var

Builtin index · Core maths

result = var(x, [on_invalid=])

Input: x, on_invalid · Output: result

What it is

Sample variance, same N-1 (unbiased) denominator as std, so var(x) == std(x)^2 always holds. x is a scalar, Vec, or Mat/Signal, flattened. On a Mat, the optional axis= keyword (0 or 1) computes the variance down each column or across each row instead, returning a length-cols/rows Vec (same support as std). on_invalid= ("propagate"/"ignore"/"error", see this section's intro) controls NaN/Inf handling.

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

var(x) — sample variance, same N-1 (unbiased) denominator as std, so var(x) == std(x)^2 always holds.

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