pyodide/benchmark/benchmarks/slowparts.py

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2018-04-05 22:07:33 +00:00
#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)
#run: slowparts(*params)
#pythran export slowparts(int, int, float [][][], float [][][], float [][], float [][], float [][][], float [][][], int)
from numpy import zeros, power, tanh
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def slowparts(d, re, preDz, preWz, SRW, RSW, yxV, xyU, resid):
""" computes the linear algebra intensive part of the gradients of the grae
"""
fprime = lambda x: 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]
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return partialDU