lmdk_bgt: Tested dynamic
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		@ -343,6 +343,58 @@ def adaptive(seq, lmdks, epsilon, inc_rt, dec_rt):
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  return rls_data, bgts, skipped
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					  return rls_data, bgts, skipped
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					def dynamic(seq, lmdks, epsilon, inc_rt, dec_rt):
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					  '''
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					    Dynamic budget allocation.
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					    Parameters:
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					      seq - The point sequence.
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					      lmdks - The landmarks.
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					      epsilon - The available privacy budget.
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					      inc_rt - Sampling rate increase rate.
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					      dec_rt - Sampling rate decrease rate.
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					    Returns:
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					      rls_data - The perturbed data.
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					      bgts - The privacy budget allocation.
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					      skipped - The number of skipped releases.
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					  '''
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					  # Uniform budget allocation
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					  bgts = uniform(seq, lmdks, epsilon)
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					  # Released
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					  rls_data = [None]*len(seq)
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					  # The sampling rate
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					  samp_rt = 1
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					  # Track landmarks
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					  lmdk_cur = 0
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					  # Track skipped releases
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					  skipped = 0
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					  for i, p in enumerate(seq):
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					    # Check if current point is a landmark
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					    if lmdk_lib.is_landmark(p, lmdks):
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					      lmdk_cur += 1
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					    # Get coordinates
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					    loc = (p[1], p[2])
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					    if i > 0:
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					      eps_dis = bgts[i]*.5
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					      dis = lmdk_lib.add_laplace_noise(distance((rls_data[i - 1][1], rls_data[i - 1][2]), loc).km*1000, 1.0, eps_dis)
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					      bgts[i] -= eps_dis
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					    if i == 0 or distance((rls_data[i - 1][1], rls_data[i - 1][2]), loc).km*1000 > 1/bgts[i]:
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					      # Add noise to original data
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					      new_loc = lmdk_lib.add_polar_noise(loc, bgts[i])
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					      rls_data[i] = [p[0], new_loc[0], new_loc[1], p[3]]
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					    else:
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					      skipped += 1
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					      # Skip current release and approximate with previous
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					      rls_data[i] = rls_data[i - 1]
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					      if lmdk_lib.is_landmark(p, lmdks):
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					        # Allocate the current budget to the following releases uniformly
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					        for j in range(i + 1, len(seq)):
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					          bgts[j] += bgts[i]/(len(lmdks) - lmdk_cur + 1)
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					      # No budget was spent
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					      bgts[i] = 0
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					  return rls_data, bgts, skipped
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def adaptive_cont(seq, lmdks, epsilon, inc_rt, dec_rt):
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					def adaptive_cont(seq, lmdks, epsilon, inc_rt, dec_rt):
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  '''
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					  '''
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    Adaptive budget allocation.
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					    Adaptive budget allocation.
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