File:MDKQ anim4.png

Summary

Description
Deutsch: Teilbild einer Animation Polynomapproximation unterschiedlicher Polynomordnung
Date
Source Own work based on: MDKQ anim.gif
Author Johannes Kalliauer
PNG development
InfoField

Licensing

I, the copyright holder of this work, hereby publish it under the following license:
Creative Commons CC-Zero 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.

Category:CC-Zero#MDKQ%20anim4.pngCategory:Self-published work

Quellen: Skript zur Bildgenerierung

Erzeugungsskript, um die Grafik zu erstellen.

Anleitung

Benötigte Open-Source-Software:

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

#This source code is public domain
import numpy, pylab
from matplotlib.font_manager import FontProperties
import matplotlib.pyplot as plt

x=[1,2,3,4,5,6,7]
y=[2.0,2.5,2.5,3.4,3.7,6.6,3]

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:])
    plt.setp(y0, color='#80d080', linewidth=1.5)
    y2=plt.plot(xneu,yneu,'r-')
    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)
    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)
Category:Regression analysis Category:Numerical analysis Category:NumPy
Category:CC-Zero Category:NumPy Category:Numerical analysis Category:PNG created with Matplotlib Category:Regression analysis Category:Self-published work