{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\n",
    "\\begin{equation*}\n",
    "\\begin{aligned}\n",
    "& \\underset{x}{\\text{maximize}}\n",
    "& & f(x) \\\\\n",
    "& \\text{subject to}\n",
    "& & g_i(x) \\leq b_i, \\; i = 1, \\ldots, m.\\\\\n",
    "\\end{aligned}\n",
    "\\end{equation*}\n",
    "$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '尤度')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 302,
       "width": 556
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = np.linspace(0, 1, 200)\n",
    "plt.figure(figsize=(6, 3))\n",
    "plt.plot(X, scipy.stats.beta.pdf(X, 150, 150), label='A: 試行300回')\n",
    "plt.plot(X, scipy.stats.beta.pdf(X, 6, 4), label='B: 試行10回')\n",
    "plt.legend(loc='upper left')\n",
    "plt.xlabel('p')\n",
    "plt.ylabel('尤度')\n",
    "#plt.title('パラメータpの尤度\\n Beta(成功回数, 失敗回数)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '尤度')"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 302,
       "width": 541
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = np.linspace(0, 1, 200)\n",
    "plt.figure(figsize=(6, 3))\n",
    "plt.plot(X, scipy.stats.beta.pdf(X, 300, 300), label='A: 試行600回')\n",
    "plt.plot(X, scipy.stats.beta.pdf(X, 700, 400), label='B: 試行1100回')\n",
    "plt.legend(loc='upper left')\n",
    "plt.xlabel('p')\n",
    "plt.ylabel('尤度')\n",
    "#plt.title('パラメータpの尤度\\n Beta(成功回数, 失敗回数)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\n",
    "y = w_0 + w_1x_1 + w_2x_2 \\cdots + \\epsilon\n",
    "$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "boston = load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _boston_dataset:\n",
      "\n",
      "Boston house prices dataset\n",
      "---------------------------\n",
      "\n",
      "**Data Set Characteristics:**  \n",
      "\n",
      "    :Number of Instances: 506 \n",
      "\n",
      "    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n",
      "\n",
      "    :Attribute Information (in order):\n",
      "        - CRIM     per capita crime rate by town\n",
      "        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n",
      "        - INDUS    proportion of non-retail business acres per town\n",
      "        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
      "        - NOX      nitric oxides concentration (parts per 10 million)\n",
      "        - RM       average number of rooms per dwelling\n",
      "        - AGE      proportion of owner-occupied units built prior to 1940\n",
      "        - DIS      weighted distances to five Boston employment centres\n",
      "        - RAD      index of accessibility to radial highways\n",
      "        - TAX      full-value property-tax rate per $10,000\n",
      "        - PTRATIO  pupil-teacher ratio by town\n",
      "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
      "        - LSTAT    % lower status of the population\n",
      "        - MEDV     Median value of owner-occupied homes in $1000's\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Creator: Harrison, D. and Rubinfeld, D.L.\n",
      "\n",
      "This is a copy of UCI ML housing dataset.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n",
      "\n",
      "\n",
      "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n",
      "\n",
      "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
      "prices and the demand for clean air', J. Environ. Economics & Management,\n",
      "vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
      "...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
      "pages 244-261 of the latter.\n",
      "\n",
      "The Boston house-price data has been used in many machine learning papers that address regression\n",
      "problems.   \n",
      "     \n",
      ".. topic:: References\n",
      "\n",
      "   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n",
      "   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(boston.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
       "       'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  \n",
       "0     15.3  396.90   4.98  \n",
       "1     17.8  396.90   9.14  \n",
       "2     17.8  392.83   4.03  \n",
       "3     18.7  394.63   2.94  \n",
       "4     18.7  396.90   5.33  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "X = pd.DataFrame(boston.data, columns=boston.feature_names)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = pd.Series(boston.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Results: Ordinary least squares\n",
      "==================================================================\n",
      "Model:              OLS              Adj. R-squared:     0.946    \n",
      "Dependent Variable: y                AIC:                3196.9303\n",
      "Date:               2019-04-04 00:40 BIC:                3230.7426\n",
      "No. Observations:   506              Log-Likelihood:     -1590.5  \n",
      "Df Model:           8                F-statistic:        1110.    \n",
      "Df Residuals:       498              Prob (F-statistic): 8.68e-312\n",
      "R-squared:          0.947            Scale:              31.960   \n",
      "--------------------------------------------------------------------\n",
      "         Coef.     Std.Err.      t      P>|t|     [0.025     0.975] \n",
      "--------------------------------------------------------------------\n",
      "CRIM     -0.1858     0.0380   -4.8884   0.0000    -0.2605    -0.1111\n",
      "ZN        0.0833     0.0146    5.7100   0.0000     0.0546     0.1119\n",
      "CHAS      3.8725     1.0130    3.8227   0.0001     1.8821     5.8629\n",
      "NOX     -18.5928     3.0070   -6.1833   0.0000   -24.5007   -12.6849\n",
      "RM        6.8287     0.2539   26.8931   0.0000     6.3298     7.3276\n",
      "DIS      -1.3713     0.1736   -7.8985   0.0000    -1.7124    -1.0302\n",
      "RAD       0.2022     0.0711    2.8420   0.0047     0.0624     0.3420\n",
      "TAX      -0.0180     0.0038   -4.7172   0.0000    -0.0255    -0.0105\n",
      "------------------------------------------------------------------\n",
      "Omnibus:              220.813      Durbin-Watson:         0.882   \n",
      "Prob(Omnibus):        0.000        Jarque-Bera (JB):      1612.249\n",
      "Skew:                 1.741        Prob(JB):              0.000   \n",
      "Kurtosis:             11.021       Condition No.:         5300    \n",
      "==================================================================\n",
      "* The condition number is large (5e+03). This might indicate\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "model = sm.OLS(y, X[['CRIM', 'ZN', 'CHAS', 'NOX', 'RM', 'DIS', 'RAD', 'TAX']])\n",
    "result = model.fit()\n",
    "print(result.summary2())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pulp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\displaystyle\n",
    "\\begin{equation*}\n",
    "\\begin{aligned}\n",
    "& \\underset{x,y,z}{\\text{maximize}}\n",
    "& 1800x + 2500y + 3000z \\\\\n",
    "& \\text{subject to}\n",
    "& 6x + 10y + 15z \\leq 12000 \\\\\n",
    "& & x \\leq 1000; y \\leq 900;z \\leq 500 \n",
    "\\end{aligned}\n",
    "\\end{equation*}\n",
    "$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal\n",
      "['X: 1000.0', 'Y: 600.0', 'Z: 0.0']\n"
     ]
    }
   ],
   "source": [
    "# Variable\n",
    "products = [pulp.LpVariable(f'{v}', cat=pulp.LpInteger, lowBound=0) for v in ['X', 'Y', 'Z']]\n",
    "# Target\n",
    "problem = pulp.LpProblem(sense=pulp.LpMaximize)\n",
    "problem += pulp.lpDot(products, [1800, 2500, 3000])\n",
    "# Constraints\n",
    "problem += pulp.lpDot(products, [6, 10, 15]) <= 200 * 60\n",
    "problem += products[0] <= 1000\n",
    "problem += products[1] <= 900\n",
    "problem += products[2] <= 500\n",
    "\n",
    "status = problem.solve()\n",
    "print(pulp.LpStatus[status])\n",
    "print([f\"{product.name}: {product.value()}\" for product in products])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3300000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1000 * 1800 + 600 * 2500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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