Notebooks lectures

.gitignore

@@ -20,3 +20,7 @@
 /.idea/vcs.xml
 /.idea/workspace.xml
 /results.csv
+/Complete_lecture 1.ipynb
+/Complete notebooks/
+/Lectures/.ipynb_checkpoints/
+/.ipynb_checkpoints/Student_lecture 1-checkpoint.ipynb

Lectures/Student_lecture 1.ipynb

@@ -0,0 +1,46 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## First Lecture\\n\",\n",
+    "    What we are going to do today:\\n\n",
+    "    - read TSP data\\n\n",
+    "    - store nodes in data structure\\n\n",
+    "    - plot raw data\\n\n",
+    "    - define distance function\\n\n",
+    "    - create distance matrix\\n\n",
+    "    - generate naive solution \\n\n",
+    "    - check if the solution is valid\\n\n",
+    "    - evaluate solution!"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "name": "pycharm-61970693",
+   "language": "python",
+   "display_name": "PyCharm (AI2020BsC)"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

Lectures/Student_lecture 2.ipynb

@@ -0,0 +1,37 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Second Lecture"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "name": "pycharm-61970693",
+   "language": "python",
+   "display_name": "PyCharm (AI2020BsC)"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

Lectures/Student_lecture 3.ipynb

@@ -0,0 +1,37 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Third Lecture"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "name": "pycharm-61970693",
+   "language": "python",
+   "display_name": "PyCharm (AI2020BsC)"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

Lectures/Student_lecture 4.ipynb

@@ -0,0 +1,37 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Fourth Lecture"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "name": "pycharm-61970693",
+   "language": "python",
+   "display_name": "PyCharm (AI2020BsC)"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

Student_lecture 1.ipynb

@@ -0,0 +1,60 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## First Lecture\n",
+    "What we are going to do today:\n",
+    "- read TSP data\n",
+    "- store nodes in data structure\n",
+    "- plot raw data\n",
+    "- define distance function\n",
+    "- create distance matrix\n",
+    "- generate naive solution \n",
+    "- check if the solution is valid\n",
+    "- evaluate solution!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### Read TSP data\n"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Read TSP data\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

run.py

@@ -23,8 +23,8 @@ def use_solver_to_compute_solution(solver, improve, index, results, name, verbos
 
 
 def run(show_plots=False, verbose=False):
-    problems = glob.glob('./problems/*.tsp')
-    # problems = ["./problems/eil76.tsp"]
+    # problems = glob.glob('./problems/*.tsp')
+    problems = ["./problems/eil76.tsp"]
     solvers_names = available_solvers.keys()
     improvers_names = available_improvers.keys()
     results = []

src/TSP_solver.py

@@ -19,7 +19,6 @@ available_improvers = {"2-opt": loop2opt,
 
 
 class SolverTSP:
-
     def __init__(self, algorithm_name, problem_instance):
         # assert algorithm_name in available_solvers, f"the {algorithm_name} initializer is not available currently."
         self.duration = np.inf
@@ -57,12 +56,6 @@ class SolverTSP:
         self.solved = True
         self.evaluate_solution()
         self._gap()
-        # if verbose:
-        #     print(f"###  solution found with {self.gap} % gap  in {self.duration} seconds ####",
-        #           f"the total length for the solution found is {self.found_length}",
-        #           f"while the optimal length is {prob_instance.best_sol}",
-        #           f"the gap is {self.gap}%")
-
         if return_value:
             return self.solution