{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import scipy.linalg as sp\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.array([0, 1, 2, 3])\n",
    "y = np.array([-1, 0.2, 0.9, 2.1])\n",
    "A = np.vstack([x, np.ones(len(x))]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1. ,  0.2,  0.9,  2.1])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  1.],\n",
       "       [ 1.,  1.],\n",
       "       [ 2.,  1.],\n",
       "       [ 3.,  1.]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 1.  , -0.95]), array([ 0.05]), 2, array([ 4.10003045,  1.09075677]))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linalg.lstsq(A, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 1.  , -0.95]),\n",
       " 0.049999999999999906,\n",
       " 2,\n",
       " array([ 4.10003045,  1.09075677]))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp.lstsq(A, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_t=np.matrix([[3,13,16],\n",
    "               [6,12,16],\n",
    "               [12,17,21]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y_t=np.matrix([[2],[6],[10]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 3)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_t.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x6156b9470>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ryiWoqNAlJGzYdZQpCYlsO5jLv4/qyh0X9qCeLkWUIKNCl6BWWuqY/fk2nvogjZaN6/PW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x615493080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([2,6,10],[12,17,21,])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "ename": "LinAlgError",
     "evalue": "Incompatible dimensions",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mLinAlgError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-33-97d0b4375571>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstsq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_t\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mY_t\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/numpy/linalg/linalg.py\u001b[0m in \u001b[0;36mlstsq\u001b[0;34m(a, b, rcond)\u001b[0m\n\u001b[1;32m   1908\u001b[0m     \u001b[0mldb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1909\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mm\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1910\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mLinAlgError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Incompatible dimensions'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1911\u001b[0m     \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_t\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_commonType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1912\u001b[0m     \u001b[0mresult_real_t\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_realType\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult_t\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mLinAlgError\u001b[0m: Incompatible dimensions"
     ]
    }
   ],
   "source": [
    "np.linalg.lstsq(X_t,Y_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "incompatible dimensions",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-34-77f836f3912e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstsq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY_t\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mX_t\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/scipy/linalg/basic.py\u001b[0m in \u001b[0;36mlstsq\u001b[0;34m(a, b, cond, overwrite_a, overwrite_b, check_finite, lapack_driver)\u001b[0m\n\u001b[1;32m    962\u001b[0m         \u001b[0mnrhs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    963\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mm\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mb1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 964\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'incompatible dimensions'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    965\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    966\u001b[0m     \u001b[0mdriver\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlapack_driver\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: incompatible dimensions"
     ]
    }
   ],
   "source": [
    "sp.lstsq(Y_t,X_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scipy.l"
   ]
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}