rub*_*bik 6 python interpolation polynomial-math
我想用Lagrange方法插值多项式,但这段代码不起作用:
def interpolate(x_values, y_values):
def _basis(j):
p = [(x - x_values[m])/(x_values[j] - x_values[m]) for m in xrange(k + 1) if m != j]
return reduce(operator.mul, p)
assert len(x_values) != 0 and (len(x_values) == len(y_values)), 'x and y cannot be empty and must have the same length'
k = len(x_values)
return sum(_basis(j) for j in xrange(k))
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我跟着维基百科,但是当我运行它时,我在第3行收到一个IndexError!
谢谢
我参加聚会晚了将近十年,但我发现这是在寻找拉格朗日插值的简单实现。@smichr 的回答很好,但是 Python 有点过时了,我还想要一些可以很好地使用的东西,np.ndarrays这样我就可以轻松地进行绘图。也许其他人会发现这很有用:
import numpy as np
import matplotlib.pyplot as plt
class LagrangePoly:
def __init__(self, X, Y):
self.n = len(X)
self.X = np.array(X)
self.Y = np.array(Y)
def basis(self, x, j):
b = [(x - self.X[m]) / (self.X[j] - self.X[m])
for m in range(self.n) if m != j]
return np.prod(b, axis=0) * self.Y[j]
def interpolate(self, x):
b = [self.basis(x, j) for j in range(self.n)]
return np.sum(b, axis=0)
X = [-9, -4, -1, 7]
Y = [5, 2, -2, 9]
plt.scatter(X, Y, c='k')
lp = LagrangePoly(X, Y)
xx = np.arange(-100, 100) / 10
plt.plot(xx, lp.basis(xx, 0))
plt.plot(xx, lp.basis(xx, 1))
plt.plot(xx, lp.basis(xx, 2))
plt.plot(xx, lp.basis(xx, 3))
plt.plot(xx, lp.interpolate(xx), linestyle=':')
plt.show()
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尝试
def interpolate(x, x_values, y_values):
def _basis(j):
p = [(x - x_values[m])/(x_values[j] - x_values[m]) for m in xrange(k) if m != j]
return reduce(operator.mul, p)
assert len(x_values) != 0 and (len(x_values) == len(y_values)), 'x and y cannot be empty and must have the same length'
k = len(x_values)
return sum(_basis(j)*y_values[j] for j in xrange(k))
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您可以如下确认:
>>> interpolate(1,[1,2,4],[1,0,2])
1.0
>>> interpolate(2,[1,2,4],[1,0,2])
0.0
>>> interpolate(4,[1,2,4],[1,0,2])
2.0
>>> interpolate(3,[1,2,4],[1,0,2])
0.33333333333333331
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因此,结果是经过给定点的基于多项式的插值。在这种情况下,这3个点定义了一个抛物线,并且前3个测试表明,对于给定的x_value,返回了指定的y_value。