commit e120708d8d1e5ed03df65f090bc9aa6081f2f5eb
Author: Wu Han <wuhanstudio@qq.com>
Date:   Sat Dec 19 18:20:11 2020 +0000

    Initial Commit

diff --git a/.dockerignore.txt b/.dockerignore.txt
new file mode 100644
index 0000000..e4c330e
--- /dev/null
+++ b/.dockerignore.txt
@@ -0,0 +1,3 @@
+.vscode/
+*.json
+*.txt
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..ebfe9e6
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,2 @@
+.vscode/
+__pycache__/
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..1157d4b
--- /dev/null
+++ b/README.md
@@ -0,0 +1,8 @@
+
+## Windows
+
+docker container run --rm -v /f/COM2014/template:/tsp python:3.8 /bin/bash -c "cd /tsp; ./run_all.sh"
+
+## Linux
+
+docker container run --rm -v $pwd:/tsp python:3.8 /bin/bash -c "cd /tsp; ./run_all.sh"
diff --git a/data/hard/dsj1000.tsp b/data/hard/dsj1000.tsp
new file mode 100644
index 0000000..41943c9
--- /dev/null
+++ b/data/hard/dsj1000.tsp
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+NAME : dsj1000
+COMMENT : Clustered random problem (Johnson)
+TYPE : TSP
+DIMENSION : 1000
+EDGE_WEIGHT_TYPE : CEIL_2D
+NODE_COORD_SECTION
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diff --git a/data/medium/a280.tsp b/data/medium/a280.tsp
new file mode 100644
index 0000000..40e506c
--- /dev/null
+++ b/data/medium/a280.tsp
@@ -0,0 +1,287 @@
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diff --git a/data/medium/pcb442.tsp b/data/medium/pcb442.tsp
new file mode 100644
index 0000000..2b74de7
--- /dev/null
+++ b/data/medium/pcb442.tsp
@@ -0,0 +1,449 @@
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+430 2.85000e+03 2.20000e+03
+431 2.85000e+03 2.70000e+03
+432 2.85000e+03 3.35000e+03
+433 2.93000e+03 9.50000e+02
+434 2.95000e+03 1.75000e+03
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+440 2.55000e+03 7.10000e+02
+441 7.50000e+02 4.90000e+02
+442 0.00000e+00 0.00000e+00
+EOF
diff --git a/data/simple/att48.tsp b/data/simple/att48.tsp
new file mode 100644
index 0000000..a1cf11e
--- /dev/null
+++ b/data/simple/att48.tsp
@@ -0,0 +1,55 @@
+NAME : att48
+COMMENT : 48 capitals of the US (Padberg/Rinaldi)
+TYPE : TSP
+DIMENSION : 48
+EDGE_WEIGHT_TYPE : ATT
+NODE_COORD_SECTION
+1 6734 1453
+2 2233 10
+3 5530 1424
+4 401 841
+5 3082 1644
+6 7608 4458
+7 7573 3716
+8 7265 1268
+9 6898 1885
+10 1112 2049
+11 5468 2606
+12 5989 2873
+13 4706 2674
+14 4612 2035
+15 6347 2683
+16 6107 669
+17 7611 5184
+18 7462 3590
+19 7732 4723
+20 5900 3561
+21 4483 3369
+22 6101 1110
+23 5199 2182
+24 1633 2809
+25 4307 2322
+26 675 1006
+27 7555 4819
+28 7541 3981
+29 3177 756
+30 7352 4506
+31 7545 2801
+32 3245 3305
+33 6426 3173
+34 4608 1198
+35 23 2216
+36 7248 3779
+37 7762 4595
+38 7392 2244
+39 3484 2829
+40 6271 2135
+41 4985 140
+42 1916 1569
+43 7280 4899
+44 7509 3239
+45 10 2676
+46 6807 2993
+47 5185 3258
+48 3023 1942
+EOF
diff --git a/data/simple/st70.tsp b/data/simple/st70.tsp
new file mode 100644
index 0000000..e3abfde
--- /dev/null
+++ b/data/simple/st70.tsp
@@ -0,0 +1,77 @@
+NAME: st70
+TYPE: TSP
+COMMENT: 70-city problem (Smith/Thompson)
+DIMENSION: 70
+EDGE_WEIGHT_TYPE : EUC_2D
+NODE_COORD_SECTION
+1 64 96
+2 80 39
+3 69 23
+4 72 42
+5 48 67
+6 58 43
+7 81 34
+8 79 17
+9 30 23
+10 42 67
+11 7 76
+12 29 51
+13 78 92
+14 64 8
+15 95 57
+16 57 91
+17 40 35
+18 68 40
+19 92 34
+20 62 1
+21 28 43
+22 76 73
+23 67 88
+24 93 54
+25 6 8
+26 87 18
+27 30 9
+28 77 13
+29 78 94
+30 55 3
+31 82 88
+32 73 28
+33 20 55
+34 27 43
+35 95 86
+36 67 99
+37 48 83
+38 75 81
+39 8 19
+40 20 18
+41 54 38
+42 63 36
+43 44 33
+44 52 18
+45 12 13
+46 25 5
+47 58 85
+48 5 67
+49 90 9
+50 41 76
+51 25 76
+52 37 64
+53 56 63
+54 10 55
+55 98 7
+56 16 74
+57 89 60
+58 48 82
+59 81 76
+60 29 60
+61 17 22
+62 5 45
+63 79 70
+64 9 100
+65 17 82
+66 74 67
+67 10 68
+68 48 19
+69 83 86
+70 84 94
+EOF
diff --git a/data/simple/ulysses16.tsp b/data/simple/ulysses16.tsp
new file mode 100644
index 0000000..f6ad82f
--- /dev/null
+++ b/data/simple/ulysses16.tsp
@@ -0,0 +1,25 @@
+NAME: ulysses16.tsp
+TYPE: TSP
+COMMENT: Odyssey of Ulysses (Groetschel/Padberg)
+DIMENSION: 16
+EDGE_WEIGHT_TYPE: GEO
+DISPLAY_DATA_TYPE: COORD_DISPLAY
+NODE_COORD_SECTION
+ 1 38.24 20.42
+ 2 39.57 26.15
+ 3 40.56 25.32
+ 4 36.26 23.12
+ 5 33.48 10.54
+ 6 37.56 12.19
+ 7 38.42 13.11
+ 8 37.52 20.44
+ 9 41.23 9.10
+ 10 41.17 13.05
+ 11 36.08 -5.21
+ 12 38.47 15.13
+ 13 38.15 15.35
+ 14 37.51 15.17
+ 15 35.49 14.32
+ 16 39.36 19.56
+ EOF
+
diff --git a/main.py b/main.py
new file mode 100644
index 0000000..44da870
--- /dev/null
+++ b/main.py
@@ -0,0 +1,98 @@
+import os
+import argparse
+
+import json
+
+import matplotlib
+# matplotlib.use('TkAgg')
+import matplotlib.pyplot as plt
+
+from utils import visualize_tsp
+
+from model.anneal_model import SimAnneal
+from model.my_model import MyModel
+
+def write_to_file(solution, fitness):
+    with open('output/' + os.path.splitext(os.path.basename(args.tsp_file))[0] + '.txt', "w") as outfile:
+        outfile.write(", ".join(str(item) for item in solution))
+        outfile.write("\n")
+        outfile.write(str(fitness))
+
+def plot_learning(fitness_list):
+    """
+    Plot the fitness through iterations.
+    """
+    plt.plot([i for i in range(len(fitness_list))], fitness_list)
+    plt.ylabel("Fitness")
+    plt.xlabel("Iteration")
+    plt.show()
+
+def load_data(file):
+    coords = []
+    with open(file, "r") as infile:
+        line = infile.readline()
+        # Skip instance header
+        while "NODE_COORD_SECTION" not in line:
+            line = infile.readline()
+
+        for line in infile.readlines():
+            line = line.replace("\n", "")
+            if line and 'EOF' not in line:
+                line = [float(x) for x in line.lstrip().split(" ", 1)[1].split(" ") if x]
+                coords.append(line)
+    return coords
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='TSP Solver')
+    parser.add_argument(
+        'tsp_file',
+        type=str,
+        help='Path to tsp file.'
+    )
+    parser.add_argument(
+        '--plot',
+        action="store_true",
+        help='Plot results'
+    )
+    args = parser.parse_args()
+
+    coords = load_data(args.tsp_file)
+
+    # model = MyModel(coords)
+    # best_solution, fitness_list = model.fit(max_it=100000)
+
+    sa = SimAnneal(coords)
+    best_solution, fitness_list = sa.fit(max_it=100000)
+
+    if best_solution:
+        if args.plot:
+            visualize_tsp.plotTSP([best_solution], coords)
+            if fitness_list:
+                plot_learning(fitness_list)
+        print("Writing the best solution to file" )
+        write_to_file(best_solution, sa.fitness(best_solution))
+
+    data = {}
+    data['nodes'] = []
+    for i in range(len(coords)):
+        data['nodes'].append({
+            'title': str(i),
+            'id': i,
+            'x': int(coords[i][0]),
+            'y': int(coords[i][1])
+        })
+    data['edges'] = []
+    for i in range(len(best_solution)):
+        if i == len(best_solution)-1:
+            data['edges'].append({
+                'source': best_solution[i],
+                'target': best_solution[0]
+            })
+        else:
+            data['edges'].append({
+                'source': best_solution[i],
+                'target': best_solution[i+1]
+            })
+    
+    with open('output/' + os.path.splitext(os.path.basename(args.tsp_file))[0] + '.json', 'w') as outfile:
+        json.dump(data, outfile)
diff --git a/model/anneal_model.py b/model/anneal_model.py
new file mode 100644
index 0000000..ba49e0a
--- /dev/null
+++ b/model/anneal_model.py
@@ -0,0 +1,83 @@
+import math
+import random
+import matplotlib.pyplot as plt
+from model.base_model import Model
+
+class SimAnneal(Model):
+    def __init__(self, coords, T=-1, alpha=-1, stopping_T=-1):
+        super().__init__(coords)
+
+        self.iteration = 0
+
+        self.T = math.sqrt(self.N) if T == -1 else T
+        self.T_save = self.T  # save inital T to reset if batch annealing is used
+        self.alpha = 0.995 if alpha == -1 else alpha
+        self.stopping_temperature = 1e-8 if stopping_T == -1 else stopping_T
+
+    def initial_solution(self):
+        """
+        Greedy algorithm to get an initial solution (closest-neighbour).
+        """
+        cur_node = random.choice(self.nodes)  # start from a random node
+        solution = [cur_node]
+
+        free_nodes = set(self.nodes)
+        free_nodes.remove(cur_node)
+        while free_nodes:
+            next_node = min(free_nodes, key=lambda x: self.dist(cur_node, x))  # nearest neighbour
+            free_nodes.remove(next_node)
+            solution.append(next_node)
+            cur_node = next_node
+
+        cur_fit = self.fitness(solution)
+        if cur_fit < self.best_fitness:  # If best found so far, update best fitness
+            self.best_fitness = cur_fit
+            self.best_solution = solution
+        self.fitness_list.append(cur_fit)
+        return solution, cur_fit
+
+    def p_accept(self, candidate_fitness):
+        """
+        Probability of accepting if the candidate is worse than current.
+        Depends on the current temperature and difference between candidate and current.
+        """
+        return math.exp(-abs(candidate_fitness - self.cur_fitness) / self.T)
+
+    def accept(self, candidate):
+        """
+        Accept with probability 1 if candidate is better than current.
+        Accept with probabilty p_accept(..) if candidate is worse.
+        """
+        candidate_fitness = self.fitness(candidate)
+        if candidate_fitness < self.cur_fitness:
+            self.cur_fitness, self.cur_solution = candidate_fitness, candidate
+            if candidate_fitness < self.best_fitness:
+                self.best_fitness, self.best_solution = candidate_fitness, candidate
+        else:
+            if random.random() < self.p_accept(candidate_fitness):
+                self.cur_fitness, self.cur_solution = candidate_fitness, candidate
+
+    def fit(self, max_it=1000):
+        """
+        Execute simulated annealing algorithm.
+        """
+        # Initialize with the greedy solution.
+        self.cur_solution, self.cur_fitness = self.initial_solution()
+
+        self.log("Starting annealing.")
+        while self.T >= self.stopping_temperature and self.iteration < max_it:
+            candidate = list(self.cur_solution)
+            l = random.randint(2, self.N - 1)
+            i = random.randint(0, self.N - l)
+            candidate[i : (i + l)] = reversed(candidate[i : (i + l)])
+            self.accept(candidate)
+            self.T *= self.alpha
+            self.iteration += 1
+
+            self.fitness_list.append(self.cur_fitness)
+
+        self.log(f"Best fitness obtained: {self.best_fitness}")
+        improvement = 100 * (self.fitness_list[0] - self.best_fitness) / (self.fitness_list[0])
+        self.log(f"Improvement over greedy heuristic: {improvement : .2f}%")
+
+        return self.best_solution, self.fitness_list
diff --git a/model/base_model.py b/model/base_model.py
new file mode 100644
index 0000000..047f95c
--- /dev/null
+++ b/model/base_model.py
@@ -0,0 +1,34 @@
+import math
+
+class Model:
+
+    def __init__(self, coords):
+        self.coords = coords
+        self.N = len(coords)
+        self.nodes = [i for i in range(self.N)]
+
+        self.best_solution = None
+        self.best_fitness = float("Inf")
+        self.fitness_list = []
+
+    def dist(self, node_0, node_1):
+        """
+        Euclidean distance between two nodes.
+        """
+        coord_0, coord_1 = self.coords[node_0], self.coords[node_1]
+        return math.sqrt((coord_0[0] - coord_1[0]) ** 2 + (coord_0[1] - coord_1[1]) ** 2)
+
+    def fitness(self, solution):
+        """
+        Total distance of the current solution path.
+        """
+        cur_fit = 0
+        for i in range(self.N):
+            cur_fit += self.dist(solution[i % self.N], solution[(i + 1) % self.N])
+        return cur_fit
+
+    def fit(self):
+        raise NotImplementedError("Your fitting method not implemented yet")
+
+    def log(self, message):
+        print('[{name}] {msg}'.format(name=self.__class__.__name__, msg=message))
diff --git a/model/my_model.py b/model/my_model.py
new file mode 100644
index 0000000..a0f99f9
--- /dev/null
+++ b/model/my_model.py
@@ -0,0 +1,19 @@
+import math
+import random
+from model.base_model import Model
+
+class MyModel(Model):
+    def __init__(self, coords):
+        super().__init__(coords)
+        """
+        Put your initialization here.
+        """
+        self.log("Nothing to initialize in your model now")
+
+    def fit(self, max_it=1000, visualize=False):
+        """
+        Put your iteration process here.
+        """
+        self.log("Nothing happens in your model now")
+
+        return self.best_solution, self.fitness_list
diff --git a/output/a280.json b/output/a280.json
new file mode 100644
index 0000000..4c6c6d0
--- /dev/null
+++ b/output/a280.json
@@ -0,0 +1 @@
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diff --git a/output/a280.txt b/output/a280.txt
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diff --git a/output/att48.json b/output/att48.json
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diff --git a/output/att48.txt b/output/att48.txt
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diff --git a/output/dsj1000.json b/output/dsj1000.json
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diff --git a/output/dsj1000.txt b/output/dsj1000.txt
new file mode 100644
index 0000000..593b6a6
--- /dev/null
+++ b/output/dsj1000.txt
@@ -0,0 +1 @@
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diff --git a/output/pcb442.json b/output/pcb442.json
new file mode 100644
index 0000000..c35d916
--- /dev/null
+++ b/output/pcb442.json
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diff --git a/output/pcb442.txt b/output/pcb442.txt
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diff --git a/output/st70.json b/output/st70.json
new file mode 100644
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+++ b/output/st70.json
@@ -0,0 +1 @@
+{"nodes": [{"title": "0", "id": 0, "x": 64, "y": 96}, {"title": "1", "id": 1, "x": 80, "y": 39}, {"title": "2", "id": 2, "x": 69, "y": 23}, {"title": "3", "id": 3, "x": 72, "y": 42}, {"title": "4", "id": 4, "x": 48, "y": 67}, {"title": "5", "id": 5, "x": 58, "y": 43}, {"title": "6", "id": 6, "x": 81, "y": 34}, {"title": "7", "id": 7, "x": 79, "y": 17}, {"title": "8", "id": 8, "x": 30, "y": 23}, {"title": "9", "id": 9, "x": 42, "y": 67}, {"title": "10", "id": 10, "x": 7, "y": 76}, {"title": "11", "id": 11, "x": 29, "y": 51}, {"title": "12", "id": 12, "x": 78, "y": 92}, {"title": "13", "id": 13, "x": 64, "y": 8}, {"title": "14", "id": 14, "x": 95, "y": 57}, {"title": "15", "id": 15, "x": 57, "y": 91}, {"title": "16", "id": 16, "x": 40, "y": 35}, {"title": "17", "id": 17, "x": 68, "y": 40}, {"title": "18", "id": 18, "x": 92, "y": 34}, {"title": "19", "id": 19, "x": 62, "y": 1}, {"title": "20", "id": 20, "x": 28, "y": 43}, {"title": "21", "id": 21, "x": 76, "y": 73}, {"title": "22", "id": 22, "x": 67, "y": 88}, {"title": "23", "id": 23, "x": 93, "y": 54}, {"title": "24", "id": 24, "x": 6, "y": 8}, {"title": "25", "id": 25, "x": 87, "y": 18}, {"title": "26", "id": 26, "x": 30, "y": 9}, {"title": "27", "id": 27, "x": 77, "y": 13}, {"title": "28", "id": 28, "x": 78, "y": 94}, {"title": "29", "id": 29, "x": 55, "y": 3}, {"title": "30", "id": 30, "x": 82, "y": 88}, {"title": "31", "id": 31, "x": 73, "y": 28}, {"title": "32", "id": 32, "x": 20, "y": 55}, {"title": "33", "id": 33, "x": 27, "y": 43}, {"title": "34", "id": 34, "x": 95, "y": 86}, {"title": "35", "id": 35, "x": 67, "y": 99}, {"title": "36", "id": 36, "x": 48, "y": 83}, {"title": "37", "id": 37, "x": 75, "y": 81}, {"title": "38", "id": 38, "x": 8, "y": 19}, {"title": "39", "id": 39, "x": 20, "y": 18}, {"title": "40", "id": 40, "x": 54, "y": 38}, {"title": "41", "id": 41, "x": 63, "y": 36}, {"title": "42", "id": 42, "x": 44, "y": 33}, {"title": "43", "id": 43, "x": 52, "y": 18}, {"title": "44", "id": 44, "x": 12, "y": 13}, {"title": "45", "id": 45, "x": 25, "y": 5}, {"title": "46", "id": 46, "x": 58, "y": 85}, {"title": "47", "id": 47, "x": 5, "y": 67}, {"title": "48", "id": 48, "x": 90, "y": 9}, {"title": "49", "id": 49, "x": 41, "y": 76}, {"title": "50", "id": 50, "x": 25, "y": 76}, {"title": "51", "id": 51, "x": 37, "y": 64}, {"title": "52", "id": 52, "x": 56, "y": 63}, {"title": "53", "id": 53, "x": 10, "y": 55}, {"title": "54", "id": 54, "x": 98, "y": 7}, {"title": "55", "id": 55, "x": 16, "y": 74}, {"title": "56", "id": 56, "x": 89, "y": 60}, {"title": "57", "id": 57, "x": 48, "y": 82}, {"title": "58", "id": 58, "x": 81, "y": 76}, {"title": "59", "id": 59, "x": 29, "y": 60}, {"title": "60", "id": 60, "x": 17, "y": 22}, {"title": "61", "id": 61, "x": 5, "y": 45}, {"title": "62", "id": 62, "x": 79, "y": 70}, {"title": "63", "id": 63, "x": 9, "y": 100}, {"title": "64", "id": 64, "x": 17, "y": 82}, {"title": "65", "id": 65, "x": 74, "y": 67}, {"title": "66", "id": 66, "x": 10, "y": 68}, {"title": "67", "id": 67, "x": 48, "y": 19}, {"title": "68", "id": 68, "x": 83, "y": 86}, {"title": "69", "id": 69, "x": 84, "y": 94}], "edges": [{"source": 63, "target": 10}, {"source": 10, "target": 66}, {"source": 66, "target": 47}, {"source": 47, "target": 53}, {"source": 53, "target": 32}, {"source": 32, "target": 59}, {"source": 59, "target": 51}, {"source": 51, "target": 9}, {"source": 9, "target": 4}, {"source": 4, "target": 52}, {"source": 52, "target": 20}, {"source": 20, "target": 16}, {"source": 16, "target": 42}, {"source": 42, "target": 40}, {"source": 40, "target": 3}, {"source": 3, "target": 17}, {"source": 17, "target": 41}, {"source": 41, "target": 5}, {"source": 5, "target": 11}, {"source": 11, "target": 33}, {"source": 33, "target": 61}, {"source": 61, "target": 60}, {"source": 60, "target": 39}, {"source": 39, "target": 38}, {"source": 38, "target": 24}, {"source": 24, "target": 44}, {"source": 44, "target": 45}, {"source": 45, "target": 26}, {"source": 26, "target": 8}, {"source": 8, "target": 67}, {"source": 67, "target": 43}, {"source": 43, "target": 29}, {"source": 29, "target": 19}, {"source": 19, "target": 13}, {"source": 13, "target": 27}, {"source": 27, "target": 7}, {"source": 7, "target": 25}, {"source": 25, "target": 48}, {"source": 48, "target": 54}, {"source": 54, "target": 18}, {"source": 18, "target": 6}, {"source": 6, "target": 31}, {"source": 31, "target": 2}, {"source": 2, "target": 1}, {"source": 1, "target": 23}, {"source": 23, "target": 14}, {"source": 14, "target": 56}, {"source": 56, "target": 62}, {"source": 62, "target": 65}, {"source": 65, "target": 21}, {"source": 21, "target": 58}, {"source": 58, "target": 37}, {"source": 37, "target": 22}, {"source": 22, "target": 12}, {"source": 12, "target": 68}, {"source": 68, "target": 30}, {"source": 30, "target": 34}, {"source": 34, "target": 69}, {"source": 69, "target": 28}, {"source": 28, "target": 35}, {"source": 35, "target": 0}, {"source": 0, "target": 15}, {"source": 15, "target": 46}, {"source": 46, "target": 36}, {"source": 36, "target": 57}, {"source": 57, "target": 49}, {"source": 49, "target": 50}, {"source": 50, "target": 55}, {"source": 55, "target": 64}, {"source": 64, "target": 63}]}
\ No newline at end of file
diff --git a/output/st70.txt b/output/st70.txt
new file mode 100644
index 0000000..03e5089
--- /dev/null
+++ b/output/st70.txt
@@ -0,0 +1 @@
+63, 10, 66, 47, 53, 32, 59, 51, 9, 4, 52, 20, 16, 42, 40, 3, 17, 41, 5, 11, 33, 61, 60, 39, 38, 24, 44, 45, 26, 8, 67, 43, 29, 19, 13, 27, 7, 25, 48, 54, 18, 6, 31, 2, 1, 23, 14, 56, 62, 65, 21, 58, 37, 22, 12, 68, 30, 34, 69, 28, 35, 0, 15, 46, 36, 57, 49, 50, 55, 64
\ No newline at end of file
diff --git a/output/ulysses16.json b/output/ulysses16.json
new file mode 100644
index 0000000..86bae59
--- /dev/null
+++ b/output/ulysses16.json
@@ -0,0 +1 @@
+{"nodes": [{"title": "0", "id": 0, "x": 38, "y": 20}, {"title": "1", "id": 1, "x": 39, "y": 26}, {"title": "2", "id": 2, "x": 40, "y": 25}, {"title": "3", "id": 3, "x": 36, "y": 23}, {"title": "4", "id": 4, "x": 33, "y": 10}, {"title": "5", "id": 5, "x": 37, "y": 12}, {"title": "6", "id": 6, "x": 38, "y": 13}, {"title": "7", "id": 7, "x": 37, "y": 20}, {"title": "8", "id": 8, "x": 41, "y": 9}, {"title": "9", "id": 9, "x": 41, "y": 13}, {"title": "10", "id": 10, "x": 36, "y": -5}, {"title": "11", "id": 11, "x": 38, "y": 15}, {"title": "12", "id": 12, "x": 38, "y": 15}, {"title": "13", "id": 13, "x": 37, "y": 15}, {"title": "14", "id": 14, "x": 35, "y": 14}, {"title": "15", "id": 15, "x": 39, "y": 19}], "edges": [{"source": 7, "target": 3}, {"source": 3, "target": 1}, {"source": 1, "target": 2}, {"source": 2, "target": 0}, {"source": 0, "target": 15}, {"source": 15, "target": 12}, {"source": 12, "target": 11}, {"source": 11, "target": 6}, {"source": 6, "target": 5}, {"source": 5, "target": 9}, {"source": 9, "target": 8}, {"source": 8, "target": 10}, {"source": 10, "target": 4}, {"source": 4, "target": 14}, {"source": 14, "target": 13}, {"source": 13, "target": 7}]}
\ No newline at end of file
diff --git a/output/ulysses16.txt b/output/ulysses16.txt
new file mode 100644
index 0000000..25ede6a
--- /dev/null
+++ b/output/ulysses16.txt
@@ -0,0 +1 @@
+7, 3, 1, 2, 0, 15, 12, 11, 6, 5, 9, 8, 10, 4, 14, 13
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..ae9a165
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,4 @@
+matplotlib 
+scikit-learn
+scipy
+numpy
diff --git a/run_all.sh b/run_all.sh
new file mode 100644
index 0000000..8778417
--- /dev/null
+++ b/run_all.sh
@@ -0,0 +1,12 @@
+#!/bin/bash
+
+pip install -r requirements.txt
+
+python main.py data/simple/ulysses16.tsp
+python main.py data/simple/att48.tsp
+python main.py data/simple/st70.tsp
+
+python main.py data/medium/a280.tsp
+python main.py data/medium/pcb442.tsp
+
+python main.py data/hard/dsj1000.tsp
diff --git a/utils/visualize_tsp.py b/utils/visualize_tsp.py
new file mode 100644
index 0000000..381a881
--- /dev/null
+++ b/utils/visualize_tsp.py
@@ -0,0 +1,54 @@
+import matplotlib.pyplot as plt
+
+def plotTSP(paths, points, num_iters=1):
+
+    """
+    path: List of lists with the different orders in which the nodes are visited
+    points: coordinates for the different nodes
+    num_iters: number of paths that are in the path list
+
+    """
+
+    # Unpack the primary TSP path and transform it into a list of ordered
+    # coordinates
+
+    x = []; y = []
+    for i in paths[0]:
+        x.append(points[i][0])
+        y.append(points[i][1])
+
+    plt.plot(x, y, 'co')
+
+    # Set a scale for the arrow heads (there should be a reasonable default for this, WTF?)
+    # a_scale = float(max(x))/float(100)
+
+    # Draw the older paths, if provided
+    if num_iters > 1:
+
+        for i in range(1, num_iters):
+
+            # Transform the old paths into a list of coordinates
+            xi = []; yi = [];
+            for j in paths[i]:
+                xi.append(points[j][0])
+                yi.append(points[j][1])
+
+            plt.arrow(xi[-1], yi[-1], (xi[0] - xi[-1]), (yi[0] - yi[-1]), 
+                    color = 'r', length_includes_head = True, ls = 'dashed',
+                    width = 0.001/float(num_iters))
+            for i in range(0, len(x) - 1):
+                plt.arrow(xi[i], yi[i], (xi[i+1] - xi[i]), (yi[i+1] - yi[i]),
+                        color = 'r', length_includes_head = True,
+                        ls = 'dashed', width = 0.001/float(num_iters))
+
+    # Draw the primary path for the TSP problem
+    plt.arrow(x[-1], y[-1], (x[0] - x[-1]), (y[0] - y[-1]),
+            color ='g', length_includes_head=True)
+    for i in range(0,len(x)-1):
+        plt.arrow(x[i], y[i], (x[i+1] - x[i]), (y[i+1] - y[i]),
+                color = 'g', length_includes_head = True)
+
+    #Set axis too slitghtly larger than the set of x and y
+    # plt.xlim(min(x)*1.1, max(x)*1.1)
+    # plt.ylim(min(y)*1.1, max(y)*1.1)
+    plt.show()