INNER CODE UNIT · Python

laplace

timeseriesAI/tsai · tsai/callback/core.py:176

        def laplace(x): return np.exp(-abs(x) / lds_sigma) / (2. * lds_sigma)
        kernel_window = list(map(laplace, np.arange(-half_ks, half_ks + 1))) / \
            max(map(laplace, np.arange(-half_ks, half_ks + 1)))

    return kernel_window


def prepare_LDS_weights(labels, n_bins=None, label_range=None, reweight='inv', lds_kernel='gaussian', lds_ks=9, lds_sigma=1, 
                        max_rel_weight=None, show_plot=True):
    
    assert reweight in {'inv', 'sqrt_inv'}
    labels_shape = labels.shape
    if n_bins is None:
        labels = labels.astype(int)
        n_bins = np.max(labels) - np.min(labels)
    num_per_label, bin_edges = np.histogram(labels, bins=n_bins, range=label_range)
    new_labels = np.searchsorted(bin_edges, labels, side='left')
    new_labels[new_labels == 0] = 1

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