First pass of refactor

.idea/.gitignore

@@ -0,0 +1,14 @@
+# Default ignored files
+/shelf/
+/workspace.xml
+# Datasource local storage ignored files
+/../../../../:\code\AI2020BsC\.idea/dataSources/
+/dataSources.local.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+/AI2020BsC.iml
+/misc.xml
+/modules.xml
+/other.xml
+/inspectionProfiles/profiles_settings.xml
+/vcs.xml

run.py

@@ -1,5 +1,7 @@
-from src import *
 import pandas as pd
+from src.io_tsp import Instance
+from src.TSP_solver import SolverTSP
+import numpy as np
 
 
 def add(solver, instance, improve, index, results, name, verbose, show_plots):
@@ -22,8 +24,8 @@ def add(solver, instance, improve, index, results, name, verbose, show_plots):
 def run(show_plots=False, verbose=False):
     # names = [name_ for name_ in os.listdir("./problems") if "tsp" in name_]
     names = ["eil76.tsp"]
-    initializers = Solver_TSP.available_initializers.keys()
-    improvements = Solver_TSP.available_improvements.keys()
+    initializers = SolverTSP.available_initializers.keys()
+    improvements = SolverTSP.available_improvements.keys()
     results = []
     index = []
     for name in names:
@@ -37,7 +39,7 @@ def run(show_plots=False, verbose=False):
 
         for init in initializers:
             for improve in improvements:
-                solver = Solver_TSP(init)
+                solver = SolverTSP(init)
                 add(solver, instance, improve, index, results, name, verbose, show_plots)
                 for improve2 in [j for j in improvements if j not in [improve]]:
                     add(solver, instance, improve2, index, results, name, verbose, show_plots)
@@ -48,7 +50,6 @@ def run(show_plots=False, verbose=False):
 
                     solver.pop()
 
-
         if instance.exist_opt and show_plots:
             solver.solution = np.concatenate([instance.optimal_tour, [instance.optimal_tour[0]]])
             solver.method = "optimal"

src/TSP_solver.py

@@ -9,7 +9,7 @@ else:
     from AI2019.src import *
 
 
-class Solver_TSP:
+class SolverTSP:
 
     solution: ndarray
     found_length: float

src/constructive_algorithms.py

@@ -36,7 +36,7 @@ class nearest_neighbor:
         for start in range(instance_.nPoints):
             new_solution = nearest_neighbor.nn(instance_, starting_node=start)
             solutions.append(new_solution)
-            lens.append(compute_lenght(new_solution, instance_.dist_matrix))
+            lens.append(compute_length(new_solution, instance_.dist_matrix))
 
         solution = solutions[np.argmin(lens)]
         return solution

src/io_tsp.py

@@ -3,6 +3,8 @@ from typing import List
 from matplotlib import pyplot as plt
 from numpy.core._multiarray_umath import ndarray
 
+from src.utils import distance_euc
+
 
 class Instance:
     nPoints: int
@@ -14,6 +16,7 @@ class Instance:
 
     def __init__(self, name_tsp):
         self.read_instance(name_tsp)
+        self.exist_opt = None  # TODO determine default value
 
     def read_instance(self, name_tsp):
         # read raw data
@@ -63,17 +66,13 @@ class Instance:
             plt.annotate(txt, (self.points[i, 1], self.points[i, 2]))
         plt.show()
 
-    @staticmethod
-    def distance_euc(zi, zj):
-        xi, xj = zi[0], zj[0]
-        yi, yj = zi[1], zj[1]
-        return round(np.sqrt((xi - xj) ** 2 + (yi - yj) ** 2), 0)
+
 
     def create_dist_matrix(self):
         self.dist_matrix = np.zeros((self.nPoints, self.nPoints))
 
         for i in range(self.nPoints):
             for j in range(i, self.nPoints):
-                self.dist_matrix[i, j] = self.distance_euc(self.points[i][1:3], self.points[j][1:3])
+                self.dist_matrix[i, j] = distance_euc(self.points[i][1:3], self.points[j][1:3])
         self.dist_matrix += self.dist_matrix.T
 

src/local_search.py

@@ -41,7 +41,7 @@ class TwoOpt:
     @staticmethod
     def loop2opt(solution, instance, max_num_of_uncrosses=10000):
         matrix_dist = instance.dist_matrix
-        new_len = compute_lenght(solution, matrix_dist)
+        new_len = compute_length(solution, matrix_dist)
         new_tsp_sequence = np.copy(np.array(solution))
         uncross = 0
         while uncross < max_num_of_uncrosses:
@@ -119,7 +119,7 @@ class TwoDotFiveOpt:
     @staticmethod
     def loop2dot5opt(solution, instance, max_num_of_changes=10000):
         matrix_dist = instance.dist_matrix
-        actual_len = compute_lenght(solution, matrix_dist)
+        actual_len = compute_length(solution, matrix_dist)
         new_tsp_sequence = np.copy(np.array(solution))
         uncross = 0
         while uncross < max_num_of_changes:

src/meta_heuristics.py

@@ -14,7 +14,7 @@ class Simulated_Annealing:
         # initial setup
         temperature = instance.best_sol / np.sqrt(instance.nPoints)
         current_sol = np.array(solution)
-        current_len = compute_lenght(solution, instance.dist_matrix)
+        current_len = compute_length(solution, instance.dist_matrix)
         best_sol = np.array(solution)
         best_len = current_len
 

src/utils.py

@@ -1,5 +1,4 @@
-
-def compute_lenght(solution, dist_matrix):
+def compute_length(solution, dist_matrix):
     total_length = 0
     starting_node = solution[0]
     from_node = starting_node
@@ -8,3 +7,8 @@ def compute_lenght(solution, dist_matrix):
         from_node = node
     return total_length
 
+
+def distance_euc(zi, zj):
+    xi, xj = zi[0], zj[0]
+    yi, yj = zi[1], zj[1]
+    return round(np.sqrt((xi - xj) ** 2 + (yi - yj) ** 2), 0)