SirStone
77e6dace01
Implement the BitBrain (Address Decoder Element + Sparse Binary Coincidence) classifier as a generic, deterministic Nim library under common_libs/bitbrain/, written from the published algorithm (Front. Neuroinform. 17:1125844), not from the GPL-3.0 reference C. - ade.nim: signed thresholded random projection (scale 64 / centre 127 defaults reproduce the reference), multi-width ADs, optional deterministic homeostatic threshold adaptation. Hebbian longevity and Metropolis-Hastings sampling are described but not implemented. - sbc.nim: packed class-bit coincidence memory; idempotent learn, counting inference. - bitbrain.nim: container over several ADs and SBCs, online learn/infer, argmax readout, memory accounting. - tests: 32 unit checks (idempotence, planted rule + monotone online curve, shuffled-label chance control, unseen input, homeostasis, memory). - tests/test_bitbrain_mnist.nim: loads the reference pretrained ADs/thresholds and MNIST from /tmp, reproduces the reference exactly - 97.210% corrected and 96.540% bug-compatible - confirming the port. No gun/wiring integration yet; inputs and outputs to be agreed separately.
204 lines
9.0 KiB
Nim
204 lines
9.0 KiB
Nim
## Address Decoder Element (ADE) layer — clean-room implementation.
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##
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## This module implements the ADE thresholded random projection as described in
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##
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## "BitBrain and Sparse Binary Coincidence (SBC) memories",
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## Frontiers in Neuroinformatics 17:1125844, 2023.
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##
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## It was written **from the published algorithm description only**. No code was
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## copied from the reference C program (`full_mnist_2048.c`) that ships alongside
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## the paper, which is GPL-3.0-or-later, (c) 2022 The University of Manchester.
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## Keeping this a clean-room implementation avoids imposing that licence on this
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## repository. The published defaults (`scale = 64`, `center = 127`) make this
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## module numerically identical to the reference's scoring rule.
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##
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## Mechanism
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## ---------
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## A *synapse* is `(inputIndex, polarity)`. An ADE with `width` synapses scores an
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## input vector as
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##
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## raw = Σ_j polarity_j * (input[inputIndex_j] - center)
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## score = scale * raw
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##
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## and **fires** iff `score >= threshold`. The reference C uses `scale = 64`
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## and `center = 127` on raw `uint8` MNIST pixels, i.e. exactly the default
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## parameters here. Multi-width ADs (the paper's best setup uses widths in
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## {6, 8, 10, 12}) detect features at different scales.
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##
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## Storage
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## -------
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## Synapses are stored as a flat `seq[int32]` of signed 1-based codes: the sign
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## is the polarity, `abs(code) - 1` is the 0-based input index. This is byte-for-
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## byte the layout of the reference `AD*_2048` weight files, so a pretrained AD
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## can be loaded with a single `memcpy`-equivalent read.
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##
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## Unsupervised phase (threshold homeostasis)
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## ------------------------------------------
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## The paper learns each ADE's homeostatic threshold so that it fires at a target
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## rate (~1%). `accumulateFiring` + `adaptThresholds` below implement the
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## paper's simple deterministic controller: over an interval of `interval` inputs
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## the firing counts are compared against `targetRate * interval`, and each ADE's
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## threshold is nudged up or down by `step`.
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##
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## The paper's *additional* unsupervised mechanisms are deliberately NOT
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## implemented here (the task only requires them to be described):
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##
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## * **Hebbian "longevity" learning.** Each synapse carries a longevity
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## counter. When an ADE fires, the smallest contributor to the threshold
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## crossing has its longevity decremented and the largest contributor has it
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## incremented. After an interval, any synapse whose longevity drops below a
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## critical value is *retired* and replaced: a new input index is drawn by the
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## same sampling mechanism used at initialisation, with longevity reset to
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## the default. This lets each ADE "home in" on a feature. It is purely
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## unsupervised.
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## * **Metropolis-Hastings input-position sampling.** Synapse input positions
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## are drawn from a distribution proportional to `sqrt` of the global input
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## activity histogram (the `sqrt` both stabilises the Poisson uncertainty and
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## flattens the distribution so that synapses can land slightly outside the
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## training support). `initRandomAddressDecoder` instead samples uniformly at
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## random, which is the paper's stated initialisation.
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## * **Spatial clustering.** For image/volumetric data the sampled positions
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## may be constrained to a local region (rejection sampling until the
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## constraint holds) so each ADE sees a coherent receptive field.
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import std/random
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const
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DefaultScale* = 64
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## Score multiplier. 64 reproduces the reference C's `yang = ±64`.
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DefaultCenter* = 127
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## Input centre. 127 reproduces the reference C's `(pixel - 127)`.
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type
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AddressDecoder* = object
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## An AD: `nAde` Address Decoder Elements, all of `width` synapses.
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nAde*: int ## the paper's `w`
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width*: int ## the paper's `n` (synapses per ADE)
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codes*: seq[int32] ## nAde*width; sign = polarity, |code|-1 = input index
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thresholds*: seq[int32] ## per-ADE firing thresholds
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scale*: int ## score multiplier (default 64)
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center*: int ## input centre (default 127)
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fireCounts*: seq[int32] ## firing accumulator for threshold homeostasis
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proc initAddressDecoder*(nAde, width: int, scale = DefaultScale,
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center = DefaultCenter): AddressDecoder =
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## Allocate an AD with zeroed synapses. Callers either fill `codes` directly
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## (e.g. from a pretrained weight file) or use `initRandomAddressDecoder`.
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doAssert nAde > 0, "nAde must be positive"
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doAssert width > 0, "width must be positive"
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result.nAde = nAde
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result.width = width
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result.scale = scale
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result.center = center
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result.codes = newSeq[int32](nAde * width)
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result.thresholds = newSeq[int32](nAde)
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result.fireCounts = newSeq[int32](nAde)
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proc synCode(inputIdx: int, polarity: int8): int32 {.inline.} =
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## Encode `(index, polarity)` as the reference's signed 1-based code.
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doAssert inputIdx >= 0
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doAssert polarity == 1'i8 or polarity == -1'i8
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result = int32(polarity) * int32(inputIdx + 1)
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proc initRandomAddressDecoder*(nAde, width, inputWidth: int, rng: var Rand,
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scale = DefaultScale, center = DefaultCenter,
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threshold: int32 = 0'i32): AddressDecoder =
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## Randomly initialise an AD. For each ADE `width` **distinct** input indices
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## are drawn uniformly from `0 ..< inputWidth` (multapses are disallowed, as in
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## the paper) with a random ± polarity. Every ADE starts at `threshold`.
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doAssert inputWidth >= width,
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"cannot draw " & $width & " distinct indices from " & $inputWidth
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result = initAddressDecoder(nAde, width, scale, center)
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var chosen = newSeq[int32](width)
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for i in 0 ..< nAde:
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result.thresholds[i] = threshold
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var k = 0
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while k < width:
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let idx = int32(rng.rand(inputWidth - 1))
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var dup = false
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for p in 0 ..< k:
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if chosen[p] == idx:
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dup = true
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break
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if not dup:
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chosen[k] = idx
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inc k
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for j in 0 ..< width:
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let pol = if rng.rand(1) == 0: 1'i8 else: -1'i8
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result.codes[i * width + j] = synCode(int(chosen[j]), pol)
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proc score*[T: SomeInteger](ad: AddressDecoder, input: openArray[T],
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ade: int): int {.inline.} =
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## Signed, centred, scaled score of ADE `ade` on `input`.
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## `score = scale * Σ_j polarity_j * (input[idx_j] - center)`.
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doAssert ade >= 0 and ade < ad.nAde
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var raw = 0
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let base = ade * ad.width
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for j in 0 ..< ad.width:
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let c = ad.codes[base + j]
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let idx =
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if c > 0'i32: int(c) - 1
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else: int(-c) - 1
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let pol = if c > 0'i32: 1 else: -1
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raw += pol * (int(input[idx]) - ad.center)
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result = ad.scale * raw
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proc fires*[T: SomeInteger](ad: AddressDecoder, input: openArray[T],
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ade: int): bool {.inline.} =
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## An ADE fires iff its score reaches its threshold (`score >= threshold`).
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score(ad, input, ade) >= int(ad.thresholds[ade])
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proc activeList*[T: SomeInteger](ad: AddressDecoder, input: openArray[T],
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active: var seq[int32]) =
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## Fill `active` with the indices of the ADEs that fire on `input`.
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## This is the sparse representation consumed by the SBC memory.
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active.setLen(0)
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for i in 0 ..< ad.nAde:
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if fires(ad, input, i):
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active.add int32(i)
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proc fireCount*[T: SomeInteger](ad: AddressDecoder, input: openArray[T]): int =
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## Number of ADEs in `ad` that fire on `input`.
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for i in 0 ..< ad.nAde:
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if fires(ad, input, i):
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inc result
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proc accumulateFiring*[T: SomeInteger](ad: var AddressDecoder,
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input: openArray[T]) =
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## Add this input's firing events to the homeostasis accumulator.
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for i in 0 ..< ad.nAde:
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if fires(ad, input, i):
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inc ad.fireCounts[i]
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proc resetFiringCounts*(ad: var AddressDecoder) =
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## Zero the homeostasis accumulator (does not touch thresholds).
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for i in 0 ..< ad.nAde:
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ad.fireCounts[i] = 0
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proc adaptThresholds*(ad: var AddressDecoder, interval: int, targetRate: float,
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step: int = 1) =
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## One step of the paper's homeostatic threshold controller.
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##
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## `fireCounts` is expected to hold the firing counts accumulated over the last
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## `interval` inputs (see `accumulateFiring`). Each ADE whose observed rate is
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## above/below `targetRate` has its threshold raised/lowered by `step`. The
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## counters are then reset, ready for the next interval.
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##
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## This is a deterministic integer controller: given the same inputs and the
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## same starting thresholds, it always produces the same thresholds.
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doAssert interval > 0, "interval must be positive"
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let expected = targetRate * float(interval)
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for i in 0 ..< ad.nAde:
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let excess = float(ad.fireCounts[i]) - expected
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if excess > 0.5:
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ad.thresholds[i] += int32(step)
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elif excess < -0.5:
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ad.thresholds[i] -= int32(step)
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ad.fireCounts[i] = 0'i32
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proc memoryBytes*(ad: AddressDecoder): int =
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## Bytes held by this AD (synapse codes, thresholds, firing counters).
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ad.codes.len * sizeof(int32) +
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ad.thresholds.len * sizeof(int32) +
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ad.fireCounts.len * sizeof(int32)
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