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name: htm-algorithms description: "Direct NuPIC legacy HTM encoders, SpatialPooler, TemporalMemory, SDRClassifier, anomaly score, and anomaly likelihood workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

HTM Algorithms

Use this sub-skill when a task asks for direct NuPIC legacy HTM algorithm APIs: encoders, SpatialPooler, TemporalMemory, SDRClassifier, raw anomaly score, or anomaly likelihood for a small streaming scalar/temporal example.

NuPIC legacy is a Python 2.7 package. Direct algorithm workflows normally require installed nupic, nupic.bindings, numpy in the legacy-compatible range, and pycapnp/Cap'n Proto only when serialization is needed.

Route first

• Stay here for: ScalarEncoder, RandomDistributedScalarEncoder, DateEncoder, CategoryEncoder, MultiEncoder, SpatialPooler.compute, TemporalMemory.compute, SDRClassifier.compute, computeRawAnomalyScore, and AnomalyLikelihood.anomalyProbability.
• Read references/api-reference.md when you need constructor signatures, input/output shapes, classifier result keys, or anomaly helper semantics.
• Read references/workflows.md when you need a direct encoders -> SP -> TM -> classifier/anomaly recipe or a debugging checklist for algorithm pipelines.
• Read references/troubleshooting.md when an algorithm example fails with encoder parameter, active-array shape, TM state/reset, classifier dict, Python 2, bindings, or serialization errors.
• Run scripts/algorithm_smoke.py when you need a deterministic installed-package smoke check for tiny encoders, SP/TM, classifier, and anomaly helpers.
• Route CSV stream headers, field metadata, OPF model parameter files, and config validation to ../data-and-configuration/.
• Route high-level ModelFactory, OPF inference dictionaries, checkpoints, and experiment workflows to ../opf-prediction/.
• For package-wide install/import failures before algorithm code runs, start with the root troubleshooting guide at ../../references/troubleshooting.md; local reference files also cross-link to that guide from their own relative paths.

Direct API checklist

1. Confirm the runtime is Python 2.7 and can import nupic, nupic.bindings, and numpy.
2. Encode every raw value into a one-dimensional binary SDR. Allocate one zero-filled NumPy array per encoder with encoder.getWidth() and call encoder.encodeIntoArray(value, bits).
3. Concatenate encoder outputs. Set SpatialPooler(inputDimensions=(encodingWidth,), columnDimensions=(columnCount,), ...) so encodingWidth == len(concatenatedEncoding).
4. Allocate activeArray = numpy.zeros(columnCount, dtype="uint32"), call sp.compute(encoding, learn, activeArray), then pass numpy.nonzero(activeArray)[0] to TemporalMemory.compute.
5. Set TemporalMemory(columnDimensions=(columnCount,), ...) to the same column count used by the SP. Use tm.getActiveCells() for classifier patternNZ; use tm.getPredictiveCells() plus tm.columnForCell(cell) to build next-step predicted columns for anomaly score.
6. For scalar predictions, call classifier.compute(recordNum, patternNZ, {"bucketIdx": bucketIdx, "actValue": rawValue}, learn=True, infer=True). Look for integer step keys such as 1 plus the "actualValues" key in the result.
7. For anomaly checks, compare current active columns with previous-step predicted columns using computeRawAnomalyScore(activeColumns, prevPredictedColumns), then optionally update AnomalyLikelihood with the raw value, anomaly score, and timestamp.

Minimal validation command

From this sub-skill directory in a prepared NuPIC legacy environment:

python scripts/algorithm_smoke.py --mode all --records 20 --encoder scalar

Expected success signal: the script prints PASS, encoder/SP/TM dimensions, a final one-step prediction summary, and bounded anomaly likelihood values. If it fails before imports, use root install/import troubleshooting; if it fails after imports, use the local troubleshooting reference.

Evidence provenance

This sub-skill distills NuPIC legacy algorithm evidence from source/doc paths such as src/nupic/algorithms/, src/nupic/encoders/, docs/source/quick-start/algorithms.rst, docs/examples/algo/complete-algo-example.py, examples/sp/hello_sp.py, examples/tm/hello_tm.py, and unit tests under tests/unit/nupic/algorithms/ and tests/unit/nupic/encoders/. Runtime guidance here is self-contained; future agents should not need to open the original checkout examples to use it.