03 Naive TSP - text improvements.

Author: mstechly

tutorials/03_Naive_approach.ipynb

@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We need this line to have a proper [bra-ket](https://en.wikipedia.org/wiki/Bra–ket_notation) formatting in markdown.\n",
+    "We need to run this line to have a proper [bra-ket](https://en.wikipedia.org/wiki/Bra–ket_notation) formatting in markdown.\n",
     "$$\n",
     "\\newcommand{\\ket}[1]{\\left| #1 \\right\\rangle}\n",
     "\\newcommand{\\bra}[1]{\\left\\langle #1 \\right|}\n",
@@ -285,7 +285,7 @@
    ],
    "source": [
     "tsp_solver = ForestTSPSolverNaive(distance_matrix, use_constraints=True)\n",
-    "solution, distribution = tsp_solver.solve_tsp()\n",
+    "solution, naive_distribution = tsp_solver.solve_tsp()\n",
     "print(\"The solution is:\", solution)"
    ]
   },
@@ -1264,7 +1264,7 @@
    ],
    "source": [
     "tsp_solver = ForestTSPSolverNaive(distance_matrix, use_constraints=True)\n",
-    "solution, distribution = tsp_solver.solve_tsp()\n",
+    "solution, naive_distribution = tsp_solver.solve_tsp()\n",
     "print(\"The solution is:\", solution)"
    ]
   },
@@ -1304,7 +1304,7 @@
    "metadata": {},
    "source": [
     "Since it might be hard for you to decode the bitstrings, let's take a look at the human-readable representation.\n",
-    "(The two might actually differ, since many bitstrings for an invalid solution may have the same representation)."
+    "The two might actually differ, since many bitstrings may have the same representation in a case of an invalid solution. That's why we call the distribution coming from tsp_solver a `naive_distribution`."
    ]
   },
   {
@@ -1325,8 +1325,8 @@
    ],
    "source": [
     "solutions_with_probs = []\n",
-    "for state in distribution:\n",
-    "    solutions_with_probs.append(['->'.join(map(str,state)), distribution[state]/10000])\n",
+    "for state in naive_distribution:\n",
+    "    solutions_with_probs.append(['->'.join(map(str,state)), naive_distribution[state]/10000])\n",
     "solutions_with_probs = sorted(solutions_with_probs,key=lambda x: -x[1])\n",
     "\n",
     "plots.plot_state_histogram(solutions_with_probs[:10][::-1])"