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#ifndef PYTHONIC_NUMPY_LINALG_MATRIX_POWER_HPP
#define PYTHONIC_NUMPY_LINALG_MATRIX_POWER_HPP
#include "pythonic/include/numpy/linalg/matrix_power.hpp"
#include "pythonic/numpy/array.hpp"
#include "pythonic/numpy/asarray.hpp"
#include "pythonic/numpy/identity.hpp"
#include "pythonic/numpy/dot.hpp"
#include "pythonic/builtins/NotImplementedError.hpp"
PYTHONIC_NS_BEGIN
namespace numpy
{
namespace linalg
{
namespace details
{
template <class E>
E fast_pow(E const &base, long n)
{
if (n == 1)
return base;
if (n == 2)
return numpy::functor::dot{}(base, base);
if (n == 3) {
auto tmp = numpy::functor::dot{}(base, base);
return numpy::functor::dot{}(tmp, base);
}
// starting from here, we know for sure that tmp will point to newly
// allocated memory
// this is used to optimize in-place dot computation in the odd case
auto tmp = fast_pow(base, n / 2);
if (n & 1) {
auto next = numpy::functor::dot{}(tmp, tmp);
return numpy::functor::dot{}(base, next, tmp);
} else {
return numpy::functor::dot{}(tmp, tmp);
}
}
}
template <class E>
auto matrix_power(E const &expr, long n)
-> decltype(numpy::functor::array{}(expr))
{
if (n == 0)
return numpy::functor::identity{}(expr.template shape<0>(),
types::dtype_t<typename E::dtype>{});
if (n > 0) {
auto base = numpy::functor::asarray{}(expr);
return details::fast_pow(base, n);
}
throw pythonic::builtins::NotImplementedError("negative power");
}
}
}
PYTHONIC_NS_END
#endif
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