Files
FYS-STK4155/doc/Programs/JupyterFiles/Examples/My Own Examples/Gaussian Noise.ipynb
T
2018-05-06 22:19:59 -04:00

91 lines
21 KiB
Plaintext

{
"cells": [
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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lpZqBLeVCK13jp7lgjp8u5amPd7Bo/QFaJcYx64YB/DStI1IxgnXfN/CSHdYD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\n3P8HRMHT1Past2QAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a13ab9080>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.005\n"
]
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import random\n",
"from sklearn.linear_model import Ridge\n",
"from sklearn.preprocessing import PolynomialFeatures\n",
"from sklearn.pipeline import make_pipeline\n",
"from sklearn.linear_model import LinearRegression\n",
"\n",
"x=np.linspace(0.02,0.98,200)\n",
"noise = np.asarray(random.sample((range(200)),200))\n",
"y=x**3*noise\n",
"yn=x**3*100\n",
"poly3 = PolynomialFeatures(degree=3)\n",
"X = poly3.fit_transform(x[:,np.newaxis])\n",
"clf3 = LinearRegression()\n",
"clf3.fit(X,y)\n",
"\n",
"Xplot=poly3.fit_transform(x[:,np.newaxis])\n",
"poly3_plot=plt.plot(x, clf3.predict(Xplot), label='Cubic Fit')\n",
"plt.plot(x,yn, color='red', label=\"True Cubic\")\n",
"plt.scatter(x, y, label='Data', color='orange', s=15)\n",
"plt.legend()\n",
"plt.show()\n",
"\n",
"def error(a):\n",
" for i in y:\n",
" err=(y-yn)/yn\n",
" return abs(np.sum(err))/len(err)\n",
"\n",
"print (error(y))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}