Minor corrections
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@ -45,7 +45,7 @@ def main(args):
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x_margin = bar_width*(len(dist_type)/2 + 1)
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x_margin = bar_width*(len(dist_type)/2 + 1)
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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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# The y axis
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# The y axis
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plt.ylabel('Privacy loss') # Set y axis label.
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plt.ylabel('Overall privacy loss') # Set y axis label.
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plt.yscale('log')
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plt.yscale('log')
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plt.ylim(epsilon/10, 100*len(seq))
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plt.ylim(epsilon/10, 100*len(seq))
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# plt.ylim(0, 10000)
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# plt.ylim(0, 10000)
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@ -55,15 +55,15 @@ def main(args):
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e = np.zeros(len(lmdk_n))
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e = np.zeros(len(lmdk_n))
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a = np.zeros(len(lmdk_n))
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a = np.zeros(len(lmdk_n))
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for i, n in enumerate(lmdk_n):
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for i, n in enumerate(lmdk_n):
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for r in range(args.reps):
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for r in range(args.iter):
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# Generate landmarks
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# Generate landmarks
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lmdks = lmdk_lib.get_lmdks(seq, n, d)
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lmdks = lmdk_lib.get_lmdks(seq, n, d)
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# Uniform budget allocation
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# Uniform budget allocation
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e_cur = lmdk_bgt.uniform(seq, lmdks, epsilon)
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e_cur = lmdk_bgt.uniform(seq, lmdks, epsilon)
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_, _, a_cur = gdp.tpl_lmdk_mem(e_cur, p, p, seq, lmdks)
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_, _, a_cur = gdp.tpl_lmdk_mem(e_cur, p, p, seq, lmdks)
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# Save privacy loss
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# Save privacy loss
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e[i] += np.sum(e_cur)/args.reps
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e[i] += np.sum(e_cur)/args.iter
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a[i] += np.sum(a_cur)/args.reps
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a[i] += np.sum(a_cur)/args.iter
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# Set label
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# Set label
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label = lmdk_lib.dist_type_to_str(d_i)
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label = lmdk_lib.dist_type_to_str(d_i)
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if d_i == 1:
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if d_i == 1:
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@ -94,21 +94,21 @@ def main(args):
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print(' [OK]', flush=True)
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print(' [OK]', flush=True)
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'''
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def parse_args():
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'''
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Parse arguments.
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Parse arguments.
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Optional:
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Optional:
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reps - The number of repetitions.
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iter - The number of repetitions.
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time - The time limit of the sequence.
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time - The time limit of the sequence.
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'''
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'''
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def parse_args():
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# Create argument parser.
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# Create argument parser.
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parser = argparse.ArgumentParser()
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parser = argparse.ArgumentParser()
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# Mandatory arguments.
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# Mandatory arguments.
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# Optional 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 repetitions.', 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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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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# Parse arguments.
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@ -1,7 +1,7 @@
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\chapter{Related work}
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\chapter{Related work}
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\label{ch:rel}
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\label{ch:rel}
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\kat{Change the way you introduce the related work chapter; do not list a series of surveys. You should speak about the several directions for privacy preserving methods (and then citing the surveys if you want). Then, you should focus on the particular configuration that you are interested in (continual observation). Summarize what we will see in the next sections by giving also the general structure of the chapter.}
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\kat{Change the way you introduce the related work chapter; do not list a series of surveys. You should speak about the several directions for privacy-preserving methods (and then citing the surveys if you want). Then, you should focus on the particular configuration that you are interested in (continual observation). Summarize what we will see in the next sections by giving also the general structure of the chapter.}
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Since the domain of data privacy is vast, several surveys have already been published with different scopes.
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Since the domain of data privacy is vast, several surveys have already been published with different scopes.
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A group of surveys focuses on specific different families of privacy-preserving algorithms and techniques.
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A group of surveys focuses on specific different families of privacy-preserving algorithms and techniques.
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