Revision history for Algorithm-Classifier-IsolationForest

0.4.0   2026-07-03/22:45
        - Implement named features and methods for testing single rows using
          tagged data.
        - The C backend can be built and installed at install time, meaning
          nothing needs built unless changing the opts. See the docs for
          more details.
        - new IF_NO_OPENMP=1 selects/builds the serial C backend: no
          libgomp linkage and no OpenMP runtime in the process at all
          (unlike OMP_NUM_THREADS=1, which just caps the thread count);
          IF_NO_OPENMP=0 re-enables OpenMP over a serial install default
        - new IF_RUNTIME_BUILD=1 ignores the prebuilt object and forces
          the classic runtime build even when the flags match
        - iforest accel updated to reflect various changes to the C backend
        - scoring: the per-leaf path-length adjustment c(size) is now
          precomputed at tree-pack time and stored in the (previously
          unused) third slot of packed leaf records, removing a log()
          call per point per tree from the C scoring hot loop -- about
          25% faster axis-mode scoring; results are bit-identical
        - scoring: score_all_xs now picks between two loop shapes based
          on total forest size: small forests keep the point-major loop
          (whole forest stays cache-resident anyway), while forests
          over 4 MB switch to a tree-blocked loop that walks a block of
          points through one tree at a time so each tree stays hot in
          L1/L2 instead of being re-streamed from memory per point --
          measured ~3.2x faster extended-mode scoring at 400 trees
          (20k points, 16 features); both shapes add the same terms in
          the same order, so scores are bit-identical either way
        - any -march (IF_ARCH / IF_NATIVE, configure- or run-time) now
          automatically adds -ffp-contract=off: with FMA available the
          compiler otherwise contracts a*b+c into fused multiply-adds
          whose different rounding silently broke the use_c bit-parity
          guarantee (one ulp in a split value builds a different tree);
          the -march win comes from vectorization, so this costs ~nothing
        - scoring: oblique nodes now prefetch both child records before
          the dot product resolves which branch is taken, hiding the
          next node's memory latency under the FMA work (~7-10% faster
          extended-mode scoring on top of the tiling; axis path is
          untouched -- its single compare has no work to hide a
          prefetch under); purely a hint, results unchanged

0.3.0   2026-07-02/23:00
        - lots of POD fixes/cleanup
        - various further C optmizations
        - fit() can now handle training data with missing (undef) feature
          cells, selectable via the new `missing =>` constructor option:
            die    :: croak on undef in the training data (default)
            zero   :: treat a missing cell as the value 0
            impute :: fill with the per-feature mean/median (see `impute_with`)
            nan    :: range over present values and route missing rows to the
                      right child, consistently at fit and score time
        - new `impute_with => 'mean'|'median'` option for impute mode ...
          missing strategy + impute fill vector are persisted in saved models;
          models from older releases load as `zero` (the prior undef -> 0
          scoring behaviour)
        - the C build is now tunable via environment variables read at first
          module load: IF_ARCH=<value> adds -march=<value>, IF_NATIVE=1 is
          shorthand for IF_ARCH=native, IF_OPT overrides the default -O3,
          and IF_NO_C=1 skips building the C backend entirely; values are
          validated (bad ones warn and fall back to the defaults) and the
          flags actually used are exposed via $OPT_LEVEL
        - Benchmarking: bench-sklearn-scoring.pl now compares pure Perl, C,
          and C+OpenMP fit/score paths against sklearn side by side

0.2.1   2026-06-30/14:30
        - derp... actually update MANIFEST so a bunch of files from last release
          are actually included

0.2.0   2026-06-30/14:15
        - C acceleration via Inline::C for core fit and predict ops
        - OpenMP support for parallel multi-threaded fitting and predict ops
        - SIMD (AVX/SSE) acceleration where available
        - Data packing support for compact model storage (new `pack` CLI command)
        - Parallel fit capability
        - New `score_predict_split` method
        - New `accel` CLI command for querying available acceleration flags
        - New `bench` CLI command for running built-in benchmarks
        - New `info` CLI command with expanded model introspection
        - Benchmarking scripts covering fit, predict, scoring, and accel modes
        - Tests: accel flag detection, accel selection, undef column handling,
          data packing, parallel fit, sklearn comparison (including undef),
          and CLI
        - minor tweaks to `csv2plot` for a bit nicer rendering
        - minor POD fixes

0.1.0   2026-06-23/03:15
        - add csv2plot helper command for graphing

0.0.1   2026-06-21/21:45
        - initial release