123 lines
14 KiB
Plaintext
123 lines
14 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "favorite-meeting",
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "anonymous-logistics",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Let's start with the same data points as before \n",
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"X = [1000, 2000] \n",
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"y = [200, 400]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "increasing-healing",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Text(0.5, 0, 'Size (feet^2)')"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": 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\n",
|
|
"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
|
|
}
|