123 lines
14 KiB
Plaintext
123 lines
14 KiB
Plaintext
|
{
|
||
|
|
"cells": [
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 3,
|
||
|
|
"id": "favorite-meeting",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"import matplotlib.pyplot as plt"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 4,
|
||
|
|
"id": "anonymous-logistics",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# Let's start with the same data points as before \n",
|
||
|
|
"X = [1000, 2000] \n",
|
||
|
|
"y = [200, 400]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 5,
|
||
|
|
"id": "increasing-healing",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"Text(0.5, 0, 'Size (feet^2)')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 5,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAiLklEQVR4nO3deZxcVZ338c+XEBZlCUiPhCQQFFABkaWH5QEcBBdABB4HEEYUtxcDD+MEdFQQZwYYnQEZjSAqxkGMigLDIsqAGjGgzAjYgRB2CdsABmmWEDYjge/zxz1dqbTdXdWhq4uu/r5fr3rVvefce+t3U1C/vufce45sExERAbBKuwOIiIhXjiSFiIioSVKIiIiaJIWIiKhJUoiIiJokhYiIqElSiGiCpLMl/eMrII7dJd3V7jiicynPKcRYIul+4GO2f1FX9qFStlu74lpZkqYD9wHPlqLHgLNtn9q2oGJcW7XdAUQEAJNsL5O0C3CVpPm2f1q/gaRVbS9rU3wxTqT5KDqOpDdJulrSYkm3Sdq/ru5qSR+rW/+QpGvLsiTNlPSopCWSbpG0dan7jqTPl+U9JD0k6ZNl20WSPlx3zNdI+kk5xm8lfb7vMxqx/RvgNmDrus/5jKRHgHP7yuo+a5qkSyT1Snpc0ll1dR+RdIekJyX9TNImjc4zIkkhOoqkicBPgJ8DfwF8HDhP0hua2P2dwFuBLYB1gUOAxwfZdsOyzRTgo8DXJK1X6r5G1Ry0IXBEeTUTuyTtCmwF3FT3OesDmwBH9tt+AnA58AAwvcRyfqk7APgs8F6gC/g18MOVOM8YZ5IUYiz6UbkKWCxpMfD1urqdgbWAU23/yfYvqX44D2viuC8AawNvpOpvu8P2oiG2PcX2C7avAJ4B3lB+qP8a+Gfbz9m+HZjdxGc/BjwB/AdwvO2rSvlL5VhLbT/fb58dgY2AT9l+1vYfbfddkRwF/Fs5h2XAvwLblquF4ZxnjDNJCjEWHWh7Ut8L+H91dRsBD9p+qa7sAaq/oodUEshZVH/pPypplqR1Btn88X7t+89RJaMuqr66B+vq6pcHs4Ht9Wy/yfaZdeW9tv84yD7TgAcG6WfYBDijLnE+AQiYMszzjHEmSSE6ze+BaZLq/9veGHi4LD8LvKqubsP6nW2faXsHYEuq5pVPDfPze4FlwNS6smnDPMYKIQ1R9yCwsaSBbhh5EPjb+uRpe03b/wMjcp7RoZIUotNcT/VX+6clTZS0B/AeSls7MB94r6RXSdqMqj8AAEl/KWmn0i/xLPBHquabptl+EbgEOKl8xhuBD768UxrUDcAi4FRJr5a0RumTADgbOEHSVgCS1pV0cFl+2ecZnStJITqK7T9RJYF9qNrpvw580PadZZOZwJ+AP1C19Z9Xt/s6wLeAJ6manB4HTl+JMP6OqgP3EeB7VB28S1fiOEMqCeg9wGbA/wIPAe8rdZcCpwHnS1oC3Er1bwIjd57RgfLwWkSLSToN2NB2U3chRbRTrhQiRpikN0raptxiuiNVE9Wl7Y4rohl5ojli5K1N1WS0EVUz1ZeAy9oaUUST0nwUERE1aT6KiIiaMd18tMEGG3j69OntDiMiYkyZN2/eY7a7Bqob00lh+vTp9PT0tDuMiIgxRdIDg9Wl+SgiImqSFCIioiZJISIiapIUIiKiJkkhImIs6f9s2Qg/a9bypCBpgqSbJF1e1jeVdL2khZIukLRaKV+9rC8s9dNbHVtExJhy0klw3HHLE4FdrZ900oh9xGhcKcwA7qhbPw2YaXszqlEa+4Yu/ijwZCmfWbaLiAioEsDixXDGGcsTw3HHVeuLF4/YFUNLk4KkqcC7qaYYRJKAPYGLyiazgQPL8gEsn7bwImCvsn1EREgwcybMmFElglVWqd5nzKjKR+jnstVXCl8BPs3yCTxeAyyumz7wIZZPkziFMm1hqX+qbL8CSUdK6pHU09vb28LQIyJeYfoSQ70RTAjQwqQgaT/gUdvzRvK4tmfZ7rbd3dU14FPaERGdqa/JqF59H8MIaOWVwq7A/pLup5oKcU/gDGBS3ZyyU1k+d+7DlLlsS/26VDNCRUREfR/CjBnw0kvLm5JGMDG0LCnYPsH2VNvTgUOBX9p+PzAXOKhsdgTLx5n/cVmn1P/SGdc7IqIiwaRJK/Yh9PUxTJo0Yk1IozKfQpk8/R9s7yfpdVRXDusDNwGH214qaQ2q+Wy3A54ADrV971DH7e7udgbEi4hxxV4xAfRfb4Kkeba7B6oblVFSbV8NXF2W7wV2HGCbPwIHj0Y8ERFjVv8EMMI3aeaJ5oiIqElSiIiImiSFiIioSVKIiIiaJIWIiKhJUoiIiJokhYiIqElSiIiImiSFiIioSVKIiIiaJIWIiKhJUoiIiJokhYiIqElSiIiImiSFiIioSVKIiIialiUFSWtIukHSzZJuk3RyKf+1pPnl9XtJPyrle0h6qq7un1oVW0REDKyVM68tBfa0/YykicC1kq60vXvfBpIuZvkczQC/tr1fC2OKiIghtOxKwZVnyurE8qpNCC1pHWBP4EetiiEiIoanpX0KkiZImg88CsyxfX1d9YHAVbaX1JXtUpqbrpS01SDHPFJSj6Se3t7elsUeETEetTQp2H7R9rbAVGBHSVvXVR8G/LBu/UZgE9tvAb7KIFcQtmfZ7rbd3dXV1ZrAIyLGqVG5+8j2YmAusDeApA2AHYH/qttmSV9zk+0rgIllu4iIGCWtvPuoS9Kksrwm8A7gzlJ9EHC57T/Wbb+hJJXlHUtsj7cqvoiI+HOtvPtoMjBb0gSqH/gLbV9e6g4FTu23/UHA0ZKWAc8Dh9o2ERExalqWFGwvALYbpG6PAcrOAs5qVTwREdFYnmiOiIiaJIWIiKhJUoiIiJokhYiIqElSiIiImiSFiIioSVKIiIiahs8pSJpK9bDZ7sBGVA+W3Uo1RMWVtl9qaYQRETFqhkwKks4FpgCXA6dRjXa6BrAF1ThGJ0o63vavWh1oRES0XqMrhS/ZvnWA8luBSyStBmw88mFFREQ7DNmnMFBCkLSepG1K/Z9sL2xVcBERMbqa6miWdLWkdSStTzXvwbckzWxtaBERMdqavfto3TJD2nuB79reCdirdWFFREQ7NJsUVpU0GTiEqtM5IiI6ULNJ4WTgZ8BC27+V9Drg7taFFRER7dDMcwoTgGm2t+krs30v8NetDCwiIkZfwysF2y8Chw33wJLWkHSDpJsl3Sbp5FL+HUn3SZpfXtuWckk6U9JCSQskbT/cz4yIiJen2ZnX/lvSWcAFwLN9hbZvHGKfpcCetp+RNBG4VtKVpe5Tti/qt/0+wObltRPwjfIeERGjpNmksG15P6WuzMCeg+1Q5ld+pqxOLK+h5lw+gOrOJgPXSZokabLtRU3GGBERL1NTHc223zbAa9CE0EfSBEnzqYbHmGP7+lL1hdJENFPS6qVsCvBg3e4PlbL+xzxSUo+knt7e3mbCj4iIJjV7pYCkdwNbUY19BIDtUwbfo9Yfsa2kScClkrYGTgAeAVYDZgGfYcUrkCHZnlX2o7u7e6grj4iIGKZmn2g+G3gf8HFAwMHAJs1+iO3FwFxgb9uLXFkKnAvsWDZ7GJhWt9vUUhYREaOk2ecU/o/tDwJP2j4Z2IVqpNRBSeoqVwhIWhN4B3BneQgOSQIOpBpcD+DHwAfLXUg7A0+lPyEiYnQ123z0fHl/TtJGwOPA5Ab7TAZml+ccVgEutH25pF9K6qK64pgPHFW2vwLYF1gIPAd8uOmziIiIEdFsUri8/NV/OtWAeAb+Y6gdbC8AthugfMAO6nLX0TFNxhMRES3QVFKw/S9l8WJJlwNr2H6qdWFFREQ7NJp57b1D1GH7kpEPKSIi2qXRlcJ7hqgzkKQQEdFBhkwKttPZGxExjjRqPvrEUPW2vzyy4URERDs1aj5ae1SiiIiIV4RGzUcnj1YgERHRfs0OczFV0qWSHi2viyVNbXVwERExupod5uJcqmEoNiqvn5SyiIjoIM0mhS7b59peVl7fAbpaGFdERLRBs0nhcUmHl/kRJkg6nGr8o4iI6CDNJoWPAIdQzYOwCDi
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Plot the data points\n",
|
||
|
|
"plt.scatter(X, y, marker='x', c='r')\n",
|
||
|
|
"\n",
|
||
|
|
"# Set the title\n",
|
||
|
|
"plt.title(\"Housing Prices\")\n",
|
||
|
|
"# Set the y-axis label\n",
|
||
|
|
"plt.ylabel('Price (in 1000s of dollars)')\n",
|
||
|
|
"# Set the x-axis label\n",
|
||
|
|
"plt.xlabel('Size (feet^2)')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "detailed-habitat",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# View the current parameter vector\n",
|
||
|
|
"tmp_w = [2,1]\n",
|
||
|
|
"print(\"View the current parameter vector\")\n",
|
||
|
|
"print(tmp_w)\n",
|
||
|
|
"print()\n",
|
||
|
|
"\n",
|
||
|
|
"# Calculate the model prediction h"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "pediatric-violin",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# Let's see how we can use gradient descent to arrive at this value of w\n",
|
||
|
|
"\n",
|
||
|
|
"# Calculate cost \n",
|
||
|
|
"\n",
|
||
|
|
"# Calculate gradient \n",
|
||
|
|
"\n",
|
||
|
|
"# alpha, direction of update\n",
|
||
|
|
"\n",
|
||
|
|
"# Show new value of tmp_w - superimpose on plot"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.9.1"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|