func solveLinear(h [][]float64, g []float64) ([]float64, error) · probabilities, returning a and b such that calibrated

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This relation defines probabilities, returning a and b such that calibrated in the source section “func solveLinear(h [][]float64, g []float64) ([]float64, error) · probabilities, returning a and b such that calibrated”. It specifies the displayed relationship between the named quantities or parameters. Its interpretation depends on the source’s stated variables and conditions; this record does not assign an unprovided physical or cognitive meaning to them.

Terms & definitions

The recovered passage does not supply a separate symbol table. Read the source context for the notation used in this version; familiar letters may carry different meanings in other builds.

How it works

1. Read probabilities, returning a and b such that calibrated as the quantity being defined or updated by this relation.

2. Substitute the source-defined variables or recorded values and follow the displayed arithmetic and indexing exactly.

3. Use this version’s variable definitions and update order. The same symbol may be assigned a different meaning in another build.

Assumptions & limits

• This source expression was recovered from the pinned code revision. Its presence does not establish that the associated program was built, run, or scientifically validated.

Original context

	for i := 0; i < n; i++ {
		x[i] = m[i][n] / m[i][i]
	}
	return x, nil
}

// FitPlatt fits Platt scaling: a one dimensional logistic map from raw model scores to calibrated
// probabilities, returning a and b such that calibrated = sigmoid(a*score + b). It reuses FitLogistic with
// the score as the single feature and recovers a and b on the raw score scale. Platt is the cold start
// calibrator: parametric, robust when the label set is thin, later replaced by isotonic past ~1000 labels.
func FitPlatt(scores []float64, y []int) (a, b float64, err error) {
	x := make([][]float64, len(scores))
	for i := range scores {
		x[i] = []float64{scores[i]}
	}
	m, e := FitLogistic(x, y, 0)
	if e != nil {
		return 0, 0, e

probabilities, returning a and b such that calibrated = sigmoid(a*score + b). It reuses FitLogistic with

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