avg_dist: Minor correction
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@ -45,7 +45,7 @@ def main(args):
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# Logging
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print('.', end='', flush=True)
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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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lmdks = lmdk_lib.get_lmdks(seq, n, d)
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# Calculate average distance
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@ -54,7 +54,7 @@ def main(args):
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t_prv, t_nxt = gdp.get_limits(t, seq, lmdks)
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avg_cur += (abs(t - t_prv) - 1 + abs(t - t_nxt) - 1 )/len(seq)
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# Normalized average based on repetitions
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avg_dist[i] += avg_cur/args.reps
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avg_dist[i] += avg_cur/args.iter
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# Rescaling (min-max normalization)
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# https://en.wikipedia.org/wiki/Feature_scaling#Rescaling_(min-max_normalization)
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avg_dist = (avg_dist - avg_dist.min())/(avg_dist.max() - avg_dist.min())
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@ -88,7 +88,7 @@ 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 total 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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@ -98,7 +98,7 @@ 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 total 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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