Evaluates the polynomial at specific values.
JAX implementations of numpy.polyval().
For the 1D-polynomial coefficients p of length M, the function returns the value:
\[p_0 x^{M - 1} + p_1 x^{M - 2} + ... + p_{M - 1}\]
p (ArrayLike) – An array of polynomial coefficients of shape (M,).
x (ArrayLike) – A number or an array of numbers.
unroll (int) – A number used to control the number of unrolled steps with lax.scan. It must be specified statically.
An array of same shape as x.
Note
The unroll parameter is JAX specific. It does not affect correctness but can have a major impact on performance for evaluating high-order polynomials. The parameter controls the number of unrolled steps with lax.scan inside the jnp.polyval implementation. Consider setting unroll=128 (or even higher) to improve runtime performance on accelerators, at the cost of increased compilation time.
Examples
>>> p = jnp.array([2, 5, 1]) >>> jnp.polyval(p, 3) Array(34., dtype=float32)
If x is a 2D array, polyval returns 2D-array with same shape as that of x:
>>> x = jnp.array([[2, 1, 5],
... [3, 4, 7],
... [1, 3, 5]])
>>> jnp.polyval(p, x)
Array([[ 19., 8., 76.],
[ 34., 53., 134.],
[ 8., 34., 76.]], dtype=float32)
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