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FYS-STK4155/doc/pub/How2ReadData/ipynb/.ipynb_checkpoints/How2ReadData-checkpoint.ipynb
T
2017-11-27 22:22:51 +00:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- dom:TITLE: Data Analysis and Machine Learning: Representing data -->\n",
"# Data Analysis and Machine Learning: Representing data\n",
"<!-- dom:AUTHOR: Morten Hjorth-Jensen at Department of Physics, University of Oslo & Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University -->\n",
"<!-- Author: --> \n",
"**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
"\n",
"Date: **Nov 26, 2017**\n",
"\n",
"Copyright 1999-2017, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
"\n",
"\n",
"\n",
"\n",
"## Representing data, overarching aims"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 1. 0. 0. 0.]\n",
" [ 0. 1. 0. 0.]\n",
" [ 0. 0. 1. 0.]\n",
" [ 0. 0. 0. 1.]]\n",
" (0, 0)\t1.0\n",
" (1, 1)\t1.0\n",
" (2, 2)\t1.0\n",
" (3, 3)\t1.0\n"
]
},
{
"data": {
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QLavUFGYG6k5aJSGFQd0JCfDdffF/mltrn4eq/Aws5tj+d6se69U1inEan/HG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\nL1bqDIYoX5wGh53H79mGT0qea+4JqS7wxplBfFL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"text/plain": [
"<matplotlib.figure.Figure at 0x111928be0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Age</th>\n",
" <th>Location</th>\n",
" <th>Name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>51</td>\n",
" <td>Roma</td>\n",
" <td>John</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>21</td>\n",
" <td>Napoli</td>\n",
" <td>Anna</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>34</td>\n",
" <td>Torino</td>\n",
" <td>Peter</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>45</td>\n",
" <td>Milano</td>\n",
" <td>Linda</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Age Location Name\n",
"0 51 Roma John\n",
"1 21 Napoli Anna\n",
"2 34 Torino Peter\n",
"3 45 Milano Linda"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy import sparse\n",
"import pandas as pd\n",
"from IPython.display import display\n",
"eye = np.eye(4)\n",
"print(eye)\n",
"sparse_mtx = sparse.csr_matrix(eye)\n",
"print(sparse_mtx)\n",
"x = np.linspace(-10,10,100)\n",
"y = np.sin(x)\n",
"plt.plot(x,y,marker='x')\n",
"plt.show()\n",
"data = {'Name': [\"John\", \"Anna\", \"Peter\", \"Linda\"], 'Location': [\"Roma\", \"Napoli\", \"Torino\", \"Milano\"], 'Age':[51, 21, 34, 45]}\n",
"data_pandas = pd.DataFrame(data)\n",
"display(data_pandas)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Representing data, overarching aims"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/site-packages/scipy/linalg/basic.py:1226: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n",
" warnings.warn(mesg, RuntimeWarning)\n"
]
},
{
"data": {
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Yl/LUNbuPs+Vga9L24X0bOGfy4KwuH7E0DaES+NhzKALmfPxdESvuhpuOzoiJ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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11472d588>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy import sparse\n",
"import pandas as pd\n",
"from IPython.display import display\n",
"import mglearn\n",
"import sklearn\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.tree import DecisionTreeRegressor\n",
"x, y = mglearn.datasets.make_wave(n_samples=100)\n",
"line = np.linspace(-3,3,1000,endpoint=False).reshape(-1,1)\n",
"reg = DecisionTreeRegressor(min_samples_split=3).fit(x,y)\n",
"plt.plot(line, reg.predict(line), label=\"decision tree\")\n",
"regline = LinearRegression().fit(x,y)\n",
"plt.plot(line, regline.predict(line), label= \"Linear Rgression\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
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