mse

std.loss.mse · Level L0

Mean squared error.

(1/n)·Σᵢ (yᵢ − tᵢ)²

Signature

mse(y: f64[n], t: f64[n]) → f64[]

Structure

The function as NOVA stores it: one box per input, operation and output, and arrows that carry values. A double border marks another library function this one runs — called once, or by Scan once per element; select it to open that function.

yf64[n]tf64[n]SubtractdMultiplyddMeanllf64[]
  • input
  • operation
  • constant
  • call
  • output

Verification

  • Signature proven by NOVA’s shape solver, for every size.
  • Equal to the reference np.mean((y - t) ** 2) in exact rational arithmetic, on all 40 test cases.
  • All 40 float64 results inside the running error bound; the closest uses 30% of it.
  • Interpreter and NumPy backend return bit-identical results.
Accuracy in detail
correctly rounded (the float64 nearest the exact value)
78%
bit-equal to the NumPy formula in float64
100%
largest error, in units in the last place
1.51

Identity

Calls
—
Called by
—
sha256:beb47019027c534e6e3fdb812e6f289ba99bb5c636648b5701afb7b648d60bd6

The semantic hash of the graph. It changes when the program changes, and never when only its documentation does.

Control handle

Symbol
Ω:std.loss.mse · Ω:mse
Pins
sha256:549006f5061f17cf030f01f393ce99e60c09f9049605fda9658f76a7bea5f5fdthis graph alone
Evidence
sha256:c7c174c54e78d642b80f423fc3bb45714878b17fa9139a89a8bc80df543c8d23the hash of its verification record
Needs
no capability: a pure function

Through NOVA’s control layer, the symbol launches this function only while the program still matches what it pins: a change to this graph, or to any graph it reaches, needs a migration first.