code: Minor corrections for HUE

code/expt/bgt_cmp_hue.py

@@ -46,15 +46,15 @@ def main(args):
   # The y axis
   plt.ylabel('Mean absolute error')  # Set y axis label.
   # plt.yscale('log')
-  plt.ylim(0, 8)
+  # plt.ylim(0, 8)
   # Bar offset
   x_offset = -(bar_width/2)*(n - 1)
 
   mae_u = np.zeros(len(lmdks_pct))
   mae_s = np.zeros(len(lmdks_pct))
   mae_a = np.zeros(len(lmdks_pct))
-  mae_evt = np.zeros(len(lmdks_pct))
-  mae_usr = np.zeros(len(lmdks_pct))
+  mae_evt = 0
+  mae_usr = 0
 
   for i, pct in enumerate(lmdks_pct):
     # Find landmarks
@@ -71,7 +71,7 @@ def main(args):
       # lmdk_bgt.validate_bgts(seq, lmdks, epsilon, bgts_u)
       mae_u[i] += lmdk_bgt.mae_cons(seq, rls_data_u)/args.iter
 
-      # # Adaptive
+      # Adaptive
       rls_data_a, _, _ = lmdk_bgt.adaptive_cons(seq, lmdks, epsilon, .5, .5)
       mae_a[i] += lmdk_bgt.mae_cons(seq, rls_data_a)/args.iter
 
@@ -79,26 +79,24 @@ def main(args):
       # Calculate once
       if i == 0:
         rls_data_evt, _ = lmdk_bgt.uniform_cons(seq, seq[seq[:, 1] < lmdks_th[0]], epsilon)
-      mae_evt[i] += lmdk_bgt.mae_cons(seq, rls_data_evt)/args.iter
+        mae_evt += lmdk_bgt.mae_cons(seq, rls_data_evt)/args.iter
       # User
       # Calculate once
       if i == 0:
         rls_data_usr, _ = lmdk_bgt.uniform_cons(seq, seq[seq[:, 1] < lmdks_th[len(lmdks_th)-1]], epsilon)
-      mae_usr[i] += lmdk_bgt.mae_cons(seq, rls_data_usr)/args.iter
+        mae_usr += lmdk_bgt.mae_cons(seq, rls_data_usr)/args.iter
 
-  plt.plot(
-    x_i,
-    mae_evt,
+  plt.axhline(
+    y = mae_evt,
     linewidth=lmdk_lib.line_width
   )
-  plt.text(x_i[-1], mae_evt[-1], '        event')
+  plt.text(x_i[-1], mae_evt, '        event')
 
-  plt.plot(
-    x_i,
-    mae_usr,
+  plt.axhline(
+    y = mae_usr,
     linewidth=lmdk_lib.line_width
   )
-  plt.text(x_i[-1], mae_usr[-1], '        user')
+  plt.text(x_i[-1], mae_usr, '        user')
 
   plt.bar(
     x_i + x_offset,

code/lib/lmdk_bgt.py

@@ -443,7 +443,7 @@ def adaptive_cons(seq, lmdks, epsilon, inc_rt, dec_rt):
     else:
       skipped += 1
       # Skip current release and approximate with previous
-      rls_data[i] = rls_data[i - 1]
+      rls_data[i] = [p[0], rls_data[i - 1][1]]
       if is_landmark:
         # Allocate the current budget to the following releases uniformly
         for j in range(i + 1, len(seq)):
@@ -538,7 +538,7 @@ def skip_cons(seq, lmdks, epsilon):
     if is_landmark:
       if i > 0:
         # Approximate with previous
-        o = rls_data[i - 1]
+        o[1] = rls_data[i - 1][1]
       bgts[i] = 0
     rls_data[i] = o
   return rls_data, bgts