Created
June 26, 2025 09:18
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#import jax | |
#import jax.numpy as jnp | |
import matplotlib.pyplot as plt | |
#import jax.numpy.linalg as linalg | |
import numpy as jnp | |
eta = 3 | |
rbf_ = lambda r: jnp.exp(-(eta*r)**2.0) | |
function = lambda x: jnp.exp(x*jnp.cos(x*3*jnp.pi)) | |
range_ = jnp.linspace(0.0,1.0,14) | |
function_values = function(range_) | |
rbf = rbf_(jnp.abs(range_[:,None] - range_[None,:])) | |
weights = jnp.linalg.solve(rbf, function_values[:,None]) | |
def interpolate(x_scalar): | |
diffs = x_scalar - range_ # shape (14,) | |
return (rbf_(jnp.abs(diffs)) * weights.squeeze(-1)).sum() | |
range2_ = jnp.linspace(0,1,100) | |
actual = function(range2_) | |
interpolated = jnp.vectorize(interpolate)(range2_) | |
fig, ax = plt.subplots() | |
plt.plot(range2_, actual) | |
plt.plot(range2_, interpolated) | |
plt.show() |
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