KNN.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pylab as plt\n",
    "from collections import Counter\n",
    "%matplotlib inline"
   ]
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   "source": [
    "df = pd.read_csv(\"data/iris.txt\", delimiter=\" \")\n",
    "#df.sample(5)\n",
    "df = df.sample(60)"
   ]
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    "def knn_learn(X, Y, predictX, k=3):\n",
    "    # finding sum of squared errors for each row\n",
    "    errors = np.sum(np.power(X - predictX, 2), axis=1)\n",
    "    \n",
    "    # finding the nearest neighbours\n",
    "    minInd = 0\n",
    "    neighbors = []\n",
    "    for i in range(k):\n",
    "        for j in range(len(errors)):\n",
    "            if errors[j] <= errors[minInd]:\n",
    "                minInd = j\n",
    "        \n",
    "        neighbors.append(\n",
    "            [float(errors[minInd]), float(Y[minInd])] )\n",
    "        errors[minInd] = np.inf\n",
    "    \n",
    "    most_frequent = Counter(np.array(neighbors)[:,1]).most_common()\n",
    "    return most_frequent[0][0]"
   ]
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       "3.0"
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     "execution_count": 192,
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   "source": [
    "x = np.mat(df.iloc[:,[0, 1, 2, 3]])\n",
    "y = np.mat(df.iloc[:,[4]])\n",
    "px = np.mat([6.7, 3.3, 5.7, 2.5])\n",
    "knn_learn(x, y, px)"
   ]
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