「学习记录」《数值分析》第二章计算实习题，Python语言

在假期利用Python完成了《数值分析》第二章的计算实习题，主要实现了牛顿插值法和三次样条插值，给出了自己的实现与调用Python包的实现——现在能搜到的基本上都是MATLAB版，或者是各种零碎的版本。

代码如下：

（第一题使用的自己的程序，第二第三题使用的Python自带库）

import math

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from numpy.linalg import solve
from scipy import interpolate
from scipy.interpolate import lagrange

plt.rc('figure',figsize=(20,15))

print("Problem I:")

given_x=[0.2,0.4,0.6,0.8,1.0]
given_y=[0.98,0.92,0.81,0.64,0.38]
given_times=4
x_range=(0,1.1,0.02)

#@brief: Convert(begin,end,interval) to a list, but interval can be float numbers.
def process_xpara(xpara):
    max_times=0
    if 0<xpara[2]<1:
        tmp_xpara_interval=xpara[2]
        while tmp_xpara_interval-int(tmp_xpara_interval)!=0:
            max_times=max_times+1
            tmp_xpara_interval=tmp_xpara_interval*10
    max_times=10**max_times
    return [i/max_times for i in range(int(xpara[0]*max_times),int(xpara[1]*max_times),int(xpara[2]*max_times))]


def divide_difference(x,y,times):
    now=[(x[i],y[i]) for i in range(len(x))]
    ans=[now[0]]
    for order in range(1,times+1):
        tmp=[]
        for i in range(1,len(now)):
            tmp.append((x[order+i-1]-x[i-1],(now[i][1]-now[i-1][1])/(x[order+i-1]-x[i-1])))
        now=tmp
        ans.append(now[0])
    return ans

def get_func_value_newton(xcoef,x,xorigin):
    ans=0
    for i in range(len(xcoef)):
        tmp=xcoef[i][1]
        for j in range(i):
            tmp=tmp*(x-xorigin[j])
        ans=ans+tmp
    return ans
"""
#@param: xpara(xbegin,xend,xinterval) fpara[f[x_1~i]]
"""
# spec_i=[0.2+0.08*x for x in (0,1,10,11)]

def newton_interpolate(xpara,fpara,xorigin):
    x_discrete_value=process_xpara(xpara)
    return [(x,get_func_value_newton(fpara,x,xorigin)) for x in x_discrete_value]

parameters=divide_difference(given_x,given_y,given_times)
newton_interpolate_value=newton_interpolate(x_range,parameters,given_x)

fig=plt.figure()
sub_fig1=fig.add_subplot(2,2,1)
sub_fig1.set_title("Problem I")
sub_fig1.plot([var[0] for var in newton_interpolate_value],[var[1] for var in newton_interpolate_value],label='Newton')

# l_f=lagrange(given_x,given_y)
# tmpara=process_xpara(x_range)
# plt.plot(tmpara,[l_f(x) for x in tmpara])

# 三次样条插值
n=len(given_x)
h=[]
f0p=0
fnp=0
for i in range(1,len(given_x)):
    h.append(given_x[i]-given_x[i-1])
miu=[0] # 0 should not be used
lam=[1]
d=[6/h[0]*((given_y[1]-given_y[0])/(given_x[1]-given_x[0])-f0p)]
for j in range(1,len(h)):
    miu.append(h[j-1]/(h[j-1]+h[j]))
    lam.append(h[j]/(h[j-1]+h[j]))
    d.append(6*((given_y[j+1]-given_y[j])/(given_x[j+1]-given_x[j])-(given_y[j-1]-given_y[j])/(given_x[j-1]-given_x[j]))/(h[j-1]+h[j]))
miu.append(1)
d.append(6/h[-1]*(fnp-(given_y[-1]-given_y[-2])/(given_x[-1]-given_x[-2])))

A=np.zeros((n,n))
for i in range(n):
    A[i][i]=2
    if i!=n-1:
        A[i][i+1]=lam[i]
    if i!=0:
        A[i][i-1]=miu[i]
C=solve(A,np.array(d).T)
# print(C)

def get_func_value_cubic_spline(mtuple, xtuple, ytuple, x):
    return mtuple[0]/(6*(xtuple[1]-xtuple[0]))*(xtuple[1]-x)**3+mtuple[1]/(6*(xtuple[1]-xtuple[0]))*(x-xtuple[0])**3+(ytuple[0]-(mtuple[0]*(xtuple[1]-xtuple[0])**2/6))*(xtuple[1]-x)/(xtuple[1]-xtuple[0])+(ytuple[1]-(mtuple[1]*(xtuple[1]-xtuple[0])**2/6))*(x-xtuple[0])/(xtuple[1]-xtuple[0])

def cubic_spline_interpolate(xpara, mpara, x, y):
    fun_value=[]
    x_discrete_value=process_xpara(xpara)
    for j in range(len(x)-1):
        ok_value=[(element,get_func_value_cubic_spline((mpara[j],mpara[j+1]),(x[j],x[j+1]),(y[j],y[j+1]),element)) for element in x_discrete_value if x[j]<=element<x[j+1]]
        fun_value=fun_value+ok_value
    return fun_value
cubic_spline_interpolate_value=cubic_spline_interpolate(x_range,C.tolist(),given_x,given_y)

sub_fig1.plot([var[0] for var in cubic_spline_interpolate_value],[var[1] for var in cubic_spline_interpolate_value],label='Cubic')

sub_fig1.legend(loc='best')

def get_func_x(x):
    return 1/(1+25*x*x)

given_x=np.linspace(-1,1,10)
given_y=get_func_x(given_x) #p.array([get_func_x(x) for x in given_x])
display_x=np.linspace(-1,1,100)
display_y=get_func_x(display_x)

sub_fig2=fig.add_subplot(2,2,2)
sub_fig2.set_title("Problem II(Alpha): Using System Functions")

c_x=interpolate.interp1d(given_x, given_y, kind = 'cubic')
l_x=lagrange(given_x,given_y)
sub_fig2.plot(display_x,l_x(display_x))
sub_fig2.plot(display_x,c_x(display_x))
sub_fig2.plot(display_x,display_y)

sub_fig3=fig.add_subplot(2,2,3)
sub_fig3.set_title("Problem II(Beta): Using System Functions")

given_x=np.linspace(-1,1,20)
given_y=get_func_x(given_x) #p.array([get_func_x(x) for x in given_x])
c_x=interpolate.interp1d(given_x, given_y, kind = 'cubic')
l_x=lagrange(given_x,given_y)
sub_fig3.plot(display_x,l_x(display_x))
sub_fig3.plot(display_x,c_x(display_x))
sub_fig3.plot(display_x,display_y)

fig_problem_three=plt.figure()

given_x=[0,1,4,9,16,25,36,49,64]
given_y=[0,1,2,3,4,5,6,7,8]
display_big_x=np.linspace(0,64,200)
display_small_x=np.linspace(0,1,50)
sub_fig4=fig_problem_three.add_subplot(2,1,1)
l_x=lagrange(given_x,given_y)
c_x=interpolate.interp1d(given_x, given_y, kind = 'cubic')
sub_fig4.plot(display_big_x,l_x(display_big_x),label='Lagrange')
sub_fig4.plot(display_big_x,c_x(display_big_x),label='Cubic')
sub_fig4.plot(display_big_x,np.sqrt(display_big_x),label='Origin')
sub_fig4.legend(loc='best')

sub_fig5=fig_problem_three.add_subplot(2,1,2)
sub_fig5.plot(display_small_x,l_x(display_small_x),label='Lagrange')
sub_fig5.plot(display_small_x,c_x(display_small_x),label='Cubic')
sub_fig5.plot(display_small_x,np.sqrt(display_small_x),label='Origin')
sub_fig5.legend(loc='best')