INNER CODE UNIT · Python

base_kernel

timeseriesAI/tsai · tsai/callback/core.py:170

        base_kernel = [0.] * half_ks + [1.] + [0.] * half_ks
        kernel_window = gaussian_filter1d(
            base_kernel, sigma=lds_sigma) / max(gaussian_filter1d(base_kernel, sigma=lds_sigma))
    elif lds_kernel == 'triang':
        kernel_window = triang(lds_ks)
    else:
        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

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