reproduce/plot_iterate_distance.py [52:76]:
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    epochs = []


    plt.cla()
    fig = plt.figure(figsize=(3.2,2))
    ax = fig.add_subplot(111)
    ax.set_prop_cycle("color", colors)


    for fname, label in zip(data_files, labels):
        print("(ALL) processing file ", fname)
        with open(fname, 'rb') as fdata:
            rd = pickle.load(fdata)
            #pdb.set_trace()
            if 'batch_indices' in rd:
                print("Has batch indices")
                # Calculate x axis for plotting
                batch_indices = np.array(rd["batch_indices"])
                nk = len(batch_indices)
                if max(batch_indices) == min(batch_indices):
                    eval_points = np.array(range(nk))/nk
                else:
                    eval_points = batch_indices/max(batch_indices)

                epochs.append(rd["epoch"])
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reproduce/plot_transform_locking.py [54:77]:
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    epochs = []

    plt.cla()
    fig = plt.figure(figsize=(3.2,2))
    ax = fig.add_subplot(111)
    ax.set_prop_cycle("color", colors)


    for fname, label in zip(data_files, labels):
        print("(ALL) processing file ", fname)
        with open(fname, 'rb') as fdata:
            rd = pickle.load(fdata)
            #pdb.set_trace()
            if 'batch_indices' in rd:
                print("Has batch indices")
                # Calculate x axis for plotting
                batch_indices = np.array(rd["batch_indices"])
                nk = len(batch_indices)
                if max(batch_indices) == min(batch_indices):
                    eval_points = np.array(range(nk))/nk
                else:
                    eval_points = batch_indices/max(batch_indices)

                epochs.append(rd["epoch"])
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