diff --git a/doc/pub/Regression/ipynb/Regression.ipynb b/doc/pub/Regression/ipynb/Regression.ipynb index 468d5907d..84b024b18 100644 --- a/doc/pub/Regression/ipynb/Regression.ipynb +++ b/doc/pub/Regression/ipynb/Regression.ipynb @@ -897,10 +897,19 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1 4]\n", + " [2 5]\n", + " [3 6]]\n", + "[[1 2 3 0 0 4 5 6]]\n" + ] + } + ], "source": [ "import numpy as np\n", "print(np.c_[np.array([1,2,3]), np.array([4,5,6])])\n", @@ -909,11 +918,20 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ZcvowPg18U9J5knZtd0FmM0b9/akXLBh5PcSWLTA4CF/7GrzvfSkgDjgAvvCFdJ3Exz8OO+889nvMnTuy72HVquHpvyPSY4eEZcq+DkPS3wKfAy4BvhYRE7i0szV8pbdNG+P1PfT0pJsB1a6g3mcfWLYMDjwwhcaOO469HylN21G7b7XNaK26DiOrD6MYxXQXcBbwT8CHJX02Ii5otgCzGaM87HU8W7ak1sbAQAqJXXZpvF2tdbBype8RYW03bgtD0q9IF9fdBlwHXA/cCZwMbBcRK9pdZI1bGDZl1F9Rvd12Y1853Uj91dRmk9TJFsYK4PZ4ZbJ8VJKvmzCDka2HRjcQmmhYwCuvpjar2LiBERG3jbH60BbWYjY19fePvOVoK1oFvpraulBTV3pHxH2tKsRsStq0Cc4+u7l9SGl4a/nGQb6a2rqQ7+ltNhFbt8LNN6epv6+8Ml0X0UyL4qSTPJLJpoyq74dh1l0a3R/i979PE/f93d+l6x723RdOOQXuuQeOPjptm0Ma3ranx2FhU45bGGY19dc0DA2lQKiN99h9dzjssDTM9cAD0/Oach9GzezZ6epsSKebTj/dp5lsSnNgmD3xBPziF+lCt0Z3l9tpJ7juOnjd61IroV6tlVAbJdXTk4LHrQebZnxKyqav0W4/+txzcPnl6bTSvvvCwoXpdNOGDY3389RT6S50jcKiZtWq4RsHbd7ssLBpyYFh01Pt9NLQUPoSHxqC446D178+tRgOPRTOPDPdm/qLX4Rrrhn9ugdfD2EG+JSUTUebN8MnPvHK00ubNqXg+OQn07xM++8Pr3rV8Povf9l3lzMbg1sYNnXUTjFJMGtW+rd2qunWW+GMM+Dww9Osr48+2ngfmzalYFi2bGRYgO8uZzaO7Nlqu4Hnkpph6udjyrHnnikMLr00dWbXW7Ik3ffBbAbp6Gy1Zm03mXAomz8/3Tti6dL0/IADfHrJrMV8Ssqq1d+fhqEeddTkwwJg/frhsACfXjJrA7cwrHMGBtJ9G4aGUkjk3BciV6ORTMuXOyDMWqjSFoakHSRdLOlOSXdIenuV9VgL1d9+dN681IoYGkrrWxkWPtVk1hFVtzBOB/4tIj4oaTbQW3E91qxGfRHNnGoaTa2FsmSJ7zBn1iGVBYakVwPvBo4FiIiXgZerqsea1N+fJuhr56g7z8dkVqkqT0ntAawDzpP0W0nfkTSnfiNJKyQNShpct25d56u0kRpdCzF3bpp8r5VhUZvVdckSWLMm7fuJJxwWZhWqMjBmAfsBZ0XEvsDzwGfqN4qI1RHRFxF9Cxcu7HSNVt8Xcfzxr+yHeP751rxXbcrviLTviHTNhEPCrCtUGRgPAg9GxPXF84tJAWJVK7cijj56eD6mJ58cnq67WbWJ/MotCE/aZ9bVKguMiHgUeEDS3sWiZcDtVdUzY9XP6NrfPzxpH7S+T6IWEFu3ugVhNsVUPUrqo8BAMULqPuC4iuuZGcrXQ0jDoTA01PhGQJNV27dHMplNC5UGRkTcBDQ9v4llGG3qjVa1IHp60r62bvUNhMymKU8NMhMMDKR7QbTieohtt03DWyEFA6QWxPnnD3dUuy/CbFpyYEx1o91Vrubhh1PLYtOm/H3W31mu3EF93nlpeGstGNwPYTZjVN2HYc2ov1huaAg+/GH4zW9SQFx1Fdx558T22dsLxxyTbmF6//1pjib3P5gZDoypodZJXf4Ch8ZXVr/4Yroaes4cePe74YQT4LTT4JFHRt+/O6fNLIMDo5s16qgeGkohsM02o3dYS2m679mz0/Ndd019GI1OS3m6DTPL5D6MbtCoH2JgII00atRRvXFjakmMZvHi4bCAFAbnnTfcWQ3p8Zo1nm7DzLL5Fq1VGm2o6/bbpxFIk5lyQ4ILLnAImNkf+RatU12tBVG+hWjNSy+N//r581Mro/x6CU480WFhZm3hU1LtMN5Q18ceg499rHFY5OjtTf0O9bcgveACX/9gZm3jFkarveENcHtpSqzaUNcbb0zXLVx5Jdx22/j7adSCqC0vd1K7NWFmHeIWRiuUZ3e9vcH8iS++mIa2nnMO7LYbfOUrsMsuo+9vtBaEO6nNrEJuYTRrrL6IMgmeegq22y49X7So8evcgjCzLuUWRiONpvwuP1+zBm66Cb7+dfjQh/L6IhYvHg4LSEHgFoSZTSEeVlsz2pTfrbJmjYPAzCrRqmG1bmFAakHU7iwHeWExfz488EBqGYxnn30cFmY25c2swBjtiupGczKNZ/162H33NPdSb+/o2y1bljcqysysy82cTu+DDkpDWmtqczJN9vTT4sXp31rLoX5yQLcozGyamT6BUe6DqKnd+Q1GhkXNxo2Te6/e3uEZYyGFgwPCzKa56REYow1t3bKl+XtUL1kChxzi+0OY2Yw3tQLjllvSKaSenhQGtfs3rFw5+Wk2xpqTydNsmJn9UeXDaiX1AIPAQxFx2Fjb9knxikG1s2fDyy9PvoA1a9K/7oMws2lqOs1WezJwB/Ank3p1M2GxbJmvqDYzy1TpsFpJuwOHAt9pemdjDW096aT009OTnvf0pOc/+1nTb2tmNlNUfR3GN4FPAVtH20DSCkmDkka/xHvJkuFpNspqwbBqVfrZvDkNod282f0TZmYTVFlgSDoMeDwibhhru4hYHRF9o55/qw1xXb4c1q5NgVD7cTCYmbVMlS2MdwDvl7QW+D5woKQ1E9pDrWXh/gczs7arLDAi4rMRsXtELAWOAK6KiKPGfWFtVteI1KJwWJiZdUQ3jJLK9+Y3Q7tmqzUzszF1RWBExC+AX1RchpmZjaHqUVJmZjZFODDMzCyLA8PMzLI4MMzMLIsDw8zMsjgwzMwsiwPDzMyyODDMzCyLA8PMzLI4MMzMLIsDw8zMsjgwzMwsiwPDzMyyODDMzCyLA8PMzLI4MMzMLIsDw8zMsjgwzMwsiwPDzMyyODDMzCxLZYEhaZGkn0u6XdJtkk6uqhYzMxvfrArfezNwSkTcKGkecIOkKyLi9gprMjOzUVTWwoiIRyLixuLxc8AdwGurqsfMzMbWFX0YkpYC+wLXN1i3QtKgpMF169Z1ujQzMytUHhiS5gI/Aj4WEc/Wr4+I1RHRFxF9Cxcu7HyBZmYGVBwYkrYlhcVARFxSZS1mZja2KkdJCfgX4I6IOK2qOszMLE+VLYx3AEcDB0q6qfg5pMJ6zMxsDJUNq42IXwKq6v3NzGxiKu/0NjOzqcGBYWZmWRwYZmaWxYFhZmZZHBhmZpbFgWFmZlkcGGZmlsWBYWZmWRwYZmaWxYFhZmZZHBhmZpbFgWFmZlkcGGZmlsWBYWZmWRwYZmaWxYFhZmZZHBhmZpbFgWFmZlkcGGZmlsWBYWZmWSoNDEkHS7pL0j2SPlNlLWZmNrbKAkNSD/A/gb8C9gGOlLRPVfWYmdnYqmxhvAW4JyLui4iXge8Dh1dYj5mZjWFWhe/9WuCB0vMHgbfWbyRpBbCieLpR0q0dqK1ZC4Anqi4ig+tsnalQI7jOVpsqde7dip1UGRhZImI1sBpA0mBE9FVc0rhcZ2tNhTqnQo3gOlttKtXZiv1UeUrqIWBR6fnuxTIzM+tCVQbGb4C9JO0haTZwBHBZhfWYmdkYKjslFRGbJX0E+HegBzg3Im4b52Wr219ZS7jO1poKdU6FGsF1ttqMqlMR0Yr9mJnZNOcrvc3MLIsDw8zMsnRNYIw3TYik7ST9oFh/vaSlpXWfLZbfJel9Fdb4cUm3S7pZ0pWSlpTWbZF0U/HT1s79jDqPlbSuVM+HSuuOkXR38XNMxXV+o1Tj7yU9XVrXkeMp6VxJj492/Y+SM4rf4WZJ+5XWdfJYjlfn8qK+WyRdI+lNpXVri+U3tWr4ZRN1vkfSM6X/tp8rrevYVEIZdX6yVOOtxedxp2JdR46npEWSfl5859wm6eQG27T28xkRlf+QOr3vBfYEZgO/A/ap26Yf+Hbx+AjgB8XjfYrttwP2KPbTU1GN7wV6i8cn1Wosnm/oomN5LHBmg9fuBNxX/Ltj8XjHquqs2/6jpIERnT6e7wb2A24dZf0hwE8AAW8Dru/0scysc//a+5Om47m+tG4tsKBLjud7gH9t9vPS7jrrtv1r4KpOH09gV2C/4vE84PcN/l9v6eezW1oYOdOEHA6cXzy+GFgmScXy70fExoj4A3BPsb+O1xgRP4+IF4qn15GuLem0ZqZceR9wRUSsj4ingCuAg7ukziOBC9tUy6gi4mpg/RibHA58L5LrgB0k7Upnj+W4dUbENUUdUN1nM+d4jqajUwlNsM6qPpuPRMSNxePngDtIM2iUtfTz2S2B0WiakPpf/I/bRMRm4BlgfuZrO1Vj2QmkZK/ZXtKgpOsk/U0b6qvJrfNviybqxZJqF1B26lhO6L2KU3t7AFeVFnfqeI5ntN+jk8dyouo/mwH8VNINSlPxVO3tkn4n6SeS3lAs68rjKamX9EX7o9Lijh9PpVP0+wLX161q6eez66cGmYokHQX0AQeUFi+JiIck7QlcJemWiLi3mgr5MXBhRGyU9I+kltuBFdWS4wjg4ojYUlrWTcdzypD0XlJgvLO0+J3FsdwZuELSncVf2FW4kfTfdoOkQ4D/DexVUS05/hr4VUSUWyMdPZ6S5pIC62MR8Wy73ge6p4WRM03IH7eRNAt4NfBk5ms7VSOSDgJWAu+PiI215RHxUPHvfcAvSH8NtMO4dUbEk6XavgO8Ofe1nayz5AjqmvwdPJ7jGe336LqpbyS9kfTf+/CIeLK2vHQsHwcupT2ndLNExLMRsaF4fDmwraQFdOHxLIz12Wz78ZS0LSksBiLikgabtPbz2e6OmczOm1mkTpc9GO7QekPdNv+FkZ3eFxWP38DITu/7aE+nd06N+5I65vaqW74jsF3xeAFwN23qsMusc9fS4w8A18VwR9gfinp3LB7vVFWdxXZ/RupEVBXHs3iPpYzeSXsoIzsVf93pY5lZ52JS/97+dcvnAPNKj68BDq6wzl1q/61JX7T3F8c26/PSqTqL9a8m9XPMqeJ4Fsfle8A3x9impZ/Pth3sSfzyh5B6+e8FVhbLvkD6Sx1ge+CHxYf+18CepdeuLF53F/BXFdb4M+Ax4Kbi57Ji+f7ALcWH/BbghIqP5VeA24p6fg78Wem1xxfH+B7guCrrLJ6fCny17nUdO56kvx4fATaRzvOeAJwInFisF+lGYPcWtfRVdCzHq/M7wFOlz+ZgsXzP4jj+rvhMrKy4zo+UPpvXUQq4Rp+XquostjmWNOCm/LqOHU/SacUAbi79dz2knZ9PTw1iZmZZuqUPw8zMupwDw8zMsjgwzMwsiwPDzMyyODDMzCyLA8PMzLI4MMzMLIsDw6wJxf0I/rJ4/E+SvlV1TWbt4skHzZrzeeALxURz+wLvr7ges7bxld5mTZL0f4G5wHsi3ZfAbFryKSmzJkj6C9Kdz152WNh058Awm6TizmUDpLuabZDUtjvqmXUDB4bZJBR3WrsEOCUi7gC+SOrPMJu23IdhZmZZ3MIwM7MsDgwzM8viwDAzsywODDMzy+LAMDOzLA4MMzPL4sAwM7Ms/x/AARw+KHcZ5gAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "%matplotlib inline\n", "\n", @@ -923,7 +941,7 @@ "import matplotlib.pyplot as plt\n", "\n", "x = 2*np.random.rand(100,1)\n", - "y = 4+3*x+np.random.randn(100,1)\n", + "y = 4+3*x+0.01*np.random.randn(100,1)\n", "\n", "xb = np.c_[np.ones((100,1)), x]\n", "beta = np.linalg.inv(xb.T.dot(xb)).dot(xb.T).dot(y)\n", @@ -957,11 +975,20 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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SJWkOpuo5mZYsGV6sNmaU2ksqIs4BzikzBrOOVmmLqF67YbiN07UsWDBwn3s1WQ2DljAkLZf0xnYEY2Y5+UFykJIFNC9ZTJiQqplccrCCilRJfQZYLOkSSXu0OiCzcaWnB6ZO7RtRPWFCX1fXZkzaV61SvVRZPMjJwoZg0IQREbdGxHuAHwM/k3SOpJ1aH5rZGJSfh2nqVDj2WHjuub7jvb2pwbm7O3WJHa7q2V0rScLzMtkIFGr0liTgXuBC4FTgPknHtTIwszEjP1ju2GP7urvmE0W1detSQmnkFa+ovb8yiG7TJk/eZ01VpA3jN8DDwNeAmcAJwEHAWyS5V5NZIwsXphJDpf1hKC66aOCkfRVTpsDFF9eej8kJwlqkSAljATAzIg6NiP8VET+OiPsj4lTgP7Y4PrPO16gdYukw/6aaMKH/kqOVfeDBclaaEU0NImmfiFjdxHga8tQg1jHy3V3rmTYtTbkxHO69ZE3UEVODtDNZmLVdrYWCKvvz3V3r2bJl6IsHSU4W1rG84p5ZLdXzMa1Zkx5ffjl86lPFu7uefHLj45XG8EpPpt5eJwvrWE4YNr5Vry5XWVnutNMGru2wfTt85CPw2GPFrj1hQvryP+WUgdNvABxySLqmezLZKOGEYeNXrdXlKivLNZrddfr0YtevTLmxZEkaJFfdm+mXvxx26GZlcMKwsa9eW0S91eXqrSpXsXhx/e6ukF7H7RA2BpW9RKtZa+R7MeWn+16zBj76UbjlluHNydTV1Vd1VFmgqLu7bxlSszGstBX3hsPdaq2ufILYYYfULjCYRutGdHXBM8/0L4FMnuwlR21U6ohutWalqlQ1SXDccX0lhqLJ4tJLUxKoNmlSWllu2bL+a0I4Wdg45yopG30qvZjyDdNDLSl3d6ceTxMm9L9WV1dKFl5ZzmwAlzCss1U3WC9cmHofNerFNJgpU1KbA6SEsGlTX8+lTZucJMzqcAnDOldlRHVlkNyaNbXXmi6i0l4xe7YbqM2GySUMK0+97q4Ajz4Kp5/enAWEurrSCG3P5Go2Ik4Y1h71qpbyU2/Mnw/vex8ccADsuWeqHhqKyrxN+Vldr7jC1UxmTVJqwpD0KklXSbpH0t2S/rLMeKyJ8j2Ydtih/8JBa9aktR6qSw8vvphGP++1F3zlK7D77rWvXWtCv3wpojKq2qUJs6Yquw3jPOBnEfFfJE0GGgyftVFj4cKUECo9l2r1YGrUq+lnP0u/Z87s34YBqcH6+OPhJz/xoDmzNistYUjaFXgXaQU/ImIbUGOeBhtVenr6J4uh6u7u2/aIarOOUmYJY29gI3CJpDcCK4HTIqLfQseSFpBW/aM7/2VinSUC7rkHTj21eLKoHmmd7+5aMW+eE4RZhyizDWMi8Gbgwog4EHgOOLP6pIhYGhFzI2LujBkz2h2j1Zv+G1JbxLJlqX1i5kzYf3944oli150yJa0VkR9J7WVHzTpamSWM9cD6iLg5e3wVNRKGtVFlPqZK9c/hh8M3v9l/XYjNm9MI6U98AjZuTPte/Wo4+OC0vsM558AjjzR+nerR1GY2KpSWMCLiMUnrJO0XEfcChwC/Lyueca3WVBuNBsn19sKzz6Yv/YMPTt1gKz2XdtppYEN1hQfNmY1qZfeSOhXoyXpIrQZOLDme8ad6NHVRW7fCxz8+cL8bqs3GLE9vPp5t357GPDz++NCfO3t2GudgZh3P05tbffWm3OjthVWr4J/+CY44AqZNG16ymDx5YG8mMxvznDBGu/yI6spP9ajqk06Ct741NU4feCB86lPwwANpDYki61PvkPuYdHV5XQizcarsNgwbrloN1fVs2wYrV6ZEcsgh8J73wKxZ6dg73tG4DWPKFHd3NTPACWN0Gk5DdW9vWmGuWnUj9bRp6fGWLW6wNrN+XCXViRpN+/3002kMxFB7NTUaJT9vXmrA7u1NM7tu2pS2PXmfmeW4hNFpai0aNH8+fO97qYH6d7+Dl18e2jVrTblhZjZELmF0mrPPrj3t9w9/mLbPPDM1XhfV1eU2CDNrCieMVmtUvQSp6uf222HxYjjyyNSOUIsEN94IX/winHtuKjU0svPOXjzIzJrKVVKtUJmTac2a/jOyrlmTqpsefzx9oV93HVx/fd+cTK95DUydmqbdqOZpv82sZB7p3SxD6eZaseeeqZvrwQenn+7u2j2g3LXVzEagWSO9XcIYrvzMrtOmwZNPDq0x+p574LWvHbjcqEsPZtahnDCKqk4QzzyTBsTB0EoVkOZh2m+/+se9aJCZdSAnjCKqq4mGmiDy3MXVzEYp95IqolZX16GoVDt5VTkzG8XGd8Ko1+U1Au68E84/H446qn5X1yK6uuDyy9M1PXLazEax8VMlVb386Gtek7q15ru8nnQSXHABrF4NGzak/fvsU7+ra96kSWna7+eeS4+9DKmZjTFjv4TR05Om8K6e8vvaa/uSRcW2bXDLLXDooXDxxfDgg2ka8IsuGjhQbtKklBSkVNV0ySUpqUSkHw+YM7MxZmyWMOoNnCsiIo2QznNXVzOzUZww6iWFqVPT3Evbt6fHQx2YWG9WV3d1NbNxbnQmjOpurvmkMFhbQyOSu7yamdVRehuGpAmS/k3Sjws/adGikXVzrR0InHyySxFmZnWUnjCA04C7C525dSt84xupGmokurrglFNSY3Wl0fryy2HJkpFd18xsDCu1SkrSLOAI4EvA3w36hLvugo99DCZMGNq8TZU2jtmz3VhtZjZMZZcwFgNnAL31TpC0QNIKSSuenTYtdXP91rcarwcxcWL/Lq8eOGdmNmKllTAkfRDYEBErJR1U77yIWAoshTS9OfvskwbTQe1eUh4wZ2bWEmVWSb0D+JCkw4EdgVdKuiIiji30bHdzNTNrq9KqpCLirIiYFRFzgKOB6wonCzMza7uy2zDMzGyU6IiBexFxA3BDyWGYmVkDLmGYmVkhThhmZlaIE4aZmRXihGFmZoU4YZiZWSFOGGZmVogThpmZFeKEYWZmhThhmJlZIU4YZmZWiBOGmZkV4oRhZmaFOGGYmVkhThhmZlaIE4aZmRXihGFmZoU4YZiZWSFOGGZmVogThpmZFeKEYWZmhZSWMCTtJel6Sb+XdJek08qKxczMBjexxNd+CfhkRNwqaRdgpaTlEfH7EmMyM7M6SithRMSjEXFrtv0McDcws6x4zMyssY5ow5A0BzgQuLnGsQWSVkhasXHjxnaHZmZmmdIThqSpwPeB0yPi6erjEbE0IuZGxNwZM2a0P0AzMwNKThiSJpGSRU9EXF1mLGZm1liZvaQEXAzcHRHnlhWHmZkVU2YJ4x3AccDBklZlP4eXGI+ZmTVQWrfaiPg1oLJe38zMhqb0Rm8zMxsdnDDMzKwQJwwzMyvECcPMzApxwjAzs0KcMMzMrBAnDDMzK8QJw8zMCnHCMDOzQpwwzMysECcMMzMrxAnDzMwKccIwM7NCnDDMzKwQJwwzMyvECcPMzApxwjAzs0KcMMzMrBAnDDMzK8QJw8zMCik1YUg6TNK9ku6XdGaZsZiZWWOlJQxJE4BvAB8A9geOkbR/WfGYmVljZZYw3gLcHxGrI2Ib8G3gqBLjMTOzBiaW+NozgXW5x+uBt1afJGkBsCB7+KKkO9sQ20hNBzaVHUQBjrN5RkOM4DibbbTEuV8zLlJmwigkIpYCSwEkrYiIuSWHNCjH2VyjIc7RECM4zmYbTXE24zplVkk9DOyVezwr22dmZh2ozITxO2BfSXtLmgwcDVxTYjxmZtZAaVVSEfGSpI8BPwcmAMsi4q5Bnra09ZE1heNsrtEQ52iIERxns42rOBURzbiOmZmNcR7pbWZmhThhmJlZIR2TMAabJkTSKyR9Jzt+s6Q5uWNnZfvvlfT+EmP8O0m/l3S7pGslzc4de1nSquynpY37BeI8QdLGXDz/I3fseEn3ZT/Hlxzn13Ix/kHSk7ljbbmfkpZJ2lBv/I+S87P3cLukN+eOtfNeDhbnvCy+OyT9VtIbc8ceyvavalb3yxHEeZCkp3L/tp/NHWvbVEIF4vx0LsY7s8/jtOxYW+6npL0kXZ9959wl6bQa5zT38xkRpf+QGr0fAPYBJgO3AftXnbMQuCjbPhr4Tra9f3b+K4C9s+tMKCnG9wBTsu1TKjFmj5/toHt5AnBBjedOA1Znv3fLtncrK86q808ldYxo9/18F/Bm4M46xw8HfgoIeBtwc7vvZcE43155fdJ0PDfnjj0ETO+Q+3kQ8OORfl5aHWfVuUcC17X7fgJ7AG/OtncB/lDj/3pTP5+dUsIoMk3IUcC3su2rgEMkKdv/7Yh4MSIeBO7Prtf2GCPi+oh4Pnt4E2lsSbuNZMqV9wPLI2JLRDwBLAcO65A4jwGubFEsdUXEr4AtDU45CrgskpuAV0nag/bey0HjjIjfZnFAeZ/NIveznrZOJTTEOMv6bD4aEbdm288Ad5Nm0Mhr6uezUxJGrWlCqt/4H8+JiJeAp4Cugs9tV4x580mZvWJHSSsk3STpwy2Ir6JonP85K6JeJakygLJd93JIr5VV7e0NXJfb3a77OZh676Od93Koqj+bAfxC0kqlqXjK9peSbpP0U0kHZPs68n5KmkL6ov1+bnfb76dSFf2BwM1Vh5r6+ez4qUFGI0nHAnOBd+d2z46IhyXtA1wn6Y6IeKCcCPkRcGVEvCjpb0glt4NLiqWIo4GrIuLl3L5Oup+jhqT3kBLGO3O735ndy1cDyyXdk/2FXYZbSf+2z0o6HPgXYN+SYiniSOA3EZEvjbT1fkqaSkpYp0fE0616HeicEkaRaUL+eI6kicCuwOaCz21XjEh6L7AI+FBEvFjZHxEPZ79XAzeQ/hpohUHjjIjNudi+Cfx50ee2M86co6kq8rfxfg6m3vvouKlvJL2B9O99VERsruzP3csNwA9oTZVuIRHxdEQ8m23/BJgkaTodeD8zjT6bLb+fkiaRkkVPRFxd45Tmfj5b3TBTsPFmIqnRZW/6GrQOqDrnb+nf6P3dbPsA+jd6r6Y1jd5FYjyQ1DC3b9X+3YBXZNvTgftoUYNdwTj3yG3/FXBT9DWEPZjFu1u2Pa2sOLPzXkdqRFQZ9zN7jTnUb6Q9gv6Nire0+14WjLOb1L739qr9OwO75LZ/CxxWYpy7V/6tSV+0a7N7W+jz0q44s+O7kto5di7jfmb35TJgcYNzmvr5bNnNHsabP5zUyv8AsCjb93nSX+oAOwLfyz70twD75J67KHvevcAHSozxl8DjwKrs55ps/9uBO7IP+R3A/JLv5T8Ad2XxXA+8Lvfck7J7fD9wYplxZo8/B3y56nltu5+kvx4fBbaT6nnnAycDJ2fHRVoI7IEslrkl3cvB4vwm8ETus7ki279Pdh9vyz4Ti0qO82O5z+ZN5BJcrc9LWXFm55xA6nCTf17b7iepWjGA23P/roe38vPpqUHMzKyQTmnDMDOzDueEYWZmhThhmJlZIU4YZmZWiBOGmZkV4oRhZmaFOGGYmVkhThhmI5CtR3Botv1FSV8vOyazVvHkg2Yjcw7w+WyiuQOBD5Ucj1nLeKS32QhJ+ldgKnBQpHUJzMYkV0mZjYCk/0Ba+Wybk4WNdU4YZsOUrVzWQ1rV7FlJLVtRz6wTOGGYDUO20trVwCcj4m7gC6T2DLMxy20YZmZWiEsYZmZWiBOGmZkV4oRhZmaFOGGYmVkhThhmZlaIE4aZmRXihGFmZoX8OwP7A67UJe89AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Importing various packages\n", "from random import random, seed\n", @@ -970,7 +997,7 @@ "from sklearn.linear_model import LinearRegression\n", "\n", "x = 2*np.random.rand(100,1)\n", - "y = 4+3*x+np.random.randn(100,1)\n", + "y = 4+3*x+0.01*np.random.randn(100,1)\n", "linreg = LinearRegression()\n", "linreg.fit(x,y)\n", "xnew = np.array([[0],[2]])\n", @@ -1041,11 +1068,20 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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97VpaQqbfLBn/1q3w6KNw113h0Qu6IxCRYmnmwXDr1sFPfwrnnw977QXHHw/f/Ca8u3dLxeiOQESKpaPEf9VVoZoo4+MDeO65UOKfPRseeSTcCQwfDpMnw9SpcOqpMGRIqM6qkwaUiUhjtbU1TyabRVu3wvz5pcz/2WfD9oMOChn/lCkwYQL07bvd2zI9oMzMRgE/BvYCHJjp7jfEvV8RSUHX0bkdXTNBwaA7a9bAvHkh4587F9auDaOaTzgBLrkkZP777hvb7mO/I4jWOX63uz9hZoOBhcCZ7v5MpffojkAkIY0uvWvKk+o9+2yp1P/b38K2bbDHHqHKZ8qUUOUzeHDVH5fpOwJ3fw14Lfr9LTNbQljprGIgEJEEVFt6ryVYFHh0bo+2bAkZ/uzZIQA891zYfuih8KUvhWqfI4/coconCYm2EZjZGOBh4GB3f7PLc61AK0BLS8u4peVKFSLSONWU3mtdpUt3BNtbtapU5TNvXuj1078/nHhiKPVPmRLOTQP05o4gsUBgZoOAh4AZ7v7L7l6rqiGRBPTpE6ZY6MosDGaE2jP2JlveseHc4c9/LpX6588P53KvvUpVPqecEiaya7BMVw0BmFk/4P8BbT0FARFJSHcDqzrUWtXTbF0zG2Hz5tCtc/bs8HghWrjxsMPgyitDlc/48SHwZlQSvYYMuBlY4u7fjHt/IlKlaqYwryZYdNVMo3PrtXIl3H13KPXfcw+8+SbsvDOcdBJcfnko/Y8alXYqq5bEHcGxwPnAn8xsUbTtSne/O4F9i0gl1ZTetd5F4A5PP13q5fPoo2Hbu94F55wTSv0nnQQDB6ad0rpoQJmIdK+oA8TeeQceeqiU+Xe0iRxxRGlg1xFHpFfl0+X/ssfSpS+ucN+vno/KbqWVZFu5hT0kn7IwG25S19sbb8Ctt8LZZ8OIETBxItx0Exx8MPzgB7B8OSxcCNOnp1vvX2ZNhRaov/uRu2fuMW7cOJcMu/129wED3MMlGB4DBoTteXb77e6jR7ubhZ95P96siPN6a293f/JJ9xkz3CdMCP9bcN9nH/eLLnKfPdt9w4be76dWPV1ro0dvfz7Ax4F7nXlu6pl+uYcCQcaVuQgdwvZGyVqmW9TglwWNvt42bXKfO9f95JPd+/Ytfd6++7pffbX7E0+EAJGWaq61joDVoECgNgKpXTX9z3sji33RNVAqPY243l5/PfTymT0b7rsPNmzY8TVpX2MdqrnWyrxmPLDAva4pSNVGILWr1HWwUWu7VlpK8KqrGvP59ejN1AlqT+mdeq43d1i0CK65Bo4+OszXf+GFoX7/E5+APffc8T1pX2MdqrnWyqyp4FB/KazeW4k4H6oayri4q0nK3PY6hO1pqbd6QlVKvVftOdy40X3OHPeLL3YfObJ0zRx9tPs117gvWlSq8sniNdah2mutS/XpCHjB68xzU8/0yz0UCJpAnHX4SbRB1KreDD2rx5Kl9pdqVErzq6+6z5zpfsYZpf/PwIHuZ53lfsst7q+/Xv7zsvh/6VDntQYscAUCyY2slqLryUCzVvLM6rmtVnu7+8KF7tOnu48bVzqGlhb3Sy5xnzfP/e23e/6crJ+HOq41BQLJn2YstZaTtZJn1tJTjQ0b3GfNcm9tdd9771IgPeYY9699zf2pp+rr5ZOXayyiQCC1y9mXILOyVvLM2h1KJcuXu994o/vkye677BLSOGiQ+9lnu996q/sbb6SdwspS+m4pEEhtspY5pSHJL2uWgm5W7wi2bXP/wx/c/+3f3A8/vJSuffd1/+d/dr/33uqqfNLWqO+WqoYUCGKX1cwgKUUOhFk69vXr3X/9a/cLL3R/17tCWvr0cT/2WPfrrnNfvDjdgV31aMR3K4XGYg0oK6K4B4RlXdEHh6U5idyyZTBnThjYdf/9YWK33XaD004Lk7hNmhTm+GlWjfhu1Xl9NsUKZbVQIIhZ0TPCogfCJLW3w+OPl1bsevLJsP097wkzeE6dCscdF5ZvTFsjAmQjvlt1Xp+9CQQaWVxEZUYl5maO+WpG8cY9Mrro1q+HX/4SPvnJMKJ3wgS49loYMgS+/nVYsiQs3H799WHt3qwEgS6zedLaWvso8EZ8t9K4PuutU4rzoTaCBGSpAbNRqq1bzVI9eV689JL7d77jPnGie//+4ZwOGeJ+7rnubW3uq1alncLuNbLdrLffLQ0o6xQI8phRSbxq+TLr+uqdrVvdf/c79y9/2f3gg0vn+oAD3L/wBfcHHnDfvDntVFYva91qE+41lM02gv328wV/+1u2Zp+U7FPdf7zefBPuvTfU9d99N6xYAX37wgc/WFqx64AD0k5l9Tq3CfTpA9u27fiaJmo3600bQRJrFtfulVdg8+btt3XMDKhAIJXUs9C6dO/FF0sNvQ8+CFu2wNChoXfP1Kmht8/uu6edytp1neq8XBDIS7tZFbIZCLoGgQ7VTPkrxaWF1ntv27awMHvHOr3PPBO2jx0Ln/tcKPV/4AOwUzazjqqVm+ocwh1Oe3ux1mYmq4Ggf//ywUAlO+lOx5e2iAut98a6dXDPPSHjnzsXVq0KGf2HPgSf+lTI/PffP+1UNlalQmV7eyGrEbMZCPbZB8q1ERShZJfmYJ88mDZN56sazz9fKvU//DBs3QrDh8Ppp4eMf+LE0N0zr1SNuJ1sjiMYNiw0DI8eHRr6Ro8uRkNxo/oyi3S1dWvI8C+/HN73Pnjve+Hznw8Frssug9/+Nvz+4x/DOefkOwhAvsfS1KPe7kZxPgo7jiDNOYDUnTJ/Vq92v+MO97//e/ehQ8O11K+f+ymnuN9wg/tf/5p2CtOVs2ue3HUfLeoUE2l1f8ziYvFxyXvV21/+Uurl88gjofF3xAiYPDlU+Zx6apjbR3JHcw3lRVpzABVl7qE8BrwtW2D+/JD5z54dpm4AOOSQkPFPnQpHHRV6w0iuKRA0s84l1GHD4K23tu8xlURGVZSBWHkJeKtXh949d90F8+bB2rWhp92HPxwy/smTw7FKoeRvQFlRdC2hrloF/fqF3hurVydXdVGUHhSVugxmfXyKO/z5z6VePvPnhwC9555w1lkh8z/5ZBg8OO2USpNSIEhTuUEtW7bAoEGwcmVy6SjKQKxmCnibN4c6/o7M/69/Ddvf/3648spQ7XPkkeFuTqSXin0VVTNlcZyyUkKdNq15u+vW8j/MepfBVavgttvg6KNhl11CKf9b34KBA+F73wvXxaJFcM014TUKAtIo9XY3ivORSPfRNKYi7tpdbfjw9LqL5kE9/8MsdRlsbw/LMV53XViesU+f8teDpsiWKqDuo3VIuuGwXI+V/v3DV33LltK2Zu/FkqRmbPzdvBkeeqjUxfPFF8P2ww8Pdf0zZ8Lrr+/4viwfk2SCViirVVtb+QwE4quWKdcesHlz6NOd1SqZtKvOepKVqrWerFgBP/oRfPSjoU//qafCTTfBQQfBjTfC8uXwxBNw9dVhdG85WTsmyZXiNRZ3lMwriavhsNIXefXqZBuGq9X1DqZjugvITqDqrvE3zYFj7rB4canU/9hjYdvee8PHPx5K/ieeuGN7RUfam6VBW/Kj3jqlah/ALcAbwOJq3xNrG0GlaRzirotNc/qIeqSR3lrr7yu1EXz608m3/7z9tvu8ee6XXLL9uRs/3n36dPeFC0ObQE+0jKbUiSwvVQkcDxyRmUBQaUk6iL+hOIkveKMaQ5Neuq/e81PueJMKYq+/7n7zze5nneU+cGDYx667up9xhvtNN7m/8kp9n5ulBm1pGpkOBCF9jMlMIMjzxG6NDDZJn6dG7i+uINbe7r5okfs117gfdVTpc0eOdL/4Yvc5c9w3buzdPkTqlItAALQCC4AFLS0tcZynIM+33o3MTJM+T43MvBt5HjZtChn8pz/tPmpU6bOOOioEhD/+sboqH5GY5SIQdH7EPo4gr7fejS4JJ3meshTEXn01VO2ccUbpcwYOdD/zzFAV9NprtadJJGYKBBJktUG6moDS6DuQWoJYe3tozL366tC427H/lpbQ+Dt3brgzEMkwBQIJsljtVUuakrwD2bDBffZs99ZW9733Lt05TZjgPmOG+5NPqspHmkpvAkHsI4vN7A7gBGAE8DfgK+5+c3fvKdQ01I2WtYVXsjT695VXYM6c0L//N7+BTZvCBH8TJ4ZJ3E4/PczoKdKEtB6BZFeaax20t4cRux0Du554ImwfMyYM6po6FY4/HnbeOd50pClrBQOJjaaYkOyqNCI2rpGyGzbArFnwqU/ByJFhquavfhV23RWuvTaM+H3hBfj2t+GUU/IfBFpbwx2Ze2l0eNamCimyrEzjUm+dUpwPtRHkSBLtFsuWuX/ve+6nn+6+yy5hH4MHu3/sY+4//rH7ihWN21cz6anzQF57zzWLBn83yHIbQT1UNZQzja6eaG+HBQtKVT6LFoXt++1XqvL54AfD7K5F1l213G235W/95mbT4PYztRFI/q1fD/fdFzL+OXPCLJ19+sCxx4aMf8oUGDs2ZHISdJfRQHYa8Yuqwe1nWrNY8mnp0tJSjQ88EKbtHjIETjstZP6TJsGwYWmnMru6W4L0/PPLv0fTXScnQzPNqrFYsmPbNnj00VCNdOihoUR76aWhcffSS+H++8Pc/nfeGaovFAS6190SpJUym2HDstF4WQQZWjpVVUOSrrfegnvvDaX+u+8OGX3fvqGOf8qUUPI/4IC0U5k/WjEvGxrYfqY2AmkuL75YqvJ58MGQ8QwdGqp6pkwJVT9Dh6adyvzrmgmtXw+rVu34ujy1G+R4XIUCQb1yfFFkyrZtYZWujl4+Tz8dto8dWyr1f+ADsJOarFKV5uC/JJS7C8rRHU9vAkHqYwbKPRIZR5DFeXkapdH9w+v5vLVr3X/2M/fzz3cfPjyc3512cj/xRPdvftP9ued6l6Zm0Gz99JOYtDDNc5LVSRkbhKxPOlfrI5FAkNeLorcBrusXtZZlH59/3v36691POilk+uA+bJj7eee533mn+5o1jTzSbGvGgkbcaU77nCS96l7CFAjqkdeLojcBrtwXtdJ5Gj3afcsW94cfdr/8cvexY0vPHXig+xVXuD/ySHhNs6unFNusBY04S+xpn5O09x8zBYJ65PWi6E2Aq3ROKj2GDQs/+/VzP/lk9xtuCHcFeVJvKTYPBY1GB4W0z0nadyQxUyCoR14vit4EuEpf1HKPPn3cP/EJ95//3H3duriPKj31ns9mL2jE8f3IwjlptnabGigQ1OP220uNmBB+z8NF0ZsvcLV3BDvvHCZzK4J6S7HNXtCII9Nu9nOScb0JBMUcWdzRjaxzn+lNm9JLTyN1N5q0O2vWwOTJYTBXZ336wHHHwT77lD7v5psrT1GQN/VOo13v/yErKk010ZspKJr9nORZvREkzkfsdwRZuEVNW3u7+5Il7t/4hvvxx7v37RvOweDBYaF2M/dRo5qjtBbn7X5RS7H6jjQdVDVUo7QbrdKyebP7b37j/rnPue+/f+m4Dz3U/aqr3B97zH3btrRTWZskMuoc1ytXlKUAWMTzXwcFgloVqbSzcqX7bbe5n3OO+267+X/X8U+a5P7d77ovXZp2CnunSP/LpGUhA85SQMq43gSCYk4xkeeh5u6wZElpOoff/S5MD7DXXqXpHE46KSzangd5nxah6Bq8eEueaT2CWnVk9nmZZ2jzZnj44VLm/8ILYfvhh4djnDoVxo0LmWbeZGhOd4lBHI3WsoNiBgIImX6zZvwQpmu+++6Q8d9zT5jOeZddQmn/iitCD6CRI9NOZfy6W3xFmp8CfSJyWETMKXdYvBiuvTbM1LnXXnDBBTB/Ppx7LsyaFbrD3nUXXHRR8wSBtrbeLYSS9S6JvT2+osvQ4i25Vm/jQpyPRAaUNYO333afN8/90kvdx4wpNZaNG+c+fbr7ggWhG2g5WWjo60neGwLzfnxJaYZrOQNQY3GO/O1vocpn9uywcteGDbDrrnDyyaGuf/Jk2Hvv7j+jWRrD894QmPfjk0zRwjRZVc3CN+7w1FOlFbv+8IewbZ99QsY/ZQqceGIIBtVqlgwo7z1+8n58kim9CQRqI4hLR6l86dKQGSxdGv5ua4O334a5c+GSS0Kmfdhh8K//GjKHq6+GJ57V07sAAAAHFElEQVSAl1+G738/3AHUEgSgeXpa1Dt9Q7Oo5fjUliBpqrdOKc5HLtoIKg102nXXUr3xgAHuZ57p/sMfur/2Wvz7TnqQVU91u3mvQ6/2+PJ+HiQRaGRxBnU3pfNnPuM+d677pk3x7DsLGUstmWCeGwKrOb5KgXv48IQTK82sN4FAbQSNtGkT3H9/qOv/4Q/Dou1dVeoX3WjVtE/EqVnaKbKgUlsCwO23Z6uBXzJLjcVpevXV0NB7113wX/8VgsGgQTB2bGgE3ry59Nos9tyJixpKq1cpaIICp1RNjcVJam+HhQth+nQYPz707rnoopDpX3hhGOW7ciU8/jjcckt2BzrFLe8NwY3U3eCorDXwSy7pjqAaGzeG0n5Hyf+110LmfswxpS6eBx0UtknQLGMZsmLEiO0XSuqgOwKpkiadi8Py5aW+/fffH7p8Dh4MEyeGzH/SJNhjj7RTmV15m9gvbjfcoDmTJDUKBB3a22HBglLmv2hR2L7ffqHqZ8oUOP546N8/3XQ2k2af2C9JjQycaXcUkKaTSNWQmZ0G3AD0BX7o7td19/rEqoY2bID77gsZ/5w5YXqHPn3g2GNLc/ePHasqn6xRRleZquQKK9O9hsysL/AX4BRgOfA48HF3f6bSe2INBMuWlUr9DzwA77wDQ4bAaaeFzH/SJBg+PJ59S+8po+ueuu0WVtYDwTHAdHefGP39ZQB3v7bSexoaCNrbw/w9HYu2PPVU2L7//qHEP3UqHHcc9OvXmP1JvJTRdU/ddgsr64Hgo8Bp7v5P0d/nA0e7+6VdXtcKtEZ/HgwsjjVhzWMEsDLtRGTAiBHQPhr2rfSChbAwyQSlqOI1cSgc0g92aMjaApufgj/FnrLk6ftR8nfuPrieN2amsdjdZwIzAcxsQb2RLW90LgIzW7BC5wHQNdGZzkWJmdVdjZLEgLJXgFGd/h4ZbRMRkQxIIhA8DrzXzPY1s/7AucCsBPYrIiJViL1qyN23mtmlwD2E7qO3uPvTPbxtZtzpaiI6F4HOQ4nORYnORUnd5yKTU0yIiEhyNOmciEjBKRCIiBRcaoHAzE4zs2fN7Hkz+59lnt/ZzH4aPf97MxuTfCqTUcW5+IKZPWNmT5nZb8xsdBrpTEJP56LT6842Mzez3HYdrOZcmNk50bXxtJn9JOk0JqWK70iLmT1gZn+Mvienp5HOJJjZLWb2hpmVHWtlwbejc/WUmR3R44fWu7RZbx6ERuO/AvsRBr88CRzY5TWfAW6Mfj8X+Gkaac3IufgwMCD6/dNFPhfR6wYDDwOPAePTTneK18V7gT8CQ6O/90w73Smei5nAp6PfDwReSjvdMZ6P44EjgMUVnj8dmAsYMAH4fU+fmdYdwVHA8+7+grtvBu4EPtLlNR8BfhT9/gvgJLNczv7W47lw9wfcvWNynccIYzHyqJrrAuAa4D+At5NMXMKqORefAr7r7msA3P2NhNOYlGrOhQO7Rb8PAV5NMH2JcveHgdXdvOQjwI89eAzY3cze3d1nphUI9gFe7vT38mhb2de4+1ZgHZDH2eCqORedXUiI9nnU47mIbnNHufucJBOWgmquiwOAA8xsvpk9Fs3ym0fVnIvpwHlmthy4G/hsMknLpFrzlOxMMSE9M7PzgPHAh9JOSxrMrA/wTeCClJOSFTsRqodOINwlPmxmh7j72lRTlY6PA7e6+39GE13eZmYHu7tm2qtCWncE1Uw78d+vMbOdCLd7Zdbya3pVTcFhZicDVwFnuPs7CaUtaT2di8GECQkfNLOXCPWfs3LaYFzNdbEcmOXuW9z9RcJ07+9NKH1JquZcXAj8DMDdHwV2IUxIV0Q1T+uTViCoZtqJWcA/RL9/FLjfo5aQnOnxXJjZ4cAPCEEgr/XA0MO5cPd17j7C3ce4+xhCe8kZ7p6hBa4bpprvyK8JdwOY2QhCVdELSSYyIdWci2XASQBm9j5CIFiRaCqzYxbwiaj30ARgnbu/1t0bUqka8grTTpjZvwML3H0WcDPh9u55QsPIuWmkNW5VnotvAIOAn0ft5cvc/YzUEh2TKs9FIVR5Lu4BTjWzZ4BtwOXunru75irPxWXATWb2eULD8QU5LThiZncQCgAjojaRrwD9ANz9RkIbyenA88BG4B97/MycnisREamSRhaLiBScAoGISMEpEIiIFJwCgYhIwSkQiIgUnAKBiEjBKRCIiBScAoFIFaK57k+Jfv+qmX0n7TSJNIomnROpzleAfzezPYHDgdyN7Jbi0shikSqZ2UOEqT5OcPe30k6PSKOoakikCmZ2CPBuYLOCgOSNAoFID6LVndoIKz+tz/ECMFJQCgQi3TCzAcAvgcvcfQlhmcyvpJsqkcZSG4GISMHpjkBEpOAUCERECk6BQESk4BQIREQKToFARKTgFAhERApOgUBEpOD+PwPBHVsGaYvcAAAAAElFTkSuQmCC\n", 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MeiBPM9Q6lgOEcLAwM+uNvL2hng78cvpzxr5kZmbWdl5W1czMxvKyqmY2Xh8mELRVeVlVM1tdHyYQtLHyTFEu4OURcVlEvDG6vKyqmS3zgmBGvgR3AC+QdJ6kszu7noVZGdrcjDPNnERtft92iLwjuD8DHM6h3WjNbDVLzThLd+dLzTjQjtHQGzcOn+10tTmJFhbg0kvhu99d3ta2922HGFuzkHTj0s9pl9m/Bm6NiA+XWTCzzmh7M07eOYmWguNgoFjSpvdth8jSDHUEgKR3AUTEQ8B7yyyUWafUPLX01PJOrjcsOA5qy/uuQ4Ob7bIEC0n6aeD8NMkN8JQSy2TWLV1YB2J+Pllg5+DB5PtqzUjjgkGb3neVGt7rLEuw+CPg70hWyHu3pIszPs/MoPappQuV5c53tWDQ1vddhYY3V4696EfE/46IEyPiD4BrgecAF5ZeMrOu6Mo6EFnvfIcFR4Bjjmnn+65Kw5src01R3iSeotysYnnWgF5YqG3dhdYqc/3uAVWswT0xSWdKulPSbkmXDfn96yTdLulWSX8ryZMUmjVNnjvfPDkOSzS8ubL0YCFpLXAVcBZwEnBeuojSoK8AcxHx88B1wJ+VXS4zy6kLifoma3hzZRU1i1OB3RFxV0QcAK4BzhncISI+FxFLmZ2bgA0VlMtsMg3u3liqht/5dkKDa2RVBIv1LE9rDrAv3TbKhcDfDPuFpG2SFiUt7t+/v8AiWq9Mc7FvePfGUjX8ztfK1agusJLOB+aAdwz7fUTsiIi5iJhbt25dtYXru67cTU97sW9498bSNfjO18pVRbC4Dzh+4PGGdNshJL0YuBzYGhGPVlAuy6pLd9PTXuwb3r3RrCxVBIubgc2STpB0OHAusGtwB0nPA95HEigeqKBMlkeX7qanvdg7yWs9VXqwiIjHgEuAG4A7gI9HxG2SrpS0Nd3tHcBRwCckfVXSrhGHszp06W562ou9k7zWU3mnKJ9IRFwPXL9i25sHfn5xFeWwCU0yRXVTbd9+6HThkO9iv9RG7wFn1jONSnBbQ3XpbrqIHj1O8loPVVKzsJbr2t30/Hx7y25WEwcLy8YXWLNeczOUmVkTNHwsk4NFFRr+IbCe8eexeVowlsnBomwt+BBYj/jz2EzTjmWq4AbA61mUraI56s0y8eexmdasSYL3SlLS6241SzcAK7uDj+jl1+j1LHqtSwParP38eWymaQaLVjTDgoNF2Tw9hDWJP49P1IQczjRjmSq6AXCwKFuXBrRZ+/nzeKim5HCmGSxa1Q1ARLTya8uWLdEaO3dGzM5GSMn3nTvrLpH1WdmfxzZ93mdnI5IwcejX7GzdJctu586ImZlDyz8zM/K8A4sxwTXXCW4zK07OZGvtpkksN8nCQuYZFiZNcDtYmNl0Bi9Ua9bA448/cZ+m9rbqYe8w94Yys+qtbPMfFiigub2tnMPJzMGiLk3ogWE2rWHdNodpam8rryuemScSrMPKdt2lHhjgD6m1y7AmnJWafqfuSTIzcc2iDl1aptTap8ha7dq1o3/nO/VOcc2iDh5Fa3UpulY7KkcB7epNZGO5ZlEHj6K1IkxSQyi6Vjs7m2+7tZaDRR3cA8OmNWzk8ateBRdfvPrziq7V+rPcGw4WdXAPDJvWsBpCBFx99eo1jKJrtf4sF6+hPSU9KM+sjUaNPIbVB5S1bYR131Tw9/GgPLM+Wa0msFqTkmsCzdbgnpIOFmZttH17crEfZlyT0vx8UvM4eDD57kDRHA3uKelgYTapOtuW5+fhooueGDCcXG63BveUdLDog4YmzFqtCesgvPe98JGPuEmpSHX/rzS5d9kk85o34atV61nUKedc95ZRF9ZBsEM15X+l5PVA8HoWNlQPp2CuRFfWQbBlPflfcW8oG67BCbPaq/zTaHDbcmHa/PeZRJP/VxrAwaLrmnpRa0Kb/zSa3LZchLb/fSbR1P+VhnCw6LqmXtQa3J88k66PV2j732cSTf1faQgHi65r6kWtC1X+Lo9X6MLfJ68m/a8U3QQ4cLyfh1MmOYQT3FaPniQTW8t/n/oUPeXHiuPNAYsRI0Z0juaahdXDVf5m898nm7w1gCz7F90EmHXp23Em6W+b9ws4E7gT2A1cNuT3LwK+DDwG/GaWY3qcRQeU3J/cpuS/z+ryjsvIur80fAyPNFk5VxxvC0Q0cZyFpLXAN4CXAPuAm4HzIuL2gX02AT8FvAHYFRHXjTuum6HMrFZ5m+qy7l90E+CK4zW5GepUYHdE3BURB4BrgHMGd4iIeyLiVsCjmcysOtMkkvN2Asi6vegmwGHHm0AVwWI9sHfg8b50W26StklalLS4f//+QgpnZj017ViSvOMysm4vulfWiuP9GA5McphWJbgjYkdEzEXE3Lp16+oujpm12bSJ5Lw1gDz7F90te+B4t8LXJzlEFcHiPuD4gccb0m1mZvWZdixJ3hpAk8ZxTOCwCl7jZmCzpBNIgsS5wCsreF0zs9E2bhyeSM4zvcf8fL6Lfd79G6T0mkVEPAZcAtwA3AF8PCJuk3SlpK0Akl4gaR/wW8D7JN1WdrnMrOc8liSXSnIWEXF9RJwYEc+OiO3ptjdHxK7055sjYkNEHBkRx0TEz1VRLhuhb7ONWj+1vFmoaq1KcFsJVgaGiy/u32yj1l9tnN+rpps5zw3VZ8PmoJGGL+rjOYHM6lfAvFGTLn7kYNFno0aKDuMV4MzqV8Dobq+UZ/nlmW7aC8CYTaeI5qMap453sOizUQFAK6aNcQ8Rs+kUtfJgjav5OVj02aiugxdd5B4iZkuKqBEUNe14jd19qxiUZ021FAAuvzypxm7cmHzoHBjMEisTyks1Asj3f1JU81GN/7NOcJuZjVLUdOENWnnQCW4zs6IVVSPowGhxBwszs1GKSih3YLS4g4WZ2ShF1gjaOFp8gIOFmdkoHagRFMXBwqwJPHljc7W8RlAUd501q1tR3TPNSuSahdlqqrjjL2rAllmJXLMwG6WqO/4a5/sxy8o1C7NRqrrjr3G+H7OsHCysOZqW5K3qjr8DA7as+xwsrBmKmpWzSFXd8bt7prWA54ayZmjQ3Dk/UcCqZGZN47mhrN2amOT1Hb/ZT7g3lDXDxo3DaxZ1J3nn5x0czHDNwlZTZcLZSV6rQ97PeJ79m9ZhY1oR0cqvLVu2hJVo586ImZmIJN2cfM3MJNvLfM3Z2Qgp+V7ma5nl/Yzn2X+1fWv+nAOLMcE11wluG66JCWezIuX9jOfZf9S+xxwDjzxSa6eJSRPcDhY23Jo1yf3QSlIyoZpZ2+X9jOfZf9S+o1R4E+beUFasNo0q7lrbsFUj72c8z/a8/yctmNrFwcKGa0vCuejBfA48/ZH3M55n/1H7HnPM8GM38SZspUkSHU34coK7Am1IOM/OHppEXPqanc1/rDqS+lavvJ/xPPsP27cBnzGc4LZeKjK34qS+VWFhIZmM8t57kxrF9u2jk9t59s3IOQvrpyJzK00cRZ6Xm9GaL+vKew2bL83BwtqtyNxKm5L6wzTs4mJTatiiWA4W1m5Fzt/UlqT+KA27uGTimtBoDavpOmdhNqiENuLKtG1sjGf1XV1JOTTnLMyKkLU9uYna1ozWxppQlRpW060kWEg6U9KdknZLumzI74+QdG36+y9J2lRFucw6pWEXl7Ea1szSOA2bIr/0YCFpLXAVcBZwEnCepJNW7HYh8P2IeA7wbuDtZZfLrHMadnEZq201oTo0qKZbRc3iVGB3RNwVEQeAa4BzVuxzDvCh9OfrgDMkqYKymXVLgy4uY7WtJtRzVSx+tB7YO/B4H/DCUftExGOSfggcAzw4uJOkbcC29OGjkv6xlBK3z7GsOFc95nOxrPHn4lh4xnGw/klw+I/hwP0PP3zfg+ef/z3OP7+El2r2uajQz0zypFatlBcRO4AdAJIWJ8nod5HPxTKfi2U+F8t8LpZJmqgbaRXNUPcBxw883pBuG7qPpMOApwLfraBsZmaWQRXB4mZgs6QTJB0OnAvsWrHPLuB30p9/E/hstHUAiJlZB5XeDJXmIC4BbgDWAh+IiNskXUky++Eu4C+Bj0jaDXyPJKCMs6O0QrePz8Uyn4tlPhfLfC6WTXQuWjuC28zMquMR3GZmNpaDhZmZjdX4YOGpQpZlOBevk3S7pFsl/a2k2TrKWYVx52Jgv1dICkmd7DaZ5TxI+g/p5+I2SR+tuoxVyfD/sVHS5yR9Jf0fObuOclZB0gckPTBqLJoS/y09V7dKev7Yg06yvF5VXyQJ8W8C/xo4HPgacNKKfS4Grk5/Phe4tu5y13gu/h0wk/786j6fi3S/o4EvAjcBc3WXu6bPxGbgK8DT08fPrLvcNZ6LHcCr059PAu6pu9wlno8XAc8H/nHE788G/gYQcBrwpXHHbHrNwlOFLBt7LiLicxGxNI3nTSRjWrooy+cC4C0k84z9qMrCVSjLefh94KqI+D5ARDxQcRmrkuVcBPBT6c9PBe6vsHyViogvkvQsHeUc4MORuAl4mqRnrXbMpgeLYVOFrB+1T0Q8BixNFdI1Wc7FoAtJ7hy6aOy5SKvVx0fEp6ssWMWyfCZOBE6U9A+SbpJ0ZmWlq1aWc/GnwPmS9gHXA/+5mqI1Ut7rSbum+7BsJJ0PzAG/UndZ6iBpDfAu4IKai9IEh5E0RZ1OUtP8oqRTIuIHtZaqHucBH4yId0r6JZKxXSdHRANXhmqeptcsPFXIsiznAkkvBi4HtkbEoxWVrWrjzsXRwMnA5yXdQ9Imu6uDSe4sn4l9wK6I+HFE3A18gyR4dE2Wc3Eh8HGAiLgReDLJBIN9lOl6MqjpwcJThSwbey4kPQ94H0mg6GrbNIw5FxHxw4g4NiI2RcQmkvzN1ojo2jq8Wf4/PklSq0DSsSTNUndVWciKZDkX9wJnAEj6WZJgsb/SUjbHLuC3015RpwE/jIhvrfaERjdDRXlThbROxnPxDuAo4BNpjv/eiNhaW6FLkvFcdF7G83AD8GuSbgceB/4wIjpX8854Ll4PvF/Sa0mS3Rd09MYSSR8juUk4Ns3RXAE8CSAiribJ2ZwN7AYeBn537DE7eq7MzKxATW+GMjOzBnCwMDOzsRwszMxsLAcLMzMby8HCzMzGcrAwM7OxHCzMzGwsBwuzgqRrJbwk/fmtkv573WUyK0qjR3CbtcwVwJWSngk8D+jc6HnrL4/gNiuQpC+QTLlyekQ8VHd5zIriZiizgkg6BXgWcMCBwrrGwcKsAOkqYwskK5D9S4cXGbKecrAwm5KkGeCvgddHxB0ky7leUW+pzIrlnIWZmY3lmoWZmY3lYGFmZmM5WJiZ2VgOFmZmNpaDhZmZjeVgYWZmYzlYmJnZWP8fxnUyTh8MM1IAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn.linear_model import LinearRegression\n", "\n", "x = np.random.rand(100,1)\n", - "y = 5*x+0.01*np.random.randn(100,1)\n", + "y = 5*x+np.random.randn(100,1)\n", "linreg = LinearRegression()\n", "linreg.fit(x,y)\n", "ypredict = linreg.predict(x)\n", @@ -1227,11 +1272,34 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The intercept alpha: \n", + " [2.00137415]\n", + "Coefficient beta : \n", + " [[4.99698001]]\n", + "Mean squared error: 0.00\n", + "Variance score: 1.00\n", + "Mean squared log error: 0.00\n", + "Mean absolute error: 0.01\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import numpy as np \n", "import matplotlib.pyplot as plt \n", @@ -1239,7 +1307,7 @@ "from sklearn.metrics import mean_squared_error, r2_score, mean_squared_log_error, mean_absolute_error\n", "\n", "x = np.random.rand(100,1)\n", - "y = 2.0+ 5*x+0.5*np.random.randn(100,1)\n", + "y = 2.0+ 5*x+0.01*np.random.randn(100,1)\n", "linreg = LinearRegression()\n", "linreg.fit(x,y)\n", "ypredict = linreg.predict(x)\n", @@ -1383,11 +1451,27 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.004999999999999991\n" + ] + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -1446,9 +1530,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Importing various packages\n", @@ -1499,9 +1581,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Importing various packages\n", @@ -1532,9 +1612,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Importing various packages\n", @@ -1572,9 +1650,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -1686,7 +1762,25 @@ ] } ], - "metadata": {}, + "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": "3.7.0" + } + }, "nbformat": 4, "nbformat_minor": 2 } diff --git a/doc/src/Statistics/Statistics.do.txt b/doc/src/Statistics/Statistics.do.txt index affe3b4a2..bd9a121bc 100644 --- a/doc/src/Statistics/Statistics.do.txt +++ b/doc/src/Statistics/Statistics.do.txt @@ -1,6 +1,6 @@ TITLE: Data Analysis and Machine Learning: Elements of Probability Theory and Statistical Data Analysis AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of Physics, University of Oslo & Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University -DATE: august 20 +DATE: today !split ===== Things to add =====