INNER CODE UNIT · Python

n_bins

timeseriesAI/tsai · tsai/callback/core.py:190

        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
    if reweight == 'sqrt_inv':
        num_per_label = np.sqrt(num_per_label)
    lds_kernel_window = get_lds_kernel_window(lds_kernel=lds_kernel, lds_ks=lds_ks, lds_sigma=lds_sigma)
    smoothed_value = convolve1d(num_per_label, weights=lds_kernel_window, mode='constant')
    if show_plot:
        plt.bar(bin_edges[:-1], num_per_label / num_per_label.sum(), width=(bin_edges[1]-bin_edges[0]), color='lime', edgecolor='black', label='original')
        plt.plot(bin_edges[:-1], smoothed_value / smoothed_value.sum(), color='red', label='smoothed')
        plt.title(f"Label distribution by bin (reweight={reweight})")
        plt.legend(loc='best')
        plt.show()
    num_per_label = smoothed_value[new_labels.flatten() - 1].reshape(*labels_shape)
    weights = 1 / num_per_label
    weights[num_per_label == 0] = 0
    if max_rel_weight is not None: 

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