lse

Builtin index · Core maths

result = lse(x)

Input: x · Output: result

What it is

Numerically stable log(sum(exp(x))), via the usual max-shift so a large input can't overflow exp. x is a scalar, Vec, or Mat/Signal, flattened.

Overloads

One call shape — this function is not overloaded.

Examples

From the documentation, and run by the build -- the output and the figure below are what the engine actually produced. Simplest first.

v = [5.0, 3.0, 8.0, 1.0, 9.0]
print("prod={prod(v)}")
print("argmedian={argmedian(v)}  argquantile(0.9)={argquantile(v, 0.9)}  argmean={argmean(v)}")
print("clamp={clamp([-5, 0, 5, 10], 0, 5)}")
print("var={var(v)}  std^2={std(v)^2}")
print("lse={lse(v)}")

prod=1080
argmedian=0  argquantile(0.9)=4  argmean=0
clamp=[0, 0, 5, 5]
var=11.2  std^2=11.2
lse=9.328591

from core-math

How it works

smooth-max family: logsumexp(x), smoothmax(x[, beta]), softmax(x).

From the note above this builtin in the interpreter, where the reasoning was written down as it was built.

See also

logsumexp — documented in the same entry.