Diabetes_Test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import datasets, linear_model\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import torch.optim as optim\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper functions\n",
    "def predict(X, W):\n",
    "    b = np.ones((X.shape[0], 1))\n",
    "    X = np.concatenate([X, b], axis = 1)\n",
    "    YPred = X.dot(W)\n",
    "    return YPred\n",
    "\n",
    "def plot(X, Y, YPredict, label):\n",
    "    plt.figure(figsize=(9, 6))\n",
    "    plt.title(label)\n",
    "    plt.xlabel(\"X\")\n",
    "    plt.ylabel(\"Y\")\n",
    "    plt.scatter(X, Y,  color='black', label = 'Actual')\n",
    "    plt.plot(X, YPredict, color='blue', linewidth=3, label = 'Prediction')\n",
    "    plt.xticks(())\n",
    "    plt.yticks(())\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "def plotCompare(X, Y, YP_ADMM, YP_SK, label):\n",
    "    plt.figure(figsize=(9, 6))\n",
    "    plt.title(label)\n",
    "    plt.xlabel(\"Data\")\n",
    "    plt.ylabel(\"Label\")\n",
    "    plt.scatter(X, Y,  color='black', label = 'Actual Data')\n",
    "    plt.plot(X, YP_ADMM, color='blue', linewidth=2, label = 'Prediction by ADMM')\n",
    "    plt.plot(X, YP_SK, color='red', linewidth=2, label = 'Prediction by SK-LEARN')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "def plotObjective(X, title):\n",
    "    plt.plot(X, label = 'Objective Function')\n",
    "    plt.title(title)\n",
    "    plt.xlabel('Iterations')\n",
    "    plt.ylabel('Objective Value')\n",
    "    plt.show()\n",
    "    \n",
    "def runAlgo(X, Y, algo, steps = 5, parallel = False):\n",
    "\n",
    "    admm = optim.ADMM([X, Y, 0.01, 1], algo, parallel = parallel)\n",
    "\n",
    "    #print(admm.getLoss())\n",
    "    start = time.time()\n",
    "    oVals = []\n",
    "    for i in xrange(0, steps):\n",
    "        s = admm.step()\n",
    "        #oVals.append(s)\n",
    "        #print('Objective Value:', s)\n",
    "    end = time.time()\n",
    "    admmTime = end - start\n",
    "    #print('Time taken', end - start)\n",
    "\n",
    "    #print('Weights: ',admm.getWeights())\n",
    "    W = admm.getParams()\n",
    "    return W, oVals, admmTime\n",
    "\n",
    "def lassoObjective(W, X, b, alpha):\n",
    "    return 0.5 * np.linalg.norm(W.dot(X) - b)**2 + alpha *  np.linalg.norm(X, 1)\n",
    "\n",
    "def ridgeObjective(W, X, b, alpha):\n",
    "    return 0.5 * np.linalg.norm(W.dot(X) - b)**2 + alpha *  np.linalg.norm(X, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# num_iterations = 5\n",
    "# N = 10000\n",
    "# D = 10\n",
    "\n",
    "# X = np.random.randn(N, D)\n",
    "# Y = np.random.randn(N, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the diabetes dataset\n",
    "diabetes = datasets.load_diabetes()\n",
    "\n",
    "# Use only one feature\n",
    "diabetes_X = diabetes.data[:, np.newaxis, 2]\n",
    "\n",
    "# Split the data into training/testing sets\n",
    "diabetes_X_train = diabetes_X[:-20]\n",
    "diabetes_X_test = diabetes_X[-20:]\n",
    "\n",
    "# Split the targets into training/testing sets\n",
    "diabetes_y_train = diabetes.target[:-20]\n",
    "diabetes_y_test = diabetes.target[-20:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(diabetes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# diabetes_X_train = X[:-20]\n",
    "# diabetes_X_test = X[-20:]\n",
    "# diabetes_y_train = Y[:-20]\n",
    "# diabetes_y_test = Y[-20:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Time taken', 0.014724969863891602)\n",
      "Mean squared error: 2553.13\n",
      "Variance score: 0.47\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run Lasso\n",
    "\n",
    "alpha = 0.01\n",
    "lasso = linear_model.LassoCV(alphas=[alpha]) #, verbose = 1000)\n",
    "\n",
    "start = time.time()\n",
    "lfit = lasso.fit(diabetes_X_train, diabetes_y_train)\n",
    "end = time.time()\n",
    "diabetes_y_pred = lfit.predict(diabetes_X_test)\n",
    "\n",
    "skTime = end- start\n",
    "print('Time taken', end - start)\n",
    "# The coefficients\n",
    "#print('Coefficients: \\n', regr.coef_)\n",
    "# The mean squared error\n",
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(diabetes_y_test, diabetes_y_pred))\n",
    "# Explained variance score: 1 is perfect prediction\n",
    "print('Variance score: %.2f' % r2_score(diabetes_y_test, diabetes_y_pred))\n",
    "\n",
    "# Plot outputs\n",
    "plot(diabetes_X_test, diabetes_y_test, diabetes_y_pred, \"Sk-learn Lasso Prediction\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "426062.6364269132\n",
      "363433.0389455327\n",
      "460500.51505585643\n"
     ]
    }
   ],
   "source": [
    "sk_w = np.array([944.21977, 872.06439, 981.638155])\n",
    "# sk_b = np.array([2, 2, 2])\n",
    "sk_b = np.array([2.3283064365386963e-10, -2.3283064365386963e-10, 0.0])\n",
    "for w, b in zip(sk_w, sk_b):\n",
    "    print(lassoObjective(np.array(w), diabetes_X_train, np.array(b), alpha))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([965.75198079]), 0.0, 1243.5769, 2)\n",
      "('Time taken', 0.016798973083496094)\n"
     ]
    }
   ],
   "source": [
    "# Create linear regression object\n",
    "\n",
    "alpha = 0.01\n",
    "#lasso = linear_model.LassoCV(alphas=[alpha], verbose = 10)\n",
    "\n",
    "#start = time.time()\n",
    "lfit = linear_model.lasso_path(diabetes_X_train, diabetes_y_train, alphas=[alpha], verbose = 10)\n",
    "#end = time.time()\n",
    "#diabetes_y_pred = lfit.predict(diabetes_X_test)\n",
    "\n",
    "skTime = end- start\n",
    "print('Time taken', end - start)\n",
    "# The coefficients\n",
    "#print('Coefficients: \\n', regr.coef_)\n",
    "# The mean squared error\n",
    "#print(\"Mean squared error: %.2f\"\n",
    "#      % mean_squared_error(diabetes_y_test, diabetes_y_pred))\n",
    "# Explained variance score: 1 is perfect prediction\n",
    "#print('Variance score: %.2f' % r2_score(diabetes_y_test, diabetes_y_pred))\n",
    "\n",
    "# Plot outputs\n",
    "#plot(diabetes_X_test, diabetes_y_test, diabetes_y_pred, \"Sk-learn Lasso Prediction\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['normalize',\n",
       " 'alphas',\n",
       " 'n_jobs',\n",
       " 'verbose',\n",
       " 'fit_intercept',\n",
       " 'selection',\n",
       " 'positive',\n",
       " 'n_alphas',\n",
       " 'max_iter',\n",
       " 'eps',\n",
       " 'precompute',\n",
       " 'random_state',\n",
       " 'tol',\n",
       " 'copy_X',\n",
       " 'cv']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso.get_params().keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Objective Value:', 983261.463164293)\n",
      "('Objective Value:', 897668.6203126734)\n",
      "('Objective Value:', 868007.8413340384)\n",
      "('Objective Value:', 856511.319478526)\n",
      "('Objective Value:', 851592.7846565667)\n",
      "('Objective Value:', 849327.9824983742)\n",
      "('Objective Value:', 848234.7107673415)\n",
      "('Objective Value:', 847692.2511785958)\n",
      "('Objective Value:', 847418.9980950987)\n",
      "('Objective Value:', 847280.2432261793)\n",
      "('Objective Value:', 847209.4891767501)\n",
      "('Objective Value:', 847173.3318901183)\n",
      "('Objective Value:', 847154.8338783506)\n",
      "('Objective Value:', 847145.3648987642)\n",
      "('Objective Value:', 847140.5163812094)\n",
      "('Objective Value:', 847138.0333613176)\n",
      "('Objective Value:', 847136.7616604941)\n",
      "('Objective Value:', 847136.1103217956)\n",
      "('Objective Value:', 847135.7767129098)\n",
      "('Objective Value:', 847135.6058401458)\n",
      "('Weights: ', array([[780.45964671],\n",
      "       [151.14402181]]))\n",
      "Mean squared error: 2715.74\n",
      "Variance score: 0.44\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = diabetes_X_train\n",
    "Y = diabetes_y_train\n",
    "\n",
    "b = np.ones((X.shape[0], 1))\n",
    "X = np.concatenate([X, b], axis = 1)\n",
    "Y = Y.reshape(-1, 1)\n",
    "\n",
    "W, oVals = runAlgo(X, Y, \"Lasso\", 20)\n",
    "\n",
    "print('Weights: ', W)\n",
    "\n",
    "# Comparison with Scikit - LR\n",
    "admm_predict = predict(diabetes_X_test, W)\n",
    "\n",
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(diabetes_y_test, admm_predict))\n",
    "# Explained variance score: 1 is perfect prediction\n",
    "print('Variance score: %.2f' % r2_score(diabetes_y_test, admm_predict))\n",
    "\n",
    "plot(diabetes_X_test, diabetes_y_test, admm_predict, \"ADMM-Lasso Prediction\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HH32U0aNHly3PzMxk06ZNYS/pfPzxx3Tq1IlOnTqVLTvhhBNYtGgRa9asAWD48OFlt19/8cUXzJgxg5YtW5bbT5s2bbjzzjsrLQ913HHHccEFF9ToO4mINBU5OTmVLs2npKSQk5OToB7VD4t2yaGhy8jIcHl5eeWWLV68mO7duyeoR03TpEmTePPNN3nxxRcT3ZW40c+ZiCSj3NxcsrOzKSwsJDU1lZycHDIzMxPdrTJmNsc5l1GTbRr1GBmJvRtvvJGpU6fyzjvvJLorIiJShczMzAYVXOqDgozUyeOPP57oLoiISBOmMTIiIiKStBRkREREJGnFLMiYWSszm21mX5rZ12b2V395FzP73My+MbOJZtbSX76n/3m5vz4Qq76JiIhI4xDLiswO4BTnXC8gHTjTzPoB9wOjnXNdgU3AVX77q4BNzrlDgdF+OxEREZGIYhZknGer/7GF/3LAKUDplMzjgNIHf5zvf8ZfP9Bi8YjbOGjevDnp6en06NGDQYMGVXokdE18+OGHnHPOOQBMnjyZkSNHRmxbcfbr1atXc/HFF9f62KECgQAbNmyo1bZLly7lpJNOIj09ne7du5OVlQWU/24Ad999N2eccQY7duwot/3y5ctJT0+vtN9LL72ULl26kJ6eTnp6OgMGDCi3/uyzz6607O6776ZTp06kp6dzxBFH8Morr5Tb38EHH1w28/batWs59NBDa/WdRUQkPmI6RsbMmpvZfOAHYBqwAvjJOVf6DPxVQOmT0joBKwH89T8D7WLZv1gpnaJg4cKFtGzZkqeffrrceuccJSUlNd7veeedx4gRIyKurxhkOnbsyKRJkyK2j5ebbrqJW2+9lfnz57N48WJuvPHGSm1ycnL47LPPeOONN2r0oL7Ro0czf/585s+fzyeffFK2fOPGjSxYsIB169ZVevz28OHDmT9/Pq+99hrXXHMNxcXFZevMjHHjxiEiIskhpkHGOVfsnEsHOgN9gXBPECt9Il+46kulp/WZWZaZ5ZlZ3vr16+uvszEyYMAAli9fTn5+Pt27d+f666+nT58+rFy5kvfee49jjz2WPn36MGjQILZu9QpY7777Lt26deP4448vm/UZYOzYsQwbNgyAdevWceGFF9KrVy969erFzJkzGTFiBCtWrCA9PZ3hw4eTn59fNvfR9u3bufLKK+nZsye9e/dmxowZZfv83e9+x5lnnknXrl254447In6XUaNG0bdvX/r27cvy5cvZsmULXbp0YdeuXYA3h1IgECj7XGrNmjV07ty57HPpbOClHnroId555x2mTJnCXnvtVdtTXc6kSZO44IILGDx4cNnUChV169aNFi1a8PPPP5ctu/XWW3nwwQfLhRsREWm44nLXknPuJ+BDoB+wn5mVPr+mM7Daf78KOBjAX78v8GOYfY1xzmU45zI6dOgQ/cBmsXlV0+7du5k6dWrZL+6lS5cydOhQ5s2bR+vWrbnvvvuYPn06c+fOJSMjg4cffpjt27dzzTXXMGXKFD755BPWrl0bdt833XQTJ554Il9++SVz587lyCOPZOTIkRxyyCHMnz+fUaNGlWv/xBNPALBgwQLGjx/P5Zdfzvbt2wFvosqJEyeyYMECJk6cyMqVK8Mec5999mH27NkMGzaMW265hTZt2nDSSSfx9ttvAzBhwgQuuugiWrRoUW67W2+9lVNOOYWzzjqL0aNH89NPP5Wt++yzz3j66aeZOnUqe++9d7XPbei+Sy8tDR06tGx56SzYQ4YMiThf1RdffEGPHj1o27Zt2bIuXbpwzDHH8PLLL9e4LyIiEn+xvGupg5nt57/fCzgVWAzMAEoHblwOvOm/n+x/xl//gUvS+RO2bdtGeno6GRkZpKamctVV3njmtLQ0+vXrB8CsWbNYtGgR/fv3Jz09nXHjxlFQUMCSJUvo0qULXbt2xcy49NJLwx7jgw8+4LrrrgO8MTn77rtv1D59+umnXHbZZYBXiUhLS2PZsmWANwHlvvvuS6tWrTjiiCMoKCgIu4/SuZSGDBnCf//7XwCuvvpqnn/+eQCef/75sBNHXnnllSxevJhBgwbx4Ycf0q9fv7JxMIceeijOOd57772o/Y8k9NLSCy+8AMD3339PYWEh/fr144gjjqC4uJglS5aUbTNq1CgOO+wwjjvuOO69995K+7zrrru4//77a3X5T0RE4iuWFZlfATPM7CvgC2Cac+4t4E7gj2a2HG8MzLN++2eBdv7yPwKRB4NUl3OxeVWhdIzM/Pnzefzxx8smOmzdunVI1xynnXZaWbtFixbx7LPeqYjFGOdomTB0TErz5s3ZvXt32Hah/Sp9379/f/Lz8/noo48oLi4uu5RVUceOHfnDH/7Am2++yR577MHChQsBOPDAA3nnnXe49dZbyy53zZw5s6zKUpupDyZOnMjGjRvp0qULgUCAwsJCJkyYULZ++PDhLFu2jNzcXIYOHVppcHG3bt044ogjyl3WExGRhimWdy195Zzr7Zw7yjnXwzn3N3/5t865vs65Q51zg5xzO/zl2/3Ph/rrv41V3xqCfv368dlnn7F8+XIAioqKWLZsGd26deO7775jxYoVABEviwwcOJCnnnoKgOLiYjZv3kybNm3YsmVL2PYnnHACubm5ACxbtozCwkIOP/zwGvW5dKzJxIkTOfbYY8uWDx06lCFDhoStxoA35qd03MzatWvZuHFjudmwDzvsMF577TUuvfRS5s+fz3HHHVcW8H7729/WqI/gnbPp06eTn59Pfn4+s2fPDnseL7nkEnr27MlLL71UaV12dnaly3MiItLw6Mm+CdKhQwfGjh3LkCFDOOqoo+jXrx9LliyhVatWjBkzhrPPPpvjjz+etLS0sNs/+uijzJgxg549e3L00Ufz9ddf065dO/r370+PHj0YPnx4ufbXX389xcXF9OzZk8GDBzN27Nga3R0EsGPHDo455hgeffRRRo8eXbY8MzOTTZs2lV16qui9996jR48e9OrVizPOOINRo0Zx0EEHlWvzm9/8hueff57zzjuvLMSFWrRoEZ07dy57vf7660D5MTLp6emsWLGCtWvXkpERnDy1a9eu7LnnnsyZM6fSfv/yl7/w0EMPVapYlQ6iFhGRhs2SdBgKABkZGS4vL6/cssWLF9O9e7iboyRWJk2axJtvvsmLL76Y6K7EjX7ORETqn5nNcc5lVN0ySLNfS53ceOONTJ06tVZjWUREROpKQUbq5PHHH090F0REpAnTGBkRERFJWgoyIiIikrQUZERERCRpKciIiIhI0lKQiZHXX38dMyv3aPxIxo4dy+rVq6tsF8mHH37IOeecE3b5vvvuS+/evTn88MM54YQTeOutt6q1v5kzZ9a6PyIiIvGiIBMj48eP5/jjjy/3aPxI6hpkohkwYADz5s1j6dKlPPbYYwwbNoz3338/6jYKMiIikiyafJDJzc0lEAjQrFkzAoFA2WP862Lr1q189tlnPPvss5WCzAMPPEDPnj3p1asXI0aMYNKkSeTl5ZGZmUl6ejrbtm0jEAiwYcMGAPLy8jjppJMAmD17Nscddxy9e/fmuOOOY+nSpTXqV3p6On/5y1/4xz/+AcCUKVM45phj6N27N6eeeirr1q0jPz+fp59+mtGjR5Oens4nn3wStp2IiEhD0KSfI5Obm0tWVhZFRUUAFBQUkJWVBXiP3a+tN954gzPPPJPDDjuMtm3bMnfuXPr06cPUqVN54403+Pzzz0lJSeHHH3+kbdu2/OMf/+DBBx8s91j9cLp168bHH3/MHnvswfTp07nrrrv497//XaO+9enTp2wOoeOPP55Zs2ZhZvzrX//igQce4KGHHuLaa69l77335vbbbwdg06ZNYduJiIgkWpMOMtnZ2WUhplRRURHZ2dl1CjLjx4/nlltuAeD3v/8948ePp0+fPkyfPp0rr7ySlJQUANq2bVuj/f78889cfvnlfPPNN5hZ2USMNRE6JcWqVasYPHgwa9asYefOnXTp0iXsNtVtJyIiEm9N+tJSYWFhjZZXx8aNG/nggw+4+uqrCQQCjBo1iokTJ+KcwzmHmVW5jz322IOSkhIAtm/fXrb8z3/+MyeffDILFy5kypQp5dZV17x588rmCLrxxhsZNmwYCxYs4Jlnnom4v+q2ExERibcmHWRSU1NrtLw6Jk2axNChQykoKCA/P5+VK1fSpUsXPv30U04//XSee+65sirQjz/+CECbNm3YsmVL2T4CgUDZTM2hl45+/vlnOnXqBHgDhGvqq6++4u9//zs33HBDpf2NGzeurF3F/kRqJyIikmhNOsjk5OSUXeYplZKSQk5OTq33OX78eC688MJyyy666CJefvllzjzzTM477zwyMjJIT0/nwQcfBOCKK67g2muvLRvse88993DzzTczYMAAmjdvXrafO+64gz/96U/079+f4uLiavXnk08+Kbv9+oYbbuCxxx5j4MCBANx7770MGjSIAQMG0L59+7Jtzj33XF5//fWywb6R2omIiCSahY6ZSDYZGRkuLy+v3LLFixeXXTqpjtzcXLKzsyksLCQ1NZWcnJw6jY+RpqGmP2ciIlI1M5vjnIt+50sFTXqwL3h3Jym4iIiIJKcmfWlJREREkpuCjIiIiCStRhlkknncjzR8+vkSEWk4Gl2QadWqFRs3btQvG4kJ5xwbN26kVatWie6KiIjQCAf7du7cmVWrVrF+/fpEd0UaqVatWtG5c+dEd0NERGiEQaZFixZ6hL6IiEgT0eguLYmIiEjToSAjIiIiSUtBRkRERJKWgoyIiIgkLQUZERERSVoKMiIiIpK0FGREREQkaSnIiIiISNJSkBEREZGkpSAjIiIiSUtBRkRERJKWgoyIiIgkLQUZERERSVoKMiIiIpK0FGREREQkaSnIiIiISNJSkBEREZGkpSAjIiIiSUtBRkRERJKWgoyIiIgkLQUZERERSVoKMiIiIpK0FGREREQkaSnIiIiISNKKWZAxs4PNbIaZLTazr83sZn/5vWb2vZnN91+/DdnmT2a23MyWmtkZseqbiIiINA57xHDfu4HbnHNzzawNMMfMpvnrRjvnHgxtbGZHAL8HjgQ6AtPN7DDnXHEM+ygiIiJJLGYVGefcGufcXP/9FmAx0CnKJucDE5xzO5xz3wHLgb6x6p+IiIgkv7iMkTGzANAb+NxfNMzMvjKz58xsf39ZJ2BlyGarCBN8zCzLzPLMLG/9+vUx7LWIiIg0dDEPMma2N/Bv4Bbn3GbgKeAQIB1YAzxU2jTM5q7SAufGOOcynHMZHTp0iFGvRUREJBnENMiYWQu8EJPrnHsNwDm3zjlX7JwrAf5J8PLRKuDgkM07A6tj2T8RERFJbrG8a8mAZ4HFzrmHQ5b/KqTZhcBC//1k4PdmtqeZdQG6ArNj1T8RERFJfrG8a6k/cBmwwMzm+8vuAoaYWTreZaN84H8BnHNfm9krwCK8O55u0B1LIiIiEk3Mgoxz7lPCj3t5J8o2OUBOrPokIiIijYue7CsiIiJJS0FGREREkpaCjIiIiCQtBRkRERFJWgoyIiIikrQUZERERCRpKciIiIhI0lKQERERkaSlICMiIiJJS0FGRESkqdi6Ff7v/2DevET3pN7Ecq4lERERaQjefx9OPTX4+YknYNWqxPWnHqkiIyIikiC5ubkEAgGaNWtGIBAgNze3/na+fTtccQWYlQ8xAH/8Y/0dJ8FUkREREUmA3NxcsrKyKCoqAqCgoICsrCwAMjMza7/jmTOhf//w6x54AG6/3Qs3jYQqMiIiIgmQnZ1dFmJKFRUVkZ2dXfOd7doFN9zgBZSKIebgg2HZMnAOhg9vVCEGFGREREQSorCwsEbLw5o7F/baC1q2hCefLL/unnuguBgKC6Fr1zr0tGFTkBEREUmA1NTUGi0vU1wMd9zhVVaOPtobC1OqbVtYsMCrvtx7LzRr/L/mG/83FBERaYBycnJISUkptywlJYWcnJzwG3z9NbRvD3vsAaNGlV93++3e5aWNG6FHjxj1uGFSkBEREUmAzMxMxowZQ1paGmZGWloaY8aMKT/Qt6QE/vpXr/rSo4cXVErtuSfk5XnVl1GjvIDTBJlzLtF9qLWMjAyXl5eX6G6IiIjUr+UeJt1iAAAgAElEQVTLYeBAb3xLRdddB4884o2LaWTMbI5zLqMm2zTN+CYiItLQOAcPPeTdWRTOJ5/A8cfHt09JQEFGREQkkRYuhL59Ydu2yusuuwyeeca7M0nC0hgZERGRRLj6am/sS8+elUPMtGleheaFFxRiqqCKjIiISLysWAGHHhp+3fnnw0svwd57x7dPSU4VGRERkVgrfaJuuBAzapRXfXnjDYWYWlBFRkREJBZWrfKmB4hkzRo46KD49aeRUkVGRKQJiOksy1Le3//uVV/ChZi77/aqL84pxNQTVWRERBq5mM2yLEHr18MBB0Ren58PaWlx605TooqMiEgjV6+zLEt5jz3mVV/ChZhhw4LVlwSHmO3bg8N0zOCssxLanXqlioyISCNXL7MsS9BPP0G7dt70AeEsWQKHHx7fPoWxcyf87W8Qbuqmn36Kf39iRRUZEZFGrtazLEt5zz/vlTP2379yiLn0Um+ZcwkNMbt2BYfo7Lln+BDzyivw3//Gv2+xoiAjItLI1XiWZQn65Rev+mIGf/hD5fXz5nnh5cUXvTYJUFwMDzzgHb5lS/jLXyq3eemlYM4aNCj+fYwlBRkRkUauWrMsS3mTJnnJYO+94ccfy687++xgKkhPT0j3Skrg0Ue9Lu6xB9x5Z+U2zz4b7GZmZsJyVsxp9msRERGAHTvgyCO9p++GM3MmHHtsfPsUwjlv2qXrrovc5skn4X//F5olaZmiNrNfJ+lXFRERqSf/+Y9XrmjVqnKI6d8fdu/2UkQCQoxzMHas171mzcKHmNGjg1287rrkDTG1pbuWRESk6dm9G447Dr74Ivz6adPg1FPj2yefczB+vHc5KJKRI+G227zLSk2dToGIiDQdn3wCJ5wQft2RR8Lcud6I2QR47TW46KLI6++9F+66C1q0iFuXkkITK0CJiEiTU1ICp53mXZ8JF2Jef90rgyxcGPcQM3p08CF14ULMXXd5D7NzDu65RyEmHFVkRESkcZozBzIijBv91a9g+XKocFt6PDzzDFx7beT1t93mPQtmr73i16dkpiAjIiKNh3MweDC8+mr49S++6D28Ls5eeAEuvzzy+i5d4MsvoU2b+PWpsVCQERGR5LdokTfGJZxWrWDdOthnn7h26dVX4ZJLIq8/4ADvalaHDvHrU2OkMTIiIpK8rr3WG2ASLsQ88YRXodm2LW4hZsqU4JiXcCFm773h+++9bq1bpxBTH1SRERGR5PLdd/DrX0dev2GDN61AnEybBqefHnm9GeTng6a2ig1VZEREJDmMGOGlgnAhZuRIr8zhXFxCzMcfBysvkULMN9943SkpUYiJJVVkRESk4Vq9Gjp1irz++++hY8e4dGX2bDjmmOhtFi2C7t3j0h3xqSIjIiINT3a2V+4IF2JGjAhWX2IcYubPD1ZeIoWY0gmwnVOISQRVZEREpGGoauzLt9969ynHWLQboEp9/jn07Rvzrkg1qCIjIiKJdd55kce+XHddsNwRwxCzYkWw8hIpxHz8cbArCjENhyoyIiISf2vXek/XjSQOkzYWFEAgEL3Ne+95sxtIwxWzioyZHWxmM8xssZl9bWY3+8vbmtk0M/vG/3N/f7mZ2WNmttzMvjKzPrHqm4iIJMg113hlj0ghpqTEK3nEKMSsWQOtW3tdiBRiJk8OVl4UYhq+WF5a2g3c5pzrDvQDbjCzI4ARwPvOua7A+/5ngLOArv4rC3gqhn0TEZF4+emn4HWbf/2r8vpJk4LJwazeD79+vfcUXTNvbHBRUeU2EycGu3DuufXeBYmhmAUZ59wa59xc//0WYDHQCTgfGOc3Gwdc4L8/H3jBeWYB+5lZlLqjiIg0aH/6k5ce9t8//Prdu73kEG7a5zratMmruJh5IWb9+sptxo0LhpdoUwlIwxaXwb5mFgB6A58DBzrn1oAXdoAD/GadgJUhm63yl1XcV5aZ5ZlZ3vpwP5kiIpI4RUXB6svIkZXX//OfwfTQvHm9HnrzZm+grhm0beuNganoqaeChx86tF4PLwkS8yBjZnsD/wZucc5tjtY0zDJXaYFzY5xzGc65jA6apEJEpGF46CEvQbRuHX79jh1eerj66no9bFGRdweRGey7r3frdEUPPxwML9deW6+HlwYgpkHGzFrghZhc59xr/uJ1pZeM/D9/8JevAg4O2bwzsDqW/RMRkTrYtStYfbn99srrH3ggmCBatqy3w+7YASefHMxNX3xRuc199wUPfeut9XZoaYBiedeSAc8Ci51zD4esmgxc7r+/HHgzZPlQ/+6lfsDPpZegRESkAXn+eS9FRAonW7d6CWL48Ho75K5dcPbZ3mFbtYIPP6zcJjs7eNNTdna9HVoauFg+R6Y/cBmwwMzm+8vuAkYCr5jZVUAhMMhf9w7wW2A5UARcGcO+iYhITZSURB/TcscdcP/99XrI4mIYMgRefTVym1tu8S4dxeBmJ0kSMQsyzrlPCT/uBWBgmPYOuCFW/RERkVp4/XX43e8ir//xx8h3JdVCcTHsUcVvpmuugaefhmZ6Nr2gJ/uKiEhFzkVPCVdeCc89V2+HKymBvfaCnTsjt8nM9G6XrucbnaQRUJARERHPBx/AwEoF86DVq6NPK1ADznkTW6+JMhKyf3+YMQNatKiXQ0ojpSAjItLU7bUXbN8eft3ZZ8Nbb9XboXr0gK+/jt7ml18gJaXeDimNnK4wiog0RV98Ebx1OlyIWbHCK5vUQ4g54YTgoSKFmJ9+Ct4urRAjNaEgIyLSlBxyiJco+vatvK5Pn2Ca+PWv63SYc88NhpdPPgnfZv364OH23bdOh5MmTEFGRKSxmzUrmCq+/bby+q++8tLEnDl1OszQocHDRCrkrF4dDC/t29fpcCKAxsiIiDRe0R6u0rEjfP99nQ8xbBg88UT0Nt99503gKBILqsiIiDQmixYFyyLhvP66Vw6pQ4jJzg4eIlKIWbIkWHlRiJFYilqRMbMtBCduLP1X4fz3zjm3Twz7JiIi1bXffvDzz5HXu0pz8NbIyJHwpz9FbzN/PvTqVafDiNRY1IqMc66Nc24f/9Um5HMbhRgRkQQrLAyWRsKFmH/+M1gWqYUnngjuPlKImTUreAiFGEmEal9aMrPjzexK/317M+sSu25JPOXm5hIIBGjWrBmBQIDc3NxEd0lEojnqKC9dpKWFX186c+LVV9d41+PGBcPLsGHh23zwQTC8HHNMjQ8hUq+qFWTM7B7gTqA0k7cEXopVpyR+cnNzycrKoqCgAOccBQUFZGVlKcyINDQbNgQTxoIFldf/3/8F00UNZ1CcNCm46yuuCN/m7beDuz/55Jp3XyRWzFWj5OjPXt0bmOuc6+0v+8o5d1SM+xdVRkaGy8vLS2QXkl4gEKCgoKDS8rS0NPLz8+PfIREp75xzvBQRya5dVc+yGMa778JZZ0VvM3EiXHJJjXctUmtmNsc5l1GTbap7aWmnPzu18w/Uuqadk4apsLCwRstFJA62bg2WSMKFmJtvDpZHahBiJk4M7jZSiHnuueCuFWIkGVQ3yLxiZs8A+5nZNcB04J+x65bES2pqao2Wi0gMZWV5KaNNm/Dri4q8hPHII9Xe5TvvBMPL738fvs1jjwXDy5VX1qLfIglUrSDjnHsQmAT8GzgM+Itz7vFYdkziIycnh5QKE5ukpKSQk5OToB6JNDE7dwaTxj/D/P/hoEHBlLHXXtXa5UcfBXd59tnh29x3X3C3N95Yh/6LJFhNHoi3APgE+Nh/L41AZmYmY8aMIS0tDTMjLS2NMWPGkJmZmeiuiTQIMbur7557vKSx557h12/a5KWMV16p1u7y8oLh5aSTwrc56aRgeMnOrlWvRRqc6g72vRr4C/AB3sPwTgT+5px7Lrbdi06DfUUklkrv6isqKipblpKSUvuwX1ICzZtHXn/ccfDZZ9Xe3aJFcOSR0dv06BH+JieRhqg2g32rG2SWAsc55zb6n9sBM51zh9eqp/VEQUZEYqne7up74onID2UBb7qAjh2rtatvv/UmsI7mwANh7drqd0+koahNkKnucPdVwJaQz1uAlTU5kIhIsqnTXX3OQbMoV+87dYJVq6rVj9WrvebRmHkFH5GmJuoYGTP7o5n9Efge+NzM7vUfjjcLWB6PDoqIJEqt7uorvcc5UohZutQLOVWEmI0bg2NeooWY0jEvCjHSVFVVkSm9B3CF/yr1Zmy6IyLScOTk5IQdIxP2rr6qnqZbjcv4W7bAPtWYxa6O8z+KNCpVTRr512iveHVSRCQRqryrb9q0YNkknDlzqpy0cdu24C6ihZjS6ZOaQojR/G9SE9Ud7NsBuAM4EmhVutw5d0rsulY1DfYVkYSoY/Vl1y5o2bLqwxQXRx9m0xjV+51iklRiOUVBLrAE6AL8FcgHvqhR70REktkXX0SvvkyfHrVkUlwc3DxaiNm5M7ibphZiALKzs8uFGICioiKy9eAbiaC6dy21c849a2Y3O+c+Aj4ys49i2TERkQahDtWX6oaRbdugVauq2zUFmv9Naqq6eX+X/+caMzvbzHoDnWPUJxGRxJo7N3r15dVXI1ZfnAtuGi3EbN4c3IVCTJDmf5Oaqm5F5j4z2xe4DXgc2Ae4JWa9EhFJhKqqLyUlEdtUtSnAhg3Qrl0t+tWE1OhOMRGqP2nkW865n51zC51zJzvnjgaqeLakiEgSWLEievXlrruCpZMKbUo3ixZiVq8Obq4QUzXN/yY1Va27lsJuaFbonEtorU93LYlIrVVVQolwy1CHDl5lJZpvv4UuXerQN5EmKpZ3LYU9Xh22FRGJv3XropdQLrss7C1DBxwQ3CxSiFm0KLipQoxI/FR3jEw4TeCxTCLSKAQCEGbyxzLbt8Oee5ZbtNde3uJo8vLg6KPr3j0Rqb2q5lraYmabw7y2ANWbqlVEJBG2bAmWUcKFmBNPDJZQ/BATCAQ3iRRiPvoouJlCjEjiRa3IOOfaRFsvItLgnHIKzJgRef3PP5ebC+DYY2HWrOi7fOEF76qTiDQ8dbm0JCLSMOzYEf1hLAcfDCEPVDvvPJgyJfouH30UbrqpnvonIjHTBB+ALSKNxoAB3nWgSCFmzRrvGlBhIVddFbxsFCnE3Htv8LKRQoxIclBFRkSSS0kJNG8evY3/WIk77oBRo6I3HTYMHn+8nvomInGnioyIJIfLL/fKKZFCzIIF4Bz3j3RllZdIIWbIkGDlRSFGJLmpIiMiDVd1Zl10jvvugz/3jN7slFPg/ffrr2si0jCoIiMiDc8990SfdfHDD3nqSYfhVV/+/OfwzXr2DFZeFGJEGidVZESk4ahi2oAJ4x1DhgAnRW7Tvj2sX1+vvRKRBkwVGRFJrDFjok4bMO+uV7zKC36IiaC08qIQI9K0qCIjIolRRfXFSmdB+X+R29RyzlsRaURUkRGR+Jk8OWr15SYeLau+RFJaeVGIERFQRUZE4qG61ZcIFFpEJBJVZCRp5ObmEggEaNasGYFAgNzc3ER3SaJ5//2o1ZcHGB61+qLKi4hUhyoykhRyc3PJysqiqKgIgIKCArKysgDIzMxMZNekoiqrLyVA+DYlJVVuLiJSTswqMmb2nJn9YGYLQ5bda2bfm9l8//XbkHV/MrPlZrbUzM6IVb8kOWVnZ5eFmFJFRUVkZ2cnqEdSzldfRa2+zOY3IdWX8m127w5WXhRiRKSmYlmRGQv8A3ihwvLRzrkHQxeY2RHA74EjgY7AdDM7zDlXHMP+SRIpDJm5uDrLJU6qSB57sIviMP+Z2bEDWraMVadEpCmJWUXGOfcx8GM1m58PTHDO7XDOfQcsB/rGqm+SfFJTU2u0XGKosDBq9WUrrcuqL6EhZvPmYOVFIUZE6ksiBvsOM7Ov/EtP+/vLOgErQ9qs8peJAJCTk0NKSkq5ZSkpKeTk5CSoR01QaXhJSwu7em+2YDjasLVs2fr1wfDSpk28OioiTUm8g8xTwCFAOrAGeMhfHu5/7cLeq2BmWWaWZ2Z56/UIzyYjMzOTMWPGkJaWhpmRlpbGmDFjNNA31n76KWr1BSirvvzC3gAUFATDS/v28eqoiDRVcb1ryTm3rvS9mf0TeMv/uAo4OKRpZ2B1hH2MAcYAZGRk6MbMJiQzM1PBJV6qGPtyIGv5gQPLPi9aBN27x7pTIiKVxbUiY2a/Cvl4IVB6R9Nk4PdmtqeZdQG6ArPj2TeRJm/HjmpXX37gQGbNClZeFGJEJFFiVpExs/F4c9S2N7NVwD3ASWaWjnfZKB/4XwDn3Ndm9gqwCNgN3KA7lkTi5JBD4NtvI64+jKV8w2EA/Oc/cPrp8eqYiEjVzCXxYzMzMjJcXl5eorshknxKSqB586hNSp+4O2ECDB4cj06JSFNnZnOccxk12UZTFIg0JWef7V06ihBi+vFfDMeTT7iyy0YKMSLSkGmKApGmoBqTNt57L8y6Jz7dERGpL6rIiDRSj9rNUQfvns8b3HC9V3ZxDu5RiBGRJKSKjEgj0q4dbPzRCy43R2hz4QWO11+HN+PXLRGRmFFFRiTJ9eoFt9lDYFYWYip6IPUfZfdKv/56nDsoIhJDqsiIJKGzz4Z33gGH8WW0hv5diXfEpVciIvGnioxIkrj7bm+4yyX2Cm+/4z2aLqw77gg+qU5EpJFTRUakAXvkEbj1Vu+9w7gvWmMFFxFpglSREWlgXngheLNR7q1f+JMCRKi+XHSRqi8i0qSpIiPSAEyZAuedF/wcMbiUKimp8tkwIiJNgSoyIgny0UfByst558EhLI9efTn33GD1RSFGRARQRUYkrubNgz59yi+rsvqycye0aBG7TomIJDFVZERibNmyYOWlNMR04Ifo1Zfu3YPVl5AQk5ubSyAQoFmzZgQCAXJzc+PwDUREGi5VZERi4PvvoXPnysurrL5s3QqtW4ddlZubS1ZWFkVFRQAUFBSQlZUFQGZmZp36KyKSrMwl8d0OGRkZLi8vL9HdEAFg40Zo377y8tZsZSttIm9o5g3erUIgEKCgoKDS8rS0NPLz82vQUxGRhsnM5jjnMmqyjS4tidTB1q3By0YVQ8xiuuGwyCFm3Trv0lE1QgxAYWFhjZaLiDQFurQkUkM7dkCrVuHX7cEudtEy+g5qWQVNTU0NW5FJTU2t1f5ERBoDVWREqqG4OFh5CRdiJnMuDoscYpYvr/OD63JyckhJSSm3LCUlhZycnFrvU0Qk2akiIxKBc9AsatR3uKr+X6Aex6CVDujNzs6msLCQ1NRUcnJyNNBXRJo0DfYVqaCqZ809yk3cxOORG3zxBWTUaKyaiIhQu8G+qsiIUL0H5VZ563QS/0+BiEiy0hgZabJKx7xECzFrbxsV/cF1U6dq0kYRkQRSRUaalOpUXj7/HPoe4zd8KEIjBRcRkQZBFRlp9KpTeZk2DdxLuTgsGGIqev55VV9ERBoYVWSkUapO5WXSJLjoIr/xaVEaKriIiDRYqshIo1GdystVV80iLS3AqRgXXRylcU6Oqi8iIklAFRlJau3be3McRTNyJNx5pzfpYuall0ZvrOAiIpJUVJGRpHPRRcHKS6QQc/vtwYLKnWfMB7OIISZ3771VfRERSVKqyEhSeOABr6oSzdChMG5cyIIqBso0Axxgv/yCno0rIpKcVJGRBuuZZ4KVl0gh5qyzgsWUceOA/PyoA2WmAea/SusvmnRRRCR5KchIgzJ+fDCHXHtt+DZXXRUML++84y8s3ahLl/Ab7dhB7ksvcYEmXRQRaVQUZCTh3normEP+53/Ct7nuumB4+de//IUbNkS/TSkQCG7UsiWZmZmMGTOGtLQ0zIy0tDTGjBmjSRdFRJKYJo2UhPjwQzj55OhtBg+GCRPCrDjmGJg9O/KGP/8M++xTl+6JiEgCaNJIadBmz/YySDSnnuo9ZbeSnTthzz2jb5zEoVxERGpHl5YkphYuDF79iRRi0tODV4AqhZg//9nbOFKI+eEH3TotItKEqSIj9W7FCjj00OhtOnWCVasirCwuhj2i/Gjutx9s2lTr/omISOOhiozUi++/D1ZeIoWYFi2CxZOwIebxx70dRAoxq1d7GyvESAW5ubkEAgGaNWtGIBAgNzc30V0SkThRRUZqbcMG6NCh6nZRr/o4B82i5Om0NO/ZMCIR5ObmkpWVRVFREQAFBQVkZWUB6I40kSZAFRmpkc2bg5WXaCGmtPISMcRMmODtJFKI+eYbb2OFGKlCdnZ2WYgpVVRURHZ2doJ6JCLxpIqMVGnbNqjwHLmwSkqqnBWg6gYatCs1VFhYWKPlItK4qCIjYe3cGay8RAsxxcXBykvEjPLee9EfXDdnju48klqLNMWEpp4QaRoUZKRMcXEwb0R7ZMuuXcHcEW14S9nOzjgj/PrSnfTpU6d+S9OWk5NDiqaeEGmyFGSaOOegTZvoNwuBd3mpNHdEa8fs2dGrLx98oOqL1CtNPSHStGmKgibIOTjkEPjuu+jttmyBvfeu5k419kVEROqoNlMUqCLThPzmN8EbhSKFmB9/DBZMqgwxS5ZEr778+9+qvtQzPS9FRKQ83bXUyD3yCNx6a/Q2P/4I++9fg5126OA9RCYSBZeY0PNSREQqU0WmERozJlgoiRRi1q0LFkuqFWJWrQruNFyIeeYZVV9iTM9LERGpTBWZRuLFF2Ho0OhtVq6Ezp1ruOOjj4a5cyOvLy6u4tYlqS96XoqISGUx+w1kZs+Z2Q9mtjBkWVszm2Zm3/h/7u8vNzN7zMyWm9lXZqb7cath0qRgkSRciDnggPKTQ1c7xGzcGNxxuBDz979X8/5rqU96XoqISGWx/C00FjizwrIRwPvOua7A+/5ngLOArv4rC3gqhv1Kam+9FcwYgwZVXt+6tTeBo3Pe5aPqzIVU5sILvR23bx9+/c6d3o7vvrtWfZe60fNSREQqi1mQcc59DPxYYfH5wDj//TjggpDlLzjPLGA/M/tVrPqWbKZPD4aXc88N3yY/38sYW7dCx4412Pn27cGdv/FG5fXDhgWrLy1a1Kb7Uk/0vBQRkcriPUbmQOfcGgDn3BozO8Bf3glYGdJulb9sTcUdmFkWXtWmUZfUP/0UBgyI3uabb+DQQ2t5gCefhBtuiLz+l1+qN8GSxFVmZqaCi4hIiIYywCHcg0jC3v7inBvjnMtwzmV0qNF1k4Yv9KG4kULM118HCyQ1DjG7dwcPEC7EXHxxcOcKMSIikgTiHWTWlV4y8v/8wV++Cjg4pF1nYHWc+5YQX34ZzBbHHBO+zbx5wXxxxBG1OEjppI2RLg1t3uzt/NVXa7FzERGRxIl3kJkMXO6/vxx4M2T5UP/upX7Az6WXoBqjxYuD4SU9PXybzz8PhpdIbaJyDi64IPKkjbfcEjxAmza1OICIiEjixfL26/HAf4HDzWyVmV0FjAROM7NvgNP8zwDvAN8Cy4F/AtfHql+JsmJFMLxEqqp89FEwW/TtW8sDLVgQnIfgzTcrr9+wwTvA6NG1PEDjocf9i4gkv5gN9nXODYmwamCYtg6IMvI0Oa1cCYEAlJREbvPee3DaaXU8kHPwhz/A2LHh1//rX3DVVXU8SOOix/2LiDQOmv06BoYNgyeeiLx+8uTIt1HXyPLl0LVr5PU1nkSp6QgEAhQUFFRanpaWRn5+fvw7JCIimv26IbjvvvAhZuLE4GWjOoeYP/7Ru3wULsQ8/HANJ1FKTnW9LKTH/YuINA6aa6meXXYZ/PnP3vQAr7wCJ55YTzteuRKiPTdn7Vo48MB6OljDVh+XhVJTU8NWZBrzs4lERBojVWTqWVpacHqAegkxf/2rV30J9wv2nnuC1ZcmEmKgfmaB1uP+RUQaB1VkGqIffogeTAoKoldnGrn6uCxUWrnJzs6msLCQ1NRUcnJyNNBXRCTJqCLTkDzyiFd9CRdibr45WH1pwiEG6m8W6MzMTPLz8ykpKSE/P18hRkQkCakik2g//RR9UO6yZdHvTGqCcnJyyo2RAV0WEhFpqlSRSZTnnvOqL+FCzBVXeA+fcU4hJgzNAi0iIqX0HJl42roVDj7Yq8KE8+WXcNRR8e2TiIhIA6HnyDRUr7ziVV/atKkcYi64IFh9UYgRERGpEY2RiZVdu6BnT1i6NPz6WbMiT3ctIiIi1aKKTH0rKfECTMuWlUPMiSfC7t1e9UUhRkREpM5Ukalv06fDwoXll73/PpxySmL6IyIi0ogpyNS3k06CnBzYuBHuvx/20CkWERGJFf2WrW8tW8JddyW6FyIiIk2CxsiIiIhI0lKQERERkaSlICMiIiJJS0FGREREkpaCjIiIiCQtBRkRERFJWgoyIiIikrQUZERERCRpKcg0crm5uQQCAZo1a0YgECA3NzfRXRIREak3erJvI5abm0tWVhZFRUUAFBQUkJWVBUBmZmYiuyYiIlIvVJFpxLKzs8tCTKmioiKys7MT1CMREZH6pSDTiBUWFtZouYiISLJRkGnEUlNTa7RcREQk2SjINGI5OTmkpKSUW5aSkkJOTk6CeiQSnQani0hNKcg0YpmZmYwZM4a0tDTMjLS0NMaMGaOBvtIglQ5OLygowDlXNjhdYUZEojHnXKL7UGsZGRkuLy8v0d2QRiQ3N5fs7GwKCwtJTU0lJydHwS9OAoEABQUFlZanpaWRn58f/w6JSNyZ2RznXEZNttHt1yI+3a6eWBqcLiK1oUtLIr5Y3K6uMR/Vp8HpIlIbCjIivvquCGjMR81ocLqI1IaCjIivvisCeiBhzWhwuojUhgb7ivgqjpEBryJQ21+mzZo1I9y/LzOjpKSkTn0VEWmMajPYVxUZEV99VwQ05kNEJPYUZERCZGZmkp+fT0lJCfn5+XW6rKExHyIisacgIxIjGvMhIhJ7GiMjIiIiDYLGyDQgen6IiIhI7OnJvjGgJ8SKiIjEhyoyMaDnh4iIiMSHgkwMaM4YERGR+FCQiQE9P0RERCQ+FGRiQM8PERERiQ8FmRjQ80NERETiIyHPkTGzfE2bAAcAAAnvSURBVGALUAzsds5lmFlbYCIQAPKBS5xzm6LtR8+RERERaTyS7TkyJzvn0kM6PAJ43znXFXjf/ywiIiISUUO6tHQ+MM5/Pw64IIF9EZF6oodDikgsJSrIOOA9M5tjZln+sgOdc2sA/D8PSFDf6kT/0RYJKn04ZEFBAc65sodD6t+FiNSXRI2R6eicW21mBwDTgBuByc65/ULabHLO7R9m2ywgCyA1NfXogoKCeHW7ShWf6Ave3Uoa6CtNVSAQINy/0bS0NPLz8+PfIRFp0GozRibhk0aa2b3AVuAa4CTn3Boz+xXwoXPu8GjbNrTBvvqPtkh5zZo1I9x/Y8yMkpKSBPRIRBqypBjsa2atzaxN6XvgdGAhMBm43G92OfBmvPtWV3qir0h5ejikiMRaIsbIHAh8amZfArOBt51z7wIjgdPM7BvgNP9zUtF/tEXK08MhRSTW4h5knHPfOud6+a8jnXM5/vKNzrmBzrmu/p8/xrtvdaX/aIuUp4dDikisJXyMTF00tDEy4A34zc7OprCwkNTUVHJycvQfbRERkWpIysG+ddEQg4yIiIjUTlIM9hURERGpLwoyIiIikrQUZERERCRpKciIiIhI0lKQERERkaSlICMiIiJJS0FGREREkpaCjIiIiCQtBRkRERFJWgoyIiIikrSSeooCM1sPFCS6HyHaAxsS3YkGQOdB5wB0DkrpPOgcgM5BqarOQ5pzrkNNdpjUQaahMbO8ms4R0RjpPOgcgM5BKZ0HnQPQOSgVi/OgS0siIiKStBRkREREJGkpyNSvMYnuQAOh86BzADoHpXQedA5A56BUvZ8HjZERERGRpKWKjIiIiCQtBZlqMLO2ZjbNzL7x/9w/QrvL/TbfmNnl/rI2ZjY/5LXBzB7x111hZutD1l0dz+9VE3U5B/7yD81sach3PcBfvqeZTTSz5Wb2uZkF4vONaqeOPwspZva2mS0xs6/NbGRI+wb/s2BmZ/p/h8vNbESY9RH/Ls3sT/7ypWZ2RnX32dDU9hyY2WlmNsfMFvh/nhKyTdh/Gw1VHc5BwMy2hXzPp0O2Odo/N8vN7DEzs/h9o9qpw3nIrPA7ocTM0v11je1n4QQzm2tmu83s4grrIv2uqPnPgnNOrypewAPACP/9COD+MG3aAt/6f+7vv98/TLs5wAn++yuAfyT6+8XjHAAfAhlhtrkeeNp//3tgYqK/a6zOA5ACnOy3aQl8ApyVDD8LQHNgBfBrv+9fAkdU5+8SOMJvvyfQxd9P8+rssyG96ngOegMd/fc9gO9Dtgn7b6Mhvup4DgLAwgj7nQ0cCxgwtfTfRUN91eU8VGjTE/i2Ef8sBICjgBeAi0OWR/tdUeOfBVVkqud8YJz/fhxwQZg2ZwDTnHM/Ouc2AdOAM0MbmFlX4AC8X2DJpl7OQRX7nQQMbOD/N1br8+CcK3LOzQBwzu0E5gKd49Dn+tAXWO6c+9bv+wS8cxEq0t/l+cAE59wO59x3wHJ/f9XZZ0NS63PgnJvnnFvtL/8aaGVme8al1/WrLj8HYZnZr/j/7d1dyBRVHMfx7y81ojR7USrSUsEwvFDIqJtAorwI9cYiLTSKLrooqOgmLIQIopuo8CIoKAzyosSygoLs5aKSwnwpkbLHgrQHA4swTUH9d3HOyrDO+qz74s5svw8MD3vmnLO7Z/4ze545Z2bg4oj4OtIv2TrK96sq6VU7rADW9/WT9s+YbRARv0bETuBkU9nSY2SnseCOTHuuiIhRgPy37HTf1cBvhdf7clrRClKvvDjDepmknZLekTS9lx+6x3rRBq/n06VPF3boU2Ui4jjwN3B5rz98D/UkFiRdAiwBNheSqxwL7cR3q23Zqmw7dVZJN21QtAzYFhHHCmll+0YVddsGMyVtk/SFpFsK+feNUWfV9CoW7ub0jswwxcLZlu0oFsa3+aZDT9InwJUlq1a3W0VJWvMlYcuBlYXX7wPrI+KYpIdIvfdbGZA+t8G9EbFf0iRgA6kd1o1RZiD6HQuSxpMOXi9HxN6cXKlYKNHOdmqVp1V62T9SVb6Msps2SCulucDzwKLC+lb7RhV10wajwDURcVDSDcC7uT0qdwxoQy9i4SbgSET8UFg/bLFwtmU7qtMdmSwibmu1TtIBSVdFxGg+9fVHSbZ9wMLC62mk8c5GHfOA8RGxtfCeBwv5XyUd4Aamn20QEfvz30OS3iKdllyXy0wH9uUf+MnAn91/m871OxZI91HYExEvFt6zUrFQorGdGqYBv7fI07wtz1R2rDqrpJs2QNI0YCOwKiJGGgXOsG9UUcdtkM9EHwOIiK2SRoDrcv7iEGvV4wC6jIVsOU1nY4YwFs5UdmFT2c/pMBY8tNSeTUBjVvV9wHsleT4GFkm6VOlKlkU5reG0sdD8Q9iwFNjds0/cex23gaTxkqYASJoALAYa/4UU670T+LRp6K1quooFSc+SDmiPFgvUIBa+BWZLminpfNJBeFNTnlbbchOwPF/FMROYTZrQ106dVdJxG+ShxA+BJyPiy0bmMfaNKuqmDaZKGgcgaRYpDvbmIdpDkm7OQymrKN+vqqSb/QFJ5wF3keaVkNOGMRZaKT1GdhwL/Z7ZPAwLaVxzM7An/70spy8AXivke4A0kfFn4P6mOvYCc5rSniNN/NsBfNa8vkpLN20AXES6Wmtn/r4vAePyuguAt3P+b4BZg/6ufWyHaaTTpLuB7Xl5sC6xANwB/ES6UmF1TnsGWDrWtiQNy40AP1K4CqGsziovnbYB8BRwuLDdt5PmV7XcN6q6dNEGywox/h2wpFDnAtKP9giwlnyz1iovXe4PC4EtTfUNYyzcSDrLchg4COwqlC39vewkFnxnXzMzM6stDy2ZmZlZbbkjY2ZmZrXljoyZmZnVljsyZmZmVlvuyJiZmVltuSNjZgMh6US+FfsuSTskPZ7vr3GmMjMk3XOuPqOZVZ87MmY2KP9GxPyImAvcTronxZoxyswA3JExs1N8HxkzGwhJ/0TExMLrWaS7hU4BrgXeJN0kDODhiPhK0hbgeuAX0vOoNpblO0dfwcwqwB0ZMxuI5o5MTvsLmAMcAk5GxFFJs0kP1FwgaSHwREQszvkvLMt3br+JmQ2SHxppZlXSePrtBGCtpPnACdLDBcu0m8/MhpQ7MmZWCXlo6QTpieJrgAPAPNJcvqMtij3WZj4zG1Ke7GtmAydpKvAKsDbSePdkYDQiTgIrgXE56yFgUqFoq3xm9j/hOTJmNhCSTgDfk4aHjpMm7b4QESfzfJcNwBHS08AfiYiJkiYAH5EmBL8BfFCW71x/FzMbHHdkzMzMrLY8tGRmZma15Y6MmZmZ1ZY7MmZmZlZb7siYmZlZbbkjY2ZmZrXljoyZmZnVljsyZmZmVlvuyJiZmVlt/QfjJmvwH6JHvQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotCompare(diabetes_X_test, diabetes_y_test, admm_predict, diabetes_y_pred, \"Prediction from Lasso Solver\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotObjective(oVals, \"Lasso Solver\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 20)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x7fad69e40ad0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "v = np.arange(20)\n",
    "o = np.array(oVals)\n",
    "v2 = np.array([v, o])\n",
    "print(v2.shape)\n",
    "plt.contour(v2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 2559.32\n",
      "Variance score: 0.47\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ridge = linear_model.Ridge(alpha=alpha)\n",
    "ridge.fit(diabetes_X_train, diabetes_y_train)\n",
    "\n",
    "diabetes_y_pred = ridge.predict(diabetes_X_test)\n",
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(diabetes_y_test, diabetes_y_pred))\n",
    "# Explained variance score: 1 is perfect prediction\n",
    "print('Variance score: %.2f' % r2_score(diabetes_y_test, diabetes_y_pred))\n",
    "\n",
    "# Plot outputs\n",
    "plot(diabetes_X_test, diabetes_y_test, diabetes_y_pred, \"Sk-learn Ridge Regression\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Objective Value:', 984938.0554013834)\n",
      "('Objective Value:', 903792.6608557915)\n",
      "('Objective Value:', 875609.5206129656)\n",
      "('Objective Value:', 864663.0861696684)\n",
      "('Objective Value:', 859971.1507052473)\n",
      "('Objective Value:', 857808.3574853624)\n",
      "('Objective Value:', 856763.9762400246)\n",
      "('Objective Value:', 856245.8749175465)\n",
      "('Objective Value:', 855985.0276359753)\n",
      "('Objective Value:', 855852.6662000592)\n",
      "('Objective Value:', 855785.2271855089)\n",
      "('Objective Value:', 855750.793946407)\n",
      "('Objective Value:', 855733.193807941)\n",
      "('Objective Value:', 855724.1926951106)\n",
      "('Objective Value:', 855719.5880063404)\n",
      "('Objective Value:', 855717.232046786)\n",
      "('Objective Value:', 855716.0265449646)\n",
      "('Objective Value:', 855715.4096879029)\n",
      "('Objective Value:', 855715.0940350482)\n",
      "('Objective Value:', 855714.9325102314)\n",
      "Mean squared error: 2746.21\n",
      "Variance score: 0.43\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = diabetes_X_train\n",
    "Y = diabetes_y_train\n",
    "\n",
    "b = np.ones((X.shape[0], 1))\n",
    "X = np.concatenate([X, b], axis = 1)\n",
    "Y = Y.reshape(-1, 1)\n",
    "\n",
    "W, oVals = runAlgo(X, Y, \"Ridge\", 20)\n",
    "\n",
    "# Comparison with Scikit - LR\n",
    "admm_predict = predict(diabetes_X_test, W)\n",
    "\n",
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(diabetes_y_test, admm_predict))\n",
    "# Explained variance score: 1 is perfect prediction\n",
    "print('Variance score: %.2f' % r2_score(diabetes_y_test, admm_predict))\n",
    "\n",
    "plot(diabetes_X_test, diabetes_y_test, admm_predict, \"ADMM-Lasso Prediction\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Su4DnI2JeGvXqMB5YXPC6JZV1V95SpBxgz4hYChARSyWNLRagpAvIekZMmDChhI+0c5ob65m3eFVu7ZuZ9Xc9JpuIeLBT0R8k3VvCcU9IupSsJ7IOmEeWpL4InFjkEBUpi50oL1lEXA5cDjB16tTcLoRpbqjj9keX0tYeVFcVC9vMbGArZRitseAxWtJJwF6lNB4RV0TElIg4FlgBPAtMAuZJehZoAh6StBdZz6S54PAmYEkP5U1FygFeTMNspOdlpcSbl+bGelrbg6WrPUnAzCpTKbPRHgTmpuc/An8HnFdK4x3DV5ImAKeTnZMZGxETI2IiWcKYEhEvALOAc9KstKOA1Wko7A7gREkNaWLAicAdad9aSUelWWjnALemt54FdMxam1FQXhZe18bMKl0pw2iTdqH9memczRbgwnSCvyu3kZ3XWQSsBz6c3n+FpK8CD6R6X4mIFWn7Y8BVQB1we3oAXALcKOk8shlv79mFz7DLOta1WbxyPUczqpyhmJmVRZfJRtLp3R0YETf31HhEHNPD/okF2wFc2EW9K4Eri5TPBQ4qUv4ycEJP8fWVcSPrqBK0ePqzmVWo7no27+xmXwA9JhvLDKquYu8RXmrAzCpXl8kmIj7cl4EMdE0Ndb6w08wqVinX2SDpr8huWTOkoywivpJXUANRc2M9/7twebnDMDMri1KmPv8n8D6ye6SJ7GT7PjnHNeA0N9Tz4ppNbNzSVu5QzMz6XClTn98UEecAKyPin4Gj2f66FytBx4y051f5vI2ZVZ5Skk3HX8f1ksaRTWPelenQFclLDZhZJSvlnM2vJI0EvgU8RDYT7Ue5RjUAbb2w0zPSzKwCdXedzaCI2BIRX01FMyX9ChjilTt33NhhtQyuqXLPxswqUnfDaM9L+pGk4zsWJYuITU40O6eqSjSN9PRnM6tM3SWb15PdE+2fgMWS/k3SkX0T1sDU1Oh1bcysMnWZbCLi5Yj4r4h4G9kKmc8A/ybpKUlf77MIB5DmhjrfjNPMKlIps9GIiCXAFWTLMK8FPpJnUANVc2M9qzdsYc3GLeUOxcysT3WbbCQNkfQeSTcDT5Hd3PILwLi+CG6g2bbUgIfSzKyydDcb7WfA24HfAT8DPhARG/sqsIFoQuO2dW0OHDeizNGYmfWd7q6zuQP4m4hY21fBDHQddxFo8SQBM6sw3d31+eq+DKQSjKgbxLDaGg+jmVnFKWmCgPUOSWn6s2ekmVllcbLpY81e18bMKlApSwzUS/onST9KrydLOjX/0Aam5sZ6WlZuIFsF28ysMpTSs/kJsIlsaQGAFuBruUU0wDU31LFhSxsvrdtc7lDMzPpMKcnmNRHxTbKlBYiIDWSLqNlO2LrUgGekmVkFKSXZbJZUR7a0AJJeQ9bT6ZGkT0taIOkxSZ9JZd+S9CdJ8yXdkpYv6Kj/BUmLJP1Z0kkF5dNS2SJJFxWUT5J0n6SFkm6QNDiV16bXi9L+iaXE2xe8ro2ZVaJSks2XgV8DzZKuBe4C/qGngyQdBJxPdl+1Q4BTJU0GZgMHRcTBwJNkdyRA0gHAWcCBwDTgB5KqJVUD3wdOBg4A3p/qAlwKfDciJgMrgfNS+XlkK4vuB3w31esXmho6rrXxjDQzqxw9JpuIuBM4HfgQcB0wNSLuKaHt1wNzImJ9RLQC9wLvjog702uAOUBT2p4OXJ+WMXgGWESWqI4AFkXE0xGxGbgemJ6WPTgeuCkdfzVwWkFbHdcJ3QSc0LFMQrnVD65h9B6D3bMxs4pSymy0WcCJwD0R8auIeKnEthcAx0oaJakeOAVo7lTnXOD2tD0eWFywryWVdVU+ClhVkLg6yrdrK+1fner3C00NXmrAzCpLKcNo3wGOAR6X9HNJZ0oa0tNBEfEE2fDVbLJhuHlAR2JA0hfT62s7ioo1sxPl3bW1HUkXSJorae7y5cu7+CS9r7mx3ksNmFlFKWUY7d6I+DiwL3A58F5gWSmNR8QVETElIo4FVgALASTNAE4Fzo5tF5y0sH3PpwlY0k35S8BISTWdyrdrK+0fkd6/c3yXR8TUiJg6ZsyYUj5Sr2huqGPJqg20tftaGzOrDCXdQSDNRjsD+ChwONvOh/R03Nj0PIHsvM91kqYBnwfeFRGFY0mzgLPSTLJJwGTgfuABYHKaeTaYbBLBrJSkfgucmY6fAdxa0NaMtH0mcHdBUiu75sZ6WtuDpavduzGzytDdXZ8BkHQDcCTZUNj3yc7dtJfY/kxJo8iu0bkwIlZK+g+gFpidztnPiYiPRsRjkm4EHicbXrswItpSDJ8guwt1NXBlRDyW2v88cL2krwEPky3wRnr+qaRFZD2as0qMt09sW9dmA01p28xsIOsx2ZDdQeADHX/4d0REHFOkbL9u6n8deNWS0xFxG3BbkfKnyWardS7fCLxnR+PtKx1LDSxeuZ6j+8+8BTOz3HS3eNrxEXE3UE821Xi7/RFxc86xDVjjRtZRJWjx9GczqxDd9WzeCtwNvLPIvgCcbHbSoOoq9h5R56UGzKxidLd42sVp8yvpIsut0gl82wVNDXU8556NmVWIUmajzSxSdlORMtsB2bU2TjZmVhm6O2ezP9l9ykZIOr1g13Cgx4s6rXvNDfUsW7uJjVvaGDKoutzhmJnlqrtzNq8ju/ByJNuft1lLdoNN2wUdM9JaVm5gv7F7lDkaM7N8dXfO5lbgVklHR8Qf+zCmijChYF0bJxszG+hKOWfz0U5rzjRIujLHmCpCx7o2nv5sZpWglGRzcESs6ngRESuBN+YXUmUYs0ctg2uqPP3ZzCpCKcmmSlJDxwtJjZR25wHrRlWVaGqo84w0M6sIpSSN7wD/J+kmsos530uRW8rYjmv2ujZmViF6TDYRcY2kuWSrYgo4PSIezz2yCtDcWMcji1f1XNHMbDdX0hIDQCPwSkR8D1juOwj0juaGelZv2MKajVvKHYqZWa5KWRb6YrJb+X8hFQ0C/jvPoCpFx4w0n7cxs4GulJ7Nu4F3Aa8ARMQSYFieQVWKwnVtzMwGslKSzea0ymUASBqab0iVY9tdBNyzMbOBrZRkc6Ok/wJGSjof+A3wo3zDqgwj6gYxrLbGw2hmNuCVMhvt25LeAawhu1/alyJidu6RVQBJNDXW+8JOMxvwSro4MyUXJ5gcNDfU8cxLr5Q7DDOzXHU5jCbp9+l5raQ1RR7PSPp434U6MO07Zg+effkVVq/39GczG7i6TDYR8Zb0PCwihnd+AFOBT/dVoAPVqQfvzZa24Jfzl5Q7FDOz3JR0UaekKZI+JemTkt4IEBEvA8flGVwlOHDccF635zBuerCl3KGYmeWmlIs6vwRcDYwCRgNXSfpHgIhY2sOxn5a0QNJjkj6TyholzZa0MD03pHJJukzSIknzJU0paGdGqr9Q0oyC8sMkPZqOuUySunuP/kgSZxw2nkcWr+Kp5evKHY6ZWS5K6dm8Hzg8Ii6OiIuBo4CzezpI0kFkK3oeARwCnCppMnARcFdETAbuSq8BTgYmp8cFwA9TO43AxcCRqa2LC5LHD1PdjuOmpfKu3qNfOu3Q8VQJZrp3Y2YDVCnJ5llgSMHrWuCpEo57PTAnItZHRCtwL9ndCKaT9ZRIz6el7enANZGZQ3Zdz97AScDsiFiR1tKZDUxL+4ZHxB/TRafXdGqr2Hv0S2OHD+HY147hloefp609yh2OmVmv62422vckXQZsAh6TdJWknwALgFLGexYAx0oaJakeOAVoBvbsGH5Lz2NT/fHA4oLjW1JZd+UtRcrp5j06f8YLJM2VNHf58uUlfKT8nDGliaWrN/LHp14uaxxmZnno7jqbuen5QeCWgvJ7Smk4Ip6QdClZT2QdMA9o7eYQFWtmJ8pLFhGXA5cDTJ06taxdinccsCfDhtQw86EW3jJ5dDlDMTPrdV0mm4i4GkDSEGA/sj/kT0XExlIbj4grgCtSO98g6328KGnviFiahsKWpeotZD2fDk3AklR+XKfye1J5U5H6dPMe/daQQdWcevA4fvHw83z1tFb2qPViqGY2cHQ3jFYj6Ztkf9SvJltWYLGkb0oaVErjksam5wnA6cB1wCygY0bZDODWtD0LOCfNSjsKWJ2GwO4ATpTUkCYGnAjckfatlXRUmoV2Tqe2ir1Hv3bmYU1s2NLGbY92O8nPzGy3090EgW+RLZo2KSIOi4g3Aq8BRgLfLrH9mZIeB34JXJhO8F8CvEPSQuAd6TXAbcDTwCKyG31+HCAiVgBfBR5Ij6+kMoCPAT9OxzwF3J7Ku3qPfm3KhJFMGj3Us9LMbMBRNpGryI7sD/Vro1MFSdXAn9K04gFj6tSpMXfu3J4r5uw/7l7It+98kv/9h7dtXVzNzKy/kvRgREztqV53PZvonGhSYRs7eCLeSvfuKU1IMPMh927MbODoLtk8LumczoWS/hr4U34hVbbxI+s4et9R3PzQ83TV6zQz2910N+XpQuBmSeeSTX8O4HCgjuziTMvJGVOa+Lufz+OBZ1dyxKTGcodjZrbLurvr8/MRcSTwFbK7CDxHdnL+iIh4vo/iq0jTDtqL+sHVnihgZgNGj7eriYi7I+J7EXFZRNzVF0FVuqG1NZx80N78z6NL2bC5rdzhmJntspKWGLC+d8Zh41m3qZU7H3+h3KGYme0yJ5t+6qhJoxg/ss7r3JjZgOBk009VVYkzpoznD4te4oXVJd8hyMysX3Ky6cdOn9JEe8DND7t3Y2a7Nyebfmzi6KFM3aeBmQ+2+JobM9utOdn0c2cc1sRTy19hXsvqcodiZrbTnGz6ub86eG9qa6p8zY2Z7dacbPq54UMGceKBezFr3hI2tfqaGzPbPTnZ7AbOmDKe1Ru2cPcT/X4NODOzopxsdgPHTB7DnsNrfSdoM9ttOdnsBqqrxGlvHM89f17OS+s2lTscM7Md5mSzmzhzShOt7cGtjywpdyhmZjvMyWY3MXnPYRzcNMK3rzGz3ZKTzW7kjClNPLF0DY8vWVPuUMzMdoiTzW7kXYeMY1C1PFHAzHY7Tja7kYahgzl+/7Hc+sjzbGlrL3c4ZmYlc7LZzZwxpYmX1m3md08uL3coZmYlyzXZSPqspMckLZB0naQhkk6Q9JCkRyT9XtJ+qW6tpBskLZJ0n6SJBe18IZX/WdJJBeXTUtkiSRcVlE9KbSxMbQ7O83P2pbftP5ZRQwd7KM3Mdiu5JRtJ44FPAVMj4iCgGjgL+CFwdkQcCvwM+Md0yHnAyojYD/gucGlq54B03IHANOAHkqolVQPfB04GDgDen+qSjv1uREwGVqa2B4RB1VW869Bx/ObxZaxav7nc4ZiZlSTvYbQaoE5SDVAPLAECGJ72j0hlANOBq9P2TcAJkpTKr4+ITRHxDLAIOCI9FkXE0xGxGbgemJ6OOT61QWrztBw/Y587Y0oTm9va+eX8peUOxcysJLklm4h4Hvg28BywFFgdEXcCHwFuk9QCfBC4JB0yHlicjm0FVgOjCsuTllTWVfkoYFVqo7D8VSRdIGmupLnLl+8+50AOHDec/fca5jtBm9luI89htAayXskkYBwwVNJfA58FTomIJuAnwL92HFKkmejF8lcXRlweEVMjYuqYMWO6+zj9iiTOmNLEI4tXsWjZunKHY2bWozyH0d4OPBMRyyNiC3Az8GbgkIi4L9W5AXhT2m4BmgHSsNsIYEVhedJENvTWVflLwMjURmH5gDL9jeOorvI1N2a2e8gz2TwHHCWpPp1HOQF4HBgh6bWpzjuAJ9L2LGBG2j4TuDuytZBnAWel2WqTgMnA/cADwOQ082ww2SSCWemY36Y2SG3emuPnLIuxw4Zw7OTR3PLQ87S1e8loM+vf8jxncx/ZSfqHgEfTe10OnA/MlDSP7JzN36dDrgBGSVoE/C1wUWrnMeBGskT1a+DCiGhL52Q+AdxBlrBuTHUBPg/8bWprVGp7wDnjsCZeWLOR/3vqpXKHYmbWLWUdAZs6dWrMnTu33GHskI1b2jji67/h+P3H8m9nvbHc4ZhZBZL0YERM7ame7yCwGxsyqJrph47nV/OX8ts/exVPM+u/nGx2c5876XW8bq9hfPSnD/LHp14udzhmZkU52ezmRtQN4qfnHcmExnrOu/oBHvzLynKHZGb2Kk6AKJQiAAAPU0lEQVQ2A0Dj0MFc+5EjGTuslg/95H4WPL+63CGZmW3HyWaAGDt8CNeefxTDhwzig1fcx5Mvri13SGZmWznZDCDjR9Zx7UeOZFB1FWf/+D6eeemVcodkZgY42Qw4E0cP5dqPHElbe3D2j+bQsnJ9uUMyM3OyGYgm7zmMa849gnWbWjn7x/fx4pqN5Q7JzCqck80AddD4EVx17hG8tHYTZ//4Pl5et6ncIZlZBXOyGcCmTGjgig8dzuIV6/ngFfezev2WcodkZhXKyWaAO2rfUfzXBw9j4bK1zPjJ/azb1NrzQWZmvczJpgIc97qx/McHpvDo86s576oH2LC5rdwhmVmFcbKpECcduBf/+t5DuP/ZFXz0vx9kU6sTjpn1HSebCjL90PFccvobuPfJ5XzquodpbWsvd0hmViGcbCrM+w6fwJffeQB3PPYif/fzeV54zcz6RE3PVWyg+dCbJ7F+Sxvf/PWfqRtUzb+c/gayxVTNzPLhZFOhPn7cfmzY3Mb37l6EBJ+ftj8j6weXOywzG6CcbCrY377jtWxubee/fvc0v3h4Ce+d2sS5b5nEPqOGljs0MxtgvCx0sjsuC91bnli6hit+/wy3PvI8re3BiQfsyfnH7Mth+zR4eM3MulXqstBONkklJ5sOy9Zs5Oo/Pst/z3mO1Ru2cEjzSM4/ZhLTDtyLmmrPJTGzV3Oy2UFONtus39zKzAdbuOL3z/Dsy+sZP7KOD795Iu87vJlhQwaVOzwz60dKTTa5/ndV0mclPSZpgaTrJA1R5uuSnpT0hKRPpbqSdJmkRZLmS5pS0M4MSQvTY0ZB+WGSHk3HXKY05iOpUdLsVH+2pIY8P+dAUz+4hg8ePZG7/u44Lv/gYYwfWcfX/ucJ3vQvd/ON255gyaoN5Q7RzHYzufVsJI0Hfg8cEBEbJN0I3AYIeBvwoYholzQ2IpZJOgX4JHAKcCTw7xFxpKRGYC4wFQjgQeCwiFgp6X7g08Cc1PZlEXG7pG8CKyLiEkkXAQ0R8fnu4nXPpnvzFq/iR//7NLcveAGAv3rD3px/zL68oWlEmSMzs3IqtWeT92y0GqBO0hagHlgCfA34QES0A0TEslR3OnBNZNlvjqSRkvYGjgNmR8QKAEmzgWmS7gGGR8QfU/k1wGnA7amt41K7VwP3AN0mG+veIc0j+Y8PTKFl5Xqu+sOzXP/AYmbNW8IRkxr54FH7cPjERvYaMaTcYZpZP5VbsomI5yV9G3gO2ADcGRF3SroOeJ+kdwPLgU9FxEJgPLC4oImWVNZdeUuRcoA9I2JpimOppLHFYpR0AXABwIQJE3bl41aMpoZ6/vHUA/j02ydzwwOL+ckfnuWT1z0MwNhhtRzcNJJDmkZwcPNIDh4/goahvnbHzHJMNuk8yXRgErAK+LmkvwZqgY0RMVXS6cCVwDFkw2udxU6UlywiLgcuh2wYbUeOrXTDhgziI8fsy4feNJF5LauZ37KK+S2rmdeyit888eLWehMa63lD04gsATWN5KDxI9ij1pd3mVWaPH/r3w48ExHLASTdDLyJrAcyM9W5BfhJ2m4BmguObyIbdmth25BYR/k9qbypSH2AFyXtnXo1ewPLsFzUVFdx2D4NHLbPtjkYazZuYUHL6q1J6JHnVvE/85cCIMF+Y/bIekDNWQLaf69hDBlUXa6PYGZ9IM9k8xxwlKR6smG0E8hO9K8Bjifr0bwVeDLVnwV8QtL1ZBMEVqdkcQfwjYIZZScCX4iIFZLWSjoKuA84B/heQVszgEvS8605fk7rZPiQQbxpv9G8ab/RW8teWreJR1PPZ37Lau59chkzH9o2Cto4dDBjh9UydvgQxg6rZc/htYwdNoQ9h9cyZutzLbU1Tkpmu6M8z9ncJ+km4CGgFXiYbMiqDrhW0meBdcBH0iG3kc1EWwSsBz6c2lkh6avAA6neVzomCwAfA65Kbd6eHpAlmRslnUeW9N6T08e0Eo3eo5a37T+Wt+2fnT6LCJas3sj8xat48sV1LFu7kRfXbGL52o08+cJalq/bVPSO1A31gxg7bAhjC5LR6D1q2aO2hqG1NdTXVmfbg2sYWlvN0Noa9qitobamyndDMCsjX9SZeOpz/9LWHqx4ZTPL1m5k2ZpNW5PRtudNLFuzkeVrN9FawjIJ1VWifnD11qQ0dHCWiIbW1lA/uJrB1VUMqqlicHUVg2uqGFQtBldXM6hGBWVVBfW0tWxQdRXVVaJKorpK1BRsV1dBdVUV1RJVVaQyUZ32V6XtKgkpG2as6ngNqcxJ0vqv/jL12WynVFeJMcOyobMDx3Vdr709WLNxC+s2tfLKpjZe2dzKK5uyx7pNbazf3Jr2pf2bWnllc9q3qZUVr6xnw5Y2trS2s7mtnc3peUtb9Ju1fqRsNsy2hCSqBCI9p8REqqdULxVt3Z8ued7a3ra2t9XvOAa6TnJb297uGBWt07nNbfvV7f5Xv+ku7e7R7p7QdzX6b5z+Bg6f2NgrsXTFycZ2a1VVYmT94FyWR2hrD7a0peTTkYRaY2tSKtzXFllyao+grR3a2tuz5wja24PW9uy5o17Hoz2yfREQpOcI2gPaY/vXQXreWpbtby84FrJ9kE3N3K7d9Lpj0mbEtv0dtrZR8D0UDn5srbtd2fY6j5a8ej/d7u+sp9GXXf4vQf/4P8VOi174AHV9MEHHycasC9mQV7Vnypn1At/K18zMcudkY2ZmuXOyMTOz3DnZmJlZ7pxszMwsd042ZmaWOycbMzPLnZONmZnlzvdGSyQtB/6yk4ePBl7qxXB6m+PbNY5v1zi+XdefY9wnIsb0VMnJphdImlvKjejKxfHtGse3axzfrtsdYuyJh9HMzCx3TjZmZpY7J5vecXm5A+iB49s1jm/XOL5dtzvE2C2fszEzs9y5Z2NmZrlzsjEzs9w52ewASdMk/VnSIkkXFdlfK+mGtP8+SRP7MLZmSb+V9ISkxyR9ukid4yStlvRIenypr+JL7/+spEfTe88tsl+SLkvf33xJU/owttcVfC+PSFoj6TOd6vTp9yfpSknLJC0oKGuUNFvSwvTc0MWxM1KdhZJm9GF835L0p/Tvd4ukkV0c2+3PQo7xfVnS8wX/hqd0cWy3v+s5xndDQWzPSnqki2Nz//56XUT4UcIDqAaeAvYFBgPzgAM61fk48J9p+yzghj6Mb29gStoeBjxZJL7jgF+V8Tt8Fhjdzf5TgNvJllQ/CrivjP/WL5BdrFa27w84FpgCLCgo+yZwUdq+CLi0yHGNwNPpuSFtN/RRfCcCNWn70mLxlfKzkGN8XwY+V8K/f7e/63nF12n/d4Avlev76+2HezalOwJYFBFPR8Rm4Hpgeqc604Gr0/ZNwAmS1BfBRcTSiHgoba8FngDG98V796LpwDWRmQOMlLR3GeI4AXgqInb2jhK9IiJ+B6zoVFz4M3Y1cFqRQ08CZkfEiohYCcwGpvVFfBFxZ0S0ppdzgKbeft9SdfH9laKU3/Vd1l186e/Ge4Hrevt9y8XJpnTjgcUFr1t49R/zrXXSL9xqYFSfRFcgDd+9EbivyO6jJc2TdLukA/s0MAjgTkkPSrqgyP5SvuO+cBZd/5KX8/sD2DMilkL2HwxgbJE6/eV7PJesp1pMTz8LefpEGua7sothyP7w/R0DvBgRC7vYX87vb6c42ZSuWA+l87zxUurkStIewEzgMxGxptPuh8iGhg4Bvgf8oi9jA94cEVOAk4ELJR3baX9/+P4GA+8Cfl5kd7m/v1L1h+/xi0ArcG0XVXr6WcjLD4HXAIcCS8mGqjor+/cHvJ/uezXl+v52mpNN6VqA5oLXTcCSrupIqgFGsHPd+J0iaRBZork2Im7uvD8i1kTEurR9GzBI0ui+ii8ilqTnZcAtZMMVhUr5jvN2MvBQRLzYeUe5v7/kxY6hxfS8rEidsn6PaULCqcDZkU4wdFbCz0IuIuLFiGiLiHbgR128b7m/vxrgdOCGruqU6/vbFU42pXsAmCxpUvrf71nArE51ZgEdM3/OBO7u6pett6Ux3iuAJyLiX7uos1fHOSRJR5D9+7/cR/ENlTSsY5vsRPKCTtVmAeekWWlHAas7hoz6UJf/oyzn91eg8GdsBnBrkTp3ACdKakjDRCemstxJmgZ8HnhXRKzvok4pPwt5xVd4DvDdXbxvKb/reXo78KeIaCm2s5zf3y4p9wyF3elBNlvqSbKZKl9MZV8h+8UCGEI2/LIIuB/Ytw9jewtZV38+8Eh6nAJ8FPhoqvMJ4DGy2TVzgDf1YXz7pvedl2Lo+P4K4xPw/fT9PgpM7eN/33qy5DGioKxs3x9Z0lsKbCH73/Z5ZOcA7wIWpufGVHcq8OOCY89NP4eLgA/3YXyLyM53dPwMdszOHAfc1t3PQh/F99P0szWfLIHs3Tm+9PpVv+t9EV8qv6rjZ66gbp9/f7398O1qzMwsdx5GMzOz3DnZmJlZ7pxszMwsd042ZmaWOycbMzPLnZONWS+RtC49T5T0gV5u+/91ev1/vdm+Wd6cbMx630Rgh5KNpOoeqmyXbCLiTTsYk1lZOdmY9b5LgGPSWiOflVSd1nl5IN0A8m9g6/o4v5X0M7ILDZH0i3Rzxcc6brAo6RKgLrV3bSrr6EUptb0grW/yvoK275F0k7L1Za4tuPvBJZIeT7F8u8+/HatINeUOwGwAuohszZRTAVLSWB0Rh0uqBf4g6c5U9wjgoIh4Jr0+NyJWSKoDHpA0MyIukvSJiDi0yHudTnZTyUOA0emY36V9bwQOJLuv1x+AN0t6nOw2LftHRKiLxc3Mept7Nmb5O5Hsnm+PkC37MAqYnPbdX5BoAD4lqeN2OM0F9bryFuC6yG4u+SJwL3B4Qdstkd108hGy4b01wEbgx5JOB4rev8ystznZmOVPwCcj4tD0mBQRHT2bV7ZWko4juwnj0ZEtY/Aw2f32emq7K5sKttvIVtBsJetNzSRbeO3XO/RJzHaSk41Z71tLtjR3hzuAj6UlIJD02nS33s5GACsjYr2k/cmWxu6wpeP4Tn4HvC+dFxpDttTw/V0FltY7GhHZEgmfIRuCM8udz9mY9b75QGsaDrsK+HeyIayH0kn65RRfzvnXwEclzQf+TDaU1uFyYL6khyLi7ILyW4Cjye4AHMA/RMQLKVkVMwy4VdIQsl7RZ3fuI5rtGN/12czMcudhNDMzy52TjZmZ5c7JxszMcudkY2ZmuXOyMTOz3DnZmJlZ7pxszMwsd/8f6zNYPcT5ujUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotObjective(oVals, \"Ridge Solver\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotCompare(diabetes_X_test, diabetes_y_test, admm_predict, diabetes_y_pred, \"Prediction from Ridge Solver\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Speed up stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0879090649973364"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lasso\n",
    "\n",
    "alpha = 0.01\n",
    " #, verbose = 1000)\n",
    "\n",
    "sk = []\n",
    "admm = []\n",
    "for i in range(0, 10000):\n",
    "    lasso = linear_model.Lasso(alpha=alpha)\n",
    "    start = time.time()\n",
    "    lfit = lasso.fit(diabetes_X_train, diabetes_y_train)\n",
    "    end = time.time()\n",
    "    \n",
    "    sk_time = end - start\n",
    "    \n",
    "    W, oVals, admm_time = runAlgo(X, Y, \"Lasso\", 3) #, parallel = True)\n",
    "    \n",
    "    sk.append(sk_time)\n",
    "    admm.append(admm_time)\n",
    "\n",
    "sk = np.array(sk)\n",
    "admm = np.array(admm)\n",
    "np.mean(sk/admm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sk / admm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "1.0539322531987703"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.42799539, 1.18829517, 1.13125   , 1.10335449, 0.9220679 ,\n",
       "       1.13853211, 1.2535461 , 1.17011701, 0.93159108, 1.07160293])"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sk / admm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00059104, 0.00033402, 0.00030208, 0.00029016, 0.00028491,\n",
       "       0.00029588, 0.00033712, 0.00030994, 0.00028896, 0.000314  ])"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00041389, 0.0002811 , 0.00026703, 0.00026298, 0.00030899,\n",
       "       0.00025988, 0.00026894, 0.00026488, 0.00031018, 0.00029302])"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "admm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.58908746285971"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ridge\n",
    "\n",
    "alpha = 0.01\n",
    " #, verbose = 1000)\n",
    "\n",
    "sk = []\n",
    "admm = []\n",
    "for i in range(0, 10000):\n",
    "    lasso = linear_model.Ridge(alpha=alpha)\n",
    "    start = time.time()\n",
    "    lfit = lasso.fit(diabetes_X_train, diabetes_y_train)\n",
    "    end = time.time()\n",
    "    \n",
    "    sk_time = end - start\n",
    "    \n",
    "    W, oVals, admm_time = runAlgo(X, Y, \"Ridge\", 3) #, parallel = True)\n",
    "    \n",
    "    sk.append(sk_time)\n",
    "    admm.append(admm_time)\n",
    "\n",
    "sk = np.array(sk)\n",
    "admm = np.array(admm)\n",
    "np.mean(sk/admm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.000633  , 0.00053191, 0.0004971 , 0.00048494, 0.00048399,\n",
       "       0.00049186, 0.000494  , 0.00049496, 0.00050712, 0.00053596])"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00037718, 0.00030708, 0.00030208, 0.00030184, 0.00031805,\n",
       "       0.00030494, 0.00030088, 0.00030708, 0.00031209, 0.0003252 ])"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "admm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.67825537, 1.73214286, 1.64561957, 1.60663507, 1.52173913,\n",
       "       1.61297889, 1.64183835, 1.61180124, 1.62490451, 1.64809384])"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sk / admm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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