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   "source": [
    "# MACHINE LEARNING LAB - 1 ( FIND-S Algorithm )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Implement and demonstrate the FIND-S algorithm for finding the most specific hypothesis based on a given set of training data samples. Read the training data from a .CSV file.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<_csv.reader object at 0x0000001781862BA8>\n",
      "\n",
      "The given training examples are:\n",
      "['sky', 'airTemp', 'humidity', 'wind', 'water', 'forecast', 'enjoySport']\n",
      "['Sunny', 'Warm', 'Normal', 'Strong', 'Warm', 'Same', 'Yes']\n",
      "['Sunny', 'Warm', 'High', 'Strong', 'Warm', 'Same', 'Yes']\n",
      "['Rainy', 'Cold', 'High', 'Strong', 'Warm', 'Change', 'No']\n",
      "['Sunny', 'Warm', 'High', 'Strong', 'Cool', 'Change', 'Yes']\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "hypo = ['%','%','%','%','%','%'];\n",
    "\n",
    "with open('trainingdata.csv') as csv_file:\n",
    "    readcsv = csv.reader(csv_file, delimiter=',')\n",
    "    print(readcsv)\n",
    "    \n",
    "    data = []\n",
    "    print(\"\\nThe given training examples are:\")\n",
    "    for row in readcsv:\n",
    "        print(row)\n",
    "        if row[len(row)-1].upper() == \"YES\":\n",
    "            data.append(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The positive examples are:\n",
      "['Sunny', 'Warm', 'Normal', 'Strong', 'Warm', 'Same', 'Yes']\n",
      "['Sunny', 'Warm', 'High', 'Strong', 'Warm', 'Same', 'Yes']\n",
      "['Sunny', 'Warm', 'High', 'Strong', 'Cool', 'Change', 'Yes']\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nThe positive examples are:\");\n",
    "for x in data:\n",
    "    print(x);\n",
    "print(\"\\n\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The steps of the Find-s algorithm are :\n",
      " ['%', '%', '%', '%', '%', '%']\n",
      "['Sunny', 'Warm', 'Normal', 'Strong', 'Warm', 'Same']\n",
      "['Sunny', 'Warm', '?', 'Strong', 'Warm', 'Same']\n",
      "['Sunny', 'Warm', '?', 'Strong', '?', '?']\n"
     ]
    }
   ],
   "source": [
    "TotalExamples = len(data);\n",
    "i=0;\n",
    "j=0;\n",
    "k=0;\n",
    "print(\"The steps of the Find-s algorithm are :\\n\",hypo);\n",
    "list = [];\n",
    "p=0;\n",
    "d=len(data[p])-1;\n",
    "for j in range(d):\n",
    "    list.append(data[i][j]);\n",
    "hypo=list;\n",
    "i=1;\n",
    "for i in range(TotalExamples):\n",
    "    for k in range(d):\n",
    "        if hypo[k]!=data[i][k]:\n",
    "            hypo[k]='?';\n",
    "            k=k+1;        \n",
    "        else:\n",
    "            hypo[k];\n",
    "    print(hypo);\n",
    "i=i+1;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The maximally specific Find-s hypothesis for the given training examples is :\n",
      "['Sunny', 'Warm', '?', 'Strong', '?', '?']\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nThe maximally specific Find-s hypothesis for the given training examples is :\");\n",
    "list=[];\n",
    "for i in range(d):\n",
    "    list.append(hypo[i]);\n",
    "print(list);"
   ]
  }
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