music_genre_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install python_speech_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from python_speech_features import mfcc\n",
    "import scipy.io.wavfile as wav\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "import os\n",
    "import pickle\n",
    "import random \n",
    "import operator\n",
    "\n",
    "import math\n",
    "import numpy as np\n",
    "import collections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def distance(instance1 , instance2 , k ):\n",
    "   \n",
    "    distance =0 \n",
    "    mm1 = instance1[0] \n",
    "    cm1 = instance1[1]\n",
    "    mm2 = instance2[0]\n",
    "    cm2 = instance2[1]\n",
    "\n",
    "    distance = np.trace(np.dot(np.linalg.inv(cm2), cm1)) \n",
    "#     print(distance)\n",
    "    distance+=(np.dot(np.dot((mm2-mm1).transpose() , np.linalg.inv(cm2)) , mm2-mm1 )) \n",
    "#     print(distance)\n",
    "    distance+= np.log(np.linalg.det(cm2)) - np.log(np.linalg.det(cm1))\n",
    "#     print(distance)\n",
    "    distance-= k\n",
    "    return distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getNeighbors(trainingSet, instance, k):\n",
    "    \n",
    "    distances = []\n",
    "    for x in range (len(trainingSet)):\n",
    "        dist = distance(trainingSet[x], instance, k )+ distance(instance, trainingSet[x], k)\n",
    "        \n",
    "        distances.append((trainingSet[x][2], dist))\n",
    "    distances.sort(key=operator.itemgetter(1))\n",
    "    \n",
    "    neighbors = []\n",
    "    for x in range(k):\n",
    "        neighbors.append(distances[x][0])\n",
    "    return neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nearestClass(neighbors):\n",
    "    classVote = {}\n",
    "\n",
    "    for x in range(len(neighbors)):\n",
    "        response = neighbors[x]\n",
    "        if response in classVote:\n",
    "            classVote[response]+=1 \n",
    "        else:\n",
    "            classVote[response]=1\n",
    "\n",
    "    sorter = sorted(classVote.items(), key = operator.itemgetter(1), reverse=True)\n",
    "    return sorter[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getAccuracy(testSet,predictions):\n",
    "    correct = 0 \n",
    "    error=0\n",
    "    for x in range (len(testSet)):\n",
    "        if testSet[x][-1]==predictions[x]:\n",
    "            correct+=1\n",
    "        else:\n",
    "            error+=1\n",
    "    \n",
    "    return [1.0*correct/len(testSet),1.0*error/len(testSet)]\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "directory = \"H:/PROJECTS/Ml package/music_speech/\"\n",
    "f= open(\"my.dat\" ,'wb')\n",
    "i=0\n",
    "\n",
    "for folder in os.listdir(directory):\n",
    "    i+=1\n",
    "    if i==11 :\n",
    "        break \n",
    "    \n",
    "    for file in os.listdir(directory+folder):  \n",
    "        (rate,sig) = wav.read(directory+folder+\"/\"+file)\n",
    "#         print(rate,sig)\n",
    "        mfcc_feat = mfcc(sig,rate ,winlen=0.020, appendEnergy = False)\n",
    "#         print(mfcc_feat)\n",
    "        covariance = np.cov(np.matrix.transpose(mfcc_feat))\n",
    "#         print(covariance)\n",
    "        mean_matrix = mfcc_feat.mean(0)\n",
    "#         print(mean_matrix)\n",
    "        feature = (mean_matrix , covariance , i)\n",
    "        \n",
    "        pickle.dump(feature , f)\n",
    "\n",
    "f.close()\n",
    "\n",
    "# plt.plot(feature)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "650\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "dataset = []\n",
    "\n",
    "def loadDataset(filename , split , trSet , teSet):\n",
    "    with open(\"my.dat\",'rb') as f:\n",
    "        while True:\n",
    "            try:\n",
    "                dataset.append(pickle.load(f))\n",
    "            except EOFError:\n",
    "                f.close()\n",
    "                break  \n",
    "\n",
    "    for x in range(len(dataset)):\n",
    "        if random.random() <split :      \n",
    "            trSet.append(dataset[x])\n",
    "        else:\n",
    "            teSet.append(dataset[x])  \n",
    "\n",
    "trainingSet = []\n",
    "testSet = []\n",
    "loadDataset(\"my.dat\" , 0.66, trainingSet, testSet)\n",
    "print(len(trainingSet))\n",
    "# print(trainingSet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "K= 1\n",
      "[[30  0  4  1  0  0  1  0  0  1]\n",
      " [ 0 32  0  1  0  2  0  0  0  0]\n",
      " [ 2  0 25  0  1  0  0  1  4  2]\n",
      " [ 1  0  3 19  3  0  2  2  1  2]\n",
      " [ 1  0  0  1 21  0  2  1  4  1]\n",
      " [ 1  3  1  0  0 26  0  0  1  1]\n",
      " [ 4  0  1  0  1  0 21  0  0  5]\n",
      " [ 0  0  1  2  2  0  0 33  1  1]\n",
      " [ 1  0  1  2  7  0  0  3 23  3]\n",
      " [ 1  0  1  8  0  2  1  0  1 20]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.73      0.81      0.77        37\n",
      "           2       0.91      0.91      0.91        35\n",
      "           3       0.68      0.71      0.69        35\n",
      "           4       0.56      0.58      0.57        33\n",
      "           5       0.60      0.68      0.64        31\n",
      "           6       0.87      0.79      0.83        33\n",
      "           7       0.78      0.66      0.71        32\n",
      "           8       0.82      0.82      0.82        40\n",
      "           9       0.66      0.57      0.61        40\n",
      "          10       0.56      0.59      0.57        34\n",
      "\n",
      "    accuracy                           0.71       350\n",
      "   macro avg       0.72      0.71      0.71       350\n",
      "weighted avg       0.72      0.71      0.71       350\n",
      "\n",
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "K= 2\n",
      "[[30  0  4  1  0  0  1  0  0  1]\n",
      " [ 0 32  0  1  0  2  0  0  0  0]\n",
      " [ 2  0 25  0  1  0  0  1  4  2]\n",
      " [ 1  0  3 19  3  0  2  2  1  2]\n",
      " [ 1  0  0  1 21  0  2  1  4  1]\n",
      " [ 1  3  1  0  0 26  0  0  1  1]\n",
      " [ 4  0  1  0  1  0 21  0  0  5]\n",
      " [ 0  0  1  2  2  0  0 33  1  1]\n",
      " [ 1  0  1  2  7  0  0  3 23  3]\n",
      " [ 1  0  1  8  0  2  1  0  1 20]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.73      0.81      0.77        37\n",
      "           2       0.91      0.91      0.91        35\n",
      "           3       0.68      0.71      0.69        35\n",
      "           4       0.56      0.58      0.57        33\n",
      "           5       0.60      0.68      0.64        31\n",
      "           6       0.87      0.79      0.83        33\n",
      "           7       0.78      0.66      0.71        32\n",
      "           8       0.82      0.82      0.82        40\n",
      "           9       0.66      0.57      0.61        40\n",
      "          10       0.56      0.59      0.57        34\n",
      "\n",
      "    accuracy                           0.71       350\n",
      "   macro avg       0.72      0.71      0.71       350\n",
      "weighted avg       0.72      0.71      0.71       350\n",
      "\n",
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "K= 3\n",
      "[[32  0  3  1  0  0  0  0  0  1]\n",
      " [ 0 31  0  1  0  2  0  0  0  1]\n",
      " [ 2  0 27  0  0  0  0  1  4  1]\n",
      " [ 1  0  3 19  4  0  2  3  0  1]\n",
      " [ 1  0  0  2 21  0  2  2  3  0]\n",
      " [ 1  4  1  0  2 23  0  0  1  1]\n",
      " [ 3  0  0  3  1  0 20  0  0  5]\n",
      " [ 0  0  1  3  1  0  0 33  1  1]\n",
      " [ 0  0  1  4 10  0  0  1 22  2]\n",
      " [ 1  0  1 10  0  1  1  0  1 19]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.78      0.86      0.82        37\n",
      "           2       0.89      0.89      0.89        35\n",
      "           3       0.73      0.77      0.75        35\n",
      "           4       0.44      0.58      0.50        33\n",
      "           5       0.54      0.68      0.60        31\n",
      "           6       0.88      0.70      0.78        33\n",
      "           7       0.80      0.62      0.70        32\n",
      "           8       0.82      0.82      0.82        40\n",
      "           9       0.69      0.55      0.61        40\n",
      "          10       0.59      0.56      0.58        34\n",
      "\n",
      "    accuracy                           0.71       350\n",
      "   macro avg       0.72      0.70      0.70       350\n",
      "weighted avg       0.72      0.71      0.71       350\n",
      "\n",
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "K= 4\n",
      "[[29  0  6  1  0  0  0  0  0  1]\n",
      " [ 0 31  0  1  0  3  0  0  0  0]\n",
      " [ 2  0 24  1  2  0  0  2  3  1]\n",
      " [ 1  0  1 18  5  0  1  6  0  1]\n",
      " [ 1  0  0  2 20  0  2  2  4  0]\n",
      " [ 2  3  1  0  1 23  0  0  2  1]\n",
      " [ 3  0  0  3  1  0 21  0  0  4]\n",
      " [ 0  0  0  4  1  0  0 34  1  0]\n",
      " [ 0  0  1  4  8  0  0  2 23  2]\n",
      " [ 1  0  3 11  0  0  1  0  1 17]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.74      0.78      0.76        37\n",
      "           2       0.91      0.89      0.90        35\n",
      "           3       0.67      0.69      0.68        35\n",
      "           4       0.40      0.55      0.46        33\n",
      "           5       0.53      0.65      0.58        31\n",
      "           6       0.88      0.70      0.78        33\n",
      "           7       0.84      0.66      0.74        32\n",
      "           8       0.74      0.85      0.79        40\n",
      "           9       0.68      0.57      0.62        40\n",
      "          10       0.63      0.50      0.56        34\n",
      "\n",
      "    accuracy                           0.69       350\n",
      "   macro avg       0.70      0.68      0.69       350\n",
      "weighted avg       0.70      0.69      0.69       350\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "K= 5\n",
      "[[28  0  5  0  2  0  0  0  0  2]\n",
      " [ 0 32  0  1  0  2  0  0  0  0]\n",
      " [ 1  0 24  0  2  0  0  1  3  4]\n",
      " [ 0  0  2 21  3  0  1  5  0  1]\n",
      " [ 1  0  0  3 22  0  2  1  2  0]\n",
      " [ 3  3  1  0  1 23  0  0  1  1]\n",
      " [ 3  0  0  3  1  0 21  0  0  4]\n",
      " [ 0  0  0  2  1  0  0 35  1  1]\n",
      " [ 0  0  1  6  6  0  0  2 24  1]\n",
      " [ 1  0  3 13  0  0  0  0  3 14]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.76      0.76      0.76        37\n",
      "           2       0.91      0.91      0.91        35\n",
      "           3       0.67      0.69      0.68        35\n",
      "           4       0.43      0.64      0.51        33\n",
      "           5       0.58      0.71      0.64        31\n",
      "           6       0.92      0.70      0.79        33\n",
      "           7       0.88      0.66      0.75        32\n",
      "           8       0.80      0.88      0.83        40\n",
      "           9       0.71      0.60      0.65        40\n",
      "          10       0.50      0.41      0.45        34\n",
      "\n",
      "    accuracy                           0.70       350\n",
      "   macro avg       0.71      0.69      0.70       350\n",
      "weighted avg       0.72      0.70      0.70       350\n",
      "\n",
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "K= 6\n",
      "[[27  0  5  1  0  0  2  0  0  2]\n",
      " [ 0 32  0  1  0  2  0  0  0  0]\n",
      " [ 1  0 24  0  2  0  0  1  4  3]\n",
      " [ 0  0  1 23  2  0  1  5  0  1]\n",
      " [ 0  0  0  3 23  0  2  0  3  0]\n",
      " [ 3  4  1  0  1 23  0  0  1  0]\n",
      " [ 3  0  0  3  1  0 22  0  0  3]\n",
      " [ 0  0  0  3  1  0  0 35  1  0]\n",
      " [ 1  0  0  5  8  0  0  2 23  1]\n",
      " [ 1  0  3 14  0  0  0  0  3 13]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.75      0.73      0.74        37\n",
      "           2       0.89      0.91      0.90        35\n",
      "           3       0.71      0.69      0.70        35\n",
      "           4       0.43      0.70      0.53        33\n",
      "           5       0.61      0.74      0.67        31\n",
      "           6       0.92      0.70      0.79        33\n",
      "           7       0.81      0.69      0.75        32\n",
      "           8       0.81      0.88      0.84        40\n",
      "           9       0.66      0.57      0.61        40\n",
      "          10       0.57      0.38      0.46        34\n",
      "\n",
      "    accuracy                           0.70       350\n",
      "   macro avg       0.72      0.70      0.70       350\n",
      "weighted avg       0.72      0.70      0.70       350\n",
      "\n",
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "K= 7\n",
      "[[29  0  5  1  0  0  1  0  0  1]\n",
      " [ 0 32  0  1  0  2  0  0  0  0]\n",
      " [ 0  0 24  1  1  0  0  2  4  3]\n",
      " [ 1  0  1 20  3  0  1  6  0  1]\n",
      " [ 0  0  0  4 22  0  2  1  2  0]\n",
      " [ 3  5  1  0  1 22  0  0  1  0]\n",
      " [ 3  0  1  3  1  0 22  0  0  2]\n",
      " [ 0  0  0  2  1  0  0 35  1  1]\n",
      " [ 1  0  0  4  8  0  0  2 24  1]\n",
      " [ 1  0  4 13  0  0  0  0  3 13]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.76      0.78      0.77        37\n",
      "           2       0.86      0.91      0.89        35\n",
      "           3       0.67      0.69      0.68        35\n",
      "           4       0.41      0.61      0.49        33\n",
      "           5       0.59      0.71      0.65        31\n",
      "           6       0.92      0.67      0.77        33\n",
      "           7       0.85      0.69      0.76        32\n",
      "           8       0.76      0.88      0.81        40\n",
      "           9       0.69      0.60      0.64        40\n",
      "          10       0.59      0.38      0.46        34\n",
      "\n",
      "    accuracy                           0.69       350\n",
      "   macro avg       0.71      0.69      0.69       350\n",
      "weighted avg       0.71      0.69      0.69       350\n",
      "\n",
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "K= 8\n",
      "[[29  0  3  1  1  0  1  0  0  2]\n",
      " [ 0 32  0  1  0  2  0  0  0  0]\n",
      " [ 1  0 24  0  0  0  0  2  5  3]\n",
      " [ 1  0  2 18  3  0  1  7  0  1]\n",
      " [ 0  0  0  4 23  0  2  0  2  0]\n",
      " [ 4  5  1  1  1 19  0  0  2  0]\n",
      " [ 3  0  0  3  1  0 22  0  0  3]\n",
      " [ 0  0  0  2  1  0  0 35  1  1]\n",
      " [ 1  0  1  3  9  0  0  1 24  1]\n",
      " [ 1  0  4 13  0  2  0  0  2 12]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.72      0.78      0.75        37\n",
      "           2       0.86      0.91      0.89        35\n",
      "           3       0.69      0.69      0.69        35\n",
      "           4       0.39      0.55      0.46        33\n",
      "           5       0.59      0.74      0.66        31\n",
      "           6       0.83      0.58      0.68        33\n",
      "           7       0.85      0.69      0.76        32\n",
      "           8       0.78      0.88      0.82        40\n",
      "           9       0.67      0.60      0.63        40\n",
      "          10       0.52      0.35      0.42        34\n",
      "\n",
      "    accuracy                           0.68       350\n",
      "   macro avg       0.69      0.68      0.68       350\n",
      "weighted avg       0.69      0.68      0.68       350\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "K= 9\n",
      "[[27  0  5  1  1  0  1  0  0  2]\n",
      " [ 0 32  0  1  0  2  0  0  0  0]\n",
      " [ 1  0 28  1  0  0  0  1  3  1]\n",
      " [ 0  0  2 20  2  0  1  7  0  1]\n",
      " [ 0  0  0  3 24  0  2  0  2  0]\n",
      " [ 4  5  1  1  2 18  0  0  2  0]\n",
      " [ 3  0  0  3  1  0 21  0  0  4]\n",
      " [ 0  0  0  2  1  0  0 35  1  1]\n",
      " [ 1  0  1  3 10  0  0  1 23  1]\n",
      " [ 1  0  4 14  0  2  0  0  2 11]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.73      0.73      0.73        37\n",
      "           2       0.86      0.91      0.89        35\n",
      "           3       0.68      0.80      0.74        35\n",
      "           4       0.41      0.61      0.49        33\n",
      "           5       0.59      0.77      0.67        31\n",
      "           6       0.82      0.55      0.65        33\n",
      "           7       0.84      0.66      0.74        32\n",
      "           8       0.80      0.88      0.83        40\n",
      "           9       0.70      0.57      0.63        40\n",
      "          10       0.52      0.32      0.40        34\n",
      "\n",
      "    accuracy                           0.68       350\n",
      "   macro avg       0.69      0.68      0.68       350\n",
      "weighted avg       0.70      0.68      0.68       350\n",
      "\n",
      "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]\n",
      "K= 10\n",
      "[[26  0  5  1  1  0  2  0  0  2]\n",
      " [ 0 33  0  1  0  1  0  0  0  0]\n",
      " [ 0  0 27  1  1  0  0  1  4  1]\n",
      " [ 1  0  0 21  3  0  1  5  0  2]\n",
      " [ 0  0  0  4 21  0  2  1  3  0]\n",
      " [ 4  5  1  2  2 16  1  0  2  0]\n",
      " [ 2  0  0  3  1  0 22  0  0  4]\n",
      " [ 0  0  0  2  0  0  0 37  1  0]\n",
      " [ 1  0  1  3 10  0  0  1 23  1]\n",
      " [ 1  0  6 14  0  1  0  2  1  9]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.74      0.70      0.72        37\n",
      "           2       0.87      0.94      0.90        35\n",
      "           3       0.68      0.77      0.72        35\n",
      "           4       0.40      0.64      0.49        33\n",
      "           5       0.54      0.68      0.60        31\n",
      "           6       0.89      0.48      0.63        33\n",
      "           7       0.79      0.69      0.73        32\n",
      "           8       0.79      0.93      0.85        40\n",
      "           9       0.68      0.57      0.62        40\n",
      "          10       0.47      0.26      0.34        34\n",
      "\n",
      "    accuracy                           0.67       350\n",
      "   macro avg       0.68      0.67      0.66       350\n",
      "weighted avg       0.69      0.67      0.67       350\n",
      "\n",
      "[0.7142857142857143, 0.7142857142857143, 0.7057142857142857, 0.6857142857142857, 0.6971428571428572, 0.7, 0.6942857142857143, 0.68, 0.6828571428571428, 0.6714285714285714]\n",
      "1\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "import pandas as pd\n",
    "from sklearn.metrics import *\n",
    "leng = len(testSet)\n",
    "\n",
    "accuracy=[]\n",
    "error=[]\n",
    "\n",
    "for j in range(10):\n",
    "    predictions = []\n",
    "    \n",
    "\n",
    "    for x in range (leng):\n",
    "        predictions.append(nearestClass(getNeighbors(trainingSet ,testSet[x] , j+1))) \n",
    "    a=[]\n",
    "    for i in range(len(testSet)):\n",
    "        a.append(testSet[i][-1])\n",
    "        \n",
    "    actual_output=[]\n",
    "    for i in range(len(testSet)):\n",
    "          actual_output.append(testSet[i][-1])\n",
    "    print(actual_output)\n",
    "    from sklearn import metrics\n",
    "    print(\"K=\",j+1)\n",
    "\n",
    "    # Printing the confusion matrix\n",
    "    # The columns will show the instances predicted for each label,\n",
    "    # and the rows will show the actual number of instances for each label.\n",
    "    print(metrics.confusion_matrix(actual_output, predictions, labels=[1,2,3,4,5,6,7,8,9,10]))\n",
    "    # Printing the precision and recall, among other metrics\n",
    "    print(metrics.classification_report(actual_output, predictions, labels=[1,2,3,4,5,6,7,8,9,10]))\n",
    "    \n",
    "    output=getAccuracy(testSet , predictions)\n",
    "    accuracy.append(output[0])\n",
    "    error.append(output[1])\n",
    "print(accuracy)\n",
    "k  = accuracy.index(max(accuracy))+1\n",
    "print(k)\n",
    "\n",
    "plot1=plt.figure(1)\n",
    "plt.plot(accuracy)\n",
    "plt.xlabel(\"K-value\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.title(\"Optimal K value\")\n",
    "\n",
    "\n",
    "plot2=plt.figure(2)\n",
    "plt.plot(error)\n",
    "plt.xlabel(\"K-value\")\n",
    "plt.ylabel(\"Error\")\n",
    "plt.title(\"Error Rate\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pop\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from collections import defaultdict\n",
    "results=defaultdict(int)\n",
    "i=1\n",
    "for folder in os.listdir(\"H:/PROJECTS/Ml package/music_speech\"):\n",
    "    results[i]=folder\n",
    "    i+=1\n",
    "\n",
    "(rate,sig)=wav.read(\"H:/PROJECTS/Ml package/file1.wav\") \n",
    "mfcc_feat=mfcc(sig,rate,winlen=0.020,appendEnergy=False) \n",
    "covariance = np.cov(np.matrix.transpose(mfcc_feat)) \n",
    "mean_matrix = mfcc_feat.mean(0) \n",
    "feature=(mean_matrix,covariance,0) \n",
    "pred=nearestClass(getNeighbors(dataset ,feature , k))\n",
    "\n",
    "print(results[pred])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting playsound\n",
      "  Downloading playsound-1.2.2-py2.py3-none-any.whl (6.0 kB)\n",
      "Installing collected packages: playsound\n",
      "Successfully installed playsound-1.2.2\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "#pip install playsound"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    " \n",
    "from playsound import playsound"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def playmusic(genre):\n",
    "    directory=\"H:/PROJECTS/Ml package/music_speech/\"\n",
    "    for file in os.listdir(directory+genre):\n",
    "            playsound(directory+genre+\"/\"+file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}