DTSA5503-Homework4.ipynb

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    "## 1. Solving CNF Satisfiability Problem \n",
    "\n",
    "We will first explore algorithms for solving the CNF Satisfiability problem. Let's recall this problem below.\n",
    "\n",
    "### CNF SAT Problem\n",
    "\n",
    "$\\newcommand\\bor{\\textsf{or}}\n",
    "\\newcommand\\band{\\textsf{and}}\n",
    "\\newcommand\\bnot{\\textsf{not}}$\n",
    "\n",
    "You are given $n$ Boolean (true/false) variables $x_1, \\ldots, x_n$ that can take values true/false. You are also given a boolean formula in a special form called conjunctive normal form (CNF) that has the following structure (see https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_158 for more details on CNF).\n",
    "  - The formula is the $\\band$ (conjunction, also written using $\\land$) of $m \\geq 0$ clauses $\\varphi: C_1 \\ \\text{and}\\ C_2\\ \\text{and}\\ \\cdots \\text{and}\\ C_m$.\n",
    "  - Each clause $C_i$ is the $\\bor$ (disjunction, also written using $\\lor$) of $k_i$ literals: $C_i: \\ell_1\\ \\text{or}\\ \\ell_2\\ \\text{or}\\ \\cdots \\text{or}\\ \\ell_k$.\n",
    "  - Each literal is a variable $x_i$ or its logical negation $\\bnot\\ x_i$ (also written as $\\overline{x_i}$).\n",
    "  \n",
    "Let's take an example.\n",
    "\n",
    "#### Example (CNF Formula)\n",
    "\n",
    "Let us take $n= 4$ with Boolean variables $x_1, x_2, x_3, x_4$ and $3$ clauses $C_1, C_2, C_3$, as shown below.\n",
    "\n",
    "$$ \\underset{C_1}{\\underbrace{(x_1\\ \\bor\\ x_2\\ \\bor\\ (\\bnot\\ x_4))}}\\ \\band\\ \\underset{C_2}{\\underbrace{((\\bnot\\ x_2)\\ \\bor\\ (\\bnot\\ x_3)\\ \\bor\\ x_1)}}\\ \\band\\ \\underset{C_3}{\\underbrace{((\\bnot\\ x_1) \\ \\bor\\ (\\bnot\\ x_2)\\ \\bor\\ (\\bnot\\ x_3))}}$$\n",
    "\n",
    "The SAT problem asks if there is a truth assignment for the variables $x_1, x_2, \\ldots, x_n$ such that \n",
    "the formula is satisfied.\n",
    " - Since it is an \"and\" of clauses, it means the each clause has to be satisfied for the entire formula to be satisfied.\n",
    " - Also, since each clause is an \"or\" of literals, at least one literal in each clause must be satisfied.\n",
    " - Finally, a literal of the form $x_i$ is satisfied if $x_i$ is assigned to be $true$ and likewise, $\\bnot\\ x_i$ is satisfied if $x_i$ is assigned to be $false$.\n",
    "\n",
    "### Question 1\n",
    "\n",
    "Find a truth assignment that satisfies the formula in the example above.\n"
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    "YOUR ANSWER HERE"
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    "SAT problems are represented by noting down (a) the number of variables $n$, (b) the number of clauses $m$, and (c) for each clause, the list of literals (variables or their negations).\n",
    "\n",
    "We will use a data structure in python that has the following fields:\n",
    "  - `n`: the number of variables.\n",
    "  - `clauses` : a list of clauses -- `[c0,...,cm-1]`\n",
    "    - Each clause `ci` is itself a list `ci: [l1, l2, .., lk]` wherein `li` is a positive number between `1` to `n` representing the variable $x_i$ OR a negative number between `-n` and `-1` representing the literal $\\bnot\\ x_i$.\n",
    "\n",
    "\n",
    "### Example\n",
    "\n",
    "Let's revisit the problem from previous example. It is represented as\n",
    " - `n=4`\n",
    " - `clauses=[ [ 1, 2, -4], [-2, -3, 1], [-1, -2, -3] ]`\n",
    " \n",
    "\n",
    "We have provided a data-structure called `SATInstance`. Your goal is to complete the function `evaluate` that \n",
    "inputs a partial truth assignment: a dictionary that maps variable numbers from `1` - `n`  (inclusive) to `true/false`. If a variable is unmapped then it is not assigned to either value.\n",
    "\n",
    "  - `evaluate` should return -1 if the partial truth assignment already violates a clause.\n",
    "  - `evaluate` should return 0 if the partial truth assignment neither violates nor satisfies the formula.\n",
    "  - `evaluate` should return +1 if the partial truth assignment satisfies each and every clause in the formula.\n",
    "\n",
    "### Example 1\n",
    "\n",
    "Revisiting the example above, let us take the partial assignment $x_1 \\mapsto \\textit{true}$. It leaves $x_2, x_3, x_4$ unassigned.\n",
    "\n",
    "Under this assignment:\n",
    "  - The clause $x_1\\ \\bor\\ x_2\\ \\bor\\ \\bnot\\ x_4$ is true since $x_1$ is true.\n",
    "  - The clause $\\bnot\\ x_2\\ \\bor\\ \\bnot\\ x_3\\ \\bor\\ x_1$ is true since $x_1$ is true.\n",
    "  - The clause $\\bnot\\ x_1\\ \\bor\\ \\bnot\\ x_2\\ \\bor\\ \\bnot\\ x_3$ is unresolved since $x_1$ is false and we do not know what $x_2$ or $x_3$ are.\n",
    "  \n",
    "Therefore your function `evaluate` must return $0$ since at least one clause is unresolved and remaining clauses are all true.\n",
    "\n",
    "### Example 2\n",
    "Revisiting the example above, let us take the partial assignment $x_1 \\mapsto \\textit{true},\\ x_2 \\mapsto \\textit{false}$. It leaves $x_3, x_4$ unassigned.\n",
    "Under this assignment:\n",
    "  - The clause $x_1\\ \\bor\\ x_2\\ \\bor\\ \\bnot\\ x_4$ is true since $x_1$ is true.\n",
    "  - The clause $\\bnot\\ x_2\\ \\bor\\ \\bnot\\ x_3\\ \\bor\\ x_1$ is true since $x_1$ is true.\n",
    "  - The clause $\\bnot\\ x_1\\ \\bor\\ \\bnot\\ x_2\\ \\bor\\ \\bnot\\ x_3$ is true because $x_2$ is false and the clause has $\\bnot\\ x_2$.\n",
    "  \n",
    "Since all clauses are true, the formula is already satisfied even though we do not know at $x_3, x_4$ are.\n",
    "`evaluate` must return `+1` for this formula and partial truth assignment.\n",
    "\n",
    "### Example 3\n",
    "Revisiting the example above, let us take the partial assignment $x_1 \\mapsto \\textit{true},\\ x_2 \\mapsto \\textit{true}, x_3 \\mapsto \\mathit{true}$. It leaves $ x_4$ unassigned.\n",
    "Under this assignment:\n",
    "  - The clause $x_1\\ \\bor\\ x_2\\ \\bor\\ \\bnot\\ x_4$ is true since $x_1$ is true.\n",
    "  - The clause $\\bnot\\ x_2\\ \\bor\\ \\bnot\\ x_3\\ \\bor\\ x_1$ is true since $x_1$ is true.\n",
    "  - The clause $\\bnot\\ x_1\\ \\bor\\ \\bnot\\ x_2\\ \\bor\\ \\bnot\\ x_3$ is __false__ since $x_1, $x_2, x_3$ are true.\n",
    "  \n",
    "  \n",
    "Since at least one clause is false, the formula is already violated even though we do not know what $ x_4$ is.\n",
    "`evaluate` must return `-1` for this formula and partial truth assignment.\n",
    "\n",
    "\n",
    "\n",
    "\n"
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    "import time\n",
    "st = time.time()\n",
    "tot = 0\n",
    "print(tot)"
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    "\n",
    "class SATInstance:\n",
    "    # Constructor: provide n the number of variables and\n",
    "    # an initial list of clauses.\n",
    "    # Note that variable numbers will go from 1 to n inclusive.\n",
    "    # we can add clauses using the add_clause method.\n",
    "    def __init__(self, n, clauses):\n",
    "        self.n = n\n",
    "        self.m = len(clauses)\n",
    "        self.clauses = clauses\n",
    "        assert self.is_valid()\n",
    "    # is_valid\n",
    "    # Check if all clauses are correct.\n",
    "    # literals in each clause must be between 1 and n or -n and -1 \n",
    "    def is_valid(self):\n",
    "        assert self.n >= 1\n",
    "        assert self.m >= 0\n",
    "        for c in self.clauses:\n",
    "            for l in c:\n",
    "                assert (1 <= l and l <= self.n) or (-self.n <= l and l <= -1)\n",
    "        return True\n",
    "    \n",
    "    # add_clause\n",
    "    # Add a new clause to the list of clauses\n",
    "    def add_clause(self, c):\n",
    "        #check the clause we are adding.\n",
    "        for l in c:\n",
    "            assert (1 <= l and l <= self.n) or (-self.n <= 1 and l <= -1)\n",
    "        self.clauses.append(c)\n",
    "    \n",
    "    ## Function: evaluate_literal\n",
    "    # Evaluate a literal against a partial truth assignment\n",
    "    # return 0 if the partial truth assignment does not have the variable corresponding to the literal\n",
    "    # return 1 if the partial truth assignment has the variable and the literal is true\n",
    "    # return -1 if the partial truth assignment has the variable and the literal is false\n",
    "    def evaluate_literal(self, partial_truth_assignment, literal):\n",
    "        var = abs(literal) # literal may be negated. First remove any negation using abs\n",
    "        if var not in partial_truth_assignment:\n",
    "            return 0\n",
    "        v = partial_truth_assignment[var]\n",
    "        if 1 <= literal <= self.n:\n",
    "            return 1 if v else -1\n",
    "        else:\n",
    "            return -1 if v else 1\n",
    "    \n",
    "    ## TODO: Write your code here\n",
    "    # Function: evaluate\n",
    "    # See description above: partial_truth_assignment is a dictionary from 1 .. n to true/false.\n",
    "    # since it is partial, we may have variables with no truth assignments.\n",
    "    # use evaluate_literal function as a useful primitive\n",
    "    # return +1 if the formula is already satisfied under partial_truth_assignment: i.e, all clauses are true\n",
    "    # return 0 if formula is indeterminate under partial_truth_assignment, all clauses are true or unresolved and at least one clause is unresolved.\n",
    "    # return -1 if formula is already violated under partial_truth_assignment, i.e, at least one clause is false\n",
    "    def evaluate(self, partial_truth_assignment):\n",
    "        clause_indeterminate = False\n",
    "        for c in self.clauses:\n",
    "            clause_status = [self.evaluate_literal(partial_truth_assignment, l) for l in c]\n",
    "            if all(c == -1 for c in clause_status):\n",
    "                # return clause violated\n",
    "                return -1\n",
    "            if any(c == 1 for c in clause_status):\n",
    "                continue\n",
    "            clause_indeterminate = True\n",
    "        if clause_indeterminate:\n",
    "            \n",
    "            return 0\n",
    "        else:\n",
    "            return 1\n",
    "        \n",
    "        # your code here\n",
    "        \n",
    "#         # full_evaluation = []\n",
    "#         for c in self.clauses:\n",
    "#             clause_evaluation = []\n",
    "#             for l in c:\n",
    "#                 current_literal = self.evaluate_literal(partial_truth_assignment, l)\n",
    "#                 clause_evaluation.append(current_literal)\n",
    "#             if 1 in clause_evaluation:\n",
    "#                 # full_evaluation.append(1) \n",
    "#                 # if x or y or z = 1 then true... want to pass those returns...\n",
    "#                 #pass\n",
    "#                 continue\n",
    "#             elif sum(clause_evaluation) == -3:\n",
    "#                 # all three are determinate false\n",
    "#                 return -1\n",
    "#             else: \n",
    "#                 # Every element of the current clause is indeterminate\n",
    "#                 return 0\n",
    "            \n",
    "#         #made it to the end without a return, return 1\n",
    "#         return 1\n",
    "\n",
    "\n",
    "      \n",
    "f1 = SATInstance(4, [ [ 1, 2, -4], [-2, -3, 1], [-1, -2, -3] ])\n",
    "t1 = {1:True, 2:False}\n",
    "e1 = f1.evaluate(t1)     \n",
    "assert e1 == 1\n",
    "\n",
    "t2 = {1:False, 2: False}\n",
    "e2 = f1.evaluate(t2)\n",
    "assert e2 == 0\n",
    "\n",
    "f2 = SATInstance(5, [[1,2,-5],[-4,-2,-1], [1, 3, 5], [-1, -5, -2], [1, 2, -4]])\n",
    "t3 = {1:True}\n",
    "e3 =f2.evaluate(t3)\n",
    "assert e3 == 0\n",
    "\n",
    "t4 = {1: True, 2: False}\n",
    "e4 =f2.evaluate(t4)\n",
    "assert e4 == 1\n",
    "\n",
    "\n",
    "t5 = {1: False, 3: False, 5:False}\n",
    "e5 = f2.evaluate(t5)\n",
    "assert e5 == -1\n",
    "\n",
    "print(tot)"
   ]
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     "text": [
      "-test1-\n",
      "-test2-\n",
      "-test3-\n",
      "-test4-\n",
      "-test5-\n",
      "All tests passed: 10 points!\n"
     ]
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    "## BEGIN TESTS\n",
    "\n",
    "print('-test1-')\n",
    "f1 = SATInstance(4, [ [ 1, 2, -4], [-2, -3, 1], [-1, -2, -3] ])\n",
    "t1 = {1:True, 2:False}\n",
    "e1 = f1.evaluate(t1)\n",
    "assert(e1 == 1, f'Expected that f1 is satisfied by t1 but your code returns: {e1}')\n",
    "\n",
    "print('-test2-')\n",
    "t2 = {1:False, 2: False}\n",
    "e2 = f1.evaluate(t2)\n",
    "assert(e2 == 0, f'Expected that f1 is indeterminate under t2. Your code returns: {e2}')\n",
    "\n",
    "print('-test3-')\n",
    "f2 = SATInstance(5, [[1,2,-5],[-4,-2,-1], [1, 3, 5], [-1, -5, -2], [1, 2, -4]])\n",
    "t3 = {1:True}\n",
    "e3 = f2.evaluate(t3)\n",
    "assert(e3 == 0, f'Expected that f2 is indeterminate under t3. Your code returns {e3}')\n",
    "\n",
    "print('-test4-')\n",
    "t4 = {1: True, 2: False}\n",
    "e4 = f2.evaluate(t4)\n",
    "assert(e4 == 1, f'Expected that f2 is satisfied by t4. Your code returns {e4}')\n",
    "\n",
    "print('-test5-')\n",
    "t5 = {1: False, 3: False, 5:False}\n",
    "e5 = f2.evaluate(t5)\n",
    "assert(e5 == -1, f'Expected that f2 is violated by t5. Your code returns {e5}')\n",
    "print('All tests passed: 10 points!')\n",
    "## END TESTS"
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    "The DPLL (Davis-Putnam-Loveland-Logemann) algorithm is a simple recursive algorithm for solving a SAT problem (for those of you who have looked up this algorithm online, we will not be considering unit propagation, pure literal elimination or other search additions that are important). The basic idea is to search for a partial truth assignment recursively. DPLL algorithm first sets the current variable\n",
    "$x_j$ to $false$. It then recursively searches for a solution. If it finds one, the solution is reported back. If not, it sets $x_j$ to $true$ and checks recursively. \n",
    "\n",
    "The pseudo code is given below. Let `n` be the number of variables in the formula.\n",
    "\n",
    "```python\n",
    "def DPLLAlgorithm(formula, truth_assign, j):\n",
    "  # j is the id of the current variable we are considering\n",
    "  # initially j is set to 1.\n",
    "  # truth_assign is a partial truth assignment\n",
    "  n = number of variables in the formula\n",
    "  assert 1 <= j <= n \n",
    "  # Try to set xj to true and check the formula\n",
    "  truth_assign0 = extend truth_assign with (xj -> true)\n",
    "  # evaluate the formula under the partial truth assignment\n",
    "  e0 = evaluate formula under truth_assign0\n",
    "  # if it is already satisfied\n",
    "  if e0 == satisfied: # end early\n",
    "     then return SUCCESS, truth_assign0\n",
    "  if e0 == indeterminate:# if it is indeterminate\n",
    "     # recursively call the DPLLAlgorithm\n",
    "     (result, final_truth_assign) = DPLLAlgorithm(formula, truth_assign0, j+1)\n",
    "     # if it succeeds, return the truth assignment\n",
    "     if result == SUCCESS:\n",
    "        return SUCCESS, final_truth_assign\n",
    "        \n",
    "  # Now try to set xj to false and check the formula\n",
    "  truth_assign1 = extend truth_assign with (xj -> false)\n",
    "  e1 = evaluate formula under truth_assign1\n",
    "  if e1 == satisfied:\n",
    "      then return SUCCESS, truth_assign1\n",
    "  if e1 == indeterminate:\n",
    "     (result, final_truth_assign) = DPLLAlgorithm(formula, truth_assign1, j+1)\n",
    "     # if it succeeds, return the truth assignment\n",
    "     if result == SUCCESS:\n",
    "        return SUCCESS, final_truth_assign\n",
    "  # Fell through all cases\n",
    "  return FAILURE, None\n",
    "```\n",
    "  \n",
    "  \n",
    "__Notes__\n",
    "\n",
    " - We will use the `SATInstance` data structure to represent our formula.\n",
    " - We will use `evaluate` function inside `SATInstance`.\n",
    " - Use a python dictionary to represent partial truth assignments.\n",
    "   - We have provided a useful function that will help you extend a truth assignment.\n",
    "   - Note that modifying a dictionary inside a function also modifies it for its caller. We have provided a helpful function `forget_var_in_truth_assign` to remove a key from a dictionary."
   ]
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     "text": [
      "-- formula 1 --\n",
      "True {1: True, 2: True, 3: False}\n"
     ]
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   "source": [
    "def extend_truth_assignment(truth_assign, j, b):\n",
    "    truth_assign[j] = b\n",
    "    return truth_assign\n",
    "    \n",
    "def forget_var_in_truth_assign(truth_assign, j):\n",
    "    # remove variable from a dictionary\n",
    "    if j in truth_assign:\n",
    "        del truth_assign[j]\n",
    "    return truth_assign\n",
    "\n",
    "# Implement the DPLL pseudo code with the following modifications\n",
    "#   return (True, partial_truth_assignment) if the formula is satisfiable\n",
    "#   return (False, None) if the formula is unsatisfiable. \n",
    "# You may use the extend_truth_assignment and forget_var_in_truth_assign functions.\n",
    "# Remember that a change in a dictionary is reflected back to the caller and this is important to keep in mind.\n",
    "# Use the evaluate function in SATInstance class to evaluate a formula under partial truth assignment.\n",
    "def dpll_algorithm(formula, partial_truth_assign, j):\n",
    "    #print(\"j is \"+str(j))\n",
    "\n",
    "    assert 1 <= j and j <= formula.n\n",
    "    assert j not in partial_truth_assign\n",
    "    \n",
    "#     #--- First Attempt\n",
    "#     try1 = extend_truth_assignment(partial_truth_assign, j, True)\n",
    "#     #print(\"Try1 = \",try1)\n",
    "#     try1e = formula.evaluate(try1)  \n",
    "#     if try1e == 1:\n",
    "#         #print(f\"success, end early on {j} True\")\n",
    "#         return [True,try1]\n",
    "#     if try1e ==0:\n",
    "#         if j<= formula.n -1:\n",
    "#             (result,lastassign)=dpll_algorithm(formula,try1,j+1)\n",
    "#             if result == True:\n",
    "#                 return [True,lastassign]    \n",
    "#     truth_assign = {}\n",
    "#     try2 = extend_truth_assignment(partial_truth_assign, j, False)\n",
    "#     #print(\"Try2: \",try2)\n",
    "#     try2e = formula.evaluate(try2)\n",
    "#     if try2e == 1:\n",
    "#         #print(f\"success, end early on {j} False\")\n",
    "#         return [True,try2]\n",
    "#     if try2e == 0:\n",
    "#         if j<= formula.n -1:\n",
    "#             (result,lastassign)=dpll_algorithm(formula,try1,j+1)\n",
    "#             if result == True:\n",
    "#                 return [True,lastassign]  \n",
    "#     #---\n",
    "# Pseudo\n",
    "# def DPLLAlgorithm(formula, truth_assign, j):\n",
    "#   # j is the id of the current variable we are considering\n",
    "#   # initially j is set to 1.\n",
    "#   # truth_assign is a partial truth assignment\n",
    "#   n = number of variables in the formula\n",
    "#   assert 1 <= j <= n \n",
    "#   # Try to set xj to true and check the formula\n",
    "#   truth_assign0 = extend truth_assign with (xj -> true)\n",
    "#   # evaluate the formula under the partial truth assignment\n",
    "#   e0 = evaluate formula under truth_assign0\n",
    "#   # if it is already satisfied\n",
    "#   if e0 == satisfied: # end early\n",
    "#      then return SUCCESS, truth_assign0\n",
    "#   if e0 == indeterminate:# if it is indeterminate\n",
    "#      # recursively call the DPLLAlgorithm\n",
    "#      (result, final_truth_assign) = DPLLAlgorithm(formula, truth_assign0, j+1)\n",
    "#      # if it succeeds, return the truth assignment\n",
    "#      if result == SUCCESS:\n",
    "#         return SUCCESS, final_truth_assign\n",
    "\n",
    "#   # Now try to set xj to false and check the formula\n",
    "#   truth_assign1 = extend truth_assign with (xj -> false)\n",
    "#   e1 = evaluate formula under truth_assign1\n",
    "#   if e1 == satisfied:\n",
    "#       then return SUCCESS, truth_assign1\n",
    "#   if e1 == indeterminate:\n",
    "#      (result, final_truth_assign) = DPLLAlgorithm(formula, truth_assign1, j+1)\n",
    "#      # if it succeeds, return the truth assignment\n",
    "#      if result == SUCCESS:\n",
    "#         return SUCCESS, final_truth_assign\n",
    "#   # Fell through all cases\n",
    "#   return FAILURE, None\n",
    "\n",
    "    try1 = extend_truth_assignment(partial_truth_assign, j, True)\n",
    "    #print(\"Try1 = \",try1)\n",
    "    try1e = formula.evaluate(try1)\n",
    "    if try1e == 1:\n",
    "        #print(f\"success, end early on {j} True\")\n",
    "        return (True,try1)\n",
    "    if try1e ==0:\n",
    "        if j<= formula.n -1:\n",
    "            (result,lastassign)=dpll_algorithm(formula,try1,j+1)\n",
    "            if result == True:\n",
    "                return (True,lastassign)    \n",
    "    try2 = extend_truth_assignment(partial_truth_assign, j, False)\n",
    "    #print(\"Try2: \",try2)\n",
    "    try2e = formula.evaluate(try2)\n",
    "    if try2e == 1:\n",
    "        #print(f\"success, end early on {j} False\")\n",
    "        return (True,try2)\n",
    "    if try2e == 0:\n",
    "        if j<= formula.n -1:\n",
    "            (result,lastassign)=dpll_algorithm(formula,try2,j+1)\n",
    "            if result == True:\n",
    "                return (True,lastassign) \n",
    "    forget_var_in_truth_assign(partial_truth_assign, j)\n",
    "    return False, None\n",
    "\n",
    "def solve_formula(formula):\n",
    "    return dpll_algorithm(formula, {}, 1)\n",
    "\n",
    "print('-- formula 1 --')\n",
    "f1 = SATInstance(4, [ [ 1, 2, -4], [-2, -3, 1], [-1, -2, -3] ])\n",
    "(e, t) = solve_formula(f1)\n",
    "print(e, t)\n",
    "assert(e, 'f1 should be satisfiable')\n",
    "assert(t != None, 'does not return a truth assignment')\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "deletable": false,
    "editable": false,
    "nbgrader": {
     "cell_type": "code",
     "checksum": "386b637764506bc128198662dbb16333",
     "grade": true,
     "grade_id": "cell-b25f50b13e32cca6",
     "locked": true,
     "points": 20,
     "schema_version": 3,
     "solution": false,
     "task": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-- formula 1 --\n",
      "True {1: True, 2: True, 3: False}\n",
      "-- formula 2 -- \n",
      "True {1: True, 2: True, 3: True, 4: False, 5: False}\n",
      "--formula 3 --\n",
      "False None\n",
      "--formula 4--\n",
      "True {1: True, 2: True, 3: True, 4: True, 5: True, 6: True, 7: True, 8: True, 9: True, 10: False}\n",
      "--formula 5--\n",
      "True {1: True, 2: True, 3: True, 4: False, 5: True, 6: True, 7: True, 8: True, 9: True, 10: True, 11: False, 12: True, 13: True, 14: True, 15: True}\n",
      "All tests passed: 20 points\n"
     ]
    }
   ],
   "source": [
    "print('-- formula 1 --')\n",
    "f1 = SATInstance(4, [ [ 1, 2, -4], [-2, -3, 1], [-1, -2, -3] ])\n",
    "(e, t) = solve_formula(f1)\n",
    "print(e, t)\n",
    "assert(e, 'f1 should be satisfiable')\n",
    "assert(t != None, 'does not return a truth assignment')\n",
    "assert(f1.evaluate(t) == 1, 'Truth assignment does not evaluate to expected value of true')\n",
    "\n",
    "print('-- formula 2 -- ')\n",
    "f2 = SATInstance(5, [[1,2,-5],[-4,-2,-1], [1, 3, 5], [-1, -5, -2], [1, 2, -4]])\n",
    "(e2, t2) = solve_formula(f2)\n",
    "print(e2, t2)\n",
    "assert(e2, 'f2 must be satisfiable')\n",
    "assert(t2 != None, 'does not return a truth assignment')\n",
    "assert(f2.evaluate(t2) == 1, 'Truth assignment does not evaluate to expected value of true')\n",
    "\n",
    "print('--formula 3 --')\n",
    "f3 = SATInstance(5, [[1, 2, -5, -4], [1, 2, -5, 4], [-1], [-2,-5], [5]])\n",
    "(e3, t3) = solve_formula(f3)\n",
    "print(e3, t3)\n",
    "assert(not e3, 'f3 is unsatisfiable')\n",
    "assert(t3 == None)\n",
    "\n",
    "print('--formula 4--')\n",
    "f4 = SATInstance(10, [\n",
    "  [-1, -5, -4, 8],\n",
    "  [1, 5, 8, 2],\n",
    "   [2, 1, 3, 9],\n",
    "    [-2, 4, 5, 6, -7],\n",
    "    [-1, 2, -1, 7, 8],\n",
    "    [2, -3, 1, 4, 9 ],\n",
    "    [1, 10],\n",
    "    [-10],\n",
    "    [1, 5, 8, 3, 10]\n",
    "])\n",
    "\n",
    "(e4, t4) = solve_formula(f4)\n",
    "print(e4, t4)\n",
    "assert(e4, 'f4 must be satisfiable')\n",
    "assert(t4 != None, 'does not return a truth assignment')\n",
    "assert(f4.evaluate(t4) == 1, 'Truth assignment does not evaluate to expected value of true')\n",
    "\n",
    "print('--formula 5--')\n",
    "f5 = SATInstance(16,[\n",
    "     [1, 2], [-2 , -4],[3, 4], [-4, -5], [5, -6], [6, -7], [6, 7], [7, -16],\n",
    "     [8, -9],[8, -14], [9, 10], [9, -10], [-10, -11], [10, 12], [11, 12], [13, 14],\n",
    "     [14, -15], [15, 16]])\n",
    "(e5, t5) = solve_formula(f5)\n",
    "print(e5, t5)\n",
    "assert(e5, 'f5 is satisfiable')\n",
    "assert(t5 != None)\n",
    "assert(f5.evaluate(t5) == 1, 'Truth assignment does not evaluate to expected value of true')\n",
    "\n",
    "print('All tests passed: 20 points')"
   ]
  },
  {
   "attachments": {
    "3-coloring-examples.png": {
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"
    }
   },
   "cell_type": "markdown",
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   "source": [
    "## 2. Use SAT solver to solve 3-coloring problem.\n",
    "\n",
    "The 3-color problem asks given a undirected graph $G$ whether it can be colored with $3$ or fewer colors. I.e, assign each vertex of the graph one of three colors say Red, Blue, Green such that vertices connected by an edge should not have the same color.\n",
    "\n",
    "### Examples\n",
    "\n",
    "We have three graphs below: the two to the left are 3-colorable and the colorings are shown. The graph to the right is a \"4-clique\" -- 4 nodes all connected to each other and is not 3-colorable.\n",
    "\n",
    "![3-coloring-examples.png](attachment:3-coloring-examples.png)\n",
    "\n",
    "### 3-Coloring Problem\n",
    "\n",
    "The 3-coloring problem inputs an undirected graph $G$, there exists a $3-$coloring (an assignment of one of three colors $R, G \\ \\text{or}\\ B$) to each vertex, such that no two vertices connected by an edge have the same color. \n",
    "\n",
    "The three coloring problem is an interesting problem in computer science: minor variants of it have very important applications ranging from compilers, computer chip design to resource allocation problems. It is also an NP complete problem, as we will see. In other words, if we find a polynomial time (\"efficient\") algorithm to solve the $3$-coloring problem, then we can solve all NP-complete problems (including SAT) in polynomial time.\n",
    "\n",
    "\n",
    "### Part A\n",
    "\n",
    "Given a graph $G$ and a proposed $3$ coloring represented as a map from vertices to colors $\\{1,2, 3\\}$, check that it is a valid three coloring of the graph $G$."
   ]
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   "execution_count": 34,
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     "cell_type": "code",
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   "source": [
    "class UndirectedGraph:\n",
    "    # n_verts: number of vertices of the graph\n",
    "    #   vertices are labeled from 0... n-1\n",
    "    # adj_list: an adjacency list represented as a list of lists.\n",
    "    #  if set to None, we will initialize it as an empty graph\n",
    "    def __init__(self, n_verts, adj_list=None):\n",
    "        self.n = n_verts\n",
    "        if adj_list == None:\n",
    "            adj_list = [ [] for j in range(self.n)] # initialize as empty list of lists\n",
    "        else:\n",
    "            assert len(self.adj_list) == n_verts\n",
    "            for lst in adj_list:\n",
    "                for elt in lst:\n",
    "                    assert 0 <= elt and elt < self.n_verts\n",
    "        \n",
    "        self.adj_list = adj_list\n",
    "    \n",
    "    def add_edge(self, i, j):\n",
    "        assert 0 <= i and i < self.n\n",
    "        assert 0 <= j and j < self.n\n",
    "        assert i != j\n",
    "        self.adj_list[i].append(j)\n",
    "        self.adj_list[j].append(i)\n",
    "        \n",
    "    def get_list_of_edges(self):\n",
    "        return [ (i, j) for i in range(self.n) for j in self.adj_list[i] if i < j ]\n",
    "            \n",
    "        \n",
    "  "
   ]
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   "execution_count": 35,
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   "source": [
    "def is_three_coloring(graph, coloring):\n",
    "    n = graph.n\n",
    "    for i in range(n):\n",
    "        if i not in coloring:\n",
    "            return False # every vertex must receive a color\n",
    "        if coloring[i] < 1 or coloring[i] > 3:\n",
    "            return False # coloring must be between 1 and 3 inclusive\n",
    "    # Your code should complete the check below\n",
    "    # use the provided function graph.get_list_of_edges() to get a list of edges\n",
    "    # or feel free to extend the graph data structure as you will.\n",
    "    # your code here\n",
    "    for i in range(len(graph.get_list_of_edges())):\n",
    "        a,b = graph.get_list_of_edges()[i]\n",
    "        if coloring[a] == [b]:\n",
    "            return False\n",
    "    #print(graph.n)\n",
    "    #print(coloring)\n",
    "    return True\n"
   ]
  },
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     "text": [
      "--- Test 1 ---\n",
      "--- Test 2 ---\n",
      "-- Test 3 --- \n",
      "All Tests Passed (10 points)!\n"
     ]
    }
   ],
   "source": [
    "print('--- Test 1 ---')\n",
    "g1 = UndirectedGraph(5)\n",
    "g1.add_edge(0, 1)\n",
    "g1.add_edge(1, 2)\n",
    "g1.add_edge(2, 0)\n",
    "g1.add_edge(1, 3)\n",
    "g1.add_edge(3, 4)\n",
    "g1.add_edge(1, 4)\n",
    "g1.add_edge(4,0)\n",
    "\n",
    "\n",
    "coloring1 = {0:1, 1:2, 2:3, 3:1, 4: 3}\n",
    "assert(is_three_coloring(g1, coloring1), 'Test 1 fail: Coloring should be valid of g1.')\n",
    "\n",
    "print('--- Test 2 ---')\n",
    "g2 = UndirectedGraph(7)\n",
    "g2.add_edge(2, 3)\n",
    "g2.add_edge(2, 1)\n",
    "g2.add_edge(2, 0)\n",
    "g2.add_edge(2, 4)\n",
    "g2.add_edge(3, 5)\n",
    "g2.add_edge(3, 6)\n",
    "g2.add_edge(5, 6)\n",
    "g2.add_edge(1, 0)\n",
    "g2.add_edge(1, 4)\n",
    "g2.add_edge(0, 4)\n",
    "\n",
    "coloring2 = {2: 1, 3: 2, 4: 2, 0: 1, 1: 3, 5: 3, 6: 1}\n",
    "\n",
    "assert(not is_three_coloring(g2, coloring2), 'Test 2 fail: Coloring should be not be valid of g2.')\n",
    "\n",
    "\n",
    "print('-- Test 3 --- ')\n",
    "\n",
    "coloring3 = {2: 3, 3: 2, 4: 2, 0: 2, 1: 1, 5: 3, 6: 1}\n",
    "\n",
    "assert(not is_three_coloring(g2, coloring3), 'Test 3 fail: Coloring should be not be valid of g2.')\n",
    "\n",
    "print('All Tests Passed (10 points)!')"
   ]
  },
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    "Note that the $3$-coloring problem belongs to the class NP. Given a $3$-coloring of a graph, we just wrote an efficient algorithm that can verify it in time that is $\\Theta(m + n)$ for $n$ vertices and $m$ edges. \n",
    "We can similarly prove that $3$-coloring is NP complete by showing that if we can solve $3$-coloring efficiently then we can also solve SAT problem efficiently. \n",
    "\n",
    "We will exercise the other direction which is often a practical approach to solving hard NP complete problems.\n",
    "\n",
    "### Part B\n",
    "\n",
    "Let $G$ be an undirected graph with vertex $V = \\{ v_1, \\ldots, v_n \\}$ and set of edges $E$ between the vertices.\n",
    "\n",
    "We will translate the 3-coloring problem for graph $G$ into a SAT problem which encodes the constraints of the 3-coloring. From the solution to the SAT problem, we will conclude that the graph is 3-colorable or not. If 3-colorable, we will also extract a 3-coloring.\n",
    "\n",
    "Let us use boolean variables of the form $x_{i, j}$ that means that vertex $i$ is being colored with color $j$ where $i \\in \\{ 1, \\ldots, n \\}$ and $j \\in \\{ 1, 2, 3 \\}$.\n",
    "\n",
    "\n",
    "### Question B\n",
    "\n",
    "Write down clauses that express the following for vertex $i$.\n",
    "  - vertex $i$ must be colored with color 1, 2 or 3.\n",
    "  - vertex $i$ cannot have more than one color: this is slightly tricky esp. if you are new to this -- think of rewording the statement so that you can write it as an or (disjunction) of literals.\n",
    "    - This will require $3$ clauses.\n",
    "  - for each edge $(i,j)$, vertices $v_i$ and $v_j$ cannot have the same color.\n",
    "    - This will require $3$ clauses.\n",
    "    \n",
    "We will not grade this problem but you should take the time to think through this and compare your answers with ours."
   ]
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    "vertex  𝑖  must be definitively shaded from set {1,2,3}\n",
    "$$ X_{𝑖,1} X_{𝑖,2} X_{𝑖,3} $$.\n",
    " \n",
    "vertex  𝑖 cannot have more than one color.\n",
    "    \n",
    "    𝗇𝗈𝗍 X𝑖1 𝗈𝗋 𝗇𝗈𝗍 X𝑖2\n",
    "    𝗇𝗈𝗍 X𝑖2 𝗈𝗋 𝗇𝗈𝗍 X𝑖3\n",
    "    𝗇𝗈𝗍 X𝑖1 𝗈𝗋 𝗇𝗈𝗍 X𝑖3\n",
    " \n",
    " \n",
    "for each edge  (𝑖,𝑗)  vertices  V𝑖,V𝑗  cannot both have the same color.\n",
    "    \n",
    "    𝗇𝗈𝗍 X𝑖1 𝗈𝗋 𝗇𝗈𝗍 X𝑗1\n",
    "    𝗇𝗈𝗍 X𝑖2 𝗈𝗋 𝗇𝗈𝗍 X𝑗2\n",
    "    𝗇𝗈𝗍 X𝑖3 𝗈𝗋 𝗇𝗈𝗍 X𝑗3\n",
    "    \n",
    "    "
   ]
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   "source": [
    "## Implement the Translation\n",
    "\n",
    "Let's use the `SATInstance` class from the previous problem and the SAT solver we constructed to solve the 3-coloring problem. "
   ]
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     "name": "stdout",
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     "text": [
      "--- Test 0 ---\n",
      "{0: 1, 1: 3, 2: 3}\n",
      "Passed\n",
      "-- Test 1 --\n",
      "Passed\n",
      "0.007778167724609375\n",
      "--Test 2--\n",
      "{0: 1, 1: 3, 2: 3, 3: 2, 4: 1, 5: 2}\n",
      "Passed\n",
      "0.010850667953491211\n",
      "-- Test 3 --\n",
      "None\n",
      "Passed\n",
      "0.020516395568847656\n",
      "--- Test 4 ---\n",
      "{0: 1, 1: 3, 2: 3, 3: 2, 4: 1}\n",
      "Passed\n",
      "0.022840261459350586\n",
      "-- Test 5 -- \n",
      "{0: 1, 1: 3, 2: 3, 3: 2, 4: 1, 5: 3, 6: 3}\n",
      "Passed\n",
      "0.057637691497802734\n",
      "--Test 6--\n",
      "passed\n",
      "All test passed: 15 points!\n",
      "0.017972469329833984\n"
     ]
    }
   ],
   "source": [
    "# Input: a graph that is an instance of the `UndirectedGraph` class\n",
    "# Output: an instance of `SATInstance` that encodes the 3 coloring problem\n",
    "# Useful functions:\n",
    "#   SATInstance class add_clause\n",
    "#   UndirectedGraph class get_list_of_edges \n",
    "def translate_three_coloring(graph):\n",
    "    n_boolean_vars = graph.n * 3 # 3 boolean variables for each vertex\n",
    "    # You can define your own scheme for translating x_i,j into the index of a prop. var.\n",
    "    # we propose using x_i,j --> 3 *i + j\n",
    "    s = SATInstance(n_boolean_vars, []) # no clauses\n",
    "    # your code here\n",
    "    \n",
    "#     for y in graph.get_list_of_edges():\n",
    "#         i = y[0]\n",
    "#         j = y[1]\n",
    "#         s.add_clause([3*i + 1, 3*i + 2, 3*i + 3]) #Neither i nor j can have any color but 1 2 or 3\n",
    "#         s.add_clause([3*j + 1, 3*j + 2, 3*j + 3])\n",
    "# #         s.add_clause([-(3*i + 1), -(3*i + 2)])  # Both i can only be one color\n",
    "# #         s.add_clause([-(3*i + 1), -(3*i + 3)])\n",
    "# #         s.add_clause([-(3*i + 2), -(3*i + 3)])\n",
    "# #         s.add_clause([-(3*j + 1), -(3*j + 2)])  # Cont'd j can only be one color\n",
    "# #         s.add_clause([-(3*j + 1), -(3*j + 3)])\n",
    "# #         s.add_clause([-(3*j + 2), -(3*j + 3)])\n",
    "        \n",
    "#         s.add_clause([-(3*i + 1), -(3*i + 2), (3*i + 3)])  # Both i can only be one color\n",
    "#         s.add_clause([-(3*i + 1), -(3*i + 3), (3*i + 2)])\n",
    "#         s.add_clause([-(3*i + 2), -(3*i + 3), (3*i + 1)])\n",
    "#         s.add_clause([-(3*j + 1), -(3*j + 2), (3*j + 3)])  # Cont'd j can only be one color\n",
    "#         s.add_clause([-(3*j + 1), -(3*j + 3), (3*j + 2)])\n",
    "#         s.add_clause([-(3*j + 2), -(3*j + 3), (3*j + 1)])\n",
    "        \n",
    "#         s.add_clause([-(3*i + 1), -(3*j + 1)]) # i and j can't both have the same color\n",
    "#         s.add_clause([-(3*i + 2), -(3*j + 2)])\n",
    "#         s.add_clause([-(3*i + 3), -(3*j + 3)])\n",
    "\n",
    "    for i in range(graph.n):\n",
    "            s.add_clause([3*i+1, 3*i+2, 3*i+3])\n",
    "            s.add_clause([-(3*i+2), -(3*i+1),(3*i+3)])\n",
    "            s.add_clause([-(3*i+2), -(3*i+3),(3*i+1)])\n",
    "            s.add_clause([-(3*i+1), -(3*i+3),(3*i+2)])\n",
    "    for (i,j) in graph.get_list_of_edges():\n",
    "        xi1 = 3*i + 1\n",
    "        xi2 = 3*i + 2\n",
    "        xi3 = 3*i + 3\n",
    "        xj1 = 3*j + 1 \n",
    "        xj2 = 3*j + 2\n",
    "        xj3 = 3*j + 3\n",
    "        s.add_clause([-xi1, -xj1])\n",
    "        s.add_clause([-xi2, -xj2])\n",
    "        s.add_clause([-xi3, -xj3])\n",
    "\n",
    "    return s\n",
    "\n",
    "# Input: graph --> an instance of UndirectedGraph with n vertices\n",
    "#         truth_assign --> dictionary with key in range 1 ... 3*n mapping each key to true/false\n",
    "#                           output from SAT solver.\n",
    "# Output: A dictionary mapping vertices 0,..., n-1 to colors {1, 2, 3}   \n",
    "# This function will be implemented based on the scheme you used in previous function translate_three_coloring\n",
    "def extract_graph_coloring_from_truth_assignment(graph, truth_assign):\n",
    "#     colors = {i:None for i in range(graph.n)}   \n",
    "    colors = {}\n",
    "#     print(truth_assign)\n",
    "#     for i in range(len(truth_assign)):\n",
    "#             current_vertex = i//3\n",
    "#             current_color = i%3 + 1\n",
    "#             colors[current_vertex] = current_color\n",
    "    for i in range(graph.n):\n",
    "        k = i\n",
    "        if truth_assign[k+1]==True:\n",
    "            colors[k]=1\n",
    "        elif truth_assign[k+2] == True:\n",
    "            colors[k]=2\n",
    "        else:\n",
    "            colors[k]=3\n",
    "    #print(colors)\n",
    "    return colors \n",
    "    \n",
    "def solve_three_coloring(graph):\n",
    "    s = translate_three_coloring(graph)\n",
    "    #print(\"clauses\",s.clauses)\n",
    "    res, truth_assign = solve_formula(s)\n",
    "    #print(\"res\",res)\n",
    "    #print(\"truth_assign\",truth_assign)\n",
    "    if res: \n",
    "        return extract_graph_coloring_from_truth_assignment(graph, truth_assign)\n",
    "    else: \n",
    "        return None\n",
    "\n",
    "##Below is Test Timing for The Cell Below.\n",
    "## Case 5 and 6 are taking > 90% of the time for the whole notebook...\n",
    "    \n",
    "st = time.time()\n",
    "print('--- Test 0 ---')\n",
    "# A simple triangle should be 3 colorable\n",
    "g0 = UndirectedGraph(3)\n",
    "g0.add_edge(0,1)\n",
    "g0.add_edge(1,2)\n",
    "g0.add_edge(0,2)\n",
    "coloring = solve_three_coloring(g0)\n",
    "print(coloring)\n",
    "assert(coloring != None)\n",
    "assert(is_three_coloring(g0, coloring))\n",
    "print('Passed')\n",
    "\n",
    "print('-- Test 1 --')\n",
    "# The \"complete\" graph on 4 vertices is not 3 colorable\n",
    "g1 = UndirectedGraph(4)\n",
    "g1.add_edge(0, 1)\n",
    "g1.add_edge(0, 2)\n",
    "g1.add_edge(0, 3)\n",
    "g1.add_edge(1, 2)\n",
    "g1.add_edge(1, 3)\n",
    "g1.add_edge(2, 3)\n",
    "coloring = solve_three_coloring(g1)\n",
    "assert(coloring == None)\n",
    "print('Passed')\n",
    "en = time.time()\n",
    "print(en-st)\n",
    "print('--Test 2--')\n",
    "# Make a chordal graph on 6 vertices\n",
    "g2 = UndirectedGraph(6)\n",
    "# make a 6 cycle\n",
    "g2.add_edge(0, 1)\n",
    "g2.add_edge(1, 2)\n",
    "g2.add_edge(2, 3)\n",
    "g2.add_edge(3, 4)\n",
    "g2.add_edge(4, 5)\n",
    "# add two chords\n",
    "g2.add_edge(0, 3)\n",
    "g2.add_edge(2, 4)\n",
    "coloring = solve_three_coloring(g2)\n",
    "print(coloring)\n",
    "assert(coloring != None)\n",
    "assert(is_three_coloring(g2, coloring))\n",
    "print('Passed')\n",
    "en = time.time()\n",
    "print(en-st)\n",
    "print('-- Test 3 --')\n",
    "g2.add_edge(1,3)\n",
    "g2.add_edge(0, 2)\n",
    "coloring = solve_three_coloring(g2)\n",
    "print(coloring)\n",
    "assert (coloring == None)\n",
    "print('Passed')\n",
    "en = time.time()\n",
    "print(en-st)\n",
    "\n",
    "print('--- Test 4 ---')\n",
    "g1 = UndirectedGraph(5)\n",
    "g1.add_edge(0, 1)\n",
    "g1.add_edge(1, 2)\n",
    "g1.add_edge(2, 0)\n",
    "g1.add_edge(1, 3)\n",
    "g1.add_edge(3, 4)\n",
    "g1.add_edge(1, 4)\n",
    "g1.add_edge(4, 0)\n",
    "coloring = solve_three_coloring(g1)\n",
    "print(coloring)\n",
    "assert( is_three_coloring(g1, coloring))\n",
    "print('Passed')\n",
    "en = time.time()\n",
    "print(en-st)\n",
    "st = en\n",
    "print('-- Test 5 -- ')\n",
    "\n",
    "g2 = UndirectedGraph(7)\n",
    "g2.add_edge(2, 3)\n",
    "g2.add_edge(2, 1)\n",
    "g2.add_edge(2, 0)\n",
    "g2.add_edge(2, 4)\n",
    "g2.add_edge(3, 5)\n",
    "g2.add_edge(3, 6)\n",
    "g2.add_edge(5, 6)\n",
    "g2.add_edge(1, 0)\n",
    "g2.add_edge(1, 4)\n",
    "\n",
    "coloring = solve_three_coloring(g2)\n",
    "print(coloring)\n",
    "assert( is_three_coloring(g2, coloring))\n",
    "print('Passed')\n",
    "en = time.time()\n",
    "print(en-st)\n",
    "st = en\n",
    "print('--Test 6--')\n",
    "g2.add_edge(0, 4)\n",
    "coloring = solve_three_coloring(g2)\n",
    "assert(coloring == None)\n",
    "print('passed')\n",
    "\n",
    "print('All test passed: 15 points!')\n",
    "en = time.time()\n",
    "print(en-st)"
   ]
  },
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    {
     "name": "stdout",
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     "text": [
      "--- Test 0 ---\n",
      "{1: True, 2: False, 3: False, 4: False, 5: True, 6: False, 7: False, 8: False, 9: True}\n",
      "{0: 1, 1: 2, 2: 3}\n",
      "Passed\n",
      "-- Test 1 --\n",
      "Passed\n",
      "--Test 2--\n",
      "{1: True, 2: False, 3: False, 4: False, 5: True, 6: False, 7: True, 8: False, 9: False, 10: False, 11: True, 12: False, 13: False, 14: False, 15: True, 16: True, 17: False, 18: False}\n",
      "{0: 1, 1: 2, 2: 1, 3: 2, 4: 3, 5: 1}\n",
      "Passed\n",
      "-- Test 3 --\n",
      "None\n",
      "Passed\n",
      "--- Test 4 ---\n",
      "{1: True, 2: False, 3: False, 4: False, 5: True, 6: False, 7: False, 8: False, 9: True, 10: True, 11: False, 12: False, 13: False, 14: False, 15: True}\n",
      "{0: 1, 1: 2, 2: 3, 3: 1, 4: 3}\n",
      "Passed\n",
      "-- Test 5 -- \n",
      "{1: True, 2: False, 3: False, 4: False, 5: True, 6: False, 7: False, 8: False, 9: True, 10: True, 11: False, 12: False, 13: True, 14: False, 15: False, 16: False, 17: True, 18: False, 19: False, 20: False, 21: True}\n",
      "{0: 1, 1: 2, 2: 3, 3: 1, 4: 1, 5: 2, 6: 3}\n",
      "Passed\n",
      "--Test 6--\n",
      "passed\n",
      "All test passed: 15 points!\n"
     ]
    }
   ],
   "source": [
    "print('--- Test 0 ---')\n",
    "# A simple triangle should be 3 colorable\n",
    "g0 = UndirectedGraph(3)\n",
    "g0.add_edge(0,1)\n",
    "g0.add_edge(1,2)\n",
    "g0.add_edge(0,2)\n",
    "coloring = solve_three_coloring(g0)\n",
    "print(coloring)\n",
    "assert(coloring != None)\n",
    "assert(is_three_coloring(g0, coloring))\n",
    "print('Passed')\n",
    "\n",
    "print('-- Test 1 --')\n",
    "# The \"complete\" graph on 4 vertices is not 3 colorable\n",
    "g1 = UndirectedGraph(4)\n",
    "g1.add_edge(0, 1)\n",
    "g1.add_edge(0, 2)\n",
    "g1.add_edge(0, 3)\n",
    "g1.add_edge(1, 2)\n",
    "g1.add_edge(1, 3)\n",
    "g1.add_edge(2, 3)\n",
    "coloring = solve_three_coloring(g1)\n",
    "assert(coloring == None)\n",
    "print('Passed')\n",
    "\n",
    "print('--Test 2--')\n",
    "# Make a chordal graph on 6 vertices\n",
    "g2 = UndirectedGraph(6)\n",
    "# make a 6 cycle\n",
    "g2.add_edge(0, 1)\n",
    "g2.add_edge(1, 2)\n",
    "g2.add_edge(2, 3)\n",
    "g2.add_edge(3, 4)\n",
    "g2.add_edge(4, 5)\n",
    "# add two chords\n",
    "g2.add_edge(0, 3)\n",
    "g2.add_edge(2, 4)\n",
    "coloring = solve_three_coloring(g2)\n",
    "print(coloring)\n",
    "assert(coloring != None)\n",
    "assert(is_three_coloring(g2, coloring))\n",
    "print('Passed')\n",
    "\n",
    "print('-- Test 3 --')\n",
    "g2.add_edge(1,3)\n",
    "g2.add_edge(0, 2)\n",
    "coloring = solve_three_coloring(g2)\n",
    "print(coloring)\n",
    "assert (coloring == None)\n",
    "print('Passed')\n",
    "\n",
    "\n",
    "print('--- Test 4 ---')\n",
    "g1 = UndirectedGraph(5)\n",
    "g1.add_edge(0, 1)\n",
    "g1.add_edge(1, 2)\n",
    "g1.add_edge(2, 0)\n",
    "g1.add_edge(1, 3)\n",
    "g1.add_edge(3, 4)\n",
    "g1.add_edge(1, 4)\n",
    "g1.add_edge(4, 0)\n",
    "coloring = solve_three_coloring(g1)\n",
    "print(coloring)\n",
    "assert( is_three_coloring(g1, coloring))\n",
    "print('Passed')\n",
    "\n",
    "print('-- Test 5 -- ')\n",
    "\n",
    "g2 = UndirectedGraph(7)\n",
    "g2.add_edge(2, 3)\n",
    "g2.add_edge(2, 1)\n",
    "g2.add_edge(2, 0)\n",
    "g2.add_edge(2, 4)\n",
    "g2.add_edge(3, 5)\n",
    "g2.add_edge(3, 6)\n",
    "g2.add_edge(5, 6)\n",
    "g2.add_edge(1, 0)\n",
    "g2.add_edge(1, 4)\n",
    "\n",
    "coloring = solve_three_coloring(g2)\n",
    "print(coloring)\n",
    "assert( is_three_coloring(g2, coloring))\n",
    "print('Passed')\n",
    "\n",
    "print('--Test 6--')\n",
    "g2.add_edge(0, 4)\n",
    "coloring = solve_three_coloring(g2)\n",
    "assert(coloring == None)\n",
    "print('passed')\n",
    "\n",
    "print('All test passed: 15 points!')"
   ]
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    "## Solution \n",
    "\n",
    "### P1 Answer 1\n",
    "\n",
    "Let us set $x_1: true,\\ x_2:\\ false$. It does not matter what we set $x_3, x_4$ to: we may set them both to be $true$ if necessary. We note that all clauses are satisfied. $C_1$ is satisfied because $x_1$ is true. $C_2, C_3$ are satisfied because $x_2$ is false.\n",
    "\n",
    "\n",
    "### P2 Answer B\n",
    "\n",
    "$\\newcommand\\bor{\\textsf{or}}\n",
    "\\newcommand\\band{\\textsf{and}}\n",
    "\\newcommand\\bnot{\\textsf{not}}$\n",
    "\n",
    "\n",
    "> vertex $i$ must be colored with color $1, 2 $ or $3$.\n",
    "  $$ x_{i, 1}\\ \\text{or}\\ x_{i,2}\\ \\text{or}\\ x_{i,3} \\,.$$\n",
    "  \n",
    "> vertex $i$ cannot have more than one color.\n",
    "$$\\begin{array}{ll}\n",
    "\\bnot\\ x_{i,1} \\ \\bor\\ \\bnot\\ x_{i,2} & \\leftarrow\\ \\text{vertex i cannot have both colors 1 and 2 } \\\\\n",
    "\\bnot\\ x_{i,2} \\ \\bor\\ \\bnot\\ x_{i,3} & \\leftarrow\\ \\text{vertex i cannot have both colors 2 and 3 } \\\\\n",
    "\\bnot\\ x_{i,1} \\ \\bor\\ \\bnot\\ x_{i,3} & \\leftarrow\\ \\text{vertex i cannot have both colors 1 and 3 } \\\\\n",
    "\\end{array}$$\n",
    "\n",
    "> edge $(i,j)$ : vertices $v_i, v_j$ cannot both have the same color.\n",
    "$$\\begin{array}{ll}\n",
    "\\bnot\\ x_{i,1}\\ \\bor\\ \\bnot\\ x_{j,1} & \\leftarrow\\ \\text{vertex i and j cannot both have color 1 } \\\\\n",
    "\\bnot\\ x_{i,2}\\ \\bor\\ \\bnot\\ x_{j,2} & \\leftarrow\\ \\text{vertex i and j cannot both have color 2 } \\\\\n",
    "\\bnot\\ x_{i,3}\\ \\bor\\ \\bnot\\ x_{j,3} & \\leftarrow\\ \\text{vertex i and j cannot both have color 3 } \\\\\n",
    "\\end{array}$$"
   ]
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   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total = 67.66168069839478\n",
      "Total in evaluate: 0\n",
      "Percent of total 0/67.66168069839478 = 0.0% \n"
     ]
    }
   ],
   "source": [
    "en=time.time()\n",
    "print(F\"Total = {en-st}\")\n",
    "print(F'Total in evaluate: {tot}')\n",
    "print(F'Percent of total {tot}/{en-st} = {100*tot/(en-st)}% ')"
   ]
  },
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   "source": [
    "### That's All Folks!"
   ]
  }
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