func TestScoreModelIntentGolden(t *testing.T) · sigmoid(0.6054651081)

Read the equation

This relation defines sigmoid(0.6054651081) in the source section “func TestScoreModelIntentGolden(t *testing.T) · sigmoid(0.6054651081)”. Applies an exponential factor. The exponent determines its real scaling or, for a complex exponent, its phase. 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 sigmoid(0.6054651081) as the quantity being defined or updated by this relation.

2. Applies an exponential factor. The exponent determines its real scaling or, for a complex exponent, its phase.

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

	}

	// The calibrated probability is sigmoid(0.6054651081) approx 0.6469. With no calibration bins the identity
	// map passes the raw through. The surfaced value is a decimal in [0, 1].
	if value.LessThan(decimal.Zero) || value.GreaterThan(decimal.NewFromInt(1)) {
		t.Fatalf("the probability must be in [0, 1], got %s", value)
	}
	// sigmoid(0.6054651081) = 1 / (1 + e^-0.6054651081). e^-0.6054651081 approx 0.54580, so p approx 0.64695.
	lo := decimal.RequireFromString("0.646")
	hi := decimal.RequireFromString("0.648")
	if value.LessThan(lo) || value.GreaterThan(hi) {
		t.Errorf("the calibrated probability must be near 0.647, got %s", value)
	}
}

// TestScoreModelIntentPriorOnlyReturnsBaseRate proves the prior only invariant: with every learned coefficient
// zero (the v0 seed), the model returns the sigmoid of the intercept alone (the calibrated base rate) no
// matter what the feature value is, because a zero coefficient makes the feature contribution zero. This is

sigmoid(0.6054651081) = 1 / (1 + e^-0.6054651081). e^-0.6054651081 approx 0.54580, so p approx 0.64695.

Related builds