size)])
        elif aggregate == 'information':
            feature = - np.log(feature)

    return feature