【数值分析】Python实现Lagrange插值
⼀直想把这⼏个插值公式⽤代码实现⼀下，今天闲着没事，尝试尝试。
先从最简单的拉格朗⽇插值开始！关于拉格朗⽇插值公式的基础知识就不赘述，百度上⼀搜⼀⼤堆。
基本思路是⾸先从⽂件读⼊给出的样本点，根据输⼊的插值次数和想要预测的点的x选择合适的样本点区间，最后计算基函数得到结果。直接看代码！（注：这⾥说样本点不是很准确，实在词穷不到⼀个更好的描述。。。）
str2double
⼀个⼩问题就是怎样将python中的str类型转换成float类型，毕竟我们给出的样本点不⼀定总是整数，⽽且也需要做⼀些容错处理，⽐如多个+、多个-等等，也应该能识别为正确的数。所以实现了⼀个str2double⽅法。
【20200903更新】如果不⽤考虑多个前置正号或者负号的情况，直接⽤float()就可以，我居然今天才知道QAQ。
import re
def str2double(str_num):
pattern = repile(r'^((\+*)|(\-*))?(\d+)(.(\d+))?$')
m = pattern.match(str_num)
if m is None:
return m
else:
sign = 1 if str_num[0] == '+' or '0' <= str_num[0] <= '9' else -1
num = re.sub(r'(\++)|(\-+)', "", m.group(0))
matchObj = re.match(r'^\d+$', num)
if matchObj is not None:
num = sign * up(0))
else:
matchObj = re.match(r'^(\d+).(\d+)$', num)
if matchObj is not None:
integer = up(1))
fraction = up(2)) * pow(10, -1*(up(2))))
num = sign * (integer + fraction)
return num
我使⽤了正则表达式来实现，pattern = repile(r'^((\+*)|(\-*))?(\d+)(.(\d+))?$')可以匹配我上⾯提到的所有类型的整数和浮点数，之后进⾏匹配，匹配成功，如果是整数，直接return整数部分，这个⽤(int)强制转换即可；如果是浮点数，那么⽤(\d+)这个正则表达式再次匹配，分别得到整数部分和⼩数部分，整数部分的处理和上⾯类似，⼩数部分则⽤乘以pow(10, -⼩数位数)得到，之后直接相加即可。这⾥为了⽀持多个+或者-，使⽤re.sub⽅法将符号去掉，所以就需要⽤sign来记录数字的正负，在最后return时乘上sign即可。
binary_searchpython正则表达式匹配小数
def binary_search(point_set, n, x):
first = 0
length = len(point_set)
last = length
while first < last:
mid = (first + last) // 2
if point_set[mid][0] < x:
first = mid + 1
elif point_set[mid][0] == x:
return mid
else:
last = mid
last =  last if last != length else last-1
head = last - 1
tail = last
while n > 0:
if head != -1:
n -= 1
head -= 1
if tail != length:
n -= 1
tail += 1
return [head+1, tail-1] if n == 0 else [head+1, tail-2]
这⾥point_set是全部样本点的集合，n是输⼊的插值次数，x是输⼊的预测点。返回合适的插值区间，即尽可能地把x包在⾥⾯。
因为要根据输⼊得到合适的插值区间，所以就涉及查⽅⾯的知识。这⾥使⽤了⼆分查，先对样本点集合point_set进⾏排序（升序），到第⼀个⼤于需要预测点的样本点，在它的两侧扩展区间，直到满⾜插值次数要求。这⾥我的实现有些问题，可能会出现n=-1因为tail多加了⼀次，就在while循环外⼜进⾏了⼀次判断，n=-1时tail-2，这个实现的确不好，可能还会有bug。。。
最后，剩下的内容⽐较好理解，直接放上全部代码。
import re
import matplotlib.pyplot as plt
import numpy as np
def str2double(str_num):
pattern = repile(r'^((\+*)|(\-*))?(\d+)(.(\d+))?$')
m = pattern.match(str_num)
if m is None:
return m
else:
sign = 1 if str_num[0] == '+' or '0' <= str_num[0] <= '9' else -1
num = re.sub(r'(\++)|(\-+)', "", m.group(0))
matchObj = re.match(r'^\d+$', num)
if matchObj is not None:
num = sign * up(0))
else:
matchObj = re.match(r'^(\d+).(\d+)$', num)
if matchObj is not None:
integer = up(1))
fraction = up(2)) * pow(10, -1*(up(2))))                num = sign * (integer + fraction)
return num
def preprocess():
f = open("", "r")
lines = f.readlines()
lines = [line.strip('\n') for line in lines]
point_set = list()
for line in lines:
point = list(filter(None, line.split(" ")))
point = [str2double(pos) for pos in point]
point_set.append(point)
return point_set
def lagrangeFit(point_set, x):
res = 0
for i in range(len(point_set)):
L = 1
for j in range(len(point_set)):
if i == j:
continue
else:
L = L * (x - point_set[j][0]) / (point_set[i][0] - point_set[j][0])
L = L * point_set[i][1]
res += L
return res
def showbasis(point_set):
print("Lagrange Basis Function:\n")
for i in range(len(point_set)):
top = ""
buttom = ""
for j in range(len(point_set)):
if i == j:
continue
else:
top += "(x-{})".format(point_set[j][0])
buttom += "({}-{})".format(point_set[i][0], point_set[j][0])
print("Basis function{}:".format(i))
print("\t\t{}".format(top))
print("\t\t{}".format(buttom))
def binary_search(point_set, n, x):
first = 0
length = len(point_set)
last = length
while first < last:
mid = (first + last) // 2
if point_set[mid][0] < x:
first = mid + 1
elif point_set[mid][0] == x:
return mid
else:
last = mid
last =  last if last != length else last-1
head = last - 1
tail = last
while n > 0:
if head != -1:
n -= 1
head -= 1
if tail != length:
n -= 1
tail += 1
return [head+1, tail-1] if n == 0 else [head+1, tail-2]
if __name__ == '__main__':
pred_x = input("Predict x:")
pred_x = float(pred_x)
n = input("Interpolation times:")
n = int(n)
point_set = preprocess()
point_set = sorted(point_set, key=lambda a: a[0])
span = binary_search(point_set, n+1, pred_x)
print("Chosen points: {}".format(point_set[span[0]:span[1]+1]))
showbasis(point_set[span[0]:span[1]+1])
X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
S = np.sin(X)
L = [lagrangeFit(point_set, x) for x in X]
L1 = [lagrangeFit(point_set[span[0]:span[1]+1], x) for x in X]
plt.figure(figsize=(8, 4))
plt.plot(X, S, label="$sin(x)$", color="red", linewidth=2)
plt.plot(X, L, label="$LagrangeFit-all$", color="blue", linewidth=2)
plt.plot(X, L1, label="$LagrangeFit-special$", color="green", linewidth=2)    plt.xlabel('x')
plt.ylabel('y')
plt.title("$sin(x)$ and Lagrange Fit")
plt.legend()
plt.show()
About Input
使⽤了进⾏样本点读⼊，每⼀⾏⼀个点，中间有⼀个空格。
结果
感觉挺好玩的hhh，过⼏天试试⽜顿插值！掰掰！

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