02장.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlwpy import *\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(1,7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "few_rolls  = np.random.randint(1,7,size=10)\n",
    "many_rolls = np.random.randint(1,7,size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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df5j5P9CJOZtOkmOBc4D3951l3JIcCZwKXAlQVb+oqh/1m0oHa9LnMjif6Wk+W7QHIMk0sA64vd8kY/Vu4E3AY30H6cEJwBzwgW5X2/uTPLXvUPrVTehcBudzL/PZoh1RkqcB1wNvqKqf9J1nHJK8GNhVVZv7ztKTw4DnA1dU1TrgfwEvLbfCTeJcBuczPc5ni3YESQ5nfmJeU1Uf7zvPGJ0CnJtkO/NXejk9yYf6jTRWO4AdVbXnXc/HmJ+oWqEmeC6D87m3+WzRLiFJmN+nv62q3tl3nnGqqsuq6tiqmmb+dH2fq6pX9BxrbKrqf4DvJjmpGzoDmLQDZwZjkucyOJ/7nM8edby0U4ALgG8k2dKNvbk7w46G7/XANd0RivcDr+o5jw6ec1m9zGfPDCVJUkPuOpYkqSGLVpKkhixaSZIasmglSWrIopUkqSGLVpKkhixaSZIasmglSWro/wBglvnfPvMr7AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "few_counts  = np.histogram(few_rolls,  bins=np.arange(.5, 7.5))[0]\n",
    "many_counts = np.histogram(many_rolls, bins=np.arange(.5, 7.5))[0]\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1,2,figsize=(8,3))\n",
    "ax1.bar(np.arange(1,7), few_counts)\n",
    "ax2.bar(np.arange(1,7), many_counts);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.stats as ss\n",
    "\n",
    "b = ss.distributions.binom\n",
    "for flips in [5, 10, 20, 40, 80]:\n",
    "    # 동전을 수없이 던지면 확률 .5를 가지는 이항분포가 그려집니다.\n",
    "    success = np.arange(flips)\n",
    "    our_distribution = b.pmf(success, flips, .5)\n",
    "    plt.hist(success, flips, weights=our_distribution)\n",
    "plt.xlim(0,55);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "b = ss.distributions.binom\n",
    "n = ss.distributions.norm\n",
    "\n",
    "for flips in [5, 10, 20, 40, 80]:\n",
    "    # 동전 던지기\n",
    "    success = np.arange(flips)\n",
    "    our_distribution = b.pmf(success, flips, .5)\n",
    "    plt.hist(success, flips, weights=our_distribution)\n",
    "\n",
    "    # 정규분포로 이항분포를 근사합니다.\n",
    "    # 평균과 표준편차를 설정합니다.\n",
    "    mu      = flips * .5, \n",
    "    std_dev = np.sqrt(flips * .5 * (1-.5))\n",
    "    \n",
    "    # 정규분포를 위해 x와 y 포인트를 설정해야 합니다.\n",
    "    # 함수에 xs를 입력하고 이를 통해 ys를 얻습니다.\n",
    "    norm_x = np.linspace(mu-3*std_dev, mu+3*std_dev, 100)\n",
    "    norm_y = n.pdf(norm_x, mu, std_dev)\n",
    "    plt.plot(norm_x, norm_y, 'k');\n",
    "\n",
    "plt.xlim(0,55);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36.25"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(2 * 12.50) + (12 * .5) + (3 * 1.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 + 12 + 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36.25"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 기본 파이썬 코드입니다.\n",
    "quantity = [2, 12, 3]\n",
    "costs    = [12.5, .5, 1.75]\n",
    "partial_cost = []\n",
    "for q,c in zip(quantity, costs):\n",
    "    partial_cost.append(q*c)\n",
    "sum(partial_cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36.25"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 요새 트렌드를 반영한 순수한 파이썬 코드입니다.\n",
    "quantity = [2, 12, 3]\n",
    "costs    = [12.5, .5, 1.75]\n",
    "sum(q*c for q,c in zip(quantity,costs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36.25"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantity = np.array([2, 12, 3]) \n",
    "costs    = np.array([12.5, .5, 1.75])\n",
    "np.sum(quantity * costs) # 요소별 곱"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36.25\n",
      "36.25\n",
      "36.25\n"
     ]
    }
   ],
   "source": [
    "print(quantity.dot(costs),       # 접곱 방식 1\n",
    "      np.dot(quantity, costs),   # 접곱 방식 2\n",
    "      quantity @ costs,          # 접곱 방식 3\n",
    "                                 # (새로 추가된 방식입니다!)\n",
    "      sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 2 12.50 --> 25.00\n",
      "12  0.50 -->  6.00\n",
      " 3  1.75 -->  5.25\n",
      "Total: 36.25\n"
     ]
    }
   ],
   "source": [
    "for q_i, c_i in zip(quantity, costs):\n",
    "    print(\"{:2d} {:5.2f} --> {:5.2f}\".format(q_i, c_i, q_i * c_i))\n",
    "\n",
    "print(\"Total:\", \n",
    "      sum(q*c for q,c in zip(quantity,costs))) # cool-kid method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weights: [0.3333 0.3333 0.3333]\n",
      "via mean: 20.0\n",
      "via weights and dot: 20.0\n"
     ]
    }
   ],
   "source": [
    "values  = np.array([10.0, 20.0, 30.0])\n",
    "weights = np.full_like(values, 1/3) # (1/3)을 반복합니다.\n",
    "\n",
    "print(\"weights:\", weights)\n",
    "print(\"via mean:\", np.mean(values))\n",
    "print(\"via weights and dot:\", np.dot(weights, values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.5"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values  = np.array([10,  20,  30])\n",
    "weights = np.array([.5, .25, .25])\n",
    "\n",
    "np.dot(weights, values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 홀, 짝\n",
    "payoffs = np.array([1.0, -.5])\n",
    "probs   = np.array([ .5,  .5])\n",
    "np.dot(payoffs, probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2542.0\n"
     ]
    }
   ],
   "source": [
    "def is_even(n):\n",
    "    # 2로 나눈 나머지가 0이면 짝수입니다.\n",
    "    return n % 2 == 0  \n",
    "\n",
    "winnings = 0.0\n",
    "for toss_ct in range(10000):\n",
    "    die_toss = np.random.randint(1,7)\n",
    "    winnings += 1.0 if is_even(die_toss) else -0.5\n",
    "print(winnings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[25  9  4  1]\n",
      "39\n",
      "39\n"
     ]
    }
   ],
   "source": [
    "values = np.array([5, -3, 2, 1])\n",
    "squares = values * values # 요소별 곱\n",
    "print(squares, \n",
    "      np.sum(squares),   # 제곱-의-합!\n",
    "      np.dot(values, values), sep=\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>errors</th>\n",
       "      <th>squared</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0000</td>\n",
       "      <td>25.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-5.0000</td>\n",
       "      <td>25.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2000</td>\n",
       "      <td>10.2400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.1000</td>\n",
       "      <td>1.2100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "{\\centering\n",
       "\\begin{tabular}{lrr}\n",
       "\\toprule\n",
       "{} &               errors &              squared \\\\\n",
       "\\midrule\n",
       "0 &               5.0000 &              25.0000 \\\\\n",
       "1 &              -5.0000 &              25.0000 \\\\\n",
       "2 &               3.2000 &              10.2400 \\\\\n",
       "3 &              -1.1000 &               1.2100 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\n",
       "\\medskip}"
      ],
      "text/plain": [
       "                errors              squared\n",
       "0               5.0000              25.0000\n",
       "1              -5.0000              25.0000\n",
       "2               3.2000              10.2400\n",
       "3              -1.1000               1.2100"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "errors = np.array([5, -5, 3.2, -1.1])\n",
    "display(pd.DataFrame({'errors':errors,\n",
    "                      'squared':errors*errors}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61.45"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(errors, errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "people = np.arange(1,11)\n",
    "total_cost = np.ones_like(people) * 40.0\n",
    "\n",
    "ax = plt.gca()\n",
    "\n",
    "ax.plot(people, total_cost)\n",
    "ax.set_xlabel(\"# People\")\n",
    "ax.set_ylabel(\"Cost\\n(Parking Only)\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "people = np.arange(1,11)\n",
    "total_cost = 80.0 * people + 40.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>total_cost</th>\n",
       "      <td>120</td>\n",
       "      <td>200</td>\n",
       "      <td>280</td>\n",
       "      <td>360</td>\n",
       "      <td>440</td>\n",
       "      <td>520</td>\n",
       "      <td>600</td>\n",
       "      <td>680</td>\n",
       "      <td>760</td>\n",
       "      <td>840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "{\\centering\n",
       "\\begin{tabular}{lrrrrrrrrrr}\n",
       "\\toprule\n",
       "{} &   1  &   2  &   3  &   4  &   5  &   6  &   7  &   8  &   9  &   10 \\\\\n",
       "\\midrule\n",
       "total\\_cost &  120 &  200 &  280 &  360 &  440 &  520 &  600 &  680 &  760 &  840 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\n",
       "\\medskip}"
      ],
      "text/plain": [
       "             1    2    3    4    5    6    7    8    9    10\n",
       "total_cost  120  200  280  360  440  520  600  680  760  840"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# .T (transpose)로 테이블을 가로로 돌려 출력합니다.\n",
    "display(pd.DataFrame({'total_cost':total_cost.astype(np.int)}, \n",
    "                     index=people).T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEGCAYAAACKB4k+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAY5UlEQVR4nO3df5Ac9X3m8fezEmAGxxY/FgpL2l1cVhEnlI3x2JFDKrlDvgRsl8VVIME3CTpKVeNUOP8IucRguerqUrd3wWWDzaVC3ZRlI+KJbUW2g8pF+VAJSOy6QvZKEDDIjhSHXa2loPUB8o8hB4LP/dHfbe2uZlezaHt6d+d5VW1196d7ej47hXj2293TrYjAzMwMoK/sBszMbPFwKJiZWc6hYGZmOYeCmZnlHApmZpZbWXYDp+OCCy6IoaGhstswM1tS9u7d++OI6G+3bkmHwtDQECMjI2W3YWa2pEganW2dDx+ZmVnOoWBmZjmHgpmZ5RwKZmaWcyiYmVnOoWBmtoQ0mzA0BH192bTZXNj9L+lLUs3MekmzCfU6tFrZ8uhotgxQqy3Me3ikYGa2RGzZciIQJrVaWX2hOBTMzJaIsbH51V8Nh4KZ2RIxMDC/+qvhUDAzWyKGh6FSmV6rVLL6QnEomJktEbUaNBowOAhSNm00Fu4kM/jqIzOzJaVWW9gQmMkjBTMzyzkUzMws51AwM7NcoaEg6Y8kPSnpe5K+JOk1ki6RtEfSAUlfkXRm2vastHwwrR8qsjczMztZYaEgaTXwYaAaEZcBK4AbgNuBOyNiHfAcsDm9ZDPwXES8CbgzbWdmZl1U9OGjlcDZklYCFeAIcBWwI63fBlyb5jemZdL6DZJUcH9mZjZFYaEQET8CPgWMkYXBMWAv8HxEHE+bjQOr0/xq4FB67fG0/fkz9yupLmlE0sjExERR7ZuZ9aQiDx+dS/bX/yXAG4BzgGvabBqTL5lj3YlCRCMiqhFR7e/vX6h2zcyMYg8fvRv454iYiIiXgK8BvwqsSoeTANYAh9P8OLAWIK1/PfBsgf2ZmdkMRYbCGLBeUiWdG9gAPAU8BFyXttkE3Jfmd6Zl0voHI+KkkYKZmRWnyHMKe8hOGO8Dnkjv1QA+Btwi6SDZOYOt6SVbgfNT/Rbg1qJ6MzOz9rSU/xivVqsxMjJSdhtmZkuKpL0RUW23zt9oNjOznEPBzMxyDgUzsw40mzA0BH192bTZLLujYvh5CmZmp9BsQr0OrVa2PDqaLUOxzzYog0cKZmansGXLiUCY1Gpl9eXGoWBmdgpjY/OrL2UOBTOzUxgYmF99KXMomJmdwvAwVCrTa5VKVl9uHApmZqdQq0GjAYODIGXTRmP5nWQGX31kZtaRWm15hsBMHimYmVnOoWBmZjmHgpmZ5RwKZmaWcyiYmVnOoWBmZjmHgpmZ5RwKZmaWcyiYmVmusFCQdKmkx6b8/ETSRyWdJ2mXpANpem7aXpLuknRQ0uOSriiqNzMza6+wUIiIH0TE5RFxOfB2oAV8HbgV2B0R64DdaRngGmBd+qkDdxfVm5mZtdetw0cbgH+KiFFgI7At1bcB16b5jcC9kXkEWCXp4i71Z2ZmdC8UbgC+lOYviogjAGl6YaqvBg5Nec14qk0jqS5pRNLIxMREgS2bmfWewkNB0pnA+4G/OdWmbWpxUiGiERHViKj29/cvRItmZpZ0Y6RwDbAvIp5Jy89MHhZK06OpPg6snfK6NcDhLvRnZotYswlDQ9DXl02bzbI7Wt66EQof4MShI4CdwKY0vwm4b0r9xnQV0nrg2ORhJjPrTc0m1OswOgoR2bRedzAUSREnHaFZuJ1LFbLzBG+MiGOpdj6wHRgAxoDrI+JZSQL+Aria7EqlmyJiZK79V6vVGBmZcxMzW8KGhrIgmGlwEJ5+utvdLB+S9kZEtd26Qp+8FhEt4PwZtf9LdjXSzG0DuLnIfsxsaRkbm1/dTp+/0Wxmi9bAwPzqdvocCma2aA0PQ6UyvVapZHUrhkPBzBatWg0ajewcgpRNG42sbsUo9JyCmdnpqtUcAt3kkYKZmeUcCmZmlnMomJlZzqFgZmY5h4KZmeUcCmZmlnMomJlZzqFgZmY5h4KZmeUcCmZmlnMomJlZzqFgZmY5h4KZmeUcCmZmlis0FCStkrRD0vcl7Zf0LknnSdol6UCanpu2laS7JB2U9LikK4rszczm1mxmz0ju68umzWbZHVk3FD1S+CzwzYj4ReCtwH7gVmB3RKwDdqdlgGuAdemnDtxdcG9mNotmE+p1GB2FiGxarzsYekFhoSDpdcCvA1sBIuLFiHge2AhsS5ttA65N8xuBeyPzCLBK0sVF9Wdms9uyBVqt6bVWK6vb8lbkSOGNwATwBUmPSvqcpHOAiyLiCECaXpi2Xw0cmvL68VSbRlJd0oikkYmJiQLbN+tdY2Pzq9vyUWQorASuAO6OiLcBP+fEoaJ21KYWJxUiGhFRjYhqf3//wnRqZtMMDMyvbstHkaEwDoxHxJ60vIMsJJ6ZPCyUpkenbL92yuvXAIcL7M/MZjE8DJXK9FqlktVteSssFCLiX4BDki5NpQ3AU8BOYFOqbQLuS/M7gRvTVUjrgWOTh5nMrLtqNWg0YHAQpGzaaGR1W95WFrz/DwFNSWcCPwRuIgui7ZI2A2PA9Wnb+4H3AAeBVtrWzEpSqzkEelGhoRARjwHVNqs2tNk2gJuL7MfMzObmbzSbmVnOoWBmZjmHgpmZ5RwKZmaWO2UoSLqnk5qZmS19nYwU3jJ1QVIf8I5i2jEzszLNGgqSPibpOeAtkp5NP88BPyb7ToGZmS0zc40UPgn0A3emaT9wQUScFxF/0o3mzMysu2YNhXQL6+PAV4GzIuJl4HckfVLS2tleZ2ZmS1cn5xQawAuS3gJ8HHgG+GKhXZmZWSk6CYXj6RYUG4HPRsSngV8oti0zMytDJ/c++rmkPwF+H/iNdPXRGcW2ZWZmZehkpPC7ZA/A+WC6lfUa4I5CuzIzs1KcMhQi4jDweeAsSVcDrYj4QuGdmfWoZhOGhqCvL5s2m2V3ZL2kk280/zawj+zw0Y3AiKR/X3RjZr2o2YR6HUZHISKb1usOBuseZeeQ59hA+gfgNyPimbR8EfBARLy1C/3NqVqtxsjISNltmC2YoaEsCGYaHISnn+52N7ZcSdobEe2eddPROYW+yUBIJjp8nZnN09jY/OpmC62Tq48ekHQ/8Ndp+QbggeJaMutdAwPtRwoDA93vxXpTJ3/x/2fgHuCdwK8A21LtlCQ9LekJSY9JGkm18yTtknQgTc9NdUm6S9JBSY9LuuJV/UZmS9jwMFQq02uVSlY364a5boj3RknvSre72B4RH46IDwFHgKF5vMe/jYjLpxy/uhXYHRHrgN1pGeAaYF36qQN3z/N3MVvyajVoNLJzCFI2bTSyulk3zDVS+CzwQpv6i8BnTuM9N5KNNkjTa6fU700h9AiwStLFp/E+ZktSrZadVH7llWzqQLBumisULomIx2YWI+I7wCUd7j/IzknslVRPtYvSl+BI0wtTfTVwaMprx1PNzMy6ZK4TzWfNsa4yx7qproyIw5IuBHZJ+v4c26pN7aTrZVO41AEGfPbNzGxBzTVS2CfppplFSf8ReLSTnadvQxMRR4Gvk52sfmbysFCaHk2bjwNTb8m9BjjcZp+NiKhGRLW/v7+TNszMrENzjRQ+CvytpBqwN9WqZHdI3XiqHUs6h+w7Dj9N878J/BmwE9gE/Hma3pdeshP4T5K+THaV07HJw0xmZtYds4ZC+h/yr0j6d8BlqXx7RHT6HYWLgK9Lmnyfv46Ib0r6LrBd0mZgDLg+bX8/8B7gINACThqlmJlZsU55m4vFzLe5MDObv9O9zYWZmfUIh4KZmeUcCmZmlpv1RLOk52jzPQGy7xNERJxXWFdmZlaKuS5JvaBrXZiZ2aIw1yWpL09dlnQe8JoppZO+WGZmZktbJ4/jfK+kfyT7xvGeNH2w6MbMzKz7OjnRPAxcCfwgItYCvwU8XGRTZmZWjk5C4XhETAB9khQRuwA/AMeWpWYze05yX182bTbL7sisuzp5HOexdO+ibwP3SjoKvFJsW2bd12xCvQ6tVrY8Opotg59pYL2jk5HCtcC/kt0g72HgR8D7CuzJrBRbtpwIhEmtVlY36xWdhMJtEfFyRLwUEVsj4g7glqIbM+u2sbH51c2Wo05C4eo2tfcudCNmZZvtmU1+lpP1kllDQdIHJT0KXCpp35SfA8BT3WvRrDuGh6Ey45mClUpWN+sVc51o3g7sBv4HcOuU+k/Tk9TMlpXJk8lbtmSHjAYGskDwSWbrJR09T0HSZcCvpcVvRcSThXbVIT9Pwcxs/k7reQqSbiYbNQykn+2S/nBhWzQzs8Wgk+8pfBB4Z0T8DEDSfwf+D/CXRTZmZmbd18nVRwJemrL8UqqZmdkyM9fVR5OjiL8CHpH0CUmfIBslbOv0DSStkPSopG+k5Usk7ZF0QNJXJJ2Z6mel5YNp/dCr/aXMzOzVmWuk8B2AiPgkUAdawAvAH0TEp+bxHh8B9k9Zvh24MyLWAc8Bm1N9M/BcRLwJuDNtZ2ZmXTRXKOSHiCLiuxFxR0R8OiK+2+nOJa0h+6Lb59KygKuAHWmTbWS30QDYyIkRyA5gQ9rezMy6ZK4Tzf2SZr2dRbrdxal8BvhT4BfS8vnA8xFxPC2PA6vT/GrgUNr3cUnH0vY/nrpDSXWykQsD/qqpmdmCmmuksAJ4Ldn/0Nv9zEnS+4CjEbF3arnNptHBuhOFiEZEVCOi2t/ff6o2zMxsHuYaKRyJiD87jX1fCbxf0nvIHuP5OrKRwypJK9NoYQ0nHus5DqwFxtNJ7tcDz57G+5uZ2Tx1dE7h1YiI2yJiTUQMATcAD0ZEDXgIuC5ttgm4L83vTMuk9Q9GJ1+3NjOzBTNXKGwo6D0/Btwi6SDZOYOtqb4VOD/Vb2H6/ZbMzKwLZj18FBELdugmIh4mPdc5In4IvLPNNv8KXL9Q72lmZvPXyTeazcysRzgUzMws51CwRaHZhKEh6OvLps1m2R2Z9aZO7pJqVqhmE+p1aLWy5dHRbBn8gBuzbvNIwUq3ZcuJQJjUamV1M+suh4KVbmxsfnUzK45DwUo32y2sfGsrs+5zKFjphoehUpleq1Syupl1l0PBSlerQaMBg4MgZdNGwyeZzcrgq49sUajVHAJmi4FHCmZmlnMomJlZzqFgZmY5h4KZmeUcCmZmlnMomJlZzqFgZmY5h4KZmeUKCwVJr5H0HUn/IOlJSf811S+RtEfSAUlfkXRmqp+Vlg+m9UNF9WZmZu0VOVL4f8BVEfFW4HLgaknrgduBOyNiHfAcsDltvxl4LiLeBNyZtjMzsy4qLBQi87O0eEb6CeAqYEeqbwOuTfMb0zJp/QZJKqo/MzM7WaHnFCStkPQYcBTYBfwT8HxEHE+bjAOr0/xq4BBAWn8MOL/NPuuSRiSNTExMFNm+mVnPKTQUIuLliLgcWAO8E3hzu83StN2oIE4qRDQiohoR1f7+/oVr1szMunP1UUQ8DzwMrAdWSZq8O+sa4HCaHwfWAqT1rwee7UZ/ZmaWKfLqo35Jq9L82cC7gf3AQ8B1abNNwH1pfmdaJq1/MCJOGinYwmo2YWgI+vqyabNZdkdmVqYin6dwMbBN0gqy8NkeEd+Q9BTwZUn/DXgU2Jq23wr8laSDZCOEGwrszcgCoF6HVitbHh3NlsHPNjDrVVrKf4xXq9UYGRkpu40la2goC4KZBgfh6ae73Y2ZdYukvRFRbbfO32juYWNj86ub2fLnUOhhAwPzq5vZ8udQ6GHDw1CpTK9VKlndzHqTQ6GH1WrQaGTnEKRs2mj4JLNZLyvy6iNbAmo1h4CZneCRgpmZ5RwKZmaWcyiYmVnOoWBmZjmHgpmZ5RwKZmaWcyiYmVnOoWBmZjmHgpmZ5RwKZmaWcyiYmVnOoWBmZjmHgpmZ5RwKZmaWKywUJK2V9JCk/ZKelPSRVD9P0i5JB9L03FSXpLskHZT0uKQriuptMWg2s2ck9/Vl02az7I7MzIodKRwH/jgi3gysB26W9EvArcDuiFgH7E7LANcA69JPHbi7wN5K1WxCvQ6joxCRTet1B4OZla+wUIiIIxGxL83/FNgPrAY2AtvSZtuAa9P8RuDeyDwCrJJ0cVH9lWnLFmi1ptdaraxuZlamrpxTkDQEvA3YA1wUEUcgCw7gwrTZauDQlJeNp9rMfdUljUgamZiYKLLtwoyNza9uZtYthYeCpNcCXwU+GhE/mWvTNrU4qRDRiIhqRFT7+/sXqs2uGhiYX93MrFsKDQVJZ5AFQjMivpbKz0weFkrTo6k+Dqyd8vI1wOEi+yvL8DBUKtNrlUpWNzMrU5FXHwnYCuyPiDumrNoJbErzm4D7ptRvTFchrQeOTR5mWm5qNWg0YHAQpGzaaGR1M7MyKeKkIzQLs2Pp14BvAU8Ar6Tyx8nOK2wHBoAx4PqIeDaFyF8AVwMt4KaIGJnrParVaoyMzLmJmZnNIGlvRFTbrVtZ1JtGxLdpf54AYEOb7QO4uah+zMzs1PyNZjMzyzkUzMws51AwM7OcQ8HMzHIOBTMzyzkUzMws51AwM7OcQ8HMzHIOBTMzyzkUzMws51AwM7OcQ8HMzHIOBTMzy/VcKDSbMDQEfX3ZtNksuyMzs8WjsFtnL0bNJtTr0Gply6Oj2TL4ATdmZtBjI4UtW04EwqRWK6ubmVmPhcLY2PzqZma9pqdCYWBgfnUzs15TWChI+ryko5K+N6V2nqRdkg6k6bmpLkl3SToo6XFJVxTR0/AwVCrTa5VKVjczs2JHCvcAV8+o3Qrsjoh1wO60DHANsC791IG7i2ioVoNGAwYHQcqmjYZPMpuZTSrs6qOI+HtJQzPKG4F/k+a3AQ8DH0v1eyMigEckrZJ0cUQcWei+ajWHgJnZbLp9TuGiyf/Rp+mFqb4aODRlu/FUO4mkuqQRSSMTExOFNmtm1msWy4lmtalFuw0johER1Yio9vf3F9yWmVlv6XYoPCPpYoA0PZrq48DaKdutAQ53uTczs57X7VDYCWxK85uA+6bUb0xXIa0HjhVxPsHMzOZW2IlmSV8iO6l8gaRx4L8Afw5sl7QZGAOuT5vfD7wHOAi0gJuK6svMzGan7IKfpUnSBDBadh+n6QLgx2U3sYj48zjBn8V0/jymO53PYzAi2p6UXdKhsBxIGomIatl9LBb+PE7wZzGdP4/pivo8FsvVR2Zmtgg4FMzMLOdQKF+j7AYWGX8eJ/izmM6fx3SFfB4+p2BmZjmPFMzMLOdQMDOznEOhJJLWSnpI0n5JT0r6SNk9lU3SCkmPSvpG2b2ULd0peIek76f/Rt5Vdk9lkvRH6d/J9yR9SdJryu6pW+bzbJqF4FAoz3HgjyPizcB64GZJv1RyT2X7CLC/7CYWic8C34yIXwTeSg9/LpJWAx8GqhFxGbACuKHcrrrqHjp/Ns1pcyiUJCKORMS+NP9Tsn/0bW8X3gskrQHeC3yu7F7KJul1wK8DWwEi4sWIeL7crkq3Ejhb0kqgQg/dMDMi/h54dkZ5I9kzaUjTaxfq/RwKi0B6GNHbgD3ldlKqzwB/CrxSdiOLwBuBCeAL6XDa5ySdU3ZTZYmIHwGfIrtf2hGyG2Y+UG5XpZvt2TSnzaFQMkmvBb4KfDQiflJ2P2WQ9D7gaETsLbuXRWIlcAVwd0S8Dfg5C3h4YKlJx8s3ApcAbwDOkfR75Xa1fDkUSiTpDLJAaEbE18rup0RXAu+X9DTwZeAqSV8st6VSjQPjETE5ctxBFhK96t3AP0fERES8BHwN+NWSeyrbbM+mOW0OhZJIEtkx4/0RcUfZ/ZQpIm6LiDURMUR2AvHBiOjZvwQj4l+AQ5IuTaUNwFMltlS2MWC9pEr6d7OBHj7xnsz2bJrTVtjzFOyUrgR+H3hC0mOp9vGIuL/Enmzx+BDQlHQm8EN6+BkjEbFH0g5gH9lVe4/SQ7e8mOezaU7//XybCzMzm+TDR2ZmlnMomJlZzqFgZmY5h4KZmeUcCmZmlnMomM1C0suSHkt35vwbSZUF3v89kq5byH2anS6HgtnsXoiIy9OdOV8E/qDshsyK5lAw68y3gDcBSPo9Sd9Jo4j/JWlFqn9A0hNpZHH75Asl/UzSpyXtk7RbUv/MnUt6u6S/k7RX0v+evIWBWbc5FMxOId2u+Rqyb5+/Gfhd4MqIuBx4GahJegNwO3AVcDnwDkmTtzM+B9gXEVcAf0f2jdSp+z8D+J/AdRHxduDzwHDxv5nZyXybC7PZnT3lFiTfIrtXVR14O/Dd7DY8nE12M7J3AA9HxASApCbZMxH+lux24F9J+/ki2Q3dproUuAzYlfa5guwW0WZd51Awm90LaTSQSzdk2xYRt82oz+chJzPvLSPgyYjo6Udu2uLgw0dm87MbuE7ShZA/K3eQ7AFJvyHpgnSO4QNkh4og+3c2eZXRfwC+PWOfPwD6J5/DLOkMSb9c8O9h1pZHCmbzEBFPSfoE8ICkPuAl4OaIeETSbcBDZH/53x8Rk7cz/jnwy5L2AsfIzklM3eeL6dLUuyS9nuzf5WeAJ7vzW5md4LukmhVM0s8i4rVl92HWCR8+MjOznEcKZmaW80jBzMxyDgUzM8s5FMzMLOdQMDOznEPBzMxy/x9pUShWHXxUzQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "ax.plot(people, total_cost, 'bo')\n",
    "ax.set_ylabel(\"Total Cost\")\n",
    "ax.set_xlabel(\"People\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -3부터 3까지 범위 내에 포인트 100개를 만듭니다.\n",
    "xs = np.linspace(-3, 3, 100)\n",
    "\n",
    "# 기울기 (m) and y절편 (b)\n",
    "m, b = 1.5, -3\n",
    "\n",
    "ax = plt.gca()\n",
    "\n",
    "ys = m*xs + b\n",
    "ax.plot(xs, ys)\n",
    "\n",
    "ax.set_ylim(-4,4)\n",
    "high_school_style(ax) # mlwpy.py의 헬퍼함수\n",
    "\n",
    "ax.plot(0, -3,'ro') # y절편\n",
    "ax.plot(2,  0,'ro') # 오른쪽으로 두 칸, 위로 세 칸 이동합니다.\n",
    "\n",
    "# y = mx + b에서 m=0이면 y = b\n",
    "ys = 0*xs + b\n",
    "ax.plot(xs, ys, 'y');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-3.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.9394</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.8788</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.8182</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.7576</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "{\\centering\n",
       "\\begin{tabular}{lrr}\n",
       "\\toprule\n",
       "{} &                    0 &                    1 \\\\\n",
       "\\midrule\n",
       "0 &              -3.0000 &               1.0000 \\\\\n",
       "1 &              -2.9394 &               1.0000 \\\\\n",
       "2 &              -2.8788 &               1.0000 \\\\\n",
       "3 &              -2.8182 &               1.0000 \\\\\n",
       "4 &              -2.7576 &               1.0000 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\n",
       "\\medskip}"
      ],
      "text/plain": [
       "                     0                    1\n",
       "0              -3.0000               1.0000\n",
       "1              -2.9394               1.0000\n",
       "2              -2.8788               1.0000\n",
       "3              -2.8182               1.0000\n",
       "4              -2.7576               1.0000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# np.c_[] 로 열별 배열을 만들 수 있습니다.\n",
    "xs    = np.linspace(-3, 3, 100)\n",
    "xs_p1 = np.c_[xs, np.ones_like(xs)]\n",
    "\n",
    "# 첫 행 몇개를 출력합니다.\n",
    "display(pd.DataFrame(xs_p1).head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "w     = np.array([1.5, -3])\n",
    "ys    = np.dot(xs_p1, w)\n",
    "\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.plot(xs, ys)\n",
    "\n",
    "# 스타일링\n",
    "ax.set_ylim(-4,4)\n",
    "high_school_style(ax)\n",
    "\n",
    "ax.plot(0, -3,'ro')  # y절편\n",
    "ax.plot(2,  0,'ro'); # 오른쪽으로 두 칸 움직이면 위로 세 칸 움직입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_people = np.arange(1,11) # 사람 수 1-10\n",
    "number_rbs    = np.arange(0,20) # 루트 비어 수 0-19\n",
    "\n",
    "# numpy 도구를 이용해서 두 배열의 외적을 구합니다.\n",
    "# 실행해보세요: np.meshgrid([0,1], [10,20])\n",
    "# 변수가 여러개인 함수에 사용하기 좋습니다.\n",
    "number_people, number_rbs = np.meshgrid(number_people, number_rbs) \n",
    "\n",
    "total_cost = 80 * number_people + 10 * number_rbs + 40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 'projection':'3d' 를 위해 라이브러리를 임포트해야 합니다.\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "fig,axes = plt.subplots(2, 3, \n",
    "                        subplot_kw={'projection':'3d'}, \n",
    "                        figsize=(9,6))\n",
    "\n",
    "angles = [0,45,90,135,180]\n",
    "for ax,angle in zip(axes.flat, angles):\n",
    "    ax.plot_surface(number_people, number_rbs, total_cost)\n",
    "    ax.set_xlabel(\"People\")\n",
    "    ax.set_ylabel(\"RootBeers\")\n",
    "    ax.set_zlabel(\"TotalCost\")\n",
    "    ax.azim = angle\n",
    "\n",
    "# 마지막 축은 사용하지 않습니다.\n",
    "axes.flat[-1].axis('off')\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_people  = np.array([2,3])\n",
    "number_rbs     = np.array([0,1,2])\n",
    "number_hotdogs = np.array([2,4])\n",
    "\n",
    "costs = np.array([80, 10, 5])\n",
    "\n",
    "columns = [\"People\", \"RootBeer\", \"HotDogs\", \"TotalCost\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>People</th>\n",
       "      <th>RootBeer</th>\n",
       "      <th>HotDogs</th>\n",
       "      <th>TotalCost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "{\\centering\n",
       "\\begin{tabular}{lrrrr}\n",
       "\\toprule\n",
       "{} &  People &  RootBeer &  HotDogs &  TotalCost \\\\\n",
       "\\midrule\n",
       "0 &       2 &         0 &        2 &        210 \\\\\n",
       "1 &       2 &         0 &        4 &        220 \\\\\n",
       "2 &       3 &         0 &        2 &        290 \\\\\n",
       "3 &       3 &         0 &        4 &        300 \\\\\n",
       "4 &       2 &         1 &        2 &        220 \\\\\n",
       "5 &       2 &         1 &        4 &        230 \\\\\n",
       "6 &       3 &         1 &        2 &        300 \\\\\n",
       "7 &       3 &         1 &        4 &        310 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\n",
       "\\medskip}"
      ],
      "text/plain": [
       "   People  RootBeer  HotDogs  TotalCost\n",
       "0       2         0        2        210\n",
       "1       2         0        4        220\n",
       "2       3         0        2        290\n",
       "3       3         0        4        300\n",
       "4       2         1        2        220\n",
       "5       2         1        4        230\n",
       "6       3         1        2        300\n",
       "7       3         1        4        310"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts = np_cartesian_product(number_people, \n",
    "                              number_rbs, \n",
    "                              number_hotdogs)\n",
    "\n",
    "totals = (costs[0] * counts[:,0] + \n",
    "          costs[1] * counts[:,1] + \n",
    "          costs[2] * counts[:,2] + 40)\n",
    "\n",
    "display(pd.DataFrame(np.c_[counts, totals],\n",
    "                     columns=columns).head(8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>People</th>\n",
       "      <th>RootBeer</th>\n",
       "      <th>HotDogs</th>\n",
       "      <th>TotalCost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/latex": [
       "{\\centering\n",
       "\\begin{tabular}{lrrrr}\n",
       "\\toprule\n",
       "{} &  People &  RootBeer &  HotDogs &  TotalCost \\\\\n",
       "\\midrule\n",
       "0 &       2 &         0 &        2 &        210 \\\\\n",
       "1 &       2 &         0 &        4 &        220 \\\\\n",
       "2 &       3 &         0 &        2 &        290 \\\\\n",
       "3 &       3 &         0 &        4 &        300 \\\\\n",
       "4 &       2 &         1 &        2 &        220 \\\\\n",
       "5 &       2 &         1 &        4 &        230 \\\\\n",
       "6 &       3 &         1 &        2 &        300 \\\\\n",
       "7 &       3 &         1 &        4 &        310 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\n",
       "\\medskip}"
      ],
      "text/plain": [
       "   People  RootBeer  HotDogs  TotalCost\n",
       "0       2         0        2        210\n",
       "1       2         0        4        220\n",
       "2       3         0        2        290\n",
       "3       3         0        4        300\n",
       "4       2         1        2        220\n",
       "5       2         1        4        230\n",
       "6       3         1        2        300\n",
       "7       3         1        4        310"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "costs = np.array([80, 10, 5])\n",
    "counts = np_cartesian_product(number_people, \n",
    "                              number_rbs, \n",
    "                              number_hotdogs)\n",
    "\n",
    "totals = np.dot(counts, costs) + 40\n",
    "display(pd.DataFrame(np.column_stack([counts, totals]), \n",
    "                     columns=columns).head(8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2,2)\n",
    "fig.tight_layout()\n",
    "\n",
    "titles = [\"$y=c_0$\", \n",
    "          \"$y=c_1x+c_0$\",\n",
    "          \"$y=c_2x^2+c_1x+c_0$\",\n",
    "          \"$y=c_3x^3+c_2x^2+c_1x+c_0$\"]\n",
    "\n",
    "xs = np.linspace(-10, 10, 100)\n",
    "for power, (ax, title) in enumerate(zip(axes.flat, titles), 1):\n",
    "    coeffs = np.random.uniform(-5, 5, power)\n",
    "    poly = np.poly1d(coeffs)\n",
    "    ax.plot(xs, poly(xs))\n",
    "    ax.set_title(title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.Figure((2,1.5))\n",
    "\n",
    "xs = np.linspace(-10,10,101)\n",
    "coeffs = np.array([2,3,4])\n",
    "ys = np.dot(coeffs, [xs**2, xs**1, xs**0])\n",
    "\n",
    "# 점곱으로 멋진 포물선을 그릴 수 있습니다.\n",
    "plt.plot(xs, ys);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4] --> [ 0  1  4  9 16]\n",
      "self dot: 30\n"
     ]
    }
   ],
   "source": [
    "oned_vec = np.arange(5)\n",
    "print(oned_vec, \"-->\", oned_vec * oned_vec)\n",
    "print(\"self dot:\", np.dot(oned_vec, oned_vec))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "row_vec = np.arange(5).reshape(1,5)\n",
    "col_vec = np.arange(0, 50, 10).reshape(5,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "row vec:\n",
      "[[0 1 2 3 4]]\n",
      "col_vec:\n",
      "[[ 0]\n",
      " [10]\n",
      " [20]\n",
      " [30]\n",
      " [40]]\n",
      "dot:\n",
      "[[300]]\n"
     ]
    }
   ],
   "source": [
    "print(\"row vec:\", row_vec, \n",
    "      \"col_vec:\", col_vec, \n",
    "      \"dot:\", np.dot(row_vec, col_vec), sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0   0   0   0   0]\n",
      " [  0  10  20  30  40]\n",
      " [  0  20  40  60  80]\n",
      " [  0  30  60  90 120]\n",
      " [  0  40  80 120 160]]\n"
     ]
    }
   ],
   "source": [
    "out = np.dot(col_vec, row_vec)\n",
    "print(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[300]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out = np.dot(row_vec, col_vec)\n",
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([300])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_vec = np.arange(0, 50, 10).reshape(5,1)\n",
    "row_vec = np.arange(0,5).reshape(1,5)\n",
    "\n",
    "oned_vec = np.arange(5)\n",
    "\n",
    "np.dot(oned_vec, col_vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I went boom: shapes (5,1) and (5,) not aligned: 1 (dim 1) != 5 (dim 0)\n"
     ]
    }
   ],
   "source": [
    "try: \n",
    "    np.dot(col_vec, oned_vec) # *쾅!*\n",
    "except ValueError as e:  \n",
    "    print(\"I went boom:\", e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5,)\n",
      "(5, 1)\n"
     ]
    }
   ],
   "source": [
    "print(oned_vec.shape,\n",
    "      col_vec.shape, sep=\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[30]\n",
      "boom\n"
     ]
    }
   ],
   "source": [
    "print(np.dot(row_vec, oned_vec))\n",
    "try: print(np.dot(oned_vec, row_vec))\n",
    "except: print(\"boom\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True True\n"
     ]
    }
   ],
   "source": [
    "print(np.allclose(np.dot(oned_vec.reshape(1,5), col_vec), \n",
    "                  np.dot(oned_vec,              col_vec)),\n",
    "      np.allclose(np.dot(row_vec, oned_vec.reshape(5,1)), \n",
    "                  np.dot(row_vec, oned_vec)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "D = np.array([[1,3],\n",
    "              [2,5],\n",
    "              [2,7],\n",
    "              [3,2]])\n",
    "weights = np.array([1.5, 2.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -7.5, -12. , -18. ,  -1.5])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(D,w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BOOM.  :sadface:\n"
     ]
    }
   ],
   "source": [
    "try: \n",
    "    np.dot(w,D)\n",
    "except ValueError:  \n",
    "    print(\"BOOM.  :sadface:\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -7.5, -12. , -18. ,  -1.5])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def rdot(arr,brr):\n",
    "    '인수를 반대(reverse)로 넣는 np.dot'\n",
    "    return np.dot(brr,arr)\n",
    "rdot(w, D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1.1 + 2.1 == 3.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(float, float, float, float)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(1.1), type(2.1), type(1.1+2.1), type(3.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.allclose(1.2 + 2.1, 3.3)"
   ]
  }
 ],
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