expt: Ready to run new experiments with HUE
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		@ -23,7 +23,7 @@ def main(args):
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  # The landmarks percentages
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					  # The landmarks percentages
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  lmdks_pct = [0, 20, 40, 60, 80, 100]
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					  lmdks_pct = [0, 20, 40, 60, 80, 100]
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  # Landmarks' thresholds
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					  # Landmarks' thresholds
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  lmdks_th = [0, .13, .15, .23, .3, 10]
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					  lmdks_th = [0, .54, .68, .88, 1.12, 10]
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  # The privacy budget
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					  # The privacy budget
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  epsilon = 1.0
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					  epsilon = 1.0
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@ -45,8 +45,8 @@ def main(args):
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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('Mean absolute error (kWh)')  # Set y axis label.
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					  plt.ylabel('Mean absolute error (kWh)')  # Set y axis label.
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  # plt.yscale('log')
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					  plt.yscale('log')
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  # plt.ylim(0, 8)
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					  plt.ylim(.1, 10000)
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  # Bar offset
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					  # Bar offset
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  x_offset = -(bar_width/2)*(n - 1)
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					  x_offset = -(bar_width/2)*(n - 1)
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@ -86,15 +86,17 @@ def main(args):
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  plt.axhline(
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					  plt.axhline(
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    y = mae_evt,
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					    y = mae_evt,
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					    color = '#212121',
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    linewidth=lmdk_lib.line_width
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					    linewidth=lmdk_lib.line_width
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  )
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					  )
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  plt.text(x_i[-1], mae_evt, '        event')
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					  plt.text(x_i[-1] + x_i[-1]*.14, mae_evt - mae_evt*.14, 'event')
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  plt.axhline(
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					  plt.axhline(
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    y = mae_usr,
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					    y = mae_usr,
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					    color = '#616161',
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    linewidth=lmdk_lib.line_width
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					    linewidth=lmdk_lib.line_width
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  )
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					  )
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  plt.text(x_i[-1], mae_usr, '        user')
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					  plt.text(x_i[-1] + x_i[-1]*.14, mae_usr - mae_usr*.14, 'user')
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  plt.bar(
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					  plt.bar(
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    x_i + x_offset,
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					    x_i + x_offset,
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@ -59,14 +59,15 @@ def main(args):
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            print('[Error: %s]' %(e))
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					            print('[Error: %s]' %(e))
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    except Exception as e:
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					    except Exception as e:
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      print('[Error: %s]' %(e))
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					      print('[Error: %s]' %(e))
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    # Save to results
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					    # Save the first 1000 rows to the results
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    lmdk_lib.save_data(args, np.array(cons, np.float32), 'cons')
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					    cons_data = np.array(cons, np.float32)[:1000]
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					    lmdk_lib.save_data(args, cons_data, 'cons')
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  '''
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					  '''
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    Find thresholds for goals.
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					    Find thresholds for goals.
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    0.2: 0.30
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					    0.2: 1.12
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    0.4: 0.23
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					    0.4: 0.88
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    0.6: 0.15
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					    0.6: 0.68
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    0.8: 0.13
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					    0.8: 0.54
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  '''
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					  '''
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  # {goal: theta}
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					  # {goal: theta}
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  lmdk = {}
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					  lmdk = {}
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@ -110,7 +111,7 @@ def parse_args():
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  # Optional arguments.
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					  # Optional arguments.
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  parser.add_argument('-a', '--arc', help='The data archive file.', type=str, default='/home/manos/Cloud/Data/HUE/Data.zip')
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					  parser.add_argument('-a', '--arc', help='The data archive file.', type=str, default='/home/manos/Cloud/Data/HUE/Data.zip')
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  parser.add_argument('-d', '--dat', help='The consumption data file.', type=str, default='Residential_26.csv')
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					  parser.add_argument('-d', '--dat', help='The consumption data file.', type=str, default='Residential_1.csv')
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  parser.add_argument('-r', '--res', help='The results archive file.', type=str, default='/home/manos/Cloud/Data/HUE/Results.zip')
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					  parser.add_argument('-r', '--res', help='The results archive file.', type=str, default='/home/manos/Cloud/Data/HUE/Results.zip')
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  # Parse arguments.
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					  # Parse arguments.
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