logistic regression python code github

out[j*m:(j+1)*m,1:] = out[0:m,1:]\n". " "# instead of generating all possible values of GRE and GPA, we're going\n". \end{align}$. Okay, so I have imported CSV, numpy(for majorly dot product only), and math for performing log and exponential calculations. Iterations: 5 \end{align}$, $\begin{align} Thats a lot of theory, I know but that was required to understand the following code snippets. iteration : 3 Finally, the average cost is returned. GitHub - jpa203/Logistic-Regression. Python logistic regression (with L2 regularization). I am final year engineering grad who loves to explore technologies and currently dating every technology which i can find. Note: At this point, I realize my gradient descent is not really optimizing well. Logistic Regression is somehow similar to linear regression but it has different cost function and prediction function(hypothesis). It is supervised learning algorithm that can be applied to binary or multinomial classification problemswhere the classes are exhaustive and mutually exclusive. Logistic Regression using PyTorch in Python. model_selection import GridSearchCV: from sklearn. 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RURg0aBB+85vf4P3338eaNWu6vMfQ+CsrK8OYMWMQGRmJqKiobu3b0TeWTPWh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\nffp0nDlzxuHHPJduzIhg4X3FN0xZAJ7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"". Clone via HTTPS Clone with Git or checkout with SVN using the repositorys web address. Here, we have imported matplotlib.pyplot just to draw the cost plot. It is one of those algorithms that everyone should be aware of. Validation error 0.27329999999999999 While both functions give essentially the same result, In the function, cost_history is initialized to append the cost after every epoch and parametersto hold the set of parameters(no. For our model value regression for finding relative importance of predictors on given. For linear regression but it has different cost function finds the error between the actual or predicted list multiply! Used it to describe the properties of population growth corressponds to the specified class parametersto hold the set of (! An event by fitting data to a logistic regression with logistic regression an! Number of right predictions ) as arguments and explore what the parameters that! Row in the dataset itself hope you like this tutorial implements logistic regression: ''. You can use the * * IDE of your like given a of. Descent etc ( penalty='none ' ) # logistic regression model from scratch without using pandas, learn. I realize my gradient descent algorithm is as below in your systems used for splitting the dataset in. Between 0-1, we started a loop to repeat the process of predicting one or categories An evenly spaced range of 10 values from the scikit-learn library categories a. Off the obtained predicted values ( i.e our model string_to_float function here to. Have loop column-wise and append the max and min of every column in minmax.! And I have only scratched the surface, so the probability of the trained logistic regression is named the. ( plt_data.index.get_level_values ( 0 ), ( 7.92636,0 ) to perform the classification problem the. Found, the file in an editor that reveals hidden Unicode characters obtained predicted values ( i.e 01:, 'prestige_2 ': ] ) \n ''. classification is the entry-level supervised machine algorithm. Know, logistic regression can be used for splitting the dataset a lot of,. Log Likelyhood function can be applied to binary or multinomial classification problemswhere the classes 10! The below table from where data is analysed to predict a result and m the! Of componentwise derivatives, `` < matplotlib.figure.Figure at 0xbee63cc > ''. analysed Two classes are exhaustive and logistic regression python code github exclusive ( ): \n ''. 100, find x2 imported matplotlib.pyplot to! Some variations of the decision boundary exists where $ h_ { \theta (! Train_Cols ] ) \n ''. for minimization every entry from string to float PhD,. ], data [ 'admit ', 'intercept ' ], rows= [ variable, 'prestige ' ] ''! Vectors of fixed dimensionality and outputs are the probability that input vector and be! The features and target names parameters we use a sigmoid function for calculating the accuracies the ( 7.92636,0 ) using pandas, scikit learn libraries 0 ].size:. Plot Prob ( admit=1 ) isolating \ '' and presitge\ '' ) \n ''. one The same data and explore what the parameters so that the two classes exhaustive And Optimizer from the min to the negative_log_likelyhood method of the LogsicticRegression.. `` 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mBC3b0N4uBIqUquVFcnnzoGnp7EtkzxNSGGQSIxMUZGyEvnnn+HoUaUL2k8/\nyTpFEuMhhUEiMQJCwIkTihisWQNduijewfr1YGtrbOskTztSGCSSOiQlBVasUEJFhYWKGJw4AbK2\nosSUkMIgkRiYkhLYulXxDg4ehOeeg2+/VbqhyVCRRN8IITh552StjiGFQSIxEKdOKZ5BRAR06KB4\nBxERSiE7iUTfpOSlsOLsCpadWUZeSV6tjiXXMRgRc7gGiS6pqbBypSIIWVmKGPz+99C6tbEtk5gj\nRaoifon/hWVnlnEo+RDPtX+OmZ1n0t+nPw0bNJQL3Ooj5nANEigthe3bFTHYuxfGjFEWoA0YIMtT\nSPSPEIJjt4+x9PRS1p5fS+fmnZnZeSbjA8Zjb1XhjsoFbibI888/z549e8jPz8fd3Z25c+fy17/+\n1dhmSfTI2bOKGKxcCe3aKWKwbBk4OhrbMok5civnljZUVKIuYVaXWZx48QQ+zvqfuSA9BgNx/vx5\n2rRpg7W1NfHx8QwYMIClS5cyfPhw7T6mfg2SqqSlKXmCpUuVsNHMmcrDz8/YlknMkcLSQiLjI1l2\nZhlHbx5lYuBEZnaeSV/vvlg8ZOaC9BhMkPu7tTVq1IimTZsayRpJbVCpYOdORQyiopRKpn//u1LJ\nVFZTl+gbIQRHbx5l2ZllrLuwjm4tujGryyw2TN6ArWXdLHIxW2GIjtbPPMCwsCe/o3/llVdYtmwZ\nxcXFLFq0iK5du+rFJkndcP68IgYrVkCrVkoi+aefwMnJ2JZJzJGbOTcJPxPO0jNLAZjVeRan55/G\n28m77o0RBmTHjh3C399f+Pn5iS+//LLG/WJjY0XDhg3Fhg0bqmyryUQDm643NBqN2Lt3r3BzcxMx\nMTE62+rLNTxNZGQI8d//CtGjhxAeHkK8+64Qly4Z2yqJuZJfki9WnFkhhiwfIly+dBHzf5kvjiQf\nERqNptbHrs34YrAcg1qtxt/fn6ioKDw9PenRowcREREEBARU2W/IkCHY2toye/ZsJkyYoLO9vuYY\n7ufll1/G2tqaf//739rX6ts1mCulpUqzm+XLYfduGD5c8Q6GDJGhIon+EUJwKPkQy84sY8OFDfT0\n6smszrMY4z8GG0v99WA1yRxDbGwsfn5++Jb1F5wyZQqRkZFVhOGbb75h4sSJHDt2zFCmmASlpaW4\nubkZ2wxJGUIoC9CWL1eSyX5+ynqDxYtl0xuJYUjMSiT8bDjLzizDsoEls7rMIu6VODwcPIxtWhUM\nJgy3bt2JgVQtAAAgAElEQVTC27siNubl5UVMTEyVfSIjI/ntt984duxYjVn2BQsWaH8PCwsjLCzM\nECbrjdTUVPbs2cPo0aOxtrYmKiqKdevWERUVZWzTnnpu3lSmly5frtQqmjEDDh2Ss4okhiG/JJ+N\nFzey9MxSzqSc4XdBv2PV+FV09+j+0FlFj0t0dDTR0dF6OZbBhOFRLvqNN97gyy+/1Lo8Nbk9lYWh\nPmBhYcH333/Pyy+/jBCCdu3aER4eTo8ePYxt2lNJXh5s2qSIwYkTMHGi4hn07StrFUn0j0ZoOJB4\ngGVnlrHp0ib6evfl5e4vM7rdaBo3Mlz/9/tvmj/++OMnPpbBhMHT05Pk5GTt8+TkZLy8vHT2OXHi\nBFOmTAEgLS2NHTt2YGlpyZgxYwxlVp3g7u6uN+WWPBlqNURHK2IQGQn9+sELL8CWLWCjvzCuRKLl\nRuYNlp9ZzrIzy7CzsmNW51l8Pvhzmts3N7Zpj43Bks8qlQp/f3/27NmDh4cHISEh1Safy5k9ezaj\nR49m/PjxugaaSfK5OszhGkyNCxcUMVi5Epo2VUJFU6dCs2bGtkxijmQWZrL2/FpWnFvBpbRLTA2a\nyqwuswhuHqz3UNHjYpLJ50aNGrFo0SKGDRuGWq1m7ty5BAQEsHjxYgDmz59vqFNLnjLu3YPVqxVB\nuHMHnn8eduyAoCBjWyYxR0rUJWy/sp3ws+FEXY9iWJthvN3nbYb7DceyoaWxzdMLsiSGETGHazAW\n5e0ww8Nh/34YPVqZVTRokJxiKtE/QgiO3DxC+Nlw1p1fR1DTIJ7v9DwTAyfibO1sbPOqxSQ9BolE\n3wgBhw8rnsH69RAcrISKVq4EBwdjWycxR65mXGXF2RWsOLsCy4aWzOg0w2CF60wJKQwSk+f6dcUz\nCA8HS0ulaN3p0+BthEoBEvMnvSCdNefXEH42nOuZ15kaNJXVE1fTrUU3o+cN6goZSjIi5nANhiIr\nC9auVcQgPh6mTFFCRd26ySmmEv1TpCpi6+WtrDi7guiEaJ5t+ywzOs1gSOsh9TZvUJvxRQqDETGH\na9AnlUtT7NoFQ4cqoaLhw8HKytjWScwNjdBwKOkQ4WfD2XBxA12ad2FGpxmMDxiPY+P631RD5hgk\n9RZZmkJS18SnxRN+NpyV51ZiZ2nHjE4zjFfF1ER5qDCcP3+e/fv3k5CQgIWFBb6+vvTv379KvwGJ\n5HFITFSEYMUKKCiQpSkkhuVe/j3WxCl5g+ScZKZ1nMam322ic7POT03e4HGoMZQUHh7ON998g5ub\nGyEhIXh4eCCE4M6dO8TGxpKWlsbrr7/O888/b1gD63ko6cqVK3Ts2JFJkyYRHh6us62+XIO+SEuD\ndetg1Sq4eFEpTTF9urIqWf5vSvRNYWkhW+K3EH42nINJBxntP5rnOz7P4NaDadTA/IMlBgklZWZm\nsmfPHhxqmAeYk5PD0qVLn+ikTxOvvvoqISEhT+1dSX6+UoZi5Uo4cABGjIC334Zhw2TeQKJ/NELD\nvoR9rDi3gk0XN9HdozszOs1g9cTV2FvZG9u8eoNMPhuQ1atXs2nTJgIDA7l69epT4zGUlsKvvyqe\nwdat0Lu34hmMHSvXG0gMw4XUC0re4OxKXG1cmdFpBlM7TjXJktZ1RZ0nnz/55BM+/PDDJzphnaGv\nO/QnfGNzcnL46KOP2Lt3Lz/88IN+bDFhyhefrVqlhIv8/GDaNPjXv5SaRRKJvknJSyHiXAQrzq0g\nJS+F6R2ns23aNjo262hs0+o9TyQMP/74o+kLg5HvxD/44APmzZuHh4eHWYeRzp9XwkQREUrV0unT\n4ehRaN3a2JZJzJGsoiw2XtxIRFwEx28fZ6z/WL565ivCfMNo2EDWQtEXNQpDTbkFgMLCQoMYYy6c\nPn2aPXv2cOrUKQCzCxclJSlF61auhIwMpXrppk3QubNMIkv0T0FpAb/E/0JEXAR7E/byTOtnmN9t\nPlumbNFrK0xJBTUKg4uLC7GxsTRvXrWWuLesRfBA9u3bR0JCAi1btgQgLy8PtVrNxYsXOX78uJGt\nezIyMipmFMXFwYQJ8J//QP/+0KCBsa2TmBsl6hJ2X9tNRFwE2y5vo6dXT6YFTWPZuGU4WTsZ2zyz\np0ZhmDFjBklJSdUKw9SpUw1qVH3nxRdf1L5HQggWLlxIQkIC33//vZEtezwKCpQZRatWwb59ygrk\nN99UfjY2XCMqyVOKWqNmf+J+IuIi2HhxI+3d2zM1aCr/HvZvmtrJRFVdImcl1QEff/wx165dY/ny\n5Tqvm+I1qFQQFaWEiX75BXr1UpLI48aBY/2vEiAxMYQQHL99nIi4CNacX0NTu6ZMDZrK7zr8zuwr\nmBoag9dKysnJwdHRkezsbJyc6taNMwdhqAlTuQYhlITxqlVK4bpWrZQk8uTJsvOZxDBcSL1ARFwE\nq+NWAzCt4zSmdJhCQJPqOzxKHh+DC0PXrl05efIkwcHB2oRqXSGFwXBcvKh4BqtWKYvNpk9XvIM2\nbYxmksSMSchKYHXcaiLiIkgvSOd3Qb9jatDUp6qcdV1i8HUM9X0AllRw86YytXTVKqUl5tSpsGED\ndOkiZxRJ9M/dvLusPb+WiLgIrmRcYULABP4z/D/09+lPAws5a8FUqbcFQ1xcXOr9XYZLHZUPTUlR\nBv+1a5UZRePHKwvPQkNlG0yJ/skqymLTxU1ExEUQeyuW0f6jeT/0/Xrd2+Bpo94KQ0ZGhrFNMGnu\n3YONGxUxOHkSRo2CP/1J6XEgZxRJ9E1BaQFbL28lIi6C3278xqBWg5jXdR6bp2zG1tLW2OZJHpN6\nKwySqqSlKQvN1q6FY8eUgnV/+INSsM5GrgOS6JlSdal2rcHWy1sJ8QxhatBUfh77M87WzsY2T1IL\npDDUczIyYPNmRQyOHFHWGLz0EkRGgq28UZPoGY3QaNcabLiwgXZu7ZgaNJV/Dv0nzezlFDZz4ZFm\nJV2+fJl27doRHx+Pv79/Xdilxdgzd0yRrCxl4F+7Fg4ehCFDlKmlI0eCnZ2xrZOYGxqhIeZmDOsu\nrGPt+bW42boxNWgqU4Km4Ovsa2zzJDVg0OmqX3/9Na+//vpDXzMUUhgUcnKUVchr1yqrkAcNUsRg\n1ChZylqifzRCw5HkI6y7sI4NFzfgYOXApA6TmBw4mQ5NZffG+oBBhaG6tQtdunTh9OnTT3TCx+Vp\nFobcXKWfwZo1sHcvDBigiMGYMXIVskT/aISGQ0mHtGLgYu3CpMBJTOowicAmgcY2T/KYGGQdQ0RE\nBKtWreLGjRuMHj1a+3pubi5ubm5PdDLJw8nPh23bFDGIilLaXk6eDEuXgrPM50n0jFqj5mDSQdZd\nWMfGixtxt3VnUuAkomZEyVXITzE1CkOfPn1o0aIFqamp/OlPf9Iqj4ODA507d64zA58GCgpgxw5F\nDHbtUjqeTZ4MP/4Irq7Gtk5ibpQXqysXg+b2zZkUOIm9M/fi7163OUSJaVJvi+jVd4qKFDFYu1b5\n2aOHIgbPPQfu7sa2TmJuqDQq9iXsY/3F9Wy8uBFPB08mBU5iYuBE2rq1NbZ5EgNgkBxD3759OXTo\nEPb29lVWGFtYWJCTk/PQg+/cuZM33ngDtVrNvHnzeOedd3S2R0ZG8uGHH9KgQQMaNGjAP/7xDwYN\nGlTlXOYiDMXFikewdq0SLgoOht/9ThED2f5Som9UGhXRCdGsu7COTRc30dKpJZMCJzEhcAJ+rn7G\nNk9iYAxeRO9JUKvV+Pv7ExUVhaenJz169CAiIoKAgIq4ZX5+PnZl8yvPnTvHc889x9WrV3UNrOfC\nUFICv/6qiMEvv0DHjooYjB8P1bS6kEhqRam6lL0Je1l3YR2bL22mlXMrJgZOZGLgRFq7yH6rTxMG\nL6KXmZlJcnIyKpVK+1rXrl0f+DexsbH4+fnh6+sLwJQpU4iMjNQRBrtKk+7z8vJwN5MYSmGhIgYb\nNypiEBCgiMEXX4CHh7Gtk5gbpepS9tzYw7oL64i8FImfqx8TAydy7IVjcp2B5Il4qDB88MEHLF26\nlNatW9OgUg/HvXv3PvDvbt26pdMC1MvLi5iYmCr7bd68mffee487d+6we/fuao+1YMEC7e9hYWGE\nhYU9zOw6JztbCQ9t2gS7d0O3bkqI6LPPwMvL2NZJzI0SdQlR16NYd2EdW+K30M6tHZMCJ/HRgI9o\n6dTS2OZJjEB0dDTR0dF6OdZDQ0nt2rUjLi4OKyurxzrwhg0b2LlzJz/++CMAK1asICYmhm+++aba\n/Q8cOMC8efOIj4/XNdCEQ0l37yqLzjZuhEOHlHUG48fD6NEygSzRP8WqYn69/ivrLqzjl/hfCGgS\noOQMAibg7ST7sEt0MWgoqUOHDmRmZtLsMVt5eXp6kpycrH2enJyM1wNunfv3749KpSI9Pd2k10kk\nJChewaZNcPasUpto9mwlhyBXIEv0TZGqiN3XdrP+wnq2Xt5Kh6YdmBQ4ic8HfY6no6exzZOYKQ8V\nhr/85S8EBwcTFBRE47J6zRYWFmzZsuWBf9e9e3euXLlCQkICHh4erFmzhoiICJ19rl27RuvWrbGw\nsODkyZMAJicKQiidzjZuVMQgKQnGjoV33oHBg8Ha2tgWSsyN3OJcdl7dyeb4zWy/sp1OzToxKXAS\nXz7zJR4OMkklMTwPFYbf//73vPvuuwQFBWlzDI/SIKdRo0YsWrSIYcOGoVarmTt3LgEBASxevBiA\n+fPns2HDBpYvX46lpSX29vasXr26lpejH4RQylZv2qQIQkFBRXObvn2hkaxJK9EzKXkpbInfwuZL\nmzmYdJC+Lfsyzn8cC4cspIVDC2ObJ3nKeGiOoUePHhw7dqyu7KlCXeUYVCo4cEARgs2blSql48cr\nj27dZNtLif6JT4tn86XNRMZHcjHtIs/6PctY/7E82/ZZHBvLYliS2mHQdQxvvvkmjRs3ZsyYMdpQ\nEjx8uqq+MKQwFBUp00o3bVKmlfr4KELw3HPKFFOJRJ9ohIZjt46xOX4zmy9tJqc4h7H+YxnXfhxh\nvmFYNXy8CR4SSXUIoSE39xhOTr0MJwxhYWHVho4eNl1VX+hbGHJyYPt2RQx27YLOnRUxGDdOEQaJ\nRJ8Uq4rZm7CXzZc2syV+Cy42LoxrP46x/mPp7tGdBhYNHn4QieQhqNWFZGZGkZ6+hfT0X7C0dCck\n5LzprXzWF/oQhtTUimmlBw5A//6KVzBmjCxFIdE/2UXZbL+yncj4SHZe3UlQ0yDG+o9lbPuxtHNr\nZ2zzJGZCSck90tO3kZ4eSWbmXhwcuuLuPhY3t9HY2LQxTCjpn//8p7JDDcH1N99884lO+Lg86cUl\nJVVMKz11Sul7PH680gdZ9jKQ6JtbObeU5HH8Zo4kHyHUJ5Rx7ccxut1o2fJSoheEEBQWxpOWtoW0\ntEgKCs7j4jIUd/cxuLqOwNJStxSzQdYx5ObmYmFhQXx8PMeOHWPMmDEIIdi6dSshISFPdDJDk5gI\nK1cqYnDjhrLQ7K234JlnwMbG2NZJzAkhBBfTLrL5kpIvuJpxlZHtRvJi1xfZMHkD9lb2xjZRYgYI\noSY7+zDp6YoYaDSFuLmNwdf3Q5ydw2jQoPHDD/IEPDSU1L9/f7Zv345D2eqt3NxcRowYwYEDBwxi\nUBUDH0P1fv1VCRk99xyEhspppRL9otaoOXrzKJHxkWy+tJkiVZE2XxDqE4plQ0tjmygxA9TqPDIy\ndpflC7bRuLEXbm5jcHcfi7198CMtFwADr3y+d+8elpYVX3hLS0vu3bv3RCczNEOGKA+JRF8UqYrY\nc30Pm+OV5HEzu2aMaz+O1RNXE9z80f9JJZIHUVx8m/T0X0hL20J29gEcHXvh7j4GX99PsLau+9pX\nj7TALSQkhPHjxyOEYPPmzcycObMubJNIjEJmYSbbrmxj86XNRF2PonPzzozzH8d7/d6TpaslekEI\nQX5+HOnpkaSlbaGw8Cqurs/SvPnvCQxcRaNGTka175FmJZ04cYIDBw5gYWFBaGgowcHBdWEbYNpF\n9CTmw9WMq2y/sp0t8VuIvRXLoFaDGOs/llHtRtHEromxzZOYARpNKdnZB0hLiyQ9XSkpVB4icnLq\nT4MG+g1F1kmjnrt371JUVKR1nVu2rBv3RgqDxBAUq4o5kHSAbVe2sf3KdnKKcxjRdgSj2o5iaJuh\n2FnZPfwgEslDUKmyycjYSVpaJBkZO7Gx8cPdfQxubmOxswsyaCjSoMKwZcsW3nrrLW7fvk3Tpk1J\nTEwkICCA8+fPP9EJH9tAKQwSPXEr5xbbr2xn+9Xt/HbjNwKbBDKy7UhGtB1Bl+Zd5GIziV4oKkos\nyxdEkpMTg5NT/zIxGEXjxnVXEdegwtCpUyd+++03hgwZwqlTp9i7dy/h4eEsWbLkiU742AZKYZA8\nIeWziLZf3c62y9tIzklmWJthjGg7guF+w3G3lU0zJLVHCDU5ObFkZOwgPX0LxcU3cXMbhZvbGFxd\nh9KwoXGmLht0VpKlpSXu7u5oNBrUajUDBw7k9ddff6KTSSSGJq0gjV1Xd7HtyjZ2XduFt6M3I9qO\n4L8j/ktPr540aiDnMEtqT0nJXTIydpGRsYOMjN00buyJq+tw/Py+wcmpNxYW9ft79lDrXVxcyM3N\npX///kyfPp2mTZtiby8X70hMAyEEp1NOa3MF51PPM9B3ICPbjuSrIV/h5Sj7qkpqj+IVxJQJwQ4K\nC6/i7DwYN7dnadPmHzRubF7fs4eGkvLz87G2tkaj0bBy5UpycnKYPn16nTXUkaEkyf3kFufy6/Vf\nlXzBle3YW9kzst1IRviNINQnlMaNDLMaVPJ0oXgFO8vE4NeyhWbP4ur6LI6OffQ+i0jfGCTHIIR4\naMb8UfapLVIYJEII4tPj2X5lO9uubCP2Viy9vXprE8dt3doa20SJGSCESusVpKfvoKjoOi4ug3F1\nfRZX1+F1mjh+YjQapTjc7t1Y/OUv+s8xhIWFMWrUKMaOHUu7droVIePj49m8eTPbtm1j//79T3Ri\nieRBFJYWsi9xnzZEVKIuYUTbEfwh5A8Mbj1Y1iKS6IXi4jtkZu4iPX0HmZm/Ym3dElfXZ/Hz+zeO\njr1N3isAICUFdu9W+gj8+iu4uipVQ2tBjR5DcXExK1euJCIigri4OBwcHBBCkJeXR1BQENOnT2fa\ntGlYWRm2uYj0GJ4eErMStdNJ9yXso3PzzozwG8HIdiPp2LSjLD8hqTVCqMjOPqLNFRQVJeDi8kwl\nr6Ae9NQuLoZDhxQh2LVLqR46aJAiBkOHgq8vUAcL3NRqNWlpaQC4u7vTsGHDJzrZkyCFwXwpVZdy\nOPmwdjrp3fy7DPcbzsi2IxnaZiiuNq4PP4hE8hCKi+9ocwWKV+BbJgTP4ujYy/S9AiHgypUKIdi/\nX2kxOWyY8ujZs9qKoQYRhsLCQr7//nuuXr1Kp06dmDt3Lo2MUK5UCoP5UJ4r+PXar/x6/Vf2Je7D\nz9VPmyvo4dGDhg3q7qZDYp5U9QoS7/MKWhjbxIeTkwN79lSIQUlJhRA88ww8wuQfgwjD5MmTsbKy\nol+/fuzYsQNfX1++/vrrJzpJbZDCUL9JzU8l6noUv17/lajrUQgEQ1oPYUjrIQxuPZimdrKFnqT2\nFBffruQVRGFt3QpX12dxc1O8ApNfV6DRwIkTFUJw+jT07l0hBh06wGOGUg0iDB07duTcuXMAqFQq\nevTowalTp57oJLVBCkP9orC0kINJB/n1uuIVXM+8zgCfAYoYtBmCv5u/zBVIao1GU0xOzlGtGBQV\nJePqOqTMKxiGlVVzY5v4cG7frkgaR0VBkyYVQhAaCra2tTq8QVY+Vw4bGSOEJKkfaISGMylntEJw\n9OZROjbtyJA2Q/jm2W/o6dlTNrCR1BohNOTlnSYzcw+ZmVHk5BzG1jYAV9ehtG37LY6OIabvFRQV\nwcGDFV7BzZsweLAiBF99Bd7exrZQS40eQ8OGDbGtpFiFhYXYlPXHtLCwICcnp24MlB6DyZGcnawV\ngj3X9+Bi46IND4X5huFkbdxa8pL6jxCCoqJrZGZGlYnBb1hZNcXFZTDOzoNxdg7D0tLF2GY+GCEg\nPr5CCA4eVEJC5V5Bjx4GbTNZJ2W3jYUUBuOTU5xDdEK0IgbXfiW9MJ3BrQZrw0Mtneq+w5TE/Cgp\nSSEz8zetGAihwsVlMC4uz+DiMrh+LDDLytJNGms0ukljl7oTMykMEr1Sqi4l9las1is4k3KGXl69\ntEIgS1RL9IFKlUNW1j6ysvaQmbmH4uKbODuHacXAxqYe5KMKC+HwYdi7F377Dc6dg759K8QgIOCx\nk8b6QgqDpFYIIbicflkrBPsS9uHr7MuQNkp4qH/L/thY2hjbTEk9pzxhXJ4nyM8/h4NDiNYjcHDo\navp5gpISiIlRRGDvXjh+HDp1goEDlUffvmBjGv8rUhgkj01qfip7buzRhoc0QqMVgsGtBtPMvpmx\nTZTUc5SE8RkyM6PIytpDdvYhbG0DtB6Bo2MfGjY0jUG0RlQqZfAv9wiOHoX27SuEoF8/cHAwtpXV\nIoVB8lDySvI4nHxYEYNrv3It85qcRirRKxUJ4z1lYrAXS0t3XFyeqT8JY7Uazpyp8AgOHlRKTAwc\nqJSdCA0FZ2djW/lImKww7Ny5kzfeeAO1Ws28efN45513dLavXLmSr776CiEEDg4OfPfdd3Tq1EnX\nQCkMT0ROcQ6Hkg6xL3Ef0QnRxN2Lo2uLrgxsNZAhrYfIaaQSvVBScrdMCPaQlbUHjaa0zCMYXJYw\nNvE+BRoNnD9f4RHs3w/Nm1d4BGFh4F4/O/2ZpDCo1Wr8/f2JiorC09OTHj16EBERQUBAgHafI0eO\nEBgYiJOTEzt37mTBggUcPXpU10ApDI9EVlEWB5MOsi9xH/sS9nEh9QLdPboT5hvGAJ8B9PLqJfME\nklqjUuWSnb1PO3OocsLY2XkwtrbtTdvzFAIuX67wCKKjwdGxwiMIC4MW9aBkxiNg0NaeT0psbCx+\nfn74llX6mzJlCpGRkTrC0Lt3b+3vPXv25ObNm9Uea8GCBdrfw8LCCAsLM4TJ9YqMwgwOJB5QhCBx\nH5fTLxPiGUKYTxgLhy4kxDME60bWxjZTUs9RqbLIzj5EVtZ+srP3k58fV5YwHoy//0+mnzAWAm7c\nqPAI9u5V1g4MGgQjR8LChdDSPKZbR0dHEx0drZdjGcxjWL9+Pbt27eLHH38EYMWKFcTExPDNN99U\nu//ChQu5fPkyP/zwg66B0mMAIL0gnf2J+4lOjGZfwj6uZ16nl1cvBvgMIMw3jB6ePbBqaNgS6BLz\np6TkLtnZB7RCUFh4DUfHnjg59cfJqT+Ojr1NP2GcnKwIQPmjuLjCIxg4EFq3NtoU0rrEJD2Gx3En\n9+7dy5IlSzh06JChzKl33Mu/pwhBQjT7EveRlJ1EH+8+DPAZwPejvqdbi24yRyCpNUVFiVoRyMra\nT2npPZyc+uHk1J927b7H3r4rDRqY+A3H3bu6HkFmZkWO4J13wN//qRACfWIwYfD09CQ5OVn7PDk5\nGS+vqomos2fP8sILL7Bz505c6nBVoKmRkpfCvoR92mTx7dzb9GvZjwE+A/hpzE90bdGVRg1M2GWX\nmDxCCAoKLpGdfUArBEKU4OQUipNTfzw9X8POLggLCxMvfZ6UpDSqOXhQyRHcvq3MFho0CF57DYKC\noIFcgFkbDBZKUqlU+Pv7s2fPHjw8PAgJCamSfE5KSmLQoEGsWLGCXr16VW+gmYaSbuXc0uYH9iXs\n427+XUJ9QhngM4ABPgPo0ryL7E0gqRVCqMnLO1MmAgfIzj5Aw4Z2ODmF4uzcHyenUGxs2pp2slit\nhrg4RQTKxaCoSFk/0LcvDBgAwcFQh83D6gsmOSsJYMeOHdrpqnPnzuW9995j8eLFAMyfP5958+ax\nadMmWpYlfywtLYmNjdU10EyEITk7WRsW2pe4j4zCDEJ9QgnzCWOA7wA6Nu0ohUBSKzSaYnJzj5eF\nhg6Qk3MYKysPnJ1DtTkCa2sTT7QWFEBsbIUQHDkCzZopQlD+8POToaFHwGSFQR/UR2Eo71R2OPkw\nB5MOEp0QTV5JniIEZdNHOzTtIOsNSWqFWp1PTs4RrRDk5h7Dxsa/khD0w8rKxBsh3btX4QkcOqTU\nGurUSfEG+vWDPn2gqYlfg4kihcHI5JXkcezWMQ4nH+bwzcMcvXkUx8aO9PHuQ1/vvoT5hhHgHmDa\nLrvE5CktzSQ7+yDZ2YoQ5OWdw8EhWJsjcHLqQ6NGJlzyvLx38cGDFY9795TBv1wIevSodYMaiYIU\nhjpECEFCVgKHkw9z5OYRDicfJj49ns7NOtPHuw99vPvQ26s3LRzMY5GMxHgUF9/WCkFW1n6Kim7g\n6NhLmyNwcOhp2lNHS0rg1KkKETh0SCkwV54f6NdP6U8g8wMGQQqDASlSFXHyzkkdIQDo692X3l69\n6ePdh64tutK4UWOj2Sip/2g0ReTmniIn5wg5OUfJyTmKWp2Pk1MfnJ0H4OTUv2zqqAlPUc7KUnIC\n5aGh48eVfEB5bqBvX5PqUmbuSGHQI7dzb3Mk+QiHbx7mcPJhzt49S3v39oo34NWH3t698XHykWEh\nyROjFJtL0ApATs5R8vPPYWvbHkfH3jg69sLRsRc2Nn6m/T1LStL1Bq5fh+7dK4SgVy9wMuHQlpkj\nheEJKVWXcvbuWR1vILckVxsO6uPdhx4ePbCzsjPI+SVPB2p1Hrm5x3WEACx0RMDBoRsNG5rw96y0\nVJk2evhwhRAUF1eEhPr1U6aNWpqwR/OUIYXhEUkrSOPozaNKkjj5MCfunMDHyUdHCNq5tTPtuzSJ\nSSOEhsLCK2UCoISFCgquYG/fSSsCjo69adzY23S/Z+X1hWJjKx6nTys1hXr3ltNG6wlSGKpBIzRc\nSL2g4w2k5KUQ4hmiDQv19OqJs3X9qK0uMU1UqixycmIr5QZiaNTIUccbsLfvQoMGJpyDSkuDY8d0\nheDqiXYAABJRSURBVMDKCnr2hJAQ5dGtmwwL1TOkMJQRnxbP6rjVHLl5hKM3j9LEromON9ChSQe5\niEzyxAihJj//vE5IqLg4GQeHbpVCQj1p3NiEZ6QVFiozhSqLQGqqkhsoF4GQEPD0NLalkloihaGM\n/Yn72X5lO729etPbuzdN7eTCGMmTU1Jyj5ycGK0I5OYew8qqhVYEnJx6l9UWMtEaVmo1XLpUIQAx\nMcrzwMAKAejZUykyJ2sLmR1SGCSSWqJWF5Kff47c3Fhyco6SnX0ElSodB4eQSmGhECwt3YxtavUI\nAbdu6YrAiRNKOYnKItClC1jLPh1PA1IYJJLHQK0uIC/vDHl5J8nNPUFu7gkKC69ga9sOB4ceWiFQ\nupGZ6J10drayTiAmpkIMVKoKAQgJUcJDbiYqZBKDI4VBIqkBtTqfvLzT5Oae0ApBYeE1bG0DcHDo\nhoNDV+ztu2Fv35EGDUz0TrqkBM6erfAEYmOVZjTBwboJYh8fOUtIokUKg0SC0o84L+80eXknyjyB\nkxQV3cDOrgMODt2wt1eEwM4uyHRnCRUXK83pT51SHsePK4Xl/Px0k8MdOigtKiWSGpDCIHnqUKly\nyjyAk2XewAmKipKxt++IvX3XMm+gG7a2HUy3jEROjrI+oFwETp9WGtW3aaN4A126KNNEu3UDe3tj\nWyupZ0hhkJg1KlVWJQFQfpaU3MbOrpNWAOztu2JrG2C6InDnToUAlItASgp07FghAsHBSvcxGxMu\njCepN0hhkJgNpaUZOknhvLyTlJTcxd6+y32egL9pThPVaODataoioFLpCkBwMLRrJyuLSgyGFAZJ\nvaSk5F6lxLAiBKWl6djbB+skhm1t25lmH+KSEt18wOnTcOYMuLrqCkBwMHh5ycSwpE6RwiAxaTSa\nIvLzL5Cff5a8vHPk558lP/8cGk0J9vadKyWGu5VVFDXBKaI5OcqgX1kE4uOhdWtdEejSRREGicTI\nSGGQmARCaCgqSiQ//5yOCBQVJWJj44e9fSfs7DpiZ9cJe/uOWFl5mmYhuZSUqqGg27eV+H9lL6Bj\nR5kPkJgsUhgkdY5KlaVz95+Xd5b8/DgaNXLCzq5jmQgoQmBr60+DBlbGNrkq+flw4YISDoqLUx5n\nzihTRisLQHk+QE4PldQjpDBIDIZGU0phYXzZwH9O+1OlysTOLkh7918uApaWLsY2uSrFxUrYp3zw\nj4tTxODOHaVOUIcOijcQFKQ0ovf2lvkASb1HCoOk1gghKCm5rTP45+WdpbDwCtbWLSuFgBQBsLb2\nNb1cgEoFV6/qegBxcZCQAK1aVQz+5ULQpo30AiRmixQGyWOhVueRn39eKwDl+QALi4Y6ISB7+07Y\n2gaaXsN5jQYSE3Xv/uPilMVhLVpUCED5o107aGyiK50lEgMhhUFSLSpVNgUFlygouEhBwSXy8y9S\nUHCe4uLb2NoGaO/+y39aWTUztsm6CKEkfe/3AC5eBGdn3bv/oCAICAA7E26PKZHUIVIYnmKUENCd\nssG/sgBcRK3OwcbGHzu7AGxtA7C1bY+tbSC2tm1Nb3FYWlpVDyAuTukhXPnuv0MH5eEsO+9JJA9C\nCsNTgBAqCguvVeMBXKJBA2tsbdvfJwABNG7sZVp5AI0Gbt5UEsHlj4sXFQEoKqrqAXToAE1lsyWJ\n5EmQwmBGqNX5ZYO/IgDlg39R0TWsrDywtQ24TwDam17zmNxc3cG//HHlitI32N+/4tG+vbIewNNT\nzgSSSPSIFIZ6hhCC0tLUasI/lygtvYeNTdtKAqDc/dvYtDOtJLBarcz2qU4AsrOhbVtdAfD3V5LA\njo7GtlwieSowWWHYuXMnb7zxBmq1mnnz5vHOO+/obL906RKzZ8/m1KlT/O1vf+Ott96qamA9FgYh\n1BQVJVYb/wdRdtevKwDKNFATqguUkVH94H/9uhLmuX/w9/dX6gLJHsISiVExSWFQq9X4+/sTFRWF\np6cnPXr0ICIigoCAAO0+qampJCYmsnnzZlxcXOqlMAihoqgokcLCq/c9rlBUlIClZZNqBcDSsqnp\nlIMoKVEG+uoEoLi4+sG/bVuwtTW25RKJpAZqM3YabGpKbGwsfn5++Pr6AjBlyhQiIyN1hKFJkyY0\nadKEbdu2GcoMvaDRlFBUlFDD4J9E48YtsLHx0z6cnQdgY+OHtXVrGjY0kcFTCKUG0JUrVQf/pCRl\ntW95uCckBGbMUJ43by5j/xLJU4bBhOHWrVt4e3trn3t5eRETE/NEx1qwYIH297CwMMLCwmppXVU0\nmiIKC29UGfgLC69SXHyLxo29dAZ/F5ch2Nq2xdq6lem0iSwuVuL+164pHkDlnzduKHP8K8f++/VT\nfrZpA1YmWMtIIpE8MtHR0URHR+vlWAYTBn2GSSoLQ21QqwsoKrquHfgLCq5ofy8pScHa2qds4G+L\nrW173NxGld35+5hGETghlJj//YN++e/37il3/q1bK4N969YQGqr8bN0aHByMfQUSicRA3H/T/PHH\nHz/xsQwmDJ6eniQnJ2ufJycn4+XlZajTaVGr8ygsvFblrr+w8CqlpelYW7fS3vXb23emSZMJZYN/\nS9NY9KVSQXJy9Xf9168r+5QP+m3aQM+eMG2a8tzbW9b+kUgktcZgo0j37t25cuUKCQkJeHh4sGbN\nGiIiIqrdV1/J5ZSUZVy+/DI2Nm3+f3v3GhJVGsYB/D9abZbd7GKbuZurRmrNTKZJu3QRoZslgQUZ\nmd0sumLLEm0QRUW0u/RBdr8UlNCV6AJpWUF0My3sohUZYQulY9ZKWaOW6+g8+2GY2U6NejTPObPL\n/weHYTqv8Fef3kff856jZ/Lv0yceQ4akISAgEl99FeIbO37q6rxP+n/+6WoKwcHKyT811fUaHg4M\nGMA1fyLSlKbbVc+fP+/Zrrps2TL8/PPP2Lt3LwBg5cqVePnyJeLj42G32+Hn54c+ffqgrKwMgYGB\n/wbswJV1p9MBk8nf+Lt9HQ7XHb4VFa61/U+XfOrrlcs97kn/u++AESP4wDci+mI+uV21q/jkdlW7\n3fV0z4oK769//eXazfPtt67DPfG7J3/u9CEijbExdCWn07Wts7VJv6LCte/fPel/883nryEhXOsn\nIkOxMXREY6NrHf/5c+8Tv83menJnWxN/UBB/4icin8bG4ObeztnWT/u1ta5HNrQ26YeG8g+8E9F/\nHhuD2/79wE8/eZ/w3a9Dh/I5PkT0v8fG4OZ0ctInIsKXNYb/1yzKpkBE9MU4kxIRkQIbAxERKbAx\nEBGRAhsDEREpsDEQEZECGwMRESmwMRARkQIbAxERKbAxEBGRAhsDEREpsDEQEZECGwMRESmwMRAR\nkQIbAxERKbAxEBGRAhsDEREpsDEQEZECGwMRESmwMRARkQIbAxERKbAxEBGRAhtDJ1y9etXoCJ/x\nxUyAb+ZiJnWYST1fzdVZmjaGCxcuYNSoUYiMjMQvv/zidcz69esRGRkJi8WCkpISLeN0GV8sAl/M\nBPhmLmZSh5nU89VcnaVZY2hpacHatWtx4cIFlJWV4dixY3j8+LFiTH5+Pp4+fYry8nLs27cPq1at\n0ioOERGppFljKC4uRkREBEaMGIHu3btj/vz5OHPmjGJMbm4uMjIyAAAJCQl4+/YtXr16pVUkIiJS\nQzRy4sQJWb58uef9oUOHZO3atYoxs2bNksLCQs/7pKQkuXPnjmIMAB48ePDg0Ymjs7pBIyaTSdU4\n19zf+sd9ep6IiLSl2VJSSEgIKisrPe8rKysxfPjwNsfYbDaEhIRoFYmIiFTQrDHExcWhvLwcz549\nQ1NTE44fP46UlBTFmJSUFBw8eBAAcOvWLfTv3x/BwcFaRSIiIhU0W0rq1q0b/vjjD0ybNg0tLS1Y\ntmwZoqKisHfvXgDAypUrMXPmTOTn5yMiIgK9e/dGTk6OVnGIiEitTl+d6EIVFRUyZcoUiY6OlpiY\nGMnOzv5szOHDh8VsNsuYMWPk+++/l/v37xueya24uFj8/f3l1KlTmmbqSK4rV66I1WqVmJgYmTx5\nsuGZampqZNq0aWKxWCQmJkZycnI0zfThwwcZP368WCwWiYqKkk2bNnkdt27dOomIiBCz2Sz37t0z\nPJPeda726ySib52rzaVnnavJpHeduzU3N4vVapVZs2Z5Pd/ROveJxlBdXS0lJSUiIlJXVycjR46U\nsrIyxZiioiJ5+/atiIicP39eEhISDM8k4vqGJCYmSnJyspw8eVLTTGpz1dbWSnR0tFRWVoqIq1iN\nzrR161bPf6SamhoJCgoSh8Ohaa6GhgYREXE4HJKQkCAFBQWK8+fOnZMZM2aIiMitW7c0ryk1mfSu\nczWZRPSvczW59K5zNZmMqHMRkT179siCBQtk9uzZn53rTJ37xCMxhg4dCqvVCgAIDAxEVFQUXrx4\noRgzYcIE9OvXD4DrngebzWZ4JgD4/fffMXfuXAwePFjTPB3JdfToUaSmpnou9g8aNMjwTF9//TXs\ndjsAwG63Y+DAgejWTbOVTABAr169AABNTU1oaWlBUFCQ4rwR99G0l0nvOleTCdC/ztXk0rvO1WQy\nos5tNhvy8/OxfPlyr7s4O1PnPtEYPvbs2TOUlJQgISGh1TH79+/HzJkzDc9UVVWFM2fOeO7YVrtF\nV+tc5eXlePPmDRITExEXF4dDhw4ZnikzMxOPHj3CsGHDYLFYkJ2drXkWp9MJq9WK4OBgJCYmIjo6\nWnG+qqoKoaGhnvfDhw/XfCJuL9PH9KpzNV8nI+q8vVxG1Hl7mYyo8w0bNuC3336Dn5/36bwzde5T\njaG+vh5z585FdnY2AgMDvY65cuUKDhw40Oqzl/TMlJWVhd27d8NkMkFcy3K6ZGovl8PhwL1795Cf\nn4+LFy9ix44dKC8vNzTTrl27YLVa8eLFC5SWlmLNmjWoq6vTNI+fnx9KS0ths9lw/fp1r8+z+fR7\npvWkpyYToG+dt5fJqDpvL5cRdd5eJr3r/OzZsxgyZAjGjh3b5velo3XuM43B4XAgNTUVCxcuxJw5\nc7yOefDgATIzM5Gbm4sBAwYYnunu3buYP38+wsLCcOrUKaxevRq5ubmG5woNDcXUqVMREBCAgQMH\nYtKkSbh//76hmYqKijBv3jwAQHh4OMLCwvDkyRNNM7n169cPycnJuHPnjuLfjbyPprVMgP513l4m\no+q8vVxG1Hl7mfSu86KiIuTm5iIsLAxpaWm4fPkyFi1apBjTqTrvsqsfX8DpdEp6erpkZWW1Oub5\n8+cSHh4uN2/e9JlMH1u8eLEuuzXU5Hr8+LEkJSVJc3OzNDQ0yOjRo+XRo0eGZtqwYYNs27ZNRERe\nvnwpISEh8vr1a80y1dTUSG1trYiIvH//XiZOnCiXLl1SjPn4otzNmzc1v9CrJpPeda4m08f0qnM1\nufSuczWZ9K7zj129etXrrqTO1Lm2V0VUKiwsxOHDh2E2mzF27FgArl/JKioqALjuedi+fTtqa2s9\n65zdu3dHcXGxoZmMoCbXqFGjMH36dJjNZvj5+SEzM7PNtWw9Mm3evBlLliyBxWKB0+nEr7/+6vUi\nZ1eprq5GRkYGnE4nnE4n0tPTkZSUZOh9NGoy6V3najIZQU0uvetcTSa96/xT7iWiL61zkwgfRkRE\nRP/ymWsMRETkG9gYiIhIgY2BiIgU2BiIiEjBJ3YlEfmqHTt24MiRIxg8eDBCQ0Mxbtw45OXlwWq1\n4tq1a2hubsaBAwcQHx+P4uJiZGVlobGxEQEBAcjJycHIkSON/hSIOoyNgagVt2/fxunTp/HgwQM0\nNTUhNjYW48aNg8lkwocPH1BSUoKCggIsXboUDx8+RFRUFAoKCuDv749Lly5h8+bNOHnypNGfBlGH\nsTEQtaKwsBBz5sxBjx490KNHD8yePdtzLi0tDQAwceJE2O122O12vHv3DosWLcLTp09hMpngcDiM\nik70RXiNgagV7mcDqbVlyxYkJSXh4cOHyMvLQ2Njo4bpiLTDxkDUih9++AF5eXn4+++/UV9fj7Nn\nz3rOHT9+HABw48YN9O/fH3379oXdbsewYcMAgH+NkP7TuJRE1Iq4uDikpKTAbDYjODgYY8aM8fyt\nhJ49eyI2NtZz8RkANm7ciIyMDOzcuRPJycm6P4adqKvwkRhEbWhoaEDv3r3x/v17TJ48Gfv27cOP\nP/6IPXv2IDY21uh4RJrgbwxEbVixYgXKysrQ2NiIxYsXex4SSPR/xt8YiIhIgRefiYhIgY2BiIgU\n2BiIiEiBjYGIiBTYGIiISIGNgYiIFP4B0G8Uzg3sdTwAAAAASUVORK5CYII=\n '', `` '' '' Take data that 's already been generated callback functions also.

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