code: Ready for copenhagen
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		@ -343,6 +343,61 @@ def adaptive(seq, lmdks, epsilon, inc_rt, dec_rt):
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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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  '''
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    Adaptive 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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    r = p[2] in lmdks
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    if r:
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      lmdk_cur += 1
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    if lmdk_lib.should_sample(samp_rt) or i == 0:
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      # Add noise to original data
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      o = lmdk_lib.randomized_response(r, bgts[i])
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      rls_data[i] = [r, o]
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      # Adjust sampling rate
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      if i > 0:
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        if rls_data[i - 1][1] == o:
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          # Decrease
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          samp_rt -= samp_rt*dec_rt
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        else:
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          # Increase
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          samp_rt += (1 - samp_rt)*inc_rt
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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 r:
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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 skip(seq, lmdks, epsilon):
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  '''
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    Skip landmarks.
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