File:MDKQ anim ohne Ausreiser2.svg
![File:MDKQ anim ohne Ausreiser2.svg](https://upload.wikimedia.org/wikipedia/commons/thumb/4/41/MDKQ_anim_ohne_Ausreiser2.svg/512px-MDKQ_anim_ohne_Ausreiser2.svg.png?20170718120319)
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Contents
Summary
[edit]DescriptionMDKQ anim ohne Ausreiser2.svg |
Deutsch: Teilbild einer Animation Polynomapproximation unterschiedlicher Polynomordnung |
Date | |
Source | MDKQ anim ohne Ausreiser.gif |
Author | Johannes Kalliauer |
Other versions | File:MDKQ anim ohne Ausreiser.gif |
![]() | This image was improved or created by the Wikigraphists of the Graphic Lab (de). You can propose images to clean up, improve, create or translate as well. |
Licensing
[edit]![]() ![]() |
This file is made available under the Creative Commons CC0 1.0 Universal Public Domain Dedication. |
The person who associated a work with this deed has dedicated the work to the public domain by waiving all of their rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law. You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission.
http://creativecommons.org/publicdomain/zero/1.0/deed.enCC0Creative Commons Zero, Public Domain Dedicationfalsefalse |
Quellen: Skript zur Bildgenerierung
[edit]Erzeugungsskript, um die Grafik zu erstellen.
Anleitung
[edit]Benötigte Open-Source-Software:
- Python
- Python-Paket: numpy
- Python-Paket: matplotlib
Nach der Installation von Python den Quelltext in eine Datei mdkq.py kopieren und starten durch Doppelklicken oder in der Konsole durch Eingabe von
python mdkq.py
Python-Skript
[edit]![](https://upload.wikimedia.org/wikipedia/commons/thumb/0/01/Created_with_Matplotlib-logo.svg/22px-Created_with_Matplotlib-logo.svg.png)
#This source code is public domain
#Created by Christian Schirm
#Edited by Johannes Kalliauer
import numpy, pylab
from matplotlib.font_manager import FontProperties
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from numpy.random import randn
x=[1,2,3,4,5,7]
y=[2.0,2.5,2.5,3.4,3.7,3]
for N in range(1,8):
A=numpy.zeros((N,N))
for i in range(N):
for j in range(N):
A[i,j]=sum(xi**(i+j) for xi in x)
b=numpy.zeros((N))
for i in range(N):
b[i]=sum(xi**(i)*yi for xi,yi in zip(x,y))
c=numpy.linalg.solve(A, b)
xr=numpy.asarray(x)
yr=numpy.sum([c[i]*xr**i for i in range(len(c))],axis=0)
residuen=[]
for i in range(len(x)): residuen+=[[xr[i],xr[i]],[y[i],yr[i]],'g-']
xneu=numpy.linspace(0, 8, num=100)
yneu=numpy.sum([c[i]*xneu**i for i in range(len(c))],axis=0)
plt.clf()
fig = plt.figure(figsize=(4.5, 3.5))
fig.subplotpars.bottom=0.13
y0=plt.plot(*residuen[:-3])
plt.setp(y0, color='#80d080', linewidth=1.5)
#y0=plt.plot(*residuen[-3:], label="Residuen")
y0,=plt.plot(*residuen[-3:])
plt.setp(y0, color='#80d080', linewidth=1.5)
#y2=plt.plot(xneu,yneu,'r-', label="Modellfunktion")
y2,=plt.plot(xneu,yneu,'r-')
#y1=plt.plot(x,y,'o', label="Messpunkte")
y1,=plt.plot(x,y,'o')
plt.xlabel('x')
plt.ylabel('y')
font = FontProperties()
font.set_size('medium')
leg = plt.legend([y1,y2,y0],['Messpunkte','Modellfunktion','Residuen'],frameon=True,loc='lower right',labelspacing=0.3,prop=font)
#leg = plt.legend(frameon=True,loc='lower right',labelspacing=0.3,prop=font)
plt.grid(True)
plt.axis([0, 8, 0, 8])
plt.text(1,7, "Polynomgrad "+str(N-1),bbox=dict(boxstyle="square,pad=0.5",color='white',ec='black',fill=True))
#plt.show()
plt.savefig('MDKQ_anim%i.png'%N)
plt.savefig('test.eps', format='eps', dpi=900)
plt.savefig("MDKQ_anim%i.svg"%N)
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Date/Time | Thumbnail | Dimensions | User | Comment | |
---|---|---|---|---|---|
current | 12:03, 18 July 2017 | ![]() | 512 × 398 (33 KB) | JoKalliauer (talk | contribs) |
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