algorithm-in-python/math/numericalAnalysis/interplotion.py
2020-04-15 12:28:20 +08:00

92 lines
2.5 KiB
Python

''' mbinary
#########################################################################
# File : interplotion.py
# Author: mbinary
# Mail: zhuheqin1@gmail.com
# Blog: https://mbinary.xyz
# Github: https://github.com/mbinary
# Created Time: 2018-10-02 21:14
# Description:
#########################################################################
'''
#########################################################################
# File : interplotion.py
# Author: mbinary
# Mail: zhuheqin1@gmail.com
# Blog: https://mbinary.xyz
# Github: https://github.com/mbinary
# Created Time: 2018-05-18 09:29
# Description: 插值计算,有牛顿插值,拉格朗日插值,以及通过插值得到的多项式估计新的函数值
#########################################################################
import sympy
from collections import namedtuple
from functools import reduce
from operator import mul
X = sympy.Symbol('x')
point = namedtuple('point', ['x', 'y'])
class interplotion:
def __init__(self, points):
self.points = [point(x, y) for x, y in points]
self.xs = [i for i, j in points]
self.poly, self.rem = self.newton(self.points, 0, len(self.points)-1)
def newton(self, li, a, b):
'''li:[(x,f(x))...]'''
qs = [li[0].y]
def quoDiff(begin, end):
if begin == end:
return li[begin].y
q = (quoDiff(begin+1, end)-quoDiff(begin, end-1)) / \
(li[end].x-li[begin].x)
if begin == a:
qs.append(q)
return q
quoDiff(a, b)
poly, base = 0, 1
for i, q in enumerate(qs):
poly += q*base
base *= X-li[i].x
return poly, base*qs[-1]
def lagrange(self, points=None):
xs = None
if points is None:
xs = self.xs
points = self.points
else:
xs = [x for x, y in points]
product = reduce(mul, [X-x for x in xs], 1)
poly = 0
for x, y in points:
tmp = product/(X-x)
coef = y/(tmp.subs(X, x))
poly += coef * tmp
return poly
def predict(self, val, poly=None):
if poly is None:
poly = self.poly
return poly.subs(X, val) # note the func subs
if __name__ == '__main__':
f = interplotion([(81, 9), (100, 10), (121, 11)])
p = f.lagrange()
print(p.subs(X, 105))
print(p)
intor = interplotion([(0, 11), (0.02, 9), (0.04, 7), (0.06, 10)])
p = intor.lagrange()
print(p)
res = intor.predict(0.08)
print(res)