pyodide/benchmark/benchmarks/slowparts.py

# from: https://groups.google.com/forum/#!topic/parakeet-python/p-flp2kdE4U
# setup: import numpy as np ;d = 10 ;re = 5 ;params = (d, re, np.ones((2*d, d+1, re)), np.ones((d, d+1, re)),  np.ones((d, 2*d)), np.ones((d, 2*d)), np.ones((d+1, re, d)), np.ones((d+1, re, d)), 1) # noqa
# run: slowparts(*params)

# pythran export slowparts(int, int, float [][][], float [][][], float
# [][], float [][], float [][][], float [][][], int)
from numpy import zeros, power, tanh


def slowparts(d, re, preDz, preWz, SRW, RSW, yxV, xyU, resid):
    """ computes the linear algebra intensive part of the gradients of the grae
    """
    def fprime(x): return 1 - power(tanh(x), 2)

    partialDU = zeros((d + 1, re, 2 * d, d))
    for k in range(2 * d):
        for i in range(d):
            partialDU[:, :, k, i] = (
                fprime(preDz[k]) * fprime(preWz[i])
                * (SRW[i, k] + RSW[i, k]) * yxV[:, :, i])

    return partialDU