knn-regression-example.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
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   "source": [
    "X = [[0,1,1], [1,2,1.5], [2,2.5,5], [3,3,4], [5,5.5,5], [5,4.5,6]]\n",
    "y = [0, 0, 1, 1,2,2]\n"
   ]
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  {
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      "text/plain": [
       "[[0, 1, 1], [1, 2, 1.5], [2, 2.5, 5], [3, 3, 4], [5, 5.5, 5], [5, 4.5, 6]]"
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     "execution_count": 50,
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    "X"
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    {
     "data": {
      "text/plain": [
       "[0, 0, 1, 1, 2, 2]"
      ]
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     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
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    "y"
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  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
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   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.66666667]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "neigh = KNeighborsRegressor(n_neighbors=3)\n",
    "neigh.fit(X, y) \n",
    "\n",
    "print(neigh.predict([[1.5,3,4]]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "          metric_params=None, n_jobs=1, n_neighbors=3, p=2,\n",
       "          weights='uniform')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neigh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.83333333333333337"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neigh.score(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[ 1.22474487,  1.5       ,  2.73861279,  3.90512484]]),\n",
       " array([[2, 3, 1, 0]]))"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neigh.kneighbors([[1.5,3,4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "neigh = NearestNeighbors(n_neighbors=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NearestNeighbors(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "         metric_params=None, n_jobs=1, n_neighbors=1, p=2, radius=1.0)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neigh.fit(samples) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([[ 0.5]]), array([[2]]))\n"
     ]
    }
   ],
   "source": [
    "print(neigh.kneighbors([[1., 1., 1.]])) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [2]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = [[0., 1., 0.], [1., 0., 1.]]\n",
    "neigh.kneighbors(X, return_distance=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  1.,  0.],\n",
       "       [ 0.,  0.,  1.]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = neigh.kneighbors_graph(X)\n",
    "A.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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