evaluation: Minor corrections and text
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		@ -20,7 +20,15 @@ def main(args):
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  # Distribution type
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  dist_type = np.array(range(0, 4))
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  # Number of landmarks
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  lmdk_n = np.array(range(int(.2*args.time), args.time, int(args.time/5)))
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  lmdk_n = np.array(range(0, args.time + 1, int(args.time/5)))
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  markers = [
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    '^', # Symmetric
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    'v', # Skewed
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    'D', # Bimodal
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    's'  # Uniform
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  ]
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  # Initialize plot
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  lmdk_lib.plot_init()
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  # Width of bars
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@ -30,11 +38,13 @@ def main(args):
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  x_margin = bar_width*(len(dist_type)/2 + 1)
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  plt.xticks(x_i, ((lmdk_n/len(seq))*100).astype(int))
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  plt.xlabel('Landmarks (%)')  # Set x axis label.
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  plt.xlim(x_i.min() - x_margin, x_i.max() + x_margin)
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  # plt.xlim(x_i.min() - x_margin, x_i.max() + x_margin)
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  plt.xlim(x_i.min(), x_i.max())
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  # The y axis
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  # plt.yscale('log')
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  plt.ylabel('Euclidean distance')  # Set y axis label.
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  # plt.ylabel('Wasserstein distance')  # Set y axis label.
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  plt.ylim(0, 1)
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  plt.ylabel('Normalized Euclidean distance')  # Set y axis label.
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  # plt.ylabel('Normalized Wasserstein distance')  # Set y axis label.
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  # Bar offset
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  x_offset = -(bar_width/2)*(len(dist_type) - 1)
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  for d_i, d in enumerate(dist_type):
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@ -47,27 +57,41 @@ def main(args):
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    print('(%d/%d) %s... ' %(d_i + 1, len(dist_type), title), end='', flush=True)
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    mae = np.zeros(len(lmdk_n))
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    for n_i, n in enumerate(lmdk_n):
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      for r in range(args.reps):
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      if n == lmdk_n[-1]:
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        break
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      for r in range(args.iter):
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        lmdks = lmdk_lib.get_lmdks(seq, n, d)
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        hist, h = lmdk_lib.get_hist(seq, lmdks)
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        opts = lmdk_sel.get_opts_from_top_h(seq, lmdks)
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        delta = 1.0
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        res, _ = exp_mech.exponential(hist, opts, exp_mech.score, delta, epsilon)
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        mae[n_i] += lmdk_lib.get_norm(hist, res)/args.reps  # Euclidean
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        # mae[n_i] += lmdk_lib.get_emd(hist, res)/args.reps  # Wasserstein
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        mae[n_i] += lmdk_lib.get_norm(hist, res)/args.iter  # Euclidean
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        # mae[n_i] += lmdk_lib.get_emd(hist, res)/args.iter  # Wasserstein
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    mae = mae/21  # Euclidean
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    # mae = mae/11.75  # Wasserstein
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    print('[OK]', flush=True)
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    # Plot bar for current distribution
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    plt.bar(
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      x_i + x_offset,
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    # # Plot bar for current distribution
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    # plt.bar(
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    #   x_i + x_offset,
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    #   mae,
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    #   bar_width,
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    #   label=label,
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    #   linewidth=lmdk_lib.line_width
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    # )
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    # # Change offset for next bar
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    # x_offset += bar_width
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    # Plot line
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    plt.plot(
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      x_i,
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      mae,
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      bar_width,
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      label=label,
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      marker=markers[d_i],
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      markersize=lmdk_lib.marker_size,
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      markeredgewidth=0,
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      linewidth=lmdk_lib.line_width
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    )
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    # Change offset for next bar
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    x_offset += bar_width
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  path = str('../../rslt/lmdk_sel_cmp/' + 'lmdk_sel_cmp-norm')
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  # path = str('../../rslt/lmdk_sel_cmp/' + 'lmdk_sel_cmp-emd')
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  path = str('../../rslt/lmdk_sel_cmp/' + 'lmdk_sel_cmp-norm-l')
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  # path = str('../../rslt/lmdk_sel_cmp/' + 'lmdk_sel_cmp-emd-l')
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  # Plot legend
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  lmdk_lib.plot_legend()
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  # Show plot
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@ -81,7 +105,7 @@ def main(args):
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  Parse arguments.
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  Optional:
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    reps - The number of repetitions.
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    iter - The number of iterations.
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    time - The time limit of the sequence.
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'''
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def parse_args():
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@ -91,7 +115,7 @@ def parse_args():
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  # Mandatory arguments.
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  # Optional arguments.
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  parser.add_argument('-r', '--reps', help='The number of repetitions.', type=int, default=1)
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  parser.add_argument('-i', '--iter', help='The number of iterations.', type=int, default=1)
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  parser.add_argument('-t', '--time', help='The time limit of the sequence.', type=int, default=100)
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  # Parse arguments.
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