138 lines
3.6 KiB
Python
138 lines
3.6 KiB
Python
#!/usr/bin/env python3
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import sys
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sys.path.insert(1, '../lib')
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import argparse
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import lmdk_lib
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import lmdk_sel
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import exp_mech
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import numpy as np
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import os
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from matplotlib import pyplot as plt
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import time
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def main(args):
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# Privacy goal
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epsilon = 1.0
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# Number of timestamps
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seq = lmdk_lib.get_seq(1, args.time)
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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(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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bar_width = 1/(len(dist_type) + 1)
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# The x axis
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x_i = np.arange(len(lmdk_n))
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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_i.max())
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# The y axis
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# plt.yscale('log')
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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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# Set label
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label = lmdk_lib.dist_type_to_str(d)
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if d_i == 1:
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label = 'Skewed'
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# Logging
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title = label + ' landmark distribution'
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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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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.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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# 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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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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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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# plt.show()
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# Save plot
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lmdk_lib.save_plot(path + '.pdf')
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'''
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Parse arguments.
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Optional:
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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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# Create argument parser.
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parser = argparse.ArgumentParser()
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# Mandatory arguments.
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# Optional arguments.
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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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args = parser.parse_args()
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return args
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if __name__ == '__main__':
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try:
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start_time = time.time()
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main(parse_args())
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end_time = time.time()
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print('##############################')
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print('Time elapsed: %s' % (time.strftime('%H:%M:%S', time.gmtime(end_time - start_time))))
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print('##############################')
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except KeyboardInterrupt:
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print('Interrupted by user.')
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exit()
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