1.1.1 - Vectors and Labeled Points.ipynb

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   "source": [
    "sc"
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  {
   "cell_type": "code",
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   "source": [
    "from pyspark.sql import SQLContext\n",
    "sqlc = SQLContext(sc)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "from pyspark.mllib.linalg import Vector, Vectors\n",
    "from pyspark.mllib.regression import LabeledPoint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vectors - Dense and Sparse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DenseVector([1.0, 0.0, 3.0])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vd1 = Vectors.dense(1.0, 0.0, 3.0)\n",
    "vd1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DenseVector([4.0, 10.0, 6.0])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vd2 = Vectors.dense([4.0, 10.0, 6.0])\n",
    "vd2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SparseVector(3, {0: 12.0, 2: 30.0})"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vs1 = Vectors.sparse(3, [0, 2], [12.0, 30.0])\n",
    "vs1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SparseVector(3, {0: 5.0, 2: 7.0})"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vs2 = Vectors.sparse(3, {0: 5.0, 2: 7.0})\n",
    "vs2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Labeled Points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabeledPoint(1.0, [1.0,0.0,3.0])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LabeledPoint(1.0, vd1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabeledPoint(0.0, (3,[0,2],[12.0,30.0]))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LabeledPoint(0.0, vs1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vector Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
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   ],
   "source": [
    "vd1.dot(vd2)"
   ]
  },
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       "26.0"
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     "execution_count": 26,
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   "source": [
    "vd1.dot(vs2)"
   ]
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   "execution_count": 43,
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       "3.1622776601683795"
      ]
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     "execution_count": 43,
     "metadata": {},
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   ],
   "source": [
    "vd1.norm(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "118.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vd1.squared_distance(vd2)"
   ]
  }
 ],
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