187 lines
34 KiB
Plaintext
187 lines
34 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Algorytm najszybszego spadku dla regresji wielomianowej. \n",
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"Zakładamy, że dysponujemy zbiorem składającym się z dwóch cech (x i y). Modelujemy zależność y od x za pomocą funkcji wielomianowej. Celem projektu jest implementacja metody najszybszego spadku dla tego problemu. Zakładamy kwadratową funkcję straty. Implementacja powinna umożliwiać podanie stopnia wielomianu, który ma być użyty do modelowania. Implementacja powinna zwracać wektor oszacowanych parametrów oraz pokazywać wizualnie zmiany wartości funkcji straty wraz z postępem uczenia."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Zbiór danych: https://www.kaggle.com/varpit94/apple-stock-data-updated-till-22jun2021?select=AAPL.csv\n",
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"This dataset provides historical data of APPLE INC. stock (AAPL). The data is available at a daily level. Currency is USD."
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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": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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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": 22,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Date</th>\n",
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" <th>Open</th>\n",
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" <th>High</th>\n",
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" <th>Low</th>\n",
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" <th>Close</th>\n",
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" <th>Adj Close</th>\n",
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" <th>Volume</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>1980-12-12</td>\n",
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" <td>0.128348</td>\n",
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" <td>0.128906</td>\n",
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" <td>0.128348</td>\n",
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" <td>0.128348</td>\n",
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" <td>0.100751</td>\n",
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" <td>469033600</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>1980-12-15</td>\n",
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" <td>0.122210</td>\n",
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" <td>0.122210</td>\n",
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" <td>0.121652</td>\n",
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" <td>0.121652</td>\n",
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" <td>0.095495</td>\n",
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" <td>175884800</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>1980-12-16</td>\n",
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" <td>0.113281</td>\n",
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" <td>0.113281</td>\n",
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" <td>0.112723</td>\n",
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" <td>0.112723</td>\n",
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" <td>0.088485</td>\n",
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" <td>105728000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>1980-12-17</td>\n",
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" <td>0.115513</td>\n",
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" <td>0.116071</td>\n",
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" <td>0.115513</td>\n",
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" <td>0.115513</td>\n",
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" <td>0.090676</td>\n",
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" <td>86441600</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>1980-12-18</td>\n",
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" <td>0.118862</td>\n",
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" <td>0.119420</td>\n",
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" <td>0.118862</td>\n",
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" <td>0.118862</td>\n",
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" <td>0.093304</td>\n",
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" <td>73449600</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Date Open High Low Close Adj Close Volume\n",
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"0 1980-12-12 0.128348 0.128906 0.128348 0.128348 0.100751 469033600\n",
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"1 1980-12-15 0.122210 0.122210 0.121652 0.121652 0.095495 175884800\n",
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"2 1980-12-16 0.113281 0.113281 0.112723 0.112723 0.088485 105728000\n",
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"3 1980-12-17 0.115513 0.116071 0.115513 0.115513 0.090676 86441600\n",
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"4 1980-12-18 0.118862 0.119420 0.118862 0.118862 0.093304 73449600"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"df = pd.read_csv('AAPL.csv')\n",
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"X = df['Low']\n",
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"Y = df['Volume']\n",
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"display(df.head(5))"
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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": 23,
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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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"<Figure size 1440x720 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"fig = plt.figure(figsize=(20,10))\n",
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"chart = fig.add_subplot()\n",
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"chart.plot(X,Y ,\"go\")\n",
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"plt.show()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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