Masud_Sir/first_follow_sets.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_first_sets(grammar):\n",
    "    first_sets = {}\n",
    "\n",
    "    # Initialize first sets with empty sets\n",
    "    for non_terminal in grammar:\n",
    "        first_sets[non_terminal] = set()\n",
    "\n",
    "    # Helper function to calculate First set for a given symbol\n",
    "    def calculate_first(symbol):\n",
    "        if symbol.isalpha() and symbol.islower():\n",
    "            # If the symbol is a terminal, add it to the First set\n",
    "            first_sets[non_terminal].add(symbol)\n",
    "        elif symbol in grammar:\n",
    "            # If the symbol is a non-terminal, calculate its First set\n",
    "            first_sets[non_terminal] |= first_sets[symbol]\n",
    "\n",
    "    # Iterate through the grammar rules to calculate First sets\n",
    "    for non_terminal, productions in grammar.items():\n",
    "        for production in productions:\n",
    "            for symbol in production:\n",
    "                calculate_first(symbol)\n",
    "                if symbol not in grammar or '0' not in first_sets[symbol]:\n",
    "                    # If symbol is a non-terminal and does not derive '0', break the loop\n",
    "                    break\n",
    "            else:\n",
    "                # If the loop completes without breaking, add '0' to the First set\n",
    "                first_sets[non_terminal].add('0')\n",
    "\n",
    "    return first_sets\n",
    "\n",
    "def calculate_follow_sets(grammar, first_sets):\n",
    "    follow_sets = {}\n",
    "\n",
    "    # Initialize follow sets with empty sets\n",
    "    for non_terminal in grammar:\n",
    "        follow_sets[non_terminal] = set()\n",
    "\n",
    "    # Add '$' to the follow set of the start symbol\n",
    "    follow_sets[list(grammar.keys())[0]].add('$')\n",
    "\n",
    "    # Helper function to calculate Follow set for a given symbol\n",
    "    def calculate_follow(non_terminal, current_production, idx):\n",
    "        if idx < len(current_production) - 1:\n",
    "            # If the current symbol is not the last in the production, calculate First set\n",
    "            for symbol in current_production[idx + 1:]:\n",
    "                follow_sets[non_terminal] |= first_sets[symbol]\n",
    "                if '0' not in first_sets[symbol]:\n",
    "                    # If '0' is not in First set, break the loop\n",
    "                    break\n",
    "            else:\n",
    "                # If the loop completes without breaking, add Follow set of the left-hand side\n",
    "                follow_sets[non_terminal] |= follow_sets[current_non_terminal]\n",
    "\n",
    "    # Iterate through the grammar rules to calculate Follow sets\n",
    "    for non_terminal, productions in grammar.items():\n",
    "        for idx, production in enumerate(productions):\n",
    "            current_non_terminal = non_terminal\n",
    "            for symbol in reversed(production):\n",
    "                if symbol.isalpha() and symbol.islower():\n",
    "                    # If the symbol is a terminal, move to the previous symbol\n",
    "                    current_non_terminal = None\n",
    "                elif symbol in grammar:\n",
    "                    # If the symbol is a non-terminal, calculate Follow set\n",
    "                    calculate_follow(non_terminal, production, idx)\n",
    "                    current_non_terminal = symbol\n",
    "\n",
    "    return follow_sets\n",
    "\n",
    "def print_sets(first_sets, follow_sets):\n",
    "    print(\"\\nFirst Sets:\")\n",
    "    for non_terminal, first_set in first_sets.items():\n",
    "        print(f\"First({non_terminal}): {', '.join(sorted(first_set))}\")\n",
    "\n",
    "    print(\"\\nFollow Sets:\")\n",
    "    for non_terminal, follow_set in follow_sets.items():\n",
    "        print(f\"Follow({non_terminal}): {', '.join(sorted(follow_set))}\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    # Grammar G: {E-> TE’, E’ -> +TE’ | 0, T-> FT’, T’-> *FT’|0, F-> (E) | id}\n",
    "    grammar = {\n",
    "        'E': ['T', 'E\\''],\n",
    "        'E\\'': ['+', 'T', 'E\\'', '0'],\n",
    "        'T': ['F', 'T\\''],\n",
    "        'T\\'': ['*', 'F', 'T\\'', '0'],\n",
    "        'F': ['(', 'E', ')', 'id']\n",
    "    }\n",
    "\n",
    "    first_sets = calculate_first_sets(grammar)\n",
    "    follow_sets = calculate_follow_sets(grammar, first_sets)\n",
    "\n",
    "    print_sets(first_sets, follow_sets)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gram = {\n",
    "\t\"E\":[\"E+T\",\"T\"],\n",
    "\t\"T\":[\"T*F\",\"F\"],\n",
    "\t\"F\":[\"(E)\",\"id\"]\n",
    "}\n",
    "\n",
    "def removeDirectLR(gramA, A):\n",
    "\ttemp = gramA[A]\n",
    "\ttempCr = []\n",
    "\ttempInCr = []\n",
    "\tfor i in temp:\n",
    "\t\tif i[0] == A:\n",
    "\t\t\t#tempInCr.append(i[1:])\n",
    "\t\t\ttempInCr.append(i[1:]+[A+\"'\"])\n",
    "\t\telse:\n",
    "\t\t\t#tempCr.append(i)\n",
    "\t\t\ttempCr.append(i+[A+\"'\"])\n",
    "\ttempInCr.append([\"e\"])\n",
    "\tgramA[A] = tempCr\n",
    "\tgramA[A+\"'\"] = tempInCr\n",
    "\treturn gramA\n",
    "\n",
    "\n",
    "def checkForIndirect(gramA, a, ai):\n",
    "\tif ai not in gramA:\n",
    "\t\treturn False\n",
    "\tif a == ai:\n",
    "\t\treturn True\n",
    "\tfor i in gramA[ai]:\n",
    "\t\tif i[0] == ai:\n",
    "\t\t\treturn False\n",
    "\t\tif i[0] in gramA:\n",
    "\t\t\treturn checkForIndirect(gramA, a, i[0])\n",
    "\treturn False\n",
    "\n",
    "def rep(gramA, A):\n",
    "\ttemp = gramA[A]\n",
    "\tnewTemp = []\n",
    "\tfor i in temp:\n",
    "\t\tif checkForIndirect(gramA, A, i[0]):\n",
    "\t\t\tt = []\n",
    "\t\t\tfor k in gramA[i[0]]:\n",
    "\t\t\t\tt=[]\n",
    "\t\t\t\tt+=k\n",
    "\t\t\t\tt+=i[1:]\n",
    "\t\t\t\tnewTemp.append(t)\n",
    "\n",
    "\t\telse:\n",
    "\t\t\tnewTemp.append(i)\n",
    "\tgramA[A] = newTemp\n",
    "\treturn gramA\n",
    "\n",
    "def rem(gram):\n",
    "\tc = 1\n",
    "\tconv = {}\n",
    "\tgramA = {}\n",
    "\trevconv = {}\n",
    "\tfor j in gram:\n",
    "\t\tconv[j] = \"A\"+str(c)\n",
    "\t\tgramA[\"A\"+str(c)] = []\n",
    "\t\tc+=1\n",
    "\n",
    "\tfor i in gram:\n",
    "\t\tfor j in gram[i]:\n",
    "\t\t\ttemp = []\n",
    "\t\t\tfor k in j:\n",
    "\t\t\t\tif k in conv:\n",
    "\t\t\t\t\ttemp.append(conv[k])\n",
    "\t\t\t\telse:\n",
    "\t\t\t\t\ttemp.append(k)\n",
    "\t\t\tgramA[conv[i]].append(temp)\n",
    "\n",
    "\n",
    "\tfor i in range(c-1,0,-1):\n",
    "\t\tai = \"A\"+str(i)\n",
    "\t\tfor j in range(0,i):\n",
    "\t\t\taj = gramA[ai][0][0]\n",
    "\t\t\tif ai!=aj :\n",
    "\t\t\t\tif aj in gramA and checkForIndirect(gramA,ai,aj):\n",
    "\t\t\t\t\tgramA = rep(gramA, ai)\n",
    "\n",
    "\tfor i in range(1,c):\n",
    "\t\tai = \"A\"+str(i)\n",
    "\t\tfor j in gramA[ai]:\n",
    "\t\t\tif ai==j[0]:\n",
    "\t\t\t\tgramA = removeDirectLR(gramA, ai)\n",
    "\t\t\t\tbreak\n",
    "\n",
    "\top = {}\n",
    "\tfor i in gramA:\n",
    "\t\ta = str(i)\n",
    "\t\tfor j in conv:\n",
    "\t\t\ta = a.replace(conv[j],j)\n",
    "\t\trevconv[i] = a\n",
    "\n",
    "\tfor i in gramA:\n",
    "\t\tl = []\n",
    "\t\tfor j in gramA[i]:\n",
    "\t\t\tk = []\n",
    "\t\t\tfor m in j:\n",
    "\t\t\t\tif m in revconv:\n",
    "\t\t\t\t\tk.append(m.replace(m,revconv[m]))\n",
    "\t\t\t\telse:\n",
    "\t\t\t\t\tk.append(m)\n",
    "\t\t\tl.append(k)\n",
    "\t\top[revconv[i]] = l\n",
    "\n",
    "\treturn op\n",
    "\n",
    "result = rem(gram)\n",
    "\n",
    "\n",
    "def first(gram, term):\n",
    "\ta = []\n",
    "\tif term not in gram:\n",
    "\t\treturn [term]\n",
    "\tfor i in gram[term]:\n",
    "\t\tif i[0] not in gram:\n",
    "\t\t\ta.append(i[0])\n",
    "\t\telif i[0] in gram:\n",
    "\t\t\ta += first(gram, i[0])\n",
    "\treturn a\n",
    "\n",
    "firsts = {}\n",
    "for i in result:\n",
    "\tfirsts[i] = first(result,i)\n",
    "\tprint(f'First({i}):',firsts[i])\n",
    "\n",
    "\n",
    "def follow(gram, term):\n",
    "\ta = []\n",
    "\tfor rule in gram:\n",
    "\t\tfor i in gram[rule]:\n",
    "\t\t\tif term in i:\n",
    "\t\t\t\ttemp = i\n",
    "\t\t\t\tindx = i.index(term)\n",
    "\t\t\t\tif indx+1!=len(i):\n",
    "\t\t\t\t\tif i[-1] in firsts:\n",
    "\t\t\t\t\t\ta+=firsts[i[-1]]\n",
    "\t\t\t\t\telse:\n",
    "\t\t\t\t\t\ta+=[i[-1]]\n",
    "\t\t\t\telse:\n",
    "\t\t\t\t\ta+=[\"e\"]\n",
    "\t\t\t\tif rule != term and \"e\" in a:\n",
    "\t\t\t\t\ta+= follow(gram,rule)\n",
    "\treturn a\n",
    "\n",
    "follows = {}\n",
    "for i in result:\n",
    "\tfollows[i] = list(set(follow(result,i)))\n",
    "\tif \"e\" in follows[i]:\n",
    "\t\tfollows[i].pop(follows[i].index(\"e\"))\n",
    "\tfollows[i]+=[\"$\"]\n",
    "\tprint(f'Follow({i}):',follows[i])"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)\n",
    "<br> Below code output like this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.setrecursionlimit(60)\n",
    "\n",
    "def first(string):\n",
    "    #print(\"first({})\".format(string))\n",
    "    first_ = set()\n",
    "    if string in non_terminals:\n",
    "        alternatives = productions_dict[string]\n",
    "\n",
    "        for alternative in alternatives:\n",
    "            first_2 = first(alternative)\n",
    "            first_ = first_ |first_2\n",
    "\n",
    "    elif string in terminals:\n",
    "        first_ = {string}\n",
    "\n",
    "    elif string=='' or string=='@':\n",
    "        first_ = {'@'}\n",
    "\n",
    "    else:\n",
    "        first_2 = first(string[0])\n",
    "        if '@' in first_2:\n",
    "            i = 1\n",
    "            while '@' in first_2:\n",
    "                #print(\"inside while\")\n",
    "\n",
    "                first_ = first_ | (first_2 - {'@'})\n",
    "                #print('string[i:]=', string[i:])\n",
    "                if string[i:] in terminals:\n",
    "                    first_ = first_ | {string[i:]}\n",
    "                    break\n",
    "                elif string[i:] == '':\n",
    "                    first_ = first_ | {'@'}\n",
    "                    break\n",
    "                first_2 = first(string[i:])\n",
    "                first_ = first_ | first_2 - {'@'}\n",
    "                i += 1\n",
    "        else:\n",
    "            first_ = first_ | first_2\n",
    "\n",
    "\n",
    "    #print(\"returning for first({})\".format(string),first_)\n",
    "    return  first_\n",
    "\n",
    "\n",
    "def follow(nT):\n",
    "    #print(\"inside follow({})\".format(nT))\n",
    "    follow_ = set()\n",
    "    #print(\"FOLLOW\", FOLLOW)\n",
    "    prods = productions_dict.items()\n",
    "    if nT==starting_symbol:\n",
    "        follow_ = follow_ | {'$'}\n",
    "    for nt,rhs in prods:\n",
    "        #print(\"nt to rhs\", nt,rhs)\n",
    "        for alt in rhs:\n",
    "            for char in alt:\n",
    "                if char==nT:\n",
    "                    following_str = alt[alt.index(char) + 1:]\n",
    "                    if following_str=='':\n",
    "                        if nt==nT:\n",
    "                            continue\n",
    "                        else:\n",
    "                            follow_ = follow_ | follow(nt)\n",
    "                    else:\n",
    "                        follow_2 = first(following_str)\n",
    "                        if '@' in follow_2:\n",
    "                            follow_ = follow_ | follow_2-{'@'}\n",
    "                            follow_ = follow_ | follow(nt)\n",
    "                        else:\n",
    "                            follow_ = follow_ | follow_2\n",
    "    #print(\"returning for follow({})\".format(nT),follow_)\n",
    "    return follow_\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "no_of_terminals=int(input(\"Enter no. of terminals: \"))\n",
    "\n",
    "terminals = []\n",
    "\n",
    "print(\"Enter the terminals :\")\n",
    "for _ in range(no_of_terminals):\n",
    "    terminals.append(input())\n",
    "\n",
    "no_of_non_terminals=int(input(\"Enter no. of non terminals: \"))\n",
    "\n",
    "non_terminals = []\n",
    "\n",
    "print(\"Enter the non terminals :\")\n",
    "for _ in range(no_of_non_terminals):\n",
    "    non_terminals.append(input())\n",
    "\n",
    "starting_symbol = input(\"Enter the starting symbol: \")\n",
    "\n",
    "no_of_productions = int(input(\"Enter no of productions: \"))\n",
    "\n",
    "productions = []\n",
    "\n",
    "print(\"Enter the productions:\")\n",
    "for _ in range(no_of_productions):\n",
    "    productions.append(input())\n",
    "\n",
    "\n",
    "#print(\"terminals\", terminals)\n",
    "\n",
    "#print(\"non terminals\", non_terminals)\n",
    "\n",
    "#print(\"productions\",productions)\n",
    "\n",
    "\n",
    "productions_dict = {}\n",
    "\n",
    "for nT in non_terminals:\n",
    "    productions_dict[nT] = []\n",
    "\n",
    "\n",
    "#print(\"productions_dict\",productions_dict)\n",
    "\n",
    "for production in productions:\n",
    "    nonterm_to_prod = production.split(\"->\")\n",
    "    alternatives = nonterm_to_prod[1].split(\"/\")\n",
    "    for alternative in alternatives:\n",
    "        productions_dict[nonterm_to_prod[0]].append(alternative)\n",
    "\n",
    "#print(\"productions_dict\",productions_dict)\n",
    "\n",
    "#print(\"nonterm_to_prod\",nonterm_to_prod)\n",
    "#print(\"alternatives\",alternatives)\n",
    "\n",
    "\n",
    "FIRST = {}\n",
    "FOLLOW = {}\n",
    "\n",
    "for non_terminal in non_terminals:\n",
    "    FIRST[non_terminal] = set()\n",
    "\n",
    "for non_terminal in non_terminals:\n",
    "    FOLLOW[non_terminal] = set()\n",
    "\n",
    "#print(\"FIRST\",FIRST)\n",
    "\n",
    "for non_terminal in non_terminals:\n",
    "    FIRST[non_terminal] = FIRST[non_terminal] | first(non_terminal)\n",
    "\n",
    "#print(\"FIRST\",FIRST)\n",
    "\n",
    "\n",
    "FOLLOW[starting_symbol] = FOLLOW[starting_symbol] | {'$'}\n",
    "for non_terminal in non_terminals:\n",
    "    FOLLOW[non_terminal] = FOLLOW[non_terminal] | follow(non_terminal)\n",
    "\n",
    "#print(\"FOLLOW\", FOLLOW)\n",
    "\n",
    "print(\"{: ^20}{: ^20}{: ^20}\".format('Non Terminals','First','Follow'))\n",
    "for non_terminal in non_terminals:\n",
    "    print(\"{: ^20}{: ^20}{: ^20}\".format(non_terminal,str(FIRST[non_terminal]),str(FOLLOW[non_terminal])))"
   ]
  }
 ],
 "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": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}