Classification refinement for configured binary values #
Every storage codec preserves classification and canonicality. The ten class characterizations and the exact numerical threshold theorems are transported from the descriptor model, with no assumptions on storage width, bias, or exceptional-value policy beyond a valid descriptor.
Configured classification is the descriptor-model classification.
Configured normality is the descriptor-model normality predicate.
Configured canonicality concerns the exact interchange word decoded by the codec.
Packing any descriptor model preserves its class.
Packing a descriptor model preserves its normality.
Packing a descriptor model preserves its canonicality.
All ten configured classification results agree with the existing configured predicates.
The signaling class is exactly the configured signaling predicate.
The quiet class is exactly the configured quiet-NaN predicate.
Negative infinity retains its stored negative sign.
Positive infinity retains its stored positive sign.
Negative zero is a zero with a negative stored sign under the configured policy.
Positive zero is a zero with a positive stored sign.
Negative subnormal classification agrees with the configured predicates.
Positive subnormal classification agrees with the configured predicates.
Negative normal classification uses the configured policy and negative sign.
Positive normal classification uses the configured policy and positive sign.
Normality excludes zeros, subnormals, infinities, and NaNs for every storage codec.
Configured finiteness is the union of the three finite classes.
Configured normality is an exact finite magnitude at or above the normal threshold.
Configured subnormality is an exact positive magnitude below the normal threshold.
Every configured class is determined by its exact value and the descriptor threshold.
Every configured implicit-leading-bit binary word is canonical.
Canonicality agrees with lossless reconstruction from the full interchange word.