feat: make a set implementation for stack

DiscreteENN_TSP_table.csv

@@ -1,18 +1,18 @@
 name,points,error,time(us),time per city(us),distance,optimal_distance
-berlin52,52,8.610000,88.000000,1.690000,8191.640137,7542.000000
-st70,70,4.720000,192.000000,2.740000,706.890015,675.000000
-eil76,76,5.760000,284.000000,3.740000,569.010010,538.000000
-pr76,76,5.540000,171.000000,2.250000,114149.859375,108159.000000
-kroA100,100,4.290000,271.000000,2.710000,22195.070312,21282.000000
-kroC100,100,3.920000,322.000000,3.220000,21562.000000,20749.000000
-kroD100,100,4.410000,339.000000,3.390000,22233.359375,21294.000000
-rd100,100,6.930000,367.000000,3.670000,8458.240234,7910.000000
-eil101,101,7.650000,376.000000,3.720000,677.090027,629.000000
-lin105,105,3.940000,419.000000,3.990000,14945.750000,14379.000000
-ch130,130,4.350000,547.000000,4.210000,6375.870117,6110.000000
-ch150,150,6.230000,723.000000,4.820000,6934.439941,6528.000000
-tsp225,225,5.100000,1611.000000,7.160000,4115.830078,3916.000000
-a280,279,6.170000,2786.000000,9.990000,2738.070068,2579.000000
-pcb442,442,6.720000,6590.000000,14.910000,54189.570312,50778.000000
-pr1002,1002,8.620000,29981.000000,29.920000,281368.750000,259045.000000
-pr2392,2392,9.290000,192723.000000,80.570000,413165.906250,378032.000000
+berlin52,52,5.720000,159.000000,3.060000,7973.629883,7542.000000
+st70,70,4.030000,238.000000,3.400000,702.210022,675.000000
+eil76,76,6.100000,274.000000,3.610000,570.799988,538.000000
+pr76,76,4.440000,204.000000,2.680000,112965.257812,108159.000000
+kroA100,100,3.480000,528.000000,5.280000,22022.210938,21282.000000
+kroC100,100,4.320000,359.000000,3.590000,21645.830078,20749.000000
+kroD100,100,5.920000,558.000000,5.580000,22554.320312,21294.000000
+rd100,100,2.960000,443.000000,4.430000,8143.919922,7910.000000
+eil101,101,5.420000,582.000000,5.760000,663.059998,629.000000
+lin105,105,3.860000,517.000000,4.920000,14933.759766,14379.000000
+ch130,130,5.600000,693.000000,5.330000,6451.919922,6110.000000
+ch150,150,6.210000,948.000000,6.320000,6933.390137,6528.000000
+tsp225,225,4.660000,4462.000000,19.830000,4098.509766,3916.000000
+a280,279,7.330000,2807.000000,10.060000,2768.139893,2579.000000
+pcb442,442,7.820000,6795.000000,15.370000,54749.230469,50778.000000
+pr1002,1002,7.480000,62663.000000,62.540001,278407.812500,259045.000000
+pr2392,2392,8.310000,199357.000000,83.339996,409444.718750,378032.000000

DiscreteENN_TSP_table.txt

@@ -1,18 +1,18 @@
 name points error time(us) time per city(us) distance optimal_distance
-berlin52 52 8.610000 88.000000 1.690000 8191.640137 7542.000000
-st70 70 4.720000 192.000000 2.740000 706.890015 675.000000
-eil76 76 5.760000 284.000000 3.740000 569.010010 538.000000
-pr76 76 5.540000 171.000000 2.250000 114149.859375 108159.000000
-kroA100 100 4.290000 271.000000 2.710000 22195.070312 21282.000000
-kroC100 100 3.920000 322.000000 3.220000 21562.000000 20749.000000
-kroD100 100 4.410000 339.000000 3.390000 22233.359375 21294.000000
-rd100 100 6.930000 367.000000 3.670000 8458.240234 7910.000000
-eil101 101 7.650000 376.000000 3.720000 677.090027 629.000000
-lin105 105 3.940000 419.000000 3.990000 14945.750000 14379.000000
-ch130 130 4.350000 547.000000 4.210000 6375.870117 6110.000000
-ch150 150 6.230000 723.000000 4.820000 6934.439941 6528.000000
-tsp225 225 5.100000 1611.000000 7.160000 4115.830078 3916.000000
-a280 279 6.170000 2786.000000 9.990000 2738.070068 2579.000000
-pcb442 442 6.720000 6590.000000 14.910000 54189.570312 50778.000000
-pr1002 1002 8.620000 29981.000000 29.920000 281368.750000 259045.000000
-pr2392 2392 9.290000 192723.000000 80.570000 413165.906250 378032.000000
+berlin52 52 5.720000 159.000000 3.060000 7973.629883 7542.000000
+st70 70 4.030000 238.000000 3.400000 702.210022 675.000000
+eil76 76 6.100000 274.000000 3.610000 570.799988 538.000000
+pr76 76 4.440000 204.000000 2.680000 112965.257812 108159.000000
+kroA100 100 3.480000 528.000000 5.280000 22022.210938 21282.000000
+kroC100 100 4.320000 359.000000 3.590000 21645.830078 20749.000000
+kroD100 100 5.920000 558.000000 5.580000 22554.320312 21294.000000
+rd100 100 2.960000 443.000000 4.430000 8143.919922 7910.000000
+eil101 101 5.420000 582.000000 5.760000 663.059998 629.000000
+lin105 105 3.860000 517.000000 4.920000 14933.759766 14379.000000
+ch130 130 5.600000 693.000000 5.330000 6451.919922 6110.000000
+ch150 150 6.210000 948.000000 6.320000 6933.390137 6528.000000
+tsp225 225 4.660000 4462.000000 19.830000 4098.509766 3916.000000
+a280 279 7.330000 2807.000000 10.060000 2768.139893 2579.000000
+pcb442 442 7.820000 6795.000000 15.370000 54749.230469 50778.000000
+pr1002 1002 7.480000 62663.000000 62.540001 278407.812500 259045.000000
+pr2392 2392 8.310000 199357.000000 83.339996 409444.718750 378032.000000

plot_data.py

@@ -45,25 +45,23 @@ def plotData(data_file, use_pandas: bool, sequence: bool):
  x = np.arange(1, len(x_labels) + 1)
 
  plt.figure(figsize=(10, 10)) # Optional: Adjusts the figure size
- plt.subplot(131)
+ plt.subplot(311)
  plt.plot(x, y1, color=color, marker='o', linestyle='-.') # Adjust markers and lines as needed
  plt.title(f"Time ({time_str}) / N")
- plt.xlabel("Cities") # Adjust with actual column name or description
  # plt.ylabel(f"Time ({time_str})") # Adjust with actual column name or description
  plt.grid(show_grid) # Optional: Adds a grid
  if sequence:
  axis = plt.gca()
- plt.xticks(x, labels = x_labels, rotation='vertical')
- plt.subplot(132)
+ plt.xticks(x)
+ plt.subplot(312)
  plt.plot(x, y2, color=color, marker='o', linestyle='-.') # Adjust markers and lines as needed
  plt.title(f"Time ({time_str}) / $N^2$")
- plt.xlabel('Cities') # Adjust with actual column name or description
  # plt.ylabel(f"Time ({time_str})") # Adjust with actual column name or description
  plt.grid(show_grid) # Optional: Adds a grid
  if sequence:
  axis = plt.gca()
- plt.xticks(x, labels = x_labels, rotation='vertical')
- plt.subplot(133)
+ plt.xticks(x)
+ plt.subplot(313)
  plt.plot(x, y3, color=color, marker='o', linestyle='-.') # Adjust markers and lines as needed
  plt.title(f"Time ({time_str}) / $N \cdot log(N)$")
  plt.xlabel('Cities') # Adjust with actual column name or description
@@ -76,6 +74,7 @@ def plotData(data_file, use_pandas: bool, sequence: bool):
  plt.suptitle("Data from current implementation")
  else:
  plt.suptitle("Data from paper")
+ plt.tight_layout()
  plt.show()
 
 def plotDataBoth(data_file1, data_file2, sequence: bool):
@@ -112,43 +111,50 @@ def plotDataBoth(data_file1, data_file2, sequence: bool):
  y2_2 = y2/(x2*x2)
  y2_3 = y2/(x2*np.log(x2))
 
- plt.figure(figsize=(10, 10)) # Optional: Adjusts the figure size
+ fig, ax = plt.subplots(3, 1, figsize=(10, 10)) # Optional: Adjusts the figure size
  print(f"{x}\n{y1_1}")
  print(f"{x}\n{y2_1}")
- plt.subplot(131)
- plt.plot(x, y1_1, color=color1, marker='o', linestyle='-.') # Adjust markers and lines as needed
- plt.plot(x, y2_1, color=color2, marker='o', linestyle='-.') # Adjust markers and lines as needed
- plt.title(f"Time ({time_str}) / N")
- plt.xlabel("Cities") # Adjust with actual column name or description
+ # plt.subplot(311)
+ ax[0].plot(x, y1_1, color=color1, label="Paper", marker='o', linestyle='-.') # Adjust markers and lines as needed
+ ax[0].plot(x, y2_1, color=color2, label="New implementation", marker='o', linestyle='-.') # Adjust markers and lines as needed
+ ax[0].set_title(f"Time ({time_str}) / N")
  # plt.ylabel(f"Time ({time_str})") # Adjust with actual column name or description
  plt.grid(show_grid) # Optional: Adds a grid
  if sequence:
  axis = plt.gca()
- plt.xticks(x, labels = x_labels, rotation='vertical')
+ plt.xticks(x)
  print(f"{x}\n{y1_2}")
  print(f"{x}\n{y2_2}")
- plt.subplot(132)
- plt.plot(x, y1_2, color=color1, marker='o', linestyle='-.') # Adjust markers and lines as needed
- plt.plot(x, y2_2, color=color2, marker='o', linestyle='-.') # Adjust markers and lines as needed
- plt.title(f"Time ({time_str}) / $N^2$")
- plt.xlabel('Cities') # Adjust with actual column name or description
+ # plt.subplot(312)
+ ax[1].plot(x, y1_2, color=color1, marker='o', linestyle='-.') # Adjust markers and lines as needed
+ ax[1].plot(x, y2_2, color=color2, marker='o', linestyle='-.') # Adjust markers and lines as needed
+ ax[1].set_title(f"Time ({time_str}) / $N^2$")
  # plt.ylabel(f"Time ({time_str})") # Adjust with actual column name or description
  plt.grid(show_grid) # Optional: Adds a grid
  if sequence:
  axis = plt.gca()
- plt.xticks(x, labels = x_labels, rotation='vertical')
+ plt.xticks(x)
  print(f"{x}\n{y1_3}")
  print(f"{x}\n{y2_3}")
- plt.subplot(133)
- plt.plot(x, y1_3, color=color1, marker='o', linestyle='-.') # Adjust markers and lines as needed
- plt.plot(x, y2_3, color=color2, marker='o', linestyle='-.') # Adjust markers and lines as needed
- plt.title(f"Time ({time_str}) / $N \cdot log(N)$")
+ # plt.subplot(313)
+ ax[2].plot(x, y1_3, color=color1, marker='o', linestyle='-.') # Adjust markers and lines as needed
+ ax[2].plot(x, y2_3, color=color2, marker='o', linestyle='-.') # Adjust markers and lines as needed
+ ax[2].set_title(f"Time ({time_str}) / $N \cdot log(N)$")
  plt.xlabel('Cities') # Adjust with actual column name or description
  # plt.ylabel(f"Time ({time_str})") # Adjust with actual column name or description
  plt.grid(show_grid) # Optional: Adds a grid
  if sequence:
  axis = plt.gca()
  plt.xticks(x, labels = x_labels, rotation='vertical')
+ # plt.suptitle("Paper vs New implementation")
+ # lines_labels = [axis.get_legend_handles_labels() for axis in fig.axes]
+ # lines, labels = [sum(lol, []) for lol in zip(*lines_labels)]
+ # print(f"lines:\n{lines}\nlabels:\n{labels}")
+ # fig.legend(lines, labels)
+ handles, labels = ax[0].get_legend_handles_labels()
+ print(f"handles:\n{handles}\nlabels:\n{labels}")
+ fig.legend(handles, labels, loc='upper right', ncol=2)
+ plt.tight_layout()
  plt.show()
 
 if __name__ == "__main__":