{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "preamble": true
   },
   "source": [
    "(Defining latex commands: not to be shown...)\n",
    "$$\n",
    "\\newcommand{\\norm}[1]{\\left \\| #1 \\right \\|}\n",
    "\\DeclareMathOperator{\\minimize}{minimize}\n",
    "\\newcommand{\\real}{\\mathbb{R}}\n",
    "\\newcommand{\\normal}{\\mathcal{N}}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Exercise Sheet 3\n",
    "Maximus Mutschler & Jan-Peter Hohloch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Gaussian Algebra (25 Points)\n",
    "\n",
    "Prove that the product of two univariate (scalar) Gaussian distributions is a Gaussian again, i.e. show, by explicitly performing the required arithmetic transformations, that\n",
    "\n",
    "\\begin{equation}\n",
    "   \\normal(x;\\mu,\\sigma^2)\\normal(x;m,s^2) = \\normal[x; (\\frac 1{\\sigma^2}+\\frac 1{s^2})^{-1}(\\frac \\mu{\\sigma^2}+\\frac m{s^2}),(\\frac 1{\\sigma^2}+\\frac 1{s^2})^{-1}]\\normal[m,\\mu,\\sigma^2+s^2].\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Proof\n",
    "\\begin{align*}\n",
    "   f(x) &= \\frac{1}{\\sqrt{2\\pi\\sigma^2}}\\cdot e^{\\frac{-(x-\\mu)^2}{2\\sigma^2}}\\cdot \\frac{1}{\\sqrt{2\\pi s^2}}\\cdot e^{\\frac{-(x-m)^2}{2s^2}}\\\\\n",
    "   &=\\frac{1}{2\\pi s\\sigma}\\cdot e^{-\\alpha}\\\\\n",
    "   \\text{where }\\alpha &= \\frac{s^2(x-\\mu)^2+\\sigma^2(x-m)^2}{2\\sigma^2 s^2}\\\\\n",
    "   &=\\frac{(s^2+\\sigma^2)x^2-2(s^2\\mu+\\sigma^2 m)x+s^2\\mu^2+\\sigma^2m^2}{2\\sigma^2s^2}\\\\\n",
    "   &=\\frac{x^2-2\\frac{s^2\\mu+\\sigma^2 m}{s^2+\\sigma^2}x+\\frac{s^2\\mu^2+\\sigma^2m^2}{s^2+\\sigma^2}}{\\frac{2\\sigma^2s^2}{s^2+\\sigma^2}}\\\\\n",
    "   &=\\frac{\\left(x-\\frac{s^2\\mu+\\sigma^2 m}{s^2+\\sigma^2}\\right)^2-\\left(\\frac{s^2\\mu+\\sigma^2 m}{s^2+\\sigma^2}\\right)^2+\\frac{s^2\\mu^2+\\sigma^2m^2}{s^2+\\sigma^2}}{\\frac{2\\sigma^2s^2}{s^2+\\sigma^2}}\\\\\n",
    "   &=\\frac{\\left(x-\\frac{s^2\\mu+\\sigma^2 m}{s^2+\\sigma^2}\\right)^2}{\\frac{2\\sigma^2s^2}{s^2+\\sigma^2}}+\\underbrace{\\frac{\\frac{s^2\\mu^2+\\sigma^2m^2}{s^2+\\sigma^2}-\\left(\\frac{s^2\\mu+\\sigma^2 m}{s^2+\\sigma^2}\\right)^2}{\\frac{2\\sigma^2s^2}{s^2+\\sigma^2}}}_\\beta\\\\\n",
    "   \\beta&= \\frac{\\frac{s^4\\mu^2+s^2\\sigma^2\\mu^2+s^2\\sigma^2m^2+\\sigma^4m^2-\\left(s^4\\mu^2+2s^2\\sigma^2\\mu m+\\sigma^4m^2\\right)}{\\left(s^2+\\sigma^2\\right)^2}}{\\frac{2\\sigma^2s^2}{s^2+\\sigma^2}}\\\\\n",
    "   &=\\frac{s^2\\sigma^2\\left(\\mu^2-2\\mu m+m^2\\right)}{2\\sigma^2s^2\\left(s^2+\\sigma^2\\right)}\\\\\n",
    "   &=\\frac{(\\mu-m)^2}{2(s^2+\\sigma^2)}\\\\\n",
    "   \\Rightarrow f(x)&=\\frac{1}{2\\pi s\\sigma}\\cdot e^{-(\\alpha-\\beta)-\\beta}\\\\\n",
    "   &=\\frac{1}{2\\pi s\\sigma}\\cdot e^{-\\frac{\\left(x-\\frac{s^2\\mu+\\sigma^2 m}{s^2+\\sigma^2}\\right)^2}{\\frac{2\\sigma^2s^2}{s^2+\\sigma^2}}}\\cdot e^{-\\frac{(\\mu-m)^2}{2(s^2+\\sigma^2)}}\\\\\n",
    "   &=\\frac{\\sqrt{2\\pi(s^2+\\sigma^2)}\\sqrt{2\\pi\\frac{\\sigma^2s^2}{s^2+\\sigma^2}}}{2\\pi s\\sigma}\\normal\\left[x;\\frac{s^2\\mu+\\sigma^2 m}{s^2+\\sigma^2},\\frac{\\sigma^2s^2}{s^2+\\sigma^2}\\right]\\normal\\left[\\mu;m,\\sigma^2+s^2\\right]\\\\\n",
    "   &=\\frac{\\sqrt{2\\pi\\cdot 2\\pi\\sigma^2s^2}}{2\\pi s\\sigma}\\normal\\left[x; \\left(\\frac 1{\\sigma^2}+\\frac 1{s^2}\\right)^{-1}\\left(\\frac \\mu{\\sigma^2}+\\frac m{s^2}\\right),\\left(\\frac 1{\\sigma^2}+\\frac 1{s^2}\\right)^{-1}\\right]\\normal\\left[\\mu;m,\\sigma^2+s^2\\right]\\\\\n",
    "   &=\\normal\\left[x; \\left(\\frac 1{\\sigma^2}+\\frac 1{s^2}\\right)^{-1}\\left(\\frac \\mu{\\sigma^2}+\\frac m{s^2}\\right),\\left(\\frac 1{\\sigma^2}+\\frac 1{s^2}\\right)^{-1}\\right]\\normal\\left[\\mu;m,\\sigma^2+s^2\\right]\\hspace{2cm}\\\\\n",
    "   &=\\normal\\left[x; \\left(\\frac 1{\\sigma^2}+\\frac 1{s^2}\\right)^{-1}\\left(\\frac \\mu{\\sigma^2}+\\frac m{s^2}\\right),\\left(\\frac 1{\\sigma^2}+\\frac 1{s^2}\\right)^{-1}\\right]\\normal\\left[m,\\mu,\\sigma^2+s^2\\right]\\hspace{2cm} q.e.d.\n",
    "\\end{align*}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Maximum Likelihood Estimator of Simple Linear Regression (25 Points)\n",
    "\n",
    "Derive the formula $\\mathbf{w}_{MLE} = (X^TX)^{-1}X^T\\mathbf{y}$ from the lecture, by calculating the derivative of $p(\\mathbf{y}\\,|X,\\mathbf{w}) = \\normal(\\mathbf{y}\\,|X\\mathbf{w}, \\sigma^2I)$ with respect to $\\mathbf{w}$, setting it to zero and solving it for $\\mathbf{w}$.\n",
    "\n",
    "\n",
    "Note: _To refresh your linear algebra you might find it useful to have a look in [here](http://webdav.tuebingen.mpg.de/lectures/ei-SS2015/pdfs/Murray_cribsheet.pdf)._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\left(2\\pi\\right)^{-\\frac{k}{2}}\\left|\\Sigma\\right|^{-\\frac{1}{2}}\\cdot e^{-\\frac{1}{2}\\left(\\vec y-\\mu\\right)^T\\Sigma^{-1}\\left(\\vec y-\\mu\\right)}\\text{, where: }k=n\\text{ number of measurments},\\ \\Sigma=\\sigma I,\\ \\mu=X\\vec w$\n",
    "\\begin{align*}\n",
    "    &\\frac{d}{d\\vec w} \\left(2\\pi\\right)^{-\\frac{k}{2}}\\left|\\Sigma\\right|^{-\\frac{1}{2}}\\cdot e^{-\\frac{1}{2}\\left(\\vec y-\\mu\\right)^T\\Sigma^{-1}\\left(\\vec y-\\mu\\right)}\\\\ &=\\underbrace{\\left(2\\pi\\right)^{-\\frac{k}{2}}\\left|\\Sigma\\right|^{-\\frac{1}{2}}\\cdot e^{-\\frac{1}{2}\\left(\\vec y-\\mu\\right)^T\\Sigma^{-1}\\left(\\vec y-\\mu\\right)}}_{>0}\\frac{d}{d\\vec w}\\left(-\\frac{1}{2}\\left(\\vec y-\\mu\\right)^T\\Sigma^{-1}\\left(\\vec y-\\mu\\right)\\right)&\\overset{!}{=}&0\\\\\n",
    "   \\Leftrightarrow &\\frac{d}{d\\vec w}-\\frac{1}{2}\\left(\\vec{y}-\\mu\\right)^T\\Sigma^{-1}\\left(\\vec{y}-\\mu\\right) &=&0\\\\\n",
    "   \\Leftrightarrow & \\frac{d}{d\\vec w}\\left(\\vec{y}-\\mu\\right)^T\\Sigma^{-1}\\left(\\vec{y}-\\mu\\right) &=&0\\ (\\Sigma\\not=\\mathcal{O})\\\\\n",
    "   \\Leftrightarrow & \\frac{d}{d\\vec w}\\left(\\vec{y}-\\mu\\right)^T\\left(\\vec{y}-\\mu\\right) &=&0\\\\\n",
    "   \\Leftrightarrow & \\frac{d}{d\\vec w}\\left(\\left(\\vec{y}-X\\vec{w}\\right)^T\\right)\\left(\\vec{y}-X\\vec{w}\\right) + \\left(\\vec{y}-X\\vec{w}\\right)^T\\frac{d}{d\\vec w}\\left(\\vec{y}-X\\vec{w}\\right)&=&0\\\\\n",
    "   \\Leftrightarrow & -X^T\\left(\\vec{y}-X\\vec{w}\\right)-\\left(\\vec{y}-X\\vec{w}\\right)^TX &=&0\\\\\n",
    "   \\Leftrightarrow & -X^T\\left(\\vec{y}-X\\vec{w}\\right)-X^T\\left(\\vec{y}-X\\vec{w}\\right)&=&0\\\\\n",
    "   \\Leftrightarrow & X^T\\left(\\vec{y}-X\\vec{w}\\right) &=&0\\\\\n",
    "   \\Leftrightarrow & \\vec{y}-X\\vec{w} &=&0\\\\\n",
    "   \\Leftrightarrow & \\vec{y}&=& X\\vec{w}\\\\\n",
    "   \\Leftrightarrow & X^T\\vec{y} &=& X^TX\\vec{w}\\\\\n",
    "   \\Leftrightarrow & \\vec{w} &=& \\left(X^TX\\right)^{-1}X^T\\vec{y}\\hspace{1cm} q.e.d.\n",
    "\\end{align*}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Linear regression (50 Points)\n",
    "\n",
    "In this exercise you will perform a regression analysis on a toy dataset. You will implement ridge regression and learn how to find a good model through a comparative performance analysis.\n",
    "\n",
    "1) Download the [training set](http://webdav.tuebingen.mpg.de/lectures/ei-SS2015/data/ex1_train.csv)! <br>\n",
    "2) Implement $\\mathbf{w}_{RIDGE}$ as a function of a given $X, \\mathbf{y}$ array and a regularization parameter $\\lambda$!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Loading the required packages\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.html.widgets import interact\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def wRidge(X,y,lamb):\n",
    "    # Change the following line and implement the ridge regression estimator wRidge\n",
    "    return np.dot(np.dot(np.linalg.pinv((np.dot(X.T,X)+lamb*np.identity(X.shape[1]))),X.T),y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3) Load \"ex1_train.csv\" into a numpy array! The first column in the csv file is $X$ and the second column is $\\mathbf{y}$, assign them to each variable!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Read ex1_train.csv and assign the first column and \n",
    "# second column to variables x and y respectively.\n",
    "\n",
    "a = np.genfromtxt('ex1_train.csv', delimiter=' ')\n",
    "x= a[:,0]\n",
    "y= a[:,1]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4) Plot the training data with appropriate labels on each axes!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f3f86afddd8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "oJt+CXUlyycAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3f86ad1cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the input data here\n",
    "plt.plot(x,y,'o')\n",
    "plt.title('Input data')\n",
    "plt.ylabel('Output y')\n",
    "plt.xlabel('Input x')   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5) Implement a function which constructs features upto a input polynomial degree $d$!<br>\n",
    "Note: _Constructing higher polynomial features is similar to what you implemented in Exercise 3 (SVM) of the previous exercise sheet._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def construct_poly(x,d):\n",
    "    ## Implement a method which given an array of size N, \n",
    "    ## returns an array of dimension (N,d)\n",
    "    poly=np.zeros((len(x),1), float)\n",
    "    xr = np.reshape(x,(len(x),1))\n",
    "\n",
    "    for i in range(0,d):\n",
    "        poly = np.append(poly, (xr**i), axis=1)\n",
    "        \n",
    "    a= np.zeros((x.shape[0],d+1))\n",
    "    for i in range (0,d):\n",
    "        a[:,i]=np.power(x,i)\n",
    "        \n",
    "    #print (a)\n",
    "    #print (poly)\n",
    "    return poly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6) Implement the Mean Squared Error Loss (MSE) as a function of the predicted and true values of the target variable! <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def MSE(y_predict,y_true):\n",
    "    ## Implement mean squared error for a given input y and its predictions.\n",
    "    return (np.linalg.norm(y_predict-y_true)**2)/y_predict.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7) By comparing the MSE find the degree $d$ for the polynomial that fits the training data best! You might find it useful to use the code below to interactively change the variable $d$, set $\\lambda = 1$ and keep it fixed. Plot the error as a function of different values of $d$!<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "minimal MSE for n=7\n"
     ]
    },
    {
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       "RU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3f86e99400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##This function provides an interactive mode to change polynomial degree. \n",
    "@interact(n=[1,16])\n",
    "def plot(n):\n",
    "    X = construct_poly(x,n) #n=d\n",
    "    w = wRidge(X,y,1.0)\n",
    "    plt.plot(x,X.dot(w))\n",
    "    plt.title(\"MSE %f\" % MSE(X.dot(w),y))\n",
    "    plt.plot(x,y,'yo')\n",
    "    print(\"minimal MSE for n=7\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8) Apply models with different values of $d$ after being trained on the training dataset, to the test data available [here](http://webdav.tuebingen.mpg.de/lectures/ei-SS2015/data/ex1_test.csv). Compare the errors on the test data to the ones from the training by plotting the error curves as functions of the polynomial degree in a single plot! What do you conclude? <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import scipy\n",
    "from scipy import linalg\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "minimal MSE for n=6\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105544490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Read test data here\n",
    "a = np.genfromtxt('ex1_test.csv', delimiter=' ')\n",
    "xt= a[:,0]\n",
    "yt= a[:,1]\n",
    "X = construct_poly(x,8)\n",
    "w = wRidge(X,y,1.0)\n",
    "@interact(n=[1,16])\n",
    "def plot(n):\n",
    "    X = construct_poly(x,n)  \n",
    "    Xt = construct_poly(xt,n) #n=d\n",
    "    w = wRidge(X,y,1.0)\n",
    "    plt.plot(x,X.dot(w),'g',label='regression training')\n",
    "    plt.plot(xt,Xt.dot(w),'b',label='regression test')\n",
    "    plt.title(\"MSE train: %f  MSE test: %f\" % (MSE(X.dot(w),y),MSE(Xt.dot(w),yt)))\n",
    "    plt.plot(xt,yt,'b*',label='data test')\n",
    "    plt.plot(x,y,'go',label='data trainig')\n",
    "    plt.ylabel('Output y') \n",
    "    plt.xlabel('Input x')  \n",
    "    plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "    print \"minimal MSE for n=6\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####Conclusion:\n",
    "We get suitable solutions for n from 5 to 7 for the test regression. For higher values we get overfitting. For lower ones underfitting. The MSE of the test regression is always higher than the one of the training regression. That's evident because the regression function is fitted to the values of the training data and so doesn't provide an optimal fitting for the test data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9) With a fixed optimal $d$, change the value of $\\lambda$ to one of the following values $[0.1, 1.0, 10.0]$ and find the minimum MSE!<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum MSE for lambda = 0.1\n"
     ]
    },
    {
     "data": {
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       "ijHHSmwAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106da2b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# optimal d = 6\n",
    "def plot():\n",
    "    X = construct_poly(x,6)  \n",
    "    Xt = construct_poly(xt,6) #n=d\n",
    "    w = wRidge(X,y,1.0)\n",
    "    plt.plot(xt,Xt.dot(w),label='$\\lambda$ = 1.0'+ \"  MSE: %f\" % MSE(X.dot(w),y))\n",
    "    \n",
    "    w = wRidge(X,y,0.1)\n",
    "    plt.plot(xt,Xt.dot(w),label='$\\lambda$ = 0.1'+ \"  MSE: %f\" % MSE(X.dot(w),y))\n",
    "    \n",
    "    w = wRidge(X,y,10.0)\n",
    "    plt.plot(xt,Xt.dot(w),label='$\\lambda$ = 10.0'+\"  MSE: %f\" % MSE(X.dot(w),y))\n",
    " \n",
    "    plt.plot(xt,yt,'b*',label='data test')\n",
    "    plt.plot(x,y,'go',label='data trainig')\n",
    "    plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plot()\n",
    "print('Minimum MSE for lambda = 0.1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hand in printed copy of completed notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "annotations": {
   "author": "",
   "categories": [
    "intelligent-systems-1-2015"
   ],
   "date": "2015-04-30",
   "location": "Beginning of next lecture",
   "parent": "IS_SS2015",
   "submission_date": "2015-05-07",
   "subtitle": "Exercise Sheet 3, Linear Regression",
   "tags": [
    "IntelligenSystems",
    "Course"
   ],
   "title": "Intelligent Systems 1 - Summer Semester 2015"
  },
  "celltoolbar": "Edit 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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}