From 0968dac538f34d4a39f60edf220027518f875c40 Mon Sep 17 00:00:00 2001
From: Almog-David <almog.david99@gmail.com>
Date: Sun, 4 Aug 2024 16:37:45 +0300
Subject: [PATCH] fixrd path to comparisons

---
 .../firefighter_problem/comparisons.py        |  36 +++---
 .../spreading_minbudget.csv                   |  20 ---
 .../spreading_minbudget.png                   | Bin 29855 -> 0 bytes
 .../spreading_minbudget_preprocessed.csv      |  19 ---
 experiments/spreading_minbudget.csv           | 114 ------------------
 5 files changed, 18 insertions(+), 171 deletions(-)
 delete mode 100644 experiments/firefighter_problem/spreading_minbudget.csv
 delete mode 100644 experiments/firefighter_problem/spreading_minbudget.png
 delete mode 100644 experiments/firefighter_problem/spreading_minbudget_preprocessed.csv
 delete mode 100644 experiments/spreading_minbudget.csv

diff --git a/experiments/firefighter_problem/comparisons.py b/experiments/firefighter_problem/comparisons.py
index 75d6876..34fed01 100644
--- a/experiments/firefighter_problem/comparisons.py
+++ b/experiments/firefighter_problem/comparisons.py
@@ -118,7 +118,7 @@ def Compare_NonSpread():
     This function runs multiple experiments on randomly generated layered networks
     and plots the results comparing the budget used by different algorithms.
     """
-    ex1 = experiments_csv.Experiment("./experiments/", "non_spreading.csv", backup_folder=None)
+    ex1 = experiments_csv.Experiment("./experiments/firefighter_problem/", "non_spreading.csv", backup_folder=None)
     ex1.clear_previous_results()  # to clear previous experiments
 
     input_ranges = {
@@ -144,7 +144,7 @@ def multiple_runs(runs=30):
     ex1.run_with_time_limit(multiple_runs, input_ranges={}, time_limit=0.9)
 
     # Preprocess the DataFrame to extract numeric budget values
-    results_csv_file = "./experiments/non_spreading_minbudget.csv"
+    results_csv_file = "./experiments/firefighter_problem/non_spreading_minbudget.csv"
     results = pd.read_csv(results_csv_file)
 
     # Extract the numeric budget from the 'Budget' column
@@ -164,7 +164,7 @@ def extract_budget_numeric(budget):
     results = results.dropna(subset=['Budget_numeric'])
 
     # Save the preprocessed DataFrame to a temporary CSV file
-    preprocessed_csv_file = "./experiments/non_spreading_minbudget_preprocessed.csv"
+    preprocessed_csv_file = "./experiments/firefighter_problem/non_spreading_minbudget_preprocessed.csv"
     results.to_csv(preprocessed_csv_file, index=False)
 
     print("\n DataFrame-NonSpread: \n", results)
@@ -177,7 +177,7 @@ def extract_budget_numeric(budget):
         y_field="Budget_numeric", 
         z_field="algorithm", 
         mean=True,
-        save_to_file="./experiments/non_spreading.png"
+        save_to_file="./experiments/firefighter_problem/non_spreading.png"
     )
     
     print("\n DataFrame-NonSpread: \n", ex1.dataFrame)
@@ -189,7 +189,7 @@ def Compare_SpreadingMaxSave():
     This function runs multiple experiments on randomly generated directed graphs
     and plots the results comparing the number of nodes saved by different algorithms.
     """
-    ex2 = experiments_csv.Experiment("./experiments/", "spreading_maxsave.csv", backup_folder=None)
+    ex2 = experiments_csv.Experiment("./experiments/firefighter_problem/", "spreading_maxsave.csv", backup_folder=None)
     ex2.clear_previous_results()  # to clear previous experiments
 
     input_ranges = {
@@ -224,7 +224,7 @@ def multiple_runs(runs=10):
 
     ## DATA ISSUE WE HAD SO THIS IS A FIX ##
     # Load the results
-    results_csv_file = "./experiments/spreading_maxsave.csv"
+    results_csv_file = "./experiments/firefighter_problem/spreading_maxsave.csv"
     results = pd.read_csv(results_csv_file)
 
     # Ensure 'algorithm' column is of type string
@@ -245,7 +245,7 @@ def multiple_runs(runs=10):
     results['Nodes_Saved'] = results['Nodes_Saved'].astype(int)
 
     # Save the cleaned DataFrame to a new CSV file (optional, for debugging)
-    cleaned_csv_file = "./experiments/spreading_maxsave_preprocessed.csv"
+    cleaned_csv_file = "./experiments/firefighter_problem/spreading_maxsave_preprocessed.csv"
     results.to_csv(cleaned_csv_file, index=False)
 
     # Plot the results using the cleaned DataFrame
@@ -261,7 +261,7 @@ def multiple_runs(runs=10):
         sharex=True,
         sharey=True,
         mean=True,
-        save_to_file="./experiments/spreading_maxsave_budget.png"
+        save_to_file="./experiments/firefighter_problem/spreading_maxsave_budget.png"
     )
 
     multi_plot_results(
@@ -276,7 +276,7 @@ def multiple_runs(runs=10):
         sharex=True,
         sharey=True,
         mean=True,
-        save_to_file="./experiments/spreading_maxsave_100_edge_prob.png"
+        save_to_file="./experiments/firefighter_problem/spreading_maxsave_100_edge_prob.png"
     )
 
     multi_plot_results(
@@ -291,7 +291,7 @@ def multiple_runs(runs=10):
         sharex=True,
         sharey=True,
         mean=True,
-        save_to_file="./experiments/spreading_maxsave_200_edge_prob.png"
+        save_to_file="./experiments/firefighter_problem/spreading_maxsave_200_edge_prob.png"
     )
 
     multi_plot_results(
@@ -306,7 +306,7 @@ def multiple_runs(runs=10):
         sharex=True,
         sharey=True,
         mean=True,
-        save_to_file="./experiments/spreading_maxsave_400_edge_prob.png"
+        save_to_file="./experiments/firefighter_problem/spreading_maxsave_400_edge_prob.png"
     )
 
     print("\n DataFrame-NonSpread: \n", ex2.dataFrame)
@@ -318,7 +318,7 @@ def Compare_SpreadingMinBudget():
     This function runs multiple experiments on randomly generated directed graphs
     and plots the results comparing the budget used by different algorithms.
     """
-    ex3 = experiments_csv.Experiment("./experiments/", "spreading_minbudget.csv", backup_folder=None)
+    ex3 = experiments_csv.Experiment("./experiments/firefighter_problem/", "spreading_minbudget.csv", backup_folder=None)
     ex3.clear_previous_results()  # to clear previous experiments
 
     input_ranges = {
@@ -349,7 +349,7 @@ def multiple_runs(runs=10):
     ex3.run_with_time_limit(multiple_runs, input_ranges={}, time_limit=0.9)
 
     # Preprocess the DataFrame to extract numeric budget values
-    results_csv_file = "./experiments/spreading_minbudget.csv"
+    results_csv_file = "./experiments/firefighter_problem/spreading_minbudget.csv"
     results = pd.read_csv(results_csv_file)
 
     # Extract the numeric budget from the 'Budget' column
@@ -369,7 +369,7 @@ def extract_budget_numeric(budget):
     results = results.dropna(subset=['Budget_numeric'])
 
     # Save the preprocessed DataFrame to a temporary CSV file
-    preprocessed_csv_file = "./experiments/spreading_minbudget_preprocessed.csv"
+    preprocessed_csv_file = "./experiments/firefighter_problem/spreading_minbudget_preprocessed.csv"
     results.to_csv(preprocessed_csv_file, index=False)
 
     print("\n DataFrame-NonSpread: \n", results)
@@ -382,17 +382,17 @@ def extract_budget_numeric(budget):
         y_field="Budget_numeric", 
         z_field="algorithm", 
         mean=True,
-        save_to_file="./experiments/spreading_minbudget.png"
+        save_to_file="./experiments/firefighter_problem/spreading_minbudget.png"
     )
 
     single_plot_results(
         results_csv_file=preprocessed_csv_file,
-        filter={"edge_probability":0.1}, 
-        x_field="graph_nodes", 
+        filter={"graph_nodes":400}, 
+        x_field="edge_probability", 
         y_field="Budget_numeric", 
         z_field="algorithm", 
         mean=True,
-        save_to_file="./experiments/spreading_minbudget_edge.png"
+        save_to_file="./experiments/firefighter_problem/spreading_minbudget_400_edge.png"
     )
     
     print("\n DataFrame-NonSpread: \n", ex3.dataFrame)
diff --git a/experiments/firefighter_problem/spreading_minbudget.csv b/experiments/firefighter_problem/spreading_minbudget.csv
deleted file mode 100644
index 7c2b261..0000000
--- a/experiments/firefighter_problem/spreading_minbudget.csv
+++ /dev/null
@@ -1,20 +0,0 @@
-algorithm,runtime,graph_nodes,Budget
-spreading_minbudget,0.4963123999768868,100,"(4, [(89, 1), (47, 1), (84, 1), (30, 1), (38, 2), (50, 2), (3, 2), (26, 2)])"
-heuristic_minbudget,0.12413459992967546,100,"(4, [(47, 1), (92, 1), (44, 1), (17, 1), (83, 2), (62, 2), (50, 2), (26, 2), (69, 3), (29, 3)])"
-spreading_minbudget,0.39317540009506047,100,"(11, [(3, 1), (15, 1), (28, 1), (54, 1), (71, 1), (96, 1), (97, 1), (17, 1), (35, 1), (45, 1), (22, 1)])"
-heuristic_minbudget,1.0460371000226587,100,"(10, [(22, 1), (35, 1), (28, 1), (54, 1), (96, 1), (17, 1), (97, 1), (15, 1), (45, 1), (71, 1)])"
-spreading_minbudget,0.2668848999310285,100,"(14, [(14, 1), (20, 1), (30, 1), (40, 1), (48, 1), (57, 1), (71, 1), (80, 1), (86, 1), (91, 1), (92, 1), (96, 1), (5, 1), (89, 1)])"
-heuristic_minbudget,3.6302230999572203,100,"(14, [(14, 1), (40, 1), (48, 1), (86, 1), (96, 1), (20, 1), (91, 1), (80, 1), (92, 1), (71, 1), (30, 1), (5, 1), (57, 1), (89, 1)])"
-spreading_minbudget,1.212209300021641,200,"(22, [(70, 1), (128, 1), (53, 1), (55, 1), (125, 1), (152, 1), (28, 1), (104, 1), (17, 1), (20, 1), (174, 1), (5, 1), (31, 1), (35, 1), (44, 1), (120, 1), (151, 1), (172, 1), (175, 1), (190, 1), (146, 1), (168, 1)])"
-heuristic_minbudget,1.0129838000284508,200,"(14, [(174, 1), (151, 1), (146, 1), (168, 1), (17, 1), (172, 1), (152, 1), (190, 1), (35, 1), (5, 1), (44, 1), (175, 1), (120, 1), (52, 1), (128, 2), (109, 2), (148, 2), (16, 2), (76, 2), (32, 2), (114, 2), (8, 2), (95, 2), (39, 2), (48, 2), (138, 2), (116, 2), (157, 2)])"
-spreading_minbudget,0.5931069999933243,200,"(5, [(5, 1), (44, 1), (193, 1), (170, 1), (24, 1)])"
-heuristic_minbudget,5.592074999934994,200,"(4, [(44, 1), (24, 1), (193, 1), (170, 1)])"
-spreading_minbudget,1.2939483999507502,200,"(40, [(89, 1), (3, 1), (4, 1), (13, 1), (17, 1), (19, 1), (27, 1), (45, 1), (50, 1), (62, 1), (74, 1), (84, 1), (92, 1), (95, 1), (97, 1), (118, 1), (119, 1), (123, 1), (124, 1), (128, 1), (138, 1), (142, 1), (144, 1), (146, 1), (152, 1), (153, 1), (180, 1), (191, 1), (198, 1), (11, 1), (29, 1), (93, 1), (100, 1), (141, 1), (164, 1), (167, 1), (173, 1), (184, 1), (102, 1), (86, 1)])"
-heuristic_minbudget,20.179923600051552,200,"(40, [(4, 1), (146, 1), (144, 1), (153, 1), (152, 1), (62, 1), (173, 1), (119, 1), (13, 1), (89, 1), (142, 1), (164, 1), (27, 1), (45, 1), (102, 1), (95, 1), (141, 1), (128, 1), (29, 1), (167, 1), (86, 1), (191, 1), (74, 1), (100, 1), (11, 1), (84, 1), (3, 1), (118, 1), (93, 1), (180, 1), (92, 1), (50, 1), (138, 1), (17, 1), (184, 1), (19, 1), (124, 1), (123, 1), (198, 1), (97, 1)])"
-spreading_minbudget,3.5869039999088272,400,"(21, [(69, 1), (116, 1), (239, 1), (159, 1), (250, 1), (37, 1), (192, 1), (144, 1), (387, 1), (29, 1), (287, 1), (7, 1), (63, 1), (300, 1), (305, 1), (93, 1), (280, 1), (112, 1), (149, 1), (11, 1), (230, 1)])"
-heuristic_minbudget,2.8195147999795154,400,"(15, [(287, 1), (305, 1), (300, 1), (149, 1), (63, 1), (11, 1), (93, 1), (280, 1), (112, 1), (230, 1), (29, 1), (343, 1), (103, 1), (364, 1), (339, 1), (190, 2), (110, 2), (121, 2), (393, 2), (193, 2), (200, 2), (354, 2), (1, 2), (147, 2), (159, 2), (367, 2), (267, 2), (204, 2), (50, 2), (373, 2)])"
-spreading_minbudget,6.544073999975808,400,"(63, [(103, 1), (333, 1), (88, 1), (2, 1), (17, 1), (27, 1), (29, 1), (66, 1), (72, 1), (76, 1), (89, 1), (135, 1), (143, 1), (148, 1), (150, 1), (152, 1), (165, 1), (166, 1), (190, 1), (222, 1), (250, 1), (257, 1), (275, 1), (289, 1), (291, 1), (292, 1), (293, 1), (306, 1), (315, 1), (326, 1), (327, 1), (363, 1), (373, 1), (380, 1), (386, 1), (40, 1), (109, 1), (112, 1), (140, 1), (182, 1), (229, 1), (238, 1), (239, 1), (325, 1), (390, 1), (9, 1), (90, 1), (171, 1), (184, 1), (359, 1), (385, 1), (393, 1), (11, 1), (51, 1), (71, 1), (212, 1), (364, 1), (230, 1), (368, 1), (391, 1), (52, 1), (353, 1), (276, 1)])"
-heuristic_minbudget,45.2434607000323,400,"(62, [(327, 1), (150, 1), (364, 1), (166, 1), (291, 1), (143, 1), (229, 1), (315, 1), (17, 1), (109, 1), (212, 1), (27, 1), (2, 1), (11, 1), (257, 1), (306, 1), (148, 1), (239, 1), (390, 1), (363, 1), (71, 1), (76, 1), (275, 1), (276, 1), (72, 1), (184, 1), (325, 1), (353, 1), (51, 1), (135, 1), (112, 1), (165, 1), (238, 1), (293, 1), (289, 1), (385, 1), (9, 1), (140, 1), (222, 1), (250, 1), (386, 1), (359, 1), (29, 1), (373, 1), (292, 1), (391, 1), (326, 1), (40, 1), (380, 1), (171, 1), (230, 1), (393, 1), (182, 1), (368, 1), (52, 1), (66, 1), (152, 1), (88, 1), (89, 1), (333, 1), (190, 1), (90, 1)])"
-spreading_minbudget,4.017668400076218,400,"(43, [(44, 1), (56, 1), (71, 1), (104, 1), (105, 1), (110, 1), (130, 1), (142, 1), (151, 1), (157, 1), (171, 1), (179, 1), (189, 1), (214, 1), (222, 1), (229, 1), (232, 1), (271, 1), (281, 1), (296, 1), (297, 1), (308, 1), (309, 1), (335, 1), (339, 1), (344, 1), (354, 1), (355, 1), (359, 1), (371, 1), (373, 1), (382, 1), (385, 1), (68, 1), (89, 1), (98, 1), (150, 1), (253, 1), (254, 1), (307, 1), (369, 1), (378, 1), (84, 1)])"
-heuristic_minbudget,96.92596789996605,400,"(43, [(56, 1), (84, 1), (222, 1), (214, 1), (307, 1), (335, 1), (369, 1), (371, 1), (309, 1), (339, 1), (104, 1), (189, 1), (229, 1), (232, 1), (382, 1), (296, 1), (130, 1), (151, 1), (171, 1), (110, 1), (179, 1), (271, 1), (71, 1), (344, 1), (354, 1), (378, 1), (254, 1), (385, 1), (68, 1), (157, 1), (281, 1), (308, 1), (44, 1), (373, 1), (105, 1), (359, 1), (142, 1), (253, 1), (297, 1), (150, 1), (355, 1), (89, 1), (98, 1)])"
-,195.34563290001824,,
diff --git a/experiments/firefighter_problem/spreading_minbudget.png b/experiments/firefighter_problem/spreading_minbudget.png
deleted file mode 100644
index a42e7c25bbc3a85acee593bf595d4519f8ff6b3c..0000000000000000000000000000000000000000
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diff --git a/experiments/firefighter_problem/spreading_minbudget_preprocessed.csv b/experiments/firefighter_problem/spreading_minbudget_preprocessed.csv
deleted file mode 100644
index 48b6414..0000000
--- a/experiments/firefighter_problem/spreading_minbudget_preprocessed.csv
+++ /dev/null
@@ -1,19 +0,0 @@
-algorithm,runtime,graph_nodes,Budget,Budget_numeric
-spreading_minbudget,0.4963123999768868,100.0,"(4, [(89, 1), (47, 1), (84, 1), (30, 1), (38, 2), (50, 2), (3, 2), (26, 2)])",4.0
-heuristic_minbudget,0.1241345999296754,100.0,"(4, [(47, 1), (92, 1), (44, 1), (17, 1), (83, 2), (62, 2), (50, 2), (26, 2), (69, 3), (29, 3)])",4.0
-spreading_minbudget,0.3931754000950604,100.0,"(11, [(3, 1), (15, 1), (28, 1), (54, 1), (71, 1), (96, 1), (97, 1), (17, 1), (35, 1), (45, 1), (22, 1)])",11.0
-heuristic_minbudget,1.0460371000226587,100.0,"(10, [(22, 1), (35, 1), (28, 1), (54, 1), (96, 1), (17, 1), (97, 1), (15, 1), (45, 1), (71, 1)])",10.0
-spreading_minbudget,0.2668848999310285,100.0,"(14, [(14, 1), (20, 1), (30, 1), (40, 1), (48, 1), (57, 1), (71, 1), (80, 1), (86, 1), (91, 1), (92, 1), (96, 1), (5, 1), (89, 1)])",14.0
-heuristic_minbudget,3.63022309995722,100.0,"(14, [(14, 1), (40, 1), (48, 1), (86, 1), (96, 1), (20, 1), (91, 1), (80, 1), (92, 1), (71, 1), (30, 1), (5, 1), (57, 1), (89, 1)])",14.0
-spreading_minbudget,1.212209300021641,200.0,"(22, [(70, 1), (128, 1), (53, 1), (55, 1), (125, 1), (152, 1), (28, 1), (104, 1), (17, 1), (20, 1), (174, 1), (5, 1), (31, 1), (35, 1), (44, 1), (120, 1), (151, 1), (172, 1), (175, 1), (190, 1), (146, 1), (168, 1)])",22.0
-heuristic_minbudget,1.0129838000284508,200.0,"(14, [(174, 1), (151, 1), (146, 1), (168, 1), (17, 1), (172, 1), (152, 1), (190, 1), (35, 1), (5, 1), (44, 1), (175, 1), (120, 1), (52, 1), (128, 2), (109, 2), (148, 2), (16, 2), (76, 2), (32, 2), (114, 2), (8, 2), (95, 2), (39, 2), (48, 2), (138, 2), (116, 2), (157, 2)])",14.0
-spreading_minbudget,0.5931069999933243,200.0,"(5, [(5, 1), (44, 1), (193, 1), (170, 1), (24, 1)])",5.0
-heuristic_minbudget,5.592074999934994,200.0,"(4, [(44, 1), (24, 1), (193, 1), (170, 1)])",4.0
-spreading_minbudget,1.2939483999507502,200.0,"(40, [(89, 1), (3, 1), (4, 1), (13, 1), (17, 1), (19, 1), (27, 1), (45, 1), (50, 1), (62, 1), (74, 1), (84, 1), (92, 1), (95, 1), (97, 1), (118, 1), (119, 1), (123, 1), (124, 1), (128, 1), (138, 1), (142, 1), (144, 1), (146, 1), (152, 1), (153, 1), (180, 1), (191, 1), (198, 1), (11, 1), (29, 1), (93, 1), (100, 1), (141, 1), (164, 1), (167, 1), (173, 1), (184, 1), (102, 1), (86, 1)])",40.0
-heuristic_minbudget,20.179923600051552,200.0,"(40, [(4, 1), (146, 1), (144, 1), (153, 1), (152, 1), (62, 1), (173, 1), (119, 1), (13, 1), (89, 1), (142, 1), (164, 1), (27, 1), (45, 1), (102, 1), (95, 1), (141, 1), (128, 1), (29, 1), (167, 1), (86, 1), (191, 1), (74, 1), (100, 1), (11, 1), (84, 1), (3, 1), (118, 1), (93, 1), (180, 1), (92, 1), (50, 1), (138, 1), (17, 1), (184, 1), (19, 1), (124, 1), (123, 1), (198, 1), (97, 1)])",40.0
-spreading_minbudget,3.5869039999088272,400.0,"(21, [(69, 1), (116, 1), (239, 1), (159, 1), (250, 1), (37, 1), (192, 1), (144, 1), (387, 1), (29, 1), (287, 1), (7, 1), (63, 1), (300, 1), (305, 1), (93, 1), (280, 1), (112, 1), (149, 1), (11, 1), (230, 1)])",21.0
-heuristic_minbudget,2.819514799979516,400.0,"(15, [(287, 1), (305, 1), (300, 1), (149, 1), (63, 1), (11, 1), (93, 1), (280, 1), (112, 1), (230, 1), (29, 1), (343, 1), (103, 1), (364, 1), (339, 1), (190, 2), (110, 2), (121, 2), (393, 2), (193, 2), (200, 2), (354, 2), (1, 2), (147, 2), (159, 2), (367, 2), (267, 2), (204, 2), (50, 2), (373, 2)])",15.0
-spreading_minbudget,6.544073999975808,400.0,"(63, [(103, 1), (333, 1), (88, 1), (2, 1), (17, 1), (27, 1), (29, 1), (66, 1), (72, 1), (76, 1), (89, 1), (135, 1), (143, 1), (148, 1), (150, 1), (152, 1), (165, 1), (166, 1), (190, 1), (222, 1), (250, 1), (257, 1), (275, 1), (289, 1), (291, 1), (292, 1), (293, 1), (306, 1), (315, 1), (326, 1), (327, 1), (363, 1), (373, 1), (380, 1), (386, 1), (40, 1), (109, 1), (112, 1), (140, 1), (182, 1), (229, 1), (238, 1), (239, 1), (325, 1), (390, 1), (9, 1), (90, 1), (171, 1), (184, 1), (359, 1), (385, 1), (393, 1), (11, 1), (51, 1), (71, 1), (212, 1), (364, 1), (230, 1), (368, 1), (391, 1), (52, 1), (353, 1), (276, 1)])",63.0
-heuristic_minbudget,45.2434607000323,400.0,"(62, [(327, 1), (150, 1), (364, 1), (166, 1), (291, 1), (143, 1), (229, 1), (315, 1), (17, 1), (109, 1), (212, 1), (27, 1), (2, 1), (11, 1), (257, 1), (306, 1), (148, 1), (239, 1), (390, 1), (363, 1), (71, 1), (76, 1), (275, 1), (276, 1), (72, 1), (184, 1), (325, 1), (353, 1), (51, 1), (135, 1), (112, 1), (165, 1), (238, 1), (293, 1), (289, 1), (385, 1), (9, 1), (140, 1), (222, 1), (250, 1), (386, 1), (359, 1), (29, 1), (373, 1), (292, 1), (391, 1), (326, 1), (40, 1), (380, 1), (171, 1), (230, 1), (393, 1), (182, 1), (368, 1), (52, 1), (66, 1), (152, 1), (88, 1), (89, 1), (333, 1), (190, 1), (90, 1)])",62.0
-spreading_minbudget,4.017668400076218,400.0,"(43, [(44, 1), (56, 1), (71, 1), (104, 1), (105, 1), (110, 1), (130, 1), (142, 1), (151, 1), (157, 1), (171, 1), (179, 1), (189, 1), (214, 1), (222, 1), (229, 1), (232, 1), (271, 1), (281, 1), (296, 1), (297, 1), (308, 1), (309, 1), (335, 1), (339, 1), (344, 1), (354, 1), (355, 1), (359, 1), (371, 1), (373, 1), (382, 1), (385, 1), (68, 1), (89, 1), (98, 1), (150, 1), (253, 1), (254, 1), (307, 1), (369, 1), (378, 1), (84, 1)])",43.0
-heuristic_minbudget,96.92596789996604,400.0,"(43, [(56, 1), (84, 1), (222, 1), (214, 1), (307, 1), (335, 1), (369, 1), (371, 1), (309, 1), (339, 1), (104, 1), (189, 1), (229, 1), (232, 1), (382, 1), (296, 1), (130, 1), (151, 1), (171, 1), (110, 1), (179, 1), (271, 1), (71, 1), (344, 1), (354, 1), (378, 1), (254, 1), (385, 1), (68, 1), (157, 1), (281, 1), (308, 1), (44, 1), (373, 1), (105, 1), (359, 1), (142, 1), (253, 1), (297, 1), (150, 1), (355, 1), (89, 1), (98, 1)])",43.0
diff --git a/experiments/spreading_minbudget.csv b/experiments/spreading_minbudget.csv
deleted file mode 100644
index ab7013e..0000000
--- a/experiments/spreading_minbudget.csv
+++ /dev/null
@@ -1,114 +0,0 @@
-algorithm,runtime,graph_nodes,edge_probability,Budget
-spreading_minbudget,0.6043815999873914,100,0.1,"(4, [(6, 1), (23, 1), (27, 1), (25, 1)])"
-heuristic_minbudget,0.08351690000563394,100,0.1,"(2, [(25, 1), (6, 1), (27, 2), (23, 2), (41, 3), (72, 3)])"
-spreading_minbudget,0.798920300003374,100,0.1,"(3, [(94, 1), (6, 1), (76, 1), (79, 2), (16, 2), (99, 2)])"
-heuristic_minbudget,0.0549452000122983,100,0.1,"(3, [(6, 1), (19, 1), (85, 1), (18, 2), (87, 2), (17, 2), (81, 3)])"
-spreading_minbudget,1.2247725000052014,100,0.1,"(7, [(6, 1), (70, 1), (23, 1), (36, 1), (47, 1), (2, 1), (25, 1)])"
-heuristic_minbudget,0.20194240000273567,100,0.1,"(4, [(47, 1), (25, 1), (19, 1), (85, 1), (17, 2), (96, 2), (61, 2), (67, 2), (42, 3)])"
-spreading_minbudget,0.5262910000019474,100,0.1,"(1, [(26, 1), (52, 2)])"
-heuristic_minbudget,0.09640729999227915,100,0.1,"(2, [(19, 1), (14, 1), (52, 2), (76, 2), (22, 3), (60, 3)])"
-spreading_minbudget,1.2042019000073196,100,0.1,"(6, [(72, 1), (91, 1), (16, 1), (14, 1), (38, 1), (25, 1)])"
-heuristic_minbudget,0.12059449999651406,100,0.1,"(4, [(14, 1), (25, 1), (19, 1), (47, 1), (90, 2), (65, 2), (91, 2), (21, 2), (42, 3), (70, 3)])"
-spreading_minbudget,0.1407623999984935,100,0.1,"(1, [(6, 1)])"
-heuristic_minbudget,0.07592089999525342,100,0.1,"(1, [(6, 1), (84, 2)])"
-spreading_minbudget,1.4573968000040622,100,0.1,"(6, [(9, 1), (13, 1), (19, 1), (23, 1), (3, 1), (47, 1)])"
-heuristic_minbudget,0.07776210000156425,100,0.1,"(4, [(19, 1), (47, 1), (6, 1), (85, 1), (78, 2), (87, 2), (71, 2), (39, 2)])"
-spreading_minbudget,0.8562765999959083,100,0.1,"(3, [(30, 1), (10, 1), (11, 1), (33, 2), (1, 2), (55, 2)])"
-heuristic_minbudget,0.05599090000032447,100,0.1,"(3, [(6, 1), (85, 1), (14, 1), (11, 2), (89, 2), (78, 2), (69, 3)])"
-spreading_minbudget,0.5544522999989567,100,0.1,"(1, [(6, 1), (78, 2)])"
-heuristic_minbudget,0.09719980000227224,100,0.1,"(2, [(25, 1), (6, 1), (93, 2), (11, 2), (72, 3), (64, 3)])"
-spreading_minbudget,0.4506552999955602,100,0.1,"(2, [(6, 1), (2, 1), (84, 2), (1, 2)])"
-heuristic_minbudget,0.08434780000243336,100,0.1,"(2, [(6, 1), (19, 1), (84, 2), (17, 2), (72, 3), (69, 3)])"
-spreading_minbudget,2.9608414999966044,100,0.5,"(29, [(54, 1), (37, 1), (9, 1), (11, 1), (12, 1), (23, 1), (31, 1), (46, 1), (60, 1), (61, 1), (88, 1), (90, 1), (2, 1), (15, 1), (24, 1), (27, 1), (45, 1), (62, 1), (20, 1), (26, 1), (32, 1), (86, 1), (94, 1), (97, 1), (28, 1), (66, 1), (93, 1), (13, 1), (65, 1)])"
-heuristic_minbudget,2.033734099997673,100,0.5,"(29, [(37, 1), (45, 1), (65, 1), (12, 1), (46, 1), (88, 1), (24, 1), (54, 1), (93, 1), (28, 1), (26, 1), (13, 1), (60, 1), (62, 1), (31, 1), (61, 1), (27, 1), (90, 1), (2, 1), (9, 1), (23, 1), (20, 1), (66, 1), (94, 1), (97, 1), (15, 1), (86, 1), (32, 1), (11, 1)])"
-spreading_minbudget,2.7890169999882346,100,0.5,"(24, [(31, 1), (38, 1), (16, 1), (46, 1), (48, 1), (61, 1), (82, 1), (84, 1), (98, 1), (2, 1), (15, 1), (45, 1), (54, 1), (62, 1), (64, 1), (77, 1), (79, 1), (85, 1), (26, 1), (32, 1), (34, 1), (78, 1), (28, 1), (93, 1)])"
-heuristic_minbudget,1.3695207999990089,100,0.5,"(22, [(85, 1), (98, 1), (54, 1), (84, 1), (46, 1), (28, 1), (45, 1), (48, 1), (15, 1), (26, 1), (93, 1), (34, 1), (61, 1), (62, 1), (79, 1), (82, 1), (16, 1), (78, 1), (2, 1), (32, 1), (77, 1), (64, 1)])"
-spreading_minbudget,0.29501640000671614,100,0.5,"(2, [(34, 1), (82, 1)])"
-heuristic_minbudget,0.8983767999889096,100,0.5,"(2, [(34, 1), (82, 1)])"
-spreading_minbudget,1.612422099991818,100,0.5,"(16, [(60, 1), (9, 1), (11, 1), (30, 1), (46, 1), (90, 1), (2, 1), (15, 1), (29, 1), (37, 1), (77, 1), (85, 1), (52, 1), (78, 1), (28, 1), (13, 1)])"
-heuristic_minbudget,1.2286775999964448,100,0.5,"(16, [(52, 1), (28, 1), (85, 1), (29, 1), (37, 1), (46, 1), (13, 1), (60, 1), (90, 1), (2, 1), (30, 1), (15, 1), (78, 1), (9, 1), (77, 1), (11, 1)])"
-spreading_minbudget,1.9392723999917507,100,0.5,"(18, [(58, 1), (22, 1), (46, 1), (48, 1), (61, 1), (2, 1), (19, 1), (37, 1), (64, 1), (77, 1), (79, 1), (85, 1), (32, 1), (72, 1), (78, 1), (28, 1), (66, 1), (13, 1)])"
-heuristic_minbudget,1.2242184999922756,100,0.5,"(17, [(22, 1), (72, 1), (13, 1), (37, 1), (46, 1), (28, 1), (48, 1), (61, 1), (85, 1), (19, 1), (32, 1), (78, 1), (2, 1), (79, 1), (77, 1), (66, 1), (64, 1)])"
-spreading_minbudget,2.1627451999956975,100,0.5,"(19, [(67, 1), (7, 1), (9, 1), (11, 1), (31, 1), (88, 1), (90, 1), (91, 1), (17, 1), (37, 1), (45, 1), (62, 1), (64, 1), (20, 1), (22, 1), (72, 1), (97, 1), (92, 1), (65, 1)])"
-heuristic_minbudget,1.7830877999949735,100,0.5,"(17, [(88, 1), (22, 1), (91, 1), (37, 1), (45, 1), (65, 1), (20, 1), (31, 1), (9, 1), (72, 1), (90, 1), (17, 1), (62, 1), (92, 1), (11, 1), (97, 1), (64, 1)])"
-spreading_minbudget,3.6650694999989355,100,0.5,"(29, [(30, 1), (46, 1), (11, 1), (12, 1), (16, 1), (41, 1), (61, 1), (82, 1), (84, 1), (88, 1), (98, 1), (2, 1), (15, 1), (24, 1), (27, 1), (45, 1), (77, 1), (79, 1), (85, 1), (22, 1), (43, 1), (72, 1), (74, 1), (78, 1), (86, 1), (97, 1), (92, 1), (13, 1), (65, 1)])"
-heuristic_minbudget,2.367954000001191,100,0.5,"(29, [(98, 1), (85, 1), (24, 1), (84, 1), (46, 1), (88, 1), (22, 1), (12, 1), (27, 1), (65, 1), (45, 1), (41, 1), (61, 1), (72, 1), (13, 1), (2, 1), (43, 1), (79, 1), (86, 1), (92, 1), (15, 1), (30, 1), (77, 1), (82, 1), (97, 1), (11, 1), (16, 1), (74, 1), (78, 1)])"
-spreading_minbudget,1.6317382999986876,100,0.5,"(13, [(12, 1), (7, 1), (11, 1), (16, 1), (88, 1), (15, 1), (19, 1), (27, 1), (54, 1), (20, 1), (26, 1), (43, 1), (28, 1)])"
-heuristic_minbudget,1.2327431000012439,100,0.5,"(13, [(27, 1), (20, 1), (88, 1), (12, 1), (28, 1), (54, 1), (26, 1), (7, 1), (43, 1), (15, 1), (19, 1), (16, 1), (11, 1)])"
-spreading_minbudget,1.377598899998702,100,0.5,"(9, [(23, 1), (82, 1), (30, 1), (40, 1), (62, 1), (79, 1), (85, 1), (22, 1), (92, 1)])"
-heuristic_minbudget,1.0871489999990445,100,0.5,"(8, [(85, 1), (79, 1), (82, 1), (40, 1), (22, 1), (62, 1), (30, 1), (92, 1)])"
-spreading_minbudget,1.249198300007265,100,0.5,"(12, [(31, 1), (16, 1), (41, 1), (84, 1), (90, 1), (24, 1), (29, 1), (37, 1), (40, 1), (85, 1), (13, 1), (65, 1)])"
-heuristic_minbudget,1.291403099996387,100,0.5,"(11, [(84, 1), (29, 1), (37, 1), (40, 1), (24, 1), (85, 1), (65, 1), (41, 1), (13, 1), (90, 1), (16, 1)])"
-spreading_minbudget,0.8987213000073098,100,0.8,"(8, [(84, 1), (16, 1), (21, 1), (68, 1), (86, 1), (87, 1), (44, 1), (52, 1)])"
-heuristic_minbudget,2.5706658999988576,100,0.8,"(8, [(52, 1), (84, 1), (21, 1), (87, 1), (16, 1), (86, 1), (44, 1), (68, 1)])"
-spreading_minbudget,0.5972516000038013,100,0.8,"(7, [(19, 1), (6, 1), (24, 1), (42, 1), (66, 1), (71, 1), (72, 1)])"
-heuristic_minbudget,2.627842400004738,100,0.8,"(7, [(24, 1), (6, 1), (71, 1), (19, 1), (66, 1), (72, 1), (42, 1)])"
-spreading_minbudget,2.4423267999954987,100,0.8,"(25, [(22, 1), (1, 1), (6, 1), (8, 1), (9, 1), (23, 1), (25, 1), (27, 1), (42, 1), (51, 1), (56, 1), (58, 1), (63, 1), (72, 1), (76, 1), (79, 1), (86, 1), (93, 1), (4, 1), (15, 1), (33, 1), (34, 1), (41, 1), (44, 1), (11, 1)])"
-heuristic_minbudget,3.5214248999982374,100,0.8,"(25, [(27, 1), (76, 1), (6, 1), (4, 1), (25, 1), (58, 1), (34, 1), (51, 1), (9, 1), (8, 1), (22, 1), (11, 1), (15, 1), (23, 1), (33, 1), (44, 1), (56, 1), (86, 1), (93, 1), (1, 1), (79, 1), (41, 1), (63, 1), (72, 1), (42, 1)])"
-spreading_minbudget,2.2762453999894205,100,0.8,"(23, [(21, 1), (2, 1), (8, 1), (9, 1), (16, 1), (22, 1), (29, 1), (37, 1), (48, 1), (53, 1), (65, 1), (71, 1), (76, 1), (85, 1), (86, 1), (89, 1), (91, 1), (95, 1), (96, 1), (97, 1), (98, 1), (74, 1), (94, 1)])"
-heuristic_minbudget,3.9915353000105824,100,0.8,"(23, [(76, 1), (2, 1), (48, 1), (98, 1), (9, 1), (29, 1), (21, 1), (22, 1), (91, 1), (37, 1), (16, 1), (65, 1), (8, 1), (71, 1), (85, 1), (89, 1), (86, 1), (94, 1), (74, 1), (97, 1), (95, 1), (53, 1), (96, 1)])"
-spreading_minbudget,3.2877872999961255,100,0.8,"(33, [(69, 1), (1, 1), (2, 1), (6, 1), (8, 1), (10, 1), (12, 1), (22, 1), (23, 1), (24, 1), (25, 1), (27, 1), (29, 1), (31, 1), (37, 1), (43, 1), (45, 1), (46, 1), (48, 1), (53, 1), (58, 1), (65, 1), (66, 1), (67, 1), (82, 1), (89, 1), (95, 1), (98, 1), (4, 1), (33, 1), (41, 1), (52, 1), (94, 1)])"
-heuristic_minbudget,4.246031399990898,100,0.8,"(33, [(6, 1), (4, 1), (25, 1), (48, 1), (2, 1), (24, 1), (52, 1), (58, 1), (27, 1), (69, 1), (22, 1), (37, 1), (29, 1), (94, 1), (43, 1), (46, 1), (65, 1), (82, 1), (98, 1), (89, 1), (8, 1), (45, 1), (10, 1), (66, 1), (12, 1), (23, 1), (33, 1), (41, 1), (1, 1), (31, 1), (67, 1), (53, 1), (95, 1)])"
-spreading_minbudget,2.8299892999930307,100,0.8,"(27, [(5, 1), (1, 1), (6, 1), (8, 1), (9, 1), (20, 1), (27, 1), (31, 1), (37, 1), (46, 1), (51, 1), (53, 1), (55, 1), (58, 1), (63, 1), (64, 1), (66, 1), (73, 1), (79, 1), (81, 1), (83, 1), (85, 1), (91, 1), (98, 1), (41, 1), (44, 1), (18, 1)])"
-heuristic_minbudget,4.0879720999946585,100,0.8,"(27, [(58, 1), (6, 1), (51, 1), (20, 1), (98, 1), (27, 1), (64, 1), (8, 1), (9, 1), (73, 1), (46, 1), (37, 1), (5, 1), (83, 1), (55, 1), (41, 1), (81, 1), (18, 1), (31, 1), (91, 1), (53, 1), (1, 1), (63, 1), (44, 1), (85, 1), (66, 1), (79, 1)])"
-spreading_minbudget,3.92984680000518,100,0.8,"(41, [(16, 1), (1, 1), (2, 1), (10, 1), (13, 1), (19, 1), (23, 1), (24, 1), (27, 1), (29, 1), (32, 1), (39, 1), (46, 1), (51, 1), (53, 1), (56, 1), (59, 1), (62, 1), (64, 1), (66, 1), (67, 1), (68, 1), (77, 1), (79, 1), (82, 1), (85, 1), (87, 1), (89, 1), (93, 1), (95, 1), (96, 1), (98, 1), (99, 1), (33, 1), (34, 1), (41, 1), (44, 1), (94, 1), (11, 1), (40, 1), (18, 1)])"
-heuristic_minbudget,3.953235400011181,100,0.8,"(41, [(67, 1), (39, 1), (34, 1), (24, 1), (2, 1), (40, 1), (51, 1), (82, 1), (29, 1), (27, 1), (46, 1), (87, 1), (64, 1), (56, 1), (16, 1), (19, 1), (23, 1), (53, 1), (94, 1), (10, 1), (98, 1), (66, 1), (1, 1), (44, 1), (33, 1), (11, 1), (13, 1), (18, 1), (32, 1), (59, 1), (93, 1), (85, 1), (41, 1), (79, 1), (89, 1), (62, 1), (68, 1), (95, 1), (96, 1), (77, 1), (99, 1)])"
-spreading_minbudget,1.3322335999982897,100,0.8,"(14, [(6, 1), (1, 1), (22, 1), (29, 1), (46, 1), (58, 1), (69, 1), (75, 1), (77, 1), (82, 1), (85, 1), (92, 1), (95, 1), (40, 1)])"
-heuristic_minbudget,2.7363971000013407,100,0.8,"(14, [(6, 1), (40, 1), (58, 1), (29, 1), (82, 1), (69, 1), (22, 1), (46, 1), (92, 1), (85, 1), (77, 1), (1, 1), (75, 1), (95, 1)])"
-spreading_minbudget,0.8161610999959521,100,0.8,"(8, [(22, 1), (31, 1), (45, 1), (85, 1), (86, 1), (91, 1), (96, 1), (44, 1)])"
-heuristic_minbudget,2.7852620000048773,100,0.8,"(8, [(45, 1), (22, 1), (91, 1), (31, 1), (86, 1), (44, 1), (85, 1), (96, 1)])"
-spreading_minbudget,1.302543899990269,100,0.8,"(14, [(2, 1), (10, 1), (45, 1), (63, 1), (64, 1), (65, 1), (79, 1), (85, 1), (91, 1), (92, 1), (96, 1), (52, 1), (74, 1), (40, 1)])"
-heuristic_minbudget,4.053526600007899,100,0.8,"(14, [(2, 1), (40, 1), (52, 1), (64, 1), (65, 1), (91, 1), (45, 1), (92, 1), (10, 1), (74, 1), (85, 1), (63, 1), (79, 1), (96, 1)])"
-spreading_minbudget,6.874619900001562,200,0.1,"(13, [(48, 1), (24, 1), (157, 1), (42, 1), (196, 1), (33, 1), (21, 1), (59, 1), (153, 1), (28, 1), (52, 1), (136, 1), (99, 1)])"
-heuristic_minbudget,0.3828627999901073,200,0.1,"(8, [(99, 1), (52, 1), (153, 1), (136, 1), (28, 1), (48, 1), (147, 1), (194, 1), (92, 2), (91, 2), (50, 2), (1, 2), (186, 2), (82, 2), (42, 2), (178, 2)])"
-spreading_minbudget,3.6053887000016402,200,0.1,"(8, [(116, 1), (153, 1), (45, 1), (49, 1), (105, 1), (95, 1), (65, 1), (99, 1)])"
-heuristic_minbudget,0.43296489999920595,200,0.1,"(5, [(99, 1), (65, 1), (153, 1), (194, 1), (76, 1), (64, 2), (29, 2), (91, 2), (168, 2), (80, 2)])"
-spreading_minbudget,6.127774299995508,200,0.1,"(12, [(57, 1), (164, 1), (21, 1), (49, 1), (108, 1), (194, 1), (11, 1), (42, 1), (68, 1), (153, 1), (65, 1), (28, 1)])"
-heuristic_minbudget,0.5014686999929836,200,0.1,"(8, [(194, 1), (153, 1), (28, 1), (65, 1), (106, 1), (48, 1), (52, 1), (76, 1), (29, 2), (177, 2), (142, 2), (171, 2), (178, 2), (120, 2), (16, 2), (128, 2)])"
-spreading_minbudget,6.081753100006608,200,0.1,"(14, [(155, 1), (131, 1), (185, 1), (6, 1), (44, 1), (76, 1), (147, 1), (153, 1), (79, 1), (161, 1), (28, 1), (112, 1), (52, 1), (106, 1)])"
-heuristic_minbudget,0.3598169000033522,200,0.1,"(8, [(147, 1), (153, 1), (52, 1), (76, 1), (106, 1), (112, 1), (28, 1), (48, 1), (192, 2), (3, 2), (101, 2), (22, 2), (122, 2), (78, 2), (195, 2), (17, 2)])"
-spreading_minbudget,6.416437199994107,200,0.1,"(10, [(194, 1), (6, 1), (52, 1), (101, 1), (4, 1), (170, 1), (46, 1), (9, 1), (41, 1), (28, 1)])"
-heuristic_minbudget,0.5401426000025822,200,0.1,"(8, [(52, 1), (4, 1), (28, 1), (194, 1), (106, 1), (147, 1), (76, 1), (48, 1), (17, 2), (2, 2), (61, 2), (50, 2), (115, 2), (143, 2), (16, 2), (93, 2)])"
-spreading_minbudget,7.155516500002705,200,0.1,"(13, [(124, 1), (196, 1), (116, 1), (66, 1), (21, 1), (120, 1), (128, 1), (138, 1), (44, 1), (72, 1), (48, 1), (65, 1), (136, 1)])"
-heuristic_minbudget,0.45384590000321623,200,0.1,"(9, [(48, 1), (44, 1), (136, 1), (65, 1), (147, 1), (52, 1), (194, 1), (76, 1), (106, 1), (170, 2), (184, 2), (12, 2), (50, 2), (2, 2), (57, 2), (129, 2), (87, 2), (69, 2)])"
-spreading_minbudget,5.644121200006339,200,0.1,"(13, [(129, 1), (199, 1), (44, 1), (82, 1), (39, 1), (76, 1), (182, 1), (194, 1), (54, 1), (161, 1), (4, 1), (28, 1), (52, 1)])"
-heuristic_minbudget,0.48520840000128374,200,0.1,"(9, [(194, 1), (76, 1), (52, 1), (4, 1), (28, 1), (161, 1), (147, 1), (48, 1), (44, 1), (17, 2), (107, 2), (57, 2), (129, 2), (122, 2), (81, 2), (108, 2), (121, 2), (2, 2)])"
-spreading_minbudget,1.8839236000058008,200,0.1,"(5, [(48, 1), (91, 1), (42, 1), (136, 1), (106, 1)])"
-heuristic_minbudget,0.398219099995913,200,0.1,"(3, [(136, 1), (106, 1), (48, 1), (157, 2), (128, 2), (120, 2)])"
-spreading_minbudget,8.769554500002414,200,0.1,"(16, [(3, 1), (157, 1), (116, 1), (106, 1), (70, 1), (44, 1), (17, 1), (43, 1), (138, 1), (194, 1), (147, 1), (76, 1), (4, 1), (48, 1), (65, 1), (112, 1)])"
-heuristic_minbudget,0.48429249999753665,200,0.1,"(10, [(48, 1), (147, 1), (194, 1), (76, 1), (106, 1), (44, 1), (65, 1), (112, 1), (4, 1), (52, 1), (142, 2), (16, 2), (107, 2), (69, 2), (42, 2), (12, 2), (143, 2), (87, 2), (6, 2), (177, 2)])"
-spreading_minbudget,4.215613599997596,200,0.1,"(9, [(116, 1), (33, 1), (70, 1), (101, 1), (21, 1), (194, 1), (4, 1), (52, 1), (106, 1)])"
-heuristic_minbudget,0.4958438000030583,200,0.1,"(6, [(52, 1), (106, 1), (4, 1), (147, 1), (194, 1), (76, 1), (59, 2), (81, 2), (2, 2), (129, 2), (61, 2), (16, 2)])"
-spreading_minbudget,0.1568801999965217,200,0.5,"(1, [(10, 1)])"
-heuristic_minbudget,3.0877527999982703,200,0.5,"(1, [(132, 1)])"
-spreading_minbudget,2.236697499989532,200,0.5,"(6, [(99, 1), (168, 1), (175, 1), (31, 1), (149, 1), (177, 1)])"
-heuristic_minbudget,3.3113985999953,200,0.5,"(5, [(149, 1), (168, 1), (177, 1), (31, 1), (175, 1)])"
-spreading_minbudget,9.140447199999471,200,0.5,"(32, [(59, 1), (144, 1), (63, 1), (19, 1), (34, 1), (41, 1), (50, 1), (65, 1), (82, 1), (85, 1), (103, 1), (116, 1), (130, 1), (143, 1), (168, 1), (180, 1), (184, 1), (191, 1), (193, 1), (28, 1), (75, 1), (139, 1), (146, 1), (149, 1), (162, 1), (61, 1), (68, 1), (81, 1), (137, 1), (186, 1), (153, 1), (192, 1)])"
-heuristic_minbudget,5.723877700002049,200,0.5,"(31, [(184, 1), (143, 1), (116, 1), (61, 1), (59, 1), (149, 1), (130, 1), (186, 1), (75, 1), (68, 1), (82, 1), (103, 1), (162, 1), (191, 1), (193, 1), (41, 1), (19, 1), (180, 1), (192, 1), (28, 1), (85, 1), (50, 1), (168, 1), (63, 1), (34, 1), (137, 1), (153, 1), (146, 1), (65, 1), (139, 1), (81, 1)])"
-spreading_minbudget,2.4964694999944186,200,0.5,"(8, [(63, 1), (85, 1), (140, 1), (180, 1), (193, 1), (197, 1), (199, 1), (119, 1)])"
-heuristic_minbudget,3.2830257000023266,200,0.5,"(7, [(180, 1), (140, 1), (193, 1), (119, 1), (199, 1), (85, 1), (197, 1)])"
-spreading_minbudget,1.0909339999925578,200,0.5,"(2, [(4, 1), (192, 1)])"
-heuristic_minbudget,3.0061140000034356,200,0.5,"(2, [(192, 1), (108, 1)])"
-spreading_minbudget,2.8638310999958776,200,0.5,"(9, [(31, 1), (13, 1), (41, 1), (45, 1), (103, 1), (116, 1), (74, 1), (127, 1), (68, 1)])"
-heuristic_minbudget,3.336111199998413,200,0.5,"(7, [(45, 1), (41, 1), (116, 1), (68, 1), (103, 1), (127, 1), (74, 1)])"
-spreading_minbudget,6.566627099993639,200,0.5,"(19, [(1, 1), (76, 1), (34, 1), (112, 1), (160, 1), (193, 1), (197, 1), (55, 1), (69, 1), (86, 1), (122, 1), (139, 1), (141, 1), (159, 1), (196, 1), (137, 1), (16, 1), (153, 1), (93, 1)])"
-heuristic_minbudget,4.1070819000015035,200,0.5,"(17, [(86, 1), (139, 1), (193, 1), (122, 1), (196, 1), (69, 1), (93, 1), (159, 1), (55, 1), (153, 1), (112, 1), (34, 1), (141, 1), (137, 1), (16, 1), (160, 1), (197, 1)])"
-spreading_minbudget,11.145615999994334,200,0.5,"(35, [(99, 1), (138, 1), (48, 1), (32, 1), (44, 1), (52, 1), (56, 1), (85, 1), (103, 1), (107, 1), (120, 1), (130, 1), (140, 1), (160, 1), (175, 1), (180, 1), (191, 1), (8, 1), (25, 1), (55, 1), (67, 1), (69, 1), (74, 1), (86, 1), (132, 1), (139, 1), (141, 1), (159, 1), (68, 1), (98, 1), (104, 1), (186, 1), (177, 1), (119, 1), (21, 1)])"
-heuristic_minbudget,6.92385840001225,200,0.5,"(33, [(103, 1), (67, 1), (68, 1), (130, 1), (132, 1), (48, 1), (186, 1), (177, 1), (69, 1), (21, 1), (191, 1), (98, 1), (104, 1), (107, 1), (119, 1), (180, 1), (32, 1), (140, 1), (159, 1), (85, 1), (120, 1), (86, 1), (175, 1), (52, 1), (8, 1), (55, 1), (25, 1), (139, 1), (160, 1), (56, 1), (44, 1), (141, 1), (74, 1)])"
-spreading_minbudget,7.714225499992608,200,0.5,"(27, [(194, 1), (30, 1), (19, 1), (41, 1), (56, 1), (65, 1), (85, 1), (87, 1), (115, 1), (116, 1), (121, 1), (130, 1), (140, 1), (143, 1), (168, 1), (175, 1), (180, 1), (181, 1), (17, 1), (67, 1), (122, 1), (138, 1), (141, 1), (196, 1), (68, 1), (81, 1), (104, 1)])"
-heuristic_minbudget,4.569759200006956,200,0.5,"(26, [(30, 1), (138, 1), (121, 1), (130, 1), (41, 1), (116, 1), (67, 1), (196, 1), (68, 1), (87, 1), (19, 1), (168, 1), (17, 1), (104, 1), (115, 1), (180, 1), (85, 1), (143, 1), (140, 1), (175, 1), (181, 1), (122, 1), (56, 1), (65, 1), (141, 1), (81, 1)])"
-spreading_minbudget,5.409246000010171,200,0.5,"(21, [(184, 1), (34, 1), (45, 1), (52, 1), (80, 1), (103, 1), (112, 1), (120, 1), (130, 1), (143, 1), (168, 1), (175, 1), (193, 1), (199, 1), (8, 1), (28, 1), (109, 1), (135, 1), (198, 1), (61, 1), (119, 1)])"
-heuristic_minbudget,4.488193300014245,200,0.5,"(21, [(184, 1), (45, 1), (193, 1), (61, 1), (130, 1), (119, 1), (103, 1), (199, 1), (80, 1), (52, 1), (28, 1), (120, 1), (143, 1), (175, 1), (34, 1), (135, 1), (168, 1), (8, 1), (109, 1), (112, 1), (198, 1)])"
-spreading_minbudget,5.363142900008825,200,0.8,"(21, [(3, 1), (21, 1), (33, 1), (44, 1), (45, 1), (68, 1), (69, 1), (70, 1), (74, 1), (138, 1), (149, 1), (150, 1), (155, 1), (171, 1), (176, 1), (179, 1), (195, 1), (198, 1), (37, 1), (162, 1), (190, 1)])"
-heuristic_minbudget,13.136786300005042,200,0.8,"(21, [(195, 1), (171, 1), (150, 1), (155, 1), (44, 1), (3, 1), (45, 1), (74, 1), (69, 1), (190, 1), (68, 1), (179, 1), (149, 1), (37, 1), (162, 1), (21, 1), (138, 1), (70, 1), (198, 1), (176, 1), (33, 1)])"
-spreading_minbudget,13.20309090000228,200,0.8,"(63, [(17, 1), (6, 1), (13, 1), (16, 1), (21, 1), (22, 1), (23, 1), (25, 1), (26, 1), (28, 1), (33, 1), (35, 1), (36, 1), (38, 1), (39, 1), (43, 1), (53, 1), (58, 1), (60, 1), (61, 1), (65, 1), (68, 1), (70, 1), (71, 1), (73, 1), (77, 1), (78, 1), (88, 1), (90, 1), (99, 1), (108, 1), (109, 1), (113, 1), (124, 1), (129, 1), (131, 1), (134, 1), (136, 1), (140, 1), (142, 1), (148, 1), (150, 1), (155, 1), (163, 1), (175, 1), (176, 1), (178, 1), (179, 1), (184, 1), (192, 1), (195, 1), (196, 1), (198, 1), (199, 1), (5, 1), (8, 1), (15, 1), (29, 1), (63, 1), (95, 1), (123, 1), (133, 1), (146, 1)])"
-heuristic_minbudget,18.206225700007053,200,0.8,"(63, [(53, 1), (26, 1), (88, 1), (5, 1), (23, 1), (38, 1), (150, 1), (155, 1), (178, 1), (39, 1), (108, 1), (134, 1), (13, 1), (36, 1), (163, 1), (17, 1), (109, 1), (175, 1), (195, 1), (63, 1), (113, 1), (8, 1), (77, 1), (71, 1), (124, 1), (136, 1), (140, 1), (21, 1), (148, 1), (22, 1), (43, 1), (60, 1), (123, 1), (142, 1), (65, 1), (6, 1), (35, 1), (196, 1), (68, 1), (95, 1), (176, 1), (179, 1), (28, 1), (146, 1), (29, 1), (58, 1), (70, 1), (61, 1), (25, 1), (73, 1), (78, 1), (199, 1), (133, 1), (33, 1), (99, 1), (131, 1), (90, 1), (184, 1), (198, 1), (15, 1), (129, 1), (16, 1), (192, 1)])"
-spreading_minbudget,1.1575602000084473,200,0.8,"(5, [(80, 1), (28, 1), (86, 1), (134, 1), (195, 1)])"
-heuristic_minbudget,9.099992300005397,200,0.8,"(5, [(80, 1), (134, 1), (28, 1), (195, 1), (86, 1)])"
-spreading_minbudget,8.297417900001165,200,0.8,"(33, [(26, 1), (19, 1), (25, 1), (30, 1), (31, 1), (35, 1), (42, 1), (61, 1), (72, 1), (76, 1), (78, 1), (93, 1), (94, 1), (98, 1), (125, 1), (128, 1), (132, 1), (134, 1), (135, 1), (138, 1), (140, 1), (155, 1), (163, 1), (172, 1), (178, 1), (195, 1), (196, 1), (81, 1), (133, 1), (146, 1), (27, 1), (51, 1), (59, 1)])"
-heuristic_minbudget,13.201126400002977,200,0.8,"(33, [(26, 1), (19, 1), (94, 1), (59, 1), (155, 1), (178, 1), (128, 1), (81, 1), (134, 1), (163, 1), (27, 1), (195, 1), (72, 1), (98, 1), (31, 1), (42, 1), (132, 1), (30, 1), (76, 1), (196, 1), (140, 1), (125, 1), (25, 1), (51, 1), (93, 1), (135, 1), (61, 1), (138, 1), (146, 1), (35, 1), (133, 1), (78, 1), (172, 1)])"
-spreading_minbudget,12.984143499998027,200,0.8,"(63, [(89, 1), (3, 1), (13, 1), (14, 1), (16, 1), (17, 1), (23, 1), (32, 1), (33, 1), (38, 1), (40, 1), (41, 1), (42, 1), (45, 1), (47, 1), (56, 1), (58, 1), (64, 1), (65, 1), (67, 1), (68, 1), (75, 1), (76, 1), (80, 1), (86, 1), (90, 1), (92, 1), (93, 1), (98, 1), (99, 1), (100, 1), (106, 1), (112, 1), (117, 1), (121, 1), (131, 1), (132, 1), (134, 1), (136, 1), (142, 1), (150, 1), (151, 1), (158, 1), (164, 1), (171, 1), (174, 1), (175, 1), (183, 1), (187, 1), (188, 1), (196, 1), (197, 1), (198, 1), (199, 1), (5, 1), (8, 1), (52, 1), (95, 1), (96, 1), (102, 1), (116, 1), (123, 1), (87, 1)])"
-heuristic_minbudget,17.87442839999858,200,0.8,"(63, [(175, 1), (5, 1), (171, 1), (89, 1), (23, 1), (102, 1), (40, 1), (38, 1), (150, 1), (47, 1), (151, 1), (197, 1), (13, 1), (52, 1), (17, 1), (41, 1), (75, 1), (96, 1), (76, 1), (65, 1), (56, 1), (80, 1), (116, 1), (132, 1), (134, 1), (8, 1), (98, 1), (67, 1), (136, 1), (42, 1), (58, 1), (174, 1), (86, 1), (32, 1), (121, 1), (164, 1), (3, 1), (196, 1), (95, 1), (112, 1), (68, 1), (64, 1), (131, 1), (100, 1), (123, 1), (142, 1), (187, 1), (117, 1), (188, 1), (14, 1), (45, 1), (199, 1), (198, 1), (16, 1), (106, 1), (33, 1), (87, 1), (158, 1), (92, 1), (99, 1), (90, 1), (93, 1), (183, 1)])"
-spreading_minbudget,5.6764264999947045,200,0.8,"(27, [(66, 1), (6, 1), (16, 1), (25, 1), (31, 1), (38, 1), (41, 1), (57, 1), (65, 1), (76, 1), (89, 1), (92, 1), (106, 1), (126, 1), (140, 1), (158, 1), (160, 1), (167, 1), (174, 1), (182, 1), (185, 1), (192, 1), (196, 1), (52, 1), (95, 1), (102, 1), (181, 1)])"
-heuristic_minbudget,13.88039399999252,200,0.8,"(27, [(76, 1), (185, 1), (66, 1), (89, 1), (102, 1), (38, 1), (174, 1), (65, 1), (6, 1), (52, 1), (182, 1), (167, 1), (41, 1), (196, 1), (25, 1), (126, 1), (140, 1), (95, 1), (31, 1), (181, 1), (16, 1), (92, 1), (158, 1), (57, 1), (160, 1), (106, 1), (192, 1)])"
-spreading_minbudget,4.208028399996692,200,0.8,"(16, [(31, 1), (13, 1), (38, 1), (58, 1), (75, 1), (76, 1), (78, 1), (88, 1), (98, 1), (101, 1), (153, 1), (164, 1), (167, 1), (197, 1), (63, 1), (159, 1)])"