In the following function using xtensor, I was expecting the compiler to express the computation graph to auto z = 3.0 * u + 100 * xt::cos(u); and evaluate the result at xt::xtensor_fixed<T, xt::xshape<nn>> out = z; when assigning the expression to an exact tensor.
template<typename T, size_t nn>
T func_cos(xt::xtensor_fixed<T, xt::xshape<nn>> &u) {
auto z = 3.0 * u;
for (auto k = 0; k < 100; ++k){
z += xt::cos(u);
}
xt::xtensor_fixed<T, xt::xshape<nn>> out = z;
return out(0);
}
However, I got an error
views_xtensor.cpp:76:11: error: no viable overloaded '+='
z += xt::cos(u);
[....]
Did I use auto in an wrong way? How could I use lazy evaluation in a for-loop?
Thank you for your help!
When you write
auto z = 3.0 * u
The type of z encodes the operation, it is something like
xfunction<multiplies, scalar<double>, xtensor_fixed>
This class does not hold any value, so it is not possible to assign it anything (even if it is a computed assignment). Now even if iw was possible, consider the following expression
auto y = z + xt::cos(u);
The type of this expression is something like
xfunction<plus, type(z), xfunction<cos, xtensor_fixed>>
So when you write
z += xt::cos(u); // equivalent to z = z + xt::cos(u);
You try to assign z something of a completely different type.
Lazy evaluation cannot be used in for loops for these reasons.