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@ofx ofx/
Last active Nov 18, 2019

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import math
import numpy as np
from numpy import linalg as LA
# Function to approximate
def f(x):
return math.exp(x * math.cos(3.0 * math.pi * x)) - 1.0
# Radial basis function (Gaussian with epsilon 3.05048)
def phi(r):
return math.exp(-math.pow(3.05048 * r, 2.0))
# Discrete points
X = np.array([x / 14.0 for x in range(0, 15)])
# Data values (right hand side)
V = np.array([f(x) for x in X])
# Matrix containing interpolation values
# with phi(||xi - xj||) at each Mij
M = np.array([[phi(abs(x - y)) for x in X] for y in X])
print('X: %s' % X)
print('V: %s' % V)
print('M: %s' % M)
# Solve M*W=V
W = LA.solve(M, V)
print('W: %s' % W)
# Compute intepolated values
S = np.array([sum([w * phi(abs(x - y)) for w, y in zip(W, X)]) for x in X])
print('S: %s' % S)
# Maximum error
print('Error: %s' % max(np.array([abs(x - y) for x, y in zip(S, V)])))
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