OpenAI Codex port of code and text of 1989 thesis.
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# Python 3 Migration Plan
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## Scope
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The original system is a cooperative composition pipeline built from three neural subsystems:
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- `Bach`: a Hopfield-Tank note generator over a 5-position by 8-note grid.
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- `Salieri`: a back-propagation critic trained against a rule-based classical-sequence supervisor.
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- `Beethoven`: an ART1 novelty/category network over the note sequence plus one classicality bit.
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The immediate goal should be a Python 3 package that reproduces the Pascal algorithms and file-driven behavior closely enough to validate compatibility, while replacing the Pascal linked-list memory model with direct numeric data structures.
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## What Exists Today
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### Core orchestration
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- `THES/ANNCOMP.PP` is the integrated driver.
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- The composition loop is effectively:
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1. Generate a candidate note with the Hopfield-Tank network.
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2. Evaluate/train the back-propagation network using the current note window and the rule-based instructor.
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3. Pass the same window plus the classical/not-classical flag into ART1.
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### Shared state
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- `THES/GLOBALS.PP` defines:
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- fixed note vocabulary of 8 notes,
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- sequence window length of 5,
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- ART1 dimensions `Max_F1_nodes = 41`, `Max_F2_nodes = 25`,
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- `Common_Area_`, which is the cross-network exchange object.
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### Hopfield-Tank subsystem
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- `THES/ANNCOMP.PP` implements `Bach` and nested `HTN`.
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- The network operates on a flattened 40-cell representation: `8 notes x 5 positions`.
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- It loads a `64 x 64` weight matrix from `HTN.DAT`, but the active note grid uses the first 40 cells.
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- The update rule uses:
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- per-neuron activation `a`,
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- output `0.5 * (1 + tanh(a / c))`,
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- resistance/capacitance/input/weight/iteration scaling factors from globals.
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- `THES/HTNDATA.PP` shows how the Hopfield weights were built from `SEQUENCE.DAT`, plus row/column inhibition and sequence reinforcement.
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### Back-propagation subsystem
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- `THES/BP_UNIT.PP` is a general BP implementation with:
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- input, hidden, and output nodes,
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- weight matrix and momentum,
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- feed-forward,
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- back-propagation,
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- file-based parameter and weight loading.
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- `THES/S61.DAT` configures Salieri as:
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- 40 input nodes,
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- 20 hidden nodes,
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- 1 output node,
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- learning rate `0.5`,
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- momentum `0.5`.
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- `THES/ANNCOMP.PP` converts the current 5-note window into a 40-bit one-hot vector and trains the network online against `Classical_instructor`.
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### Rule-based supervisor
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- `THES/CLASINST.PP` loads `SEQUENCE.DAT`.
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- It converts the 5-note sequence to a digit string and returns `1` if the target suffix matches any stored example sequence, else `0`.
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- This acts as the teaching signal for the BP network.
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### ART1 subsystem
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- `THES/ANNCOMP.PP` implements `ART1`.
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- F1 input is the 40-bit one-hot sequence plus one bit for `Is_classical`, for a total vector length of 41.
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- F2 supports up to 25 committed categories.
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- The implementation includes a nonstandard compatibility detail: when all categories are saturated and none remain eligible, vigilance is reduced by 1 percent and matching is retried.
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### Legacy data model problem
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- `THES/STRUCT.PP` provides generic linked-list vectors and matrices (`DVE`, `HVE`) used to work around Turbo Pascal memory constraints.
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- `THES/BP_UNIT.PP` stores nodes, IO vectors, and weights through those linked structures rather than direct arrays.
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- That representation should not be preserved in Python except where needed for compatibility tests.
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## Recommended Python Representation
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Use explicit typed structures and dense arrays:
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- `numpy.ndarray` for:
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- Hopfield state vectors and weight matrices,
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- BP activations, deltas, biases, and weights,
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- ART1 F1/F2 activations and top-down/bottom-up LTM weights.
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- `dataclasses.dataclass` for stable API/state containers.
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- `Enum` for note identifiers only if it does not complicate file compatibility.
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Recommended canonical encodings:
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- `NoteSequence`: shape `(5,)`, integer values `0..8`.
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- `SequenceOneHot`: shape `(40,)`, binary.
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- `ArtInputVector`: shape `(41,)`, binary.
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- `HopfieldWeights`: shape `(40, 40)` as the normalized active subset of the legacy file.
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- `BPWeightsIH`, `BPWeightsHO` or one legacy-compatible dense square matrix, depending on whether fidelity or clarity is prioritized in a given layer of the codebase.
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## Package Layout
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```text
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composer_ans/
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__init__.py
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types.py
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encoding.py
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io/
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__init__.py
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legacy_files.py
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hopfield.py
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backprop.py
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art1.py
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classical_rules.py
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pipeline.py
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compatibility.py
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tests/
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data/
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test_encoding.py
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test_classical_rules.py
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test_hopfield.py
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test_backprop.py
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test_art1.py
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test_pipeline.py
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```
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## API Design
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Keep the public API small and deterministic.
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```python
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from composer_ans.pipeline import CompositionContext, CompositionPipeline
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ctx = CompositionContext(notes=[0, 0, 0, 0, 0])
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pipeline = CompositionPipeline.from_legacy_data("THES")
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result = pipeline.step(ctx)
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```
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Suggested subsystem APIs:
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```python
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candidate = hopfield.generate_next_note(notes, params)
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is_classical, bp_state = salieri.evaluate_and_train(notes, target=None)
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art_result = beethoven.categorize(notes, is_classical)
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```
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Where:
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- `target=None` means "derive target from the classical instructor", matching the Pascal integrated flow.
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- Each call returns structured state useful for debugging and test baselines, not just the final scalar.
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## Migration Strategy
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### Phase 1: Preserve semantics, not implementation style
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- Recreate file readers for:
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- `SEQUENCE.DAT`,
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- `S61.DAT`,
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- `S61.WT`,
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- `HTN.DAT`.
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- Recreate sequence encodings exactly:
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- 5-note rolling window,
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- 40-bit one-hot flattening,
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- ART1 extra classicality bit.
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- Recreate the rule-based instructor exactly before porting the trainable models.
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Deliverable:
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- A Python package that can parse legacy files and reproduce the same encoded inputs the Pascal code would produce.
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### Phase 2: Port Hopfield-Tank
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- Implement the continuous-time iterative update as written.
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- Preserve:
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- noise injection behavior,
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- stop condition using epsilon on alternating time buffers,
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- "pick max cell in each column" post-processing.
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- Isolate random number generation behind an injectable RNG so deterministic tests are possible.
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Deliverable:
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- `generate_next_note()` producing the same result as Pascal for fixed seeds and known sequences.
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### Phase 3: Port Salieri back-propagation
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- First implement a legacy-compatible execution mode mirroring the square-node storage and update order.
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- Then wrap it with a clearer façade that exposes standard layer matrices.
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- Preserve:
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- sigmoid behavior,
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- theta updates,
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- momentum handling,
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- online training after every presentation,
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- periodic weight dumping capability.
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Deliverable:
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- `evaluate_and_train()` matching legacy outputs and weight updates for a controlled presentation sequence.
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### Phase 4: Port Beethoven ART1
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- Port the F1/F2 STM and LTM equations directly.
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- Preserve:
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- 41-bit input vector,
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- eligibility and commitment logic,
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- resonance loop,
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- modified vigilance-reduction behavior on saturation.
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- Keep ART1 state persistent across calls, because the Pascal version learns over the composition session.
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Deliverable:
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- `categorize()` returning winner, new-category flag, vigilance-change flag, and current category count.
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### Phase 5: Rebuild the integrated pipeline
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- Recreate `Common_Area_` as a Python dataclass.
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- Implement a single-step pipeline equivalent to one iteration of the Pascal composition loop.
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- Add an optional batch runner that emits a complete composition and an event log.
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Deliverable:
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- End-to-end run over a fixed number of notes using legacy data assets.
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## Compatibility Plan
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Compatibility should be measured in layers:
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- Encoding compatibility:
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- identical one-hot vectors and ART input vectors for the same note windows.
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- File compatibility:
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- legacy `.DAT` and `.WT` files load without manual editing.
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- Behavioral compatibility:
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- same classical instructor decisions,
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- same Hopfield winner for fixed seed/input,
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- same BP output progression for replayed presentations,
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- same ART1 category decisions for replayed inputs.
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- Pipeline compatibility:
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- same sequence of generated notes for a fixed random seed, or if exact replication is blocked by legacy RNG differences, same per-step subsystem outputs within defined tolerances.
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## Known Risks
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- Pascal `Single`, file layout, and RNG behavior may not map exactly to Python defaults.
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- `HTN.DAT` is written as a Pascal binary `FILE OF ARRAY[1..64,1..64] OF REAL`; a dedicated reader may be needed to confirm element size and ordering.
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- The BP code relies on update order within linked structures. A mathematically equivalent refactor may still diverge numerically unless a legacy mode preserves operation order.
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- ART1 has thesis-specific modifications; replacing them with textbook ART1 would break compatibility.
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## Recommended Delivery Order
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1. Build legacy readers and encoders.
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2. Port `Classical_instructor`.
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3. Port Hopfield-Tank and verify with controlled seeds.
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4. Port BP in legacy-compatible mode and replay known presentations.
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5. Port ART1 with persistent state.
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6. Assemble the integrated pipeline.
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7. Add a second, cleaner API layer only after compatibility tests pass.
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## Immediate Next Step
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Implement the non-neural compatibility layer first:
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- legacy file parsers,
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- note/sequence encoders,
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- rule-based classical instructor,
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- golden tests based on the files already in `THES`.
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That gives a stable foundation for porting the three neural subsystems without losing track of what the original program actually did.
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99
README.md
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README.md
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# TriuneCadence
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A Python3 port of the music composition code from my 1989 master's thesis, 'Integration and Hybridization in Neural Network Modelling'.
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TriuneCadence is a Python implementation of a modular neural music-composition system inspired by Wesley R. Elsberry's 1989 master's thesis on constrained melodic composition.
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It combines three different network families in one pipeline:
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- a Hopfield-Tank note generator
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- a back-propagation critic (`Salieri`)
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- an ART1 novelty/category module (`Beethoven`)
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The repository includes:
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- a modern Python codebase with generic network modules and thesis-specific adapters
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- legacy thesis source, text, and data files in [`THES/`](./THES)
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- timing, entropy, and predictability analysis for generated note sequences
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- JSON serialization for learned model state and run reports
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## Why This Repo Exists
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The original system was implemented in Turbo Pascal on late-1980s hardware under severe memory constraints. That led to pointer-heavy data structures and implementation complexity that obscured what was, architecturally, a strong multi-network design.
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This repository keeps the core ideas accessible:
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- generic reusable implementations of the underlying network families
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- a thesis-faithful composition pipeline built on top of those generic modules
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- a practical environment for experimentation, analysis, and historical comparison
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## Quick Start
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Run a short composition from the thesis data:
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```bash
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python -m composer_ans --thes-root THES --notes 16
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```
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Or, if installed as a package:
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```bash
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triune-cadence --thes-root THES --notes 16
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```
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Save model state and a run report:
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```bash
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triune-cadence \
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--thes-root THES \
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--notes 32 \
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--save-salieri salieri.json \
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--save-beethoven beethoven.json \
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--save-report run.json
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```
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## Sweepable Parameters
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The CLI currently exposes a few parameters that are useful for experiments:
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- `--object-threshold`
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- `--max-attempts-per-note`
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- `--art-vigilance`
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- `--art-vigilance-decay`
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Saved run reports include those parameters along with:
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- note sequence
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- per-note generation time
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- total runtime
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- unigram entropy
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- first-order conditional entropy
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- normalized entropy
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- predictability
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- redundancy
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## Project Layout
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Core Python modules live in [`composer_ans/`](./composer_ans):
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- generic Hopfield-Tank core: [`composer_ans/hopfield.py`](./composer_ans/hopfield.py)
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- generic back-propagation core: [`composer_ans/backprop.py`](./composer_ans/backprop.py)
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- generic ART1 core: [`composer_ans/art1.py`](./composer_ans/art1.py)
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- thesis-specific wrappers: [`composer_ans/salieri.py`](./composer_ans/salieri.py), [`composer_ans/beethoven.py`](./composer_ans/beethoven.py)
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- integrated composition pipeline: [`composer_ans/pipeline.py`](./composer_ans/pipeline.py)
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- analysis and reporting: [`composer_ans/analysis.py`](./composer_ans/analysis.py), [`composer_ans/reporting.py`](./composer_ans/reporting.py)
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Legacy materials are in [`THES/`](./THES).
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## Historical Context
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The thesis reports that the integrated system generated 152 notes in about three hours on a 16 MHz 80386-class machine, and in about fifteen hours on an 8088-based machine with an 8087 coprocessor. This Python version can report per-note generation time directly so present-day runs can be compared against those historical figures.
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## Development
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Run the test suite with:
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```bash
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pytest -q
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```
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## Related Repo Notes
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The original migration planning artifact is preserved in [`MIGRATION_PLAN.md`](./MIGRATION_PLAN.md).
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UNIT ANN;
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{
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This unit provides several functions of general use in Artificial Neural
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Network (ANN) modelling.
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}
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{
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Copyright 1989 by Wesley R. Elsberry. All rights reserved.
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Commercial use of this software is prohibited without written consent of
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the author.
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For information, bug reports, and updates contact
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Wesley R. Elsberry
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528 Chambers Creek Drive South
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Everman, Texas 76140
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Telephone: (817) 551-7018
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}
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INTERFACE
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{PUBLIC DECLARATIONS}
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FUNCTION Close_enough (target, plus_minus, x : DOUBLE) : BOOLEAN;
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FUNCTION Gaussian_noise (mean, variance : DOUBLE) :DOUBLE;
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FUNCTION Tanh (rr : DOUBLE) :DOUBLE ;
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FUNCTION Linear (m, B, X : DOUBLE):DOUBLE;
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FUNCTION Linear_ramp (low, high, x : DOUBLE) : DOUBLE;
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FUNCTION Threshold (low, high, thresh, x : DOUBLE):DOUBLE;
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FUNCTION Sigmoid (range,slope_mod,shift,X : DOUBLE):DOUBLE;
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FUNCTION Signum (xx : DOUBLE):INTEGER;
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IMPLEMENTATION
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{PRIVATE DECLARATIONS}
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CONST
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mach_inf = 1E37;
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exp_max = 80.0;
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TYPE
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REAL = DOUBLE;
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{IMPLEMENTATIONS OF PROCEDURES AND FUNCTIONS}
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FUNCTION Close_enough (target, plus_minus, x : REAL) : BOOLEAN;
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{
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Given a target and an absolute value of allowed deviation (plus_minus),
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Close_enough returns TRUE if the tested value (x) is within the
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defined interval.
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}
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BEGIN {}
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IF (x >= (target - plus_minus)) AND (x <= (target + plus_minus))
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THEN {}
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BEGIN
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Close_enough := TRUE;
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END
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ELSE {}
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BEGIN
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Close_enough := FALSE;
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END;
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END; {}
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FUNCTION Gaussian_noise(mean, variance : REAL) :REAL;
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{Produces random numbers which conform to a Gaussian distribution}
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VAR
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u1, u2, x : REAL;
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BEGIN {Gaussian_noise}
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u1 := Random;
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u2 := Random;
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x := Sqrt(-2*Ln(u1))*Cos(2*Pi*u2);
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x := variance*x + mean;
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Gaussian_noise := x;
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END; {Gaussian_noise}
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{
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Activation functions
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}
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FUNCTION tanh(rr : REAL) :REAL ;
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{returns the hyperbolic tangent of rr}
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BEGIN {tanh}
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IF (rr > Exp_Max) THEN {}
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BEGIN
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rr := Exp_Max;
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END;
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IF (rr < -Exp_Max) THEN {}
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BEGIN
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rr := -Exp_max;
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END;
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tanh := (Exp(rr) - Exp(-rr)) / (Exp(rr) + Exp(-rr));
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END;
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FUNCTION Linear (m, B, X : REAL):REAL;
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{
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Linear returns the parameter value times slope, plus intercept.
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}
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BEGIN {Linear}
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Linear := X*m + B;
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END; {Linear}
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FUNCTION Linear_ramp (LOW, HIGH, X : REAL) : REAL;
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{
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Returns X when X is between LOW and HIGH, the appropriate bound
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otherwise.
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}
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BEGIN {Linear_ramp}
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IF (X < HIGH) AND (X > LOW) THEN
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{}
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BEGIN
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Linear_ramp := X;
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END
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ELSE {}
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BEGIN
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IF (x >= HIGH) THEN {}
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BEGIN
|
||||
Linear_ramp := HIGH;
|
||||
END
|
||||
ELSE {}
|
||||
BEGIN
|
||||
Linear_ramp := LOW;
|
||||
END;
|
||||
END;
|
||||
END; {Linear_ramp}
|
||||
|
||||
FUNCTION Threshold(LOW,HIGH,THRESH,X : REAL):REAL;
|
||||
{
|
||||
Returns LOW when X is below THRESH and HIGH when X is greater
|
||||
than or equal to THRESH.
|
||||
}
|
||||
|
||||
BEGIN {Threshold}
|
||||
IF (X < THRESH) THEN {}
|
||||
BEGIN
|
||||
Threshold := LOW;
|
||||
END
|
||||
ELSE {}
|
||||
BEGIN
|
||||
Threshold := HIGH;
|
||||
END;
|
||||
END; {Threshold}
|
||||
|
||||
FUNCTION Sigmoid(range,slope_mod,shift,X : REAL):REAL;
|
||||
{
|
||||
Function of the form :
|
||||
|
||||
[ range / (1 + exp(-slope_mod * X)) ] - shift
|
||||
|
||||
range - determines the range of values, 0..range
|
||||
slope_mod - modifies the slope of the curve
|
||||
shift - changes the range from 0..range to (0-shift)..(range-shift)
|
||||
}
|
||||
|
||||
CONST
|
||||
Machine_Infinity = 1E37;
|
||||
|
||||
VAR
|
||||
Temp : REAL;
|
||||
|
||||
BEGIN {Sigmoid}
|
||||
Temp := 0.0 - (Slope_mod * X);
|
||||
IF Temp > Exp_Max THEN Temp := Exp_Max;
|
||||
IF Temp < -Exp_Max THEN Temp := -Exp_Max;
|
||||
Temp := Exp(Temp);
|
||||
Sigmoid := (range/(1+(Temp))) - shift;
|
||||
END; {Sigmoid}
|
||||
|
||||
FUNCTION signum(xx : REAL):INTEGER;
|
||||
|
||||
BEGIN
|
||||
IF xx >= 0.0 THEN signum := 1
|
||||
ELSE signum := -1;
|
||||
END;
|
||||
|
||||
|
||||
BEGIN {INITIALIZATION}
|
||||
END.
|
||||
|
||||
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,377 @@
|
|||
UNIT ANSI_Z;
|
||||
{
|
||||
This unit provides certain ANSI and VT-52 screen control functions.
|
||||
}
|
||||
{
|
||||
|
||||
Copyright 1989 by Wesley R. Elsberry. All rights reserved.
|
||||
|
||||
Commercial use of this software is prohibited without written consent of
|
||||
the author.
|
||||
|
||||
For information, bug reports, and updates contact
|
||||
|
||||
Wesley R. Elsberry
|
||||
528 Chambers Creek Drive South
|
||||
Everman, Texas 76140
|
||||
Telephone: (817) 551-7018
|
||||
|
||||
}
|
||||
|
||||
|
||||
INTERFACE
|
||||
|
||||
USES DOS, MISC1;
|
||||
|
||||
TYPE
|
||||
ANSI_MODE_ = (NULL_,ANSI_,VT52_,IBM_PC_);
|
||||
|
||||
CONST
|
||||
ANSI_MODE : ANSI_MODE_ = ANSI_;
|
||||
|
||||
PROCEDURE ANSI_CLRSCR;
|
||||
{Clear the screen using ANSI control}
|
||||
|
||||
PROCEDURE ANSI_CUU(VAR ii : INTEGER);
|
||||
{Cursor up}
|
||||
|
||||
PROCEDURE ANSI_CUD(VAR ii : INTEGER);
|
||||
{Cursor down}
|
||||
|
||||
PROCEDURE ANSI_CUF(VAR ii : INTEGER);
|
||||
{Cursor forward or right}
|
||||
|
||||
PROCEDURE ANSI_CUB(VAR ii : INTEGER);
|
||||
{Cursor backward or left}
|
||||
|
||||
PROCEDURE ANSI_EEOL;
|
||||
{Erase to End Of Line (VT52)}
|
||||
|
||||
PROCEDURE ANSI_CUH;
|
||||
{Cursor home}
|
||||
|
||||
PROCEDURE ANSI_CUP(line, col : INTEGER);
|
||||
{Cursor position}
|
||||
|
||||
|
||||
|
||||
IMPLEMENTATION
|
||||
|
||||
|
||||
TYPE
|
||||
Position_ = RECORD
|
||||
l : BYTE;
|
||||
c : BYTE;
|
||||
END;
|
||||
|
||||
CONST
|
||||
C_pos : Position_ = (l : 0;
|
||||
c : 0);
|
||||
|
||||
VAR
|
||||
inch : CHAR;
|
||||
|
||||
{ ANSI Control sequences
|
||||
|
||||
ESC [ Pn ; Pn R -> Cursor Position Report (CPR)
|
||||
ESC [ Pn D -> Cursor Backward (CUB)
|
||||
ESC [ Pn B -> Cursor Down (CUD)
|
||||
ESC [ Pn C -> Cursor Forward (CUF)
|
||||
ESC [ Pn ; Pn H -> Cursor Position (CUP)
|
||||
ESC [ Pn A -> Cursor Up (CUU)
|
||||
ESC [ Pn c -> Device Attributes (DA)
|
||||
ESC # 8 -> Screen Alignment Display (DECALN)
|
||||
ESC Z -> Identify Terminal (DECID)
|
||||
ESC = -> Keypad Application Mode (DECKPAM)
|
||||
ESC > -> Keypad Numeric Mode (DECKPNM)
|
||||
ESC 8 -> Restore Cursor (DECRC)
|
||||
ESC [ <sol> ; <par> ; <nbits> ; <xspeed> ; <rspeed> ; <clkmul> ; <flags> x
|
||||
-> Report Terminal Parameters (DECREPTPARM)
|
||||
ESC [ <sol> x -> Request Terminal Parameters (DECREQTPARM)
|
||||
ESC 7 -> Save Cursor (DECSC)
|
||||
ESC [ Pn ; Pn r -> Set Top and Bottom Margins (DECSTBM)
|
||||
ESC [ Ps n -> Device Status Report (DSR)
|
||||
ESC [ Ps J -> Erase in Display (ED)
|
||||
ESC [ Ps K -> Erase in Line (EL)
|
||||
ESC H -> Horizontal Tabulation Set (HTS)
|
||||
ESC [ Pn ; Pn f -> Horizontal and Vertical Position (HVP)
|
||||
ESC D -> Index (IND)
|
||||
ESC E -> Next Line (NEL)
|
||||
ESC M -> Reverse Index (RI)
|
||||
ESC c -> Reset to Initial State (RIS)
|
||||
ESC [ Ps ; Ps ; ... ; Ps l -> Reset Mode (RM)
|
||||
ESC ( A | B | 0 | 1 | 2 -> Select Character Set (SCS)
|
||||
ESC [ Ps ; ... ; Ps m -> Select Graphic Rendition (SGR)
|
||||
ESC Ps ; ... Ps h -> Select Mode (SM)
|
||||
ESC [ Ps g -> Tabulation Clear (TBC)
|
||||
|
||||
|
||||
VT52 Mode control sequences
|
||||
ESC A -> Cursor Up
|
||||
ESC B -> Cursor Down
|
||||
ESC C -> Cursor Right
|
||||
ESC D -> Cursor Left
|
||||
ESC F -> Enter Graphics Mode
|
||||
ESC G -> Exit Graphics Mode
|
||||
ESC H -> Cursor to Home
|
||||
ESC I -> Reverse Line Feed
|
||||
ESC J -> Erase To End Of Screen
|
||||
ESC K -> Erase to End Of Line
|
||||
ESC Y line column -> Direct Cursor Address
|
||||
ESC Z -> Identify
|
||||
ESC = -> Enter Alternate Keypad Mode
|
||||
ESC > -> Exit Alternate Keypad Mode
|
||||
ESC < -> Enter ANSI Mode
|
||||
|
||||
}
|
||||
|
||||
{
|
||||
CASE ANSI_MODE OF
|
||||
ANSI_ : BEGIN
|
||||
END;
|
||||
VT52_ : BEGIN
|
||||
END;
|
||||
IBM_PC_ : BEGIN
|
||||
END;
|
||||
END;
|
||||
}
|
||||
|
||||
|
||||
{$V-}
|
||||
|
||||
FUNCTION INT_TO_STR (ii : INTEGER):STRING;
|
||||
|
||||
VAR
|
||||
tstr : STRING;
|
||||
|
||||
BEGIN
|
||||
Str(ii,tstr);
|
||||
int_to_str := tstr;
|
||||
END;
|
||||
{$V-}
|
||||
|
||||
|
||||
PROCEDURE ANSI_CLRSCR ;
|
||||
{Clear the screen using ANSI control}
|
||||
|
||||
BEGIN {}
|
||||
CASE ANSI_MODE OF
|
||||
ANSI_ : BEGIN
|
||||
Write(Output,ascii_esc,ascii_obracket,ascii_two,'J');
|
||||
END;
|
||||
VT52_ : BEGIN
|
||||
Write(Output,ascii_esc,'H',ascii_esc,'J',ascii_esc,'H');
|
||||
END;
|
||||
{ IBM_PC_ : BEGIN
|
||||
CRT.CLRSCR;
|
||||
END; }
|
||||
END;
|
||||
c_pos.l := 0;
|
||||
c_pos.c := 0;
|
||||
END; {}
|
||||
|
||||
|
||||
|
||||
PROCEDURE ANSI_CUU(VAR ii : INTEGER);
|
||||
{Cursor up}
|
||||
|
||||
BEGIN {}
|
||||
IF ii <= 1 THEN ii := 1;
|
||||
CASE ANSI_MODE OF
|
||||
ANSI_ : BEGIN
|
||||
Write(Output,ascii_esc,ascii_obracket);
|
||||
IF (ii > 1) THEN Write(Output,int_to_str(ii));
|
||||
Write(Output,'A');
|
||||
END;
|
||||
VT52_ : BEGIN
|
||||
Write(Output,ascii_esc,'A');
|
||||
END;
|
||||
{ IBM_PC_ : BEGIN
|
||||
c_pos.l := CRT.WHEREY;
|
||||
c_pos.c := CRT.WHEREX;
|
||||
IF c_pos.l-ii >= 0 THEN
|
||||
c_pos.l := c_pos.l - ii
|
||||
ELSE
|
||||
c_pos.l := 0;
|
||||
CRT.GOTOXY(c_pos.c,c_pos.l);
|
||||
END; }
|
||||
END;
|
||||
END; {}
|
||||
|
||||
PROCEDURE ANSI_CUD(VAR ii : INTEGER);
|
||||
{Cursor down}
|
||||
|
||||
BEGIN {}
|
||||
CASE ANSI_MODE OF
|
||||
ANSI_ : BEGIN
|
||||
Write(Output,ascii_esc,ascii_obracket);
|
||||
IF (ii > 1) THEN Write(Output,int_to_str(ii));
|
||||
Write(Output,'B');
|
||||
END;
|
||||
VT52_ : BEGIN
|
||||
Write(Output,ascii_esc,'B');
|
||||
END;
|
||||
{ IBM_PC_ : BEGIN
|
||||
c_pos.l := CRT.WHEREY;
|
||||
c_pos.c := CRT.WHEREX;
|
||||
IF c_pos.l+ii <= 24 THEN
|
||||
c_pos.l := c_pos.l + ii
|
||||
ELSE
|
||||
c_pos.l := 24;
|
||||
CRT.GOTOXY(c_pos.c,c_pos.l);
|
||||
END; }
|
||||
END;
|
||||
END; {}
|
||||
|
||||
PROCEDURE ANSI_CUF(VAR ii : INTEGER);
|
||||
{Cursor forward or right}
|
||||
|
||||
BEGIN {}
|
||||
CASE ANSI_MODE OF
|
||||
ANSI_ : BEGIN
|
||||
Write(Output,ascii_esc,ascii_obracket);
|
||||
IF (ii > 1) THEN Write(Output,int_to_str(ii));
|
||||
Write(Output,'C');
|
||||
END;
|
||||
VT52_ : BEGIN
|
||||
Write(Output,ascii_esc,'C');
|
||||
END;
|
||||
{ IBM_PC_ : BEGIN
|
||||
c_pos.l := CRT.WHEREY;
|
||||
c_pos.c := CRT.WHEREX;
|
||||
IF c_pos.c+ii <= 79 THEN
|
||||
c_pos.c := c_pos.c + ii
|
||||
ELSE
|
||||
c_pos.c := 79;
|
||||
CRT.GOTOXY(c_pos.c,c_pos.l);
|
||||
END;}
|
||||
END;
|
||||
END; {}
|
||||
|
||||
PROCEDURE ANSI_CUB(VAR ii : INTEGER);
|
||||
{Cursor backward or left}
|
||||
|
||||
BEGIN {}
|
||||
CASE ANSI_MODE OF
|
||||
ANSI_ : BEGIN
|
||||
Write(Output,ascii_esc,ascii_obracket);
|
||||
IF (ii > 1) THEN Write(Output,int_to_str(ii));
|
||||
Write(Output,'D');
|
||||
END;
|
||||
VT52_ : BEGIN
|
||||
Write(Output,ascii_esc,'D');
|
||||
END;
|
||||
{ IBM_PC_ : BEGIN
|
||||
c_pos.l := CRT.WHEREY;
|
||||
c_pos.c := CRT.WHEREX;
|
||||
IF ((c_pos.c-ii) >= 0) THEN
|
||||
c_pos.c := c_pos.c - ii
|
||||
ELSE
|
||||
c_pos.c := 0;
|
||||
CRT.GOTOXY(c_pos.c,c_pos.l);
|
||||
END; }
|
||||
END;
|
||||
END; {}
|
||||
|
||||
PROCEDURE ANSI_EEOL;
|
||||
{Erase to End Of Line (VT52)}
|
||||
|
||||
BEGIN {}
|
||||
CASE ANSI_MODE OF
|
||||
ANSI_ : BEGIN
|
||||
Write(Output,ascii_esc,ascii_obracket,ascii_zero,'K');
|
||||
END;
|
||||
VT52_ : BEGIN
|
||||
Write(Output,ascii_esc,'K');
|
||||
END;
|
||||
{ IBM_PC_ : BEGIN
|
||||
CRT.CLREOL;
|
||||
END; }
|
||||
END;
|
||||
END; {}
|
||||
|
||||
PROCEDURE ANSI_EEOS;
|
||||
{Erase to End Of Screen (VT52)}
|
||||
|
||||
BEGIN {}
|
||||
CASE ANSI_MODE OF
|
||||
ANSI_ : BEGIN
|
||||
END;
|
||||
VT52_ : BEGIN
|
||||
Write(Output,ascii_esc,'J');
|
||||
END;
|
||||
{ IBM_PC_ : BEGIN
|
||||
END; }
|
||||
END;
|
||||
END; {}
|
||||
|
||||
PROCEDURE ANSI_CUH;
|
||||
{Cursor home}
|
||||
|
||||
BEGIN {}
|
||||
CASE ANSI_MODE OF
|
||||
ANSI_ : BEGIN
|
||||
Write(Output,ascii_esc,ascii_obracket,'0;0','H');
|
||||
END;
|
||||
VT52_ : BEGIN
|
||||
Write(Output,ascii_esc,'H');
|
||||
END;
|
||||
{ IBM_PC_ : BEGIN
|
||||
CRT.GOTOXY(BYTE(1),BYTE(1));
|
||||
END; }
|
||||
END;
|
||||
c_pos.l := 0;
|
||||
c_pos.c := 0;
|
||||
END; {}
|
||||
|
||||
PROCEDURE ANSI_CUP(line, col : INTEGER);
|
||||
{Cursor position}
|
||||
|
||||
BEGIN {}
|
||||
line := line MOD 256;
|
||||
col := col MOD 256;
|
||||
CASE ANSI_MODE OF
|
||||
ANSI_ : BEGIN
|
||||
Write(Output,ascii_esc,ascii_obracket,int_to_str(line),';',
|
||||
int_to_str(col),'f');
|
||||
END;
|
||||
VT52_ : BEGIN
|
||||
Write(Output,ascii_esc,'Y',Chr(Ord(BYTE(line+32))),Chr(Ord(
|
||||
BYTE(col+32))));
|
||||
END;
|
||||
{ IBM_PC_ : BEGIN
|
||||
CRT.GOTOXY(BYTE(col+1),BYTE(line+1));
|
||||
END; }
|
||||
END;
|
||||
c_pos.l := line;
|
||||
c_pos.c := col;
|
||||
END; {}
|
||||
|
||||
|
||||
{
|
||||
IF (ANSI_MODE = ANSI) THEN BEGIN
|
||||
END
|
||||
ELSE BEGIN {VT52}
|
||||
END;
|
||||
}
|
||||
|
||||
BEGIN {Initialization}
|
||||
ASSIGN (INPUT,'');
|
||||
RESET(INPUT);
|
||||
ASSIGN(OUTPUT,'');
|
||||
REWRITE(OUTPUT);
|
||||
WRITELN(OUTPUT);
|
||||
REPEAT
|
||||
WRITE('Is this machine''s video 1) ANSI or 2) VT-52 compatible ?');
|
||||
READLN(inch);
|
||||
inch := UPCASE(inch);
|
||||
UNTIL (inch IN ['1','2','3']);
|
||||
CASE inch OF
|
||||
'1' : ANSI_MODE := ANSI_;
|
||||
'2' : ANSI_MODE := VT52_;
|
||||
{ '3' : ANSI_MODE := IBM_PC_; }
|
||||
END;
|
||||
ANSI_CLRSCR;
|
||||
END.
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,153 @@
|
|||
8
|
||||
1
|
||||
4
|
||||
1
|
||||
1
|
||||
2
|
||||
5
|
||||
1
|
||||
5
|
||||
8
|
||||
2
|
||||
5
|
||||
1
|
||||
4
|
||||
2
|
||||
3
|
||||
4
|
||||
3
|
||||
2
|
||||
3
|
||||
8
|
||||
4
|
||||
3
|
||||
2
|
||||
2
|
||||
3
|
||||
2
|
||||
6
|
||||
5
|
||||
2
|
||||
7
|
||||
8
|
||||
8
|
||||
2
|
||||
3
|
||||
4
|
||||
1
|
||||
4
|
||||
8
|
||||
8
|
||||
2
|
||||
2
|
||||
1
|
||||
4
|
||||
7
|
||||
8
|
||||
4
|
||||
1
|
||||
5
|
||||
1
|
||||
3
|
||||
1
|
||||
8
|
||||
8
|
||||
5
|
||||
1
|
||||
4
|
||||
6
|
||||
7
|
||||
1
|
||||
4
|
||||
8
|
||||
7
|
||||
5
|
||||
1
|
||||
4
|
||||
8
|
||||
2
|
||||
5
|
||||
5
|
||||
8
|
||||
1
|
||||
8
|
||||
4
|
||||
1
|
||||
4
|
||||
5
|
||||
4
|
||||
1
|
||||
4
|
||||
2
|
||||
8
|
||||
3
|
||||
1
|
||||
4
|
||||
8
|
||||
1
|
||||
4
|
||||
1
|
||||
7
|
||||
5
|
||||
2
|
||||
4
|
||||
2
|
||||
8
|
||||
2
|
||||
7
|
||||
6
|
||||
4
|
||||
3
|
||||
6
|
||||
3
|
||||
2
|
||||
7
|
||||
8
|
||||
5
|
||||
1
|
||||
6
|
||||
4
|
||||
2
|
||||
2
|
||||
2
|
||||
4
|
||||
7
|
||||
5
|
||||
8
|
||||
8
|
||||
6
|
||||
7
|
||||
8
|
||||
4
|
||||
6
|
||||
6
|
||||
3
|
||||
5
|
||||
4
|
||||
2
|
||||
1
|
||||
6
|
||||
2
|
||||
4
|
||||
5
|
||||
2
|
||||
2
|
||||
4
|
||||
3
|
||||
6
|
||||
8
|
||||
4
|
||||
6
|
||||
8
|
||||
7
|
||||
5
|
||||
1
|
||||
1
|
||||
2
|
||||
8
|
||||
5
|
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|
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|
|||
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|
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|
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|
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|
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|
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|
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|
||||
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|
||||
4
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,94 @@
|
|||
|
||||
|
||||
PROGRAM classical_composition (Input,Output);
|
||||
{
|
||||
This program composes note sequences in a manner designed to conform as
|
||||
far as possible to a set of example sequences.
|
||||
}
|
||||
|
||||
{
|
||||
|
||||
Copyright 1989 by Wesley R. Elsberry. All rights reserved.
|
||||
|
||||
Commercial use of this software is prohibited without written consent of
|
||||
the author.
|
||||
|
||||
For information, bug reports, and updates contact
|
||||
|
||||
Wesley R. Elsberry
|
||||
528 Chambers Creek Drive South
|
||||
Everman, Texas 76140
|
||||
Telephone: (817) 551-7018
|
||||
|
||||
}
|
||||
|
||||
|
||||
USES
|
||||
DOS, misc1, ANSI_Z, globals, clasinst;
|
||||
|
||||
{General}
|
||||
|
||||
|
||||
VAR
|
||||
ii, jj, kk : INTEGER;
|
||||
notes : notes_;
|
||||
sinch : CHAR;
|
||||
scon, outfname : STRING;
|
||||
outf : TEXT;
|
||||
cnt,success : INTEGER;
|
||||
ana : ARRAY[1..8] OF INTEGER;
|
||||
ana_cnt : INTEGER;
|
||||
|
||||
PROCEDURE fill_ana (notes : notes_);
|
||||
|
||||
VAR
|
||||
ii, tr : INTEGER;
|
||||
|
||||
BEGIN
|
||||
ana_cnt := 0;
|
||||
FOR ii := 1 TO 8 DO BEGIN
|
||||
notes[5] := ii;
|
||||
ana[ii] := classical_instructor(notes);
|
||||
END;
|
||||
FOR ii := 1 TO 8 DO BEGIN
|
||||
IF ana[ii] = 1 THEN BEGIN
|
||||
INC(ana_cnt);
|
||||
ana[ana_cnt] := ii;
|
||||
END;
|
||||
END;
|
||||
END;
|
||||
|
||||
|
||||
BEGIN
|
||||
Randomize;
|
||||
FOR ii := 1 TO 5 DO BEGIN
|
||||
notes[ii] := 0;
|
||||
END;
|
||||
|
||||
{Get filename to test}
|
||||
Write('Name of file to process: ');
|
||||
Readln(outfname);
|
||||
{Open for input}
|
||||
Assign(outf,outfname);
|
||||
Rewrite(outf);
|
||||
|
||||
FOR ii := 1 TO 10000 DO BEGIN
|
||||
fill_ana(notes);
|
||||
IF ana_cnt <> 0 THEN BEGIN
|
||||
notes[5] := ana[(Random(ana_cnt) + 1)];
|
||||
END
|
||||
ELSE BEGIN
|
||||
notes[5] := Random(8) + 1;
|
||||
END;
|
||||
FOR jj := 1 TO 4 DO BEGIN
|
||||
notes[jj] := notes[jj+1];
|
||||
END;
|
||||
Writeln(outf,notes[5]);
|
||||
END;
|
||||
|
||||
Close(outf);
|
||||
|
||||
END.
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,120 @@
|
|||
|
||||
|
||||
UNIT ClasInst; {Classical_Instructor}
|
||||
{
|
||||
This unit provides a critique of note sequences based on a data file of
|
||||
example sequences, 'SEQUENCE.DAT'.
|
||||
}
|
||||
{
|
||||
|
||||
Copyright 1989 by Wesley R. Elsberry. All rights reserved.
|
||||
|
||||
Commercial use of this software is prohibited without written consent of
|
||||
the author.
|
||||
|
||||
For information, bug reports, and updates contact
|
||||
|
||||
Wesley R. Elsberry
|
||||
528 Chambers Creek Drive South
|
||||
Everman, Texas 76140
|
||||
Telephone: (817) 551-7018
|
||||
|
||||
}
|
||||
|
||||
|
||||
INTERFACE
|
||||
|
||||
USES DOS, misc1, ANSI_Z, globals;
|
||||
|
||||
{General}
|
||||
FUNCTION Classical_instructor(Seq : Notes_):INTEGER;
|
||||
{This function does a look-up of a sequence of notes and returns a 1 if it
|
||||
is a listed sequence, a 0 if it is not recognized as being a classical
|
||||
sequence. This function is used in the training of Salieri, the PDP network.}
|
||||
|
||||
|
||||
IMPLEMENTATION
|
||||
|
||||
CONST
|
||||
Max_Seq = 100;
|
||||
|
||||
TYPE
|
||||
N_str_ = STRING[v_len_in];
|
||||
N_Str_Ary_Ptr_ = ^N_Str_Ary_;
|
||||
N_Str_Ary_ = ARRAY[1..Max_Seq] OF N_Str_;
|
||||
|
||||
CONST
|
||||
Seq_Table : N_Str_ary_ptr_ = NIL;
|
||||
Number_of_seqs : INTEGER = 0;
|
||||
|
||||
VAR
|
||||
Target, Instring : N_Str_;
|
||||
Inf : TEXT;
|
||||
Inchar : CHAR;
|
||||
ii, jj : INTEGER;
|
||||
Found : BOOLEAN;
|
||||
nchar : CHAR;
|
||||
|
||||
FUNCTION Classical_instructor(Seq : Notes_):INTEGER;
|
||||
{This function does a look-up of a sequence of notes and returns a 1 if it
|
||||
is a listed sequence, a 0 if it is not recognized as being a classical
|
||||
sequence. This function is used in the training of Salieri, the PDP
|
||||
network.}
|
||||
|
||||
{Determine if a sequence of notes can be considered to be classical in
|
||||
form. Does this by executing a look-up matching against the last
|
||||
n notes in the passed-in sequence. Returns 0 if not found, 1 if found.
|
||||
|
||||
Requires the following TYPE definition:
|
||||
|
||||
Notes_ = ARRAY[1..v_len_in] OF INTEGER;
|
||||
}
|
||||
|
||||
BEGIN {Classical_instructor}
|
||||
|
||||
{Convert notes to string representation}
|
||||
Target := ''; {Clear string}
|
||||
FOR ii := 1 TO v_len_out DO BEGIN
|
||||
nchar := Chr(seq[ii]+48);
|
||||
Target := Target + nchar;
|
||||
END;
|
||||
|
||||
ANSI_CUP(0,39);
|
||||
Write('Classical_Instructor: Target: ',target);
|
||||
ANSI_CUP(23,0);
|
||||
{Run through possible sequences, mark if found}
|
||||
ii := 1;
|
||||
Found := FALSE;
|
||||
REPEAT {}
|
||||
jj := Length(Seq_Table^[ii]);
|
||||
Found := (Copy(Target,v_len_out-jj+1,jj) = Seq_Table^[ii]);
|
||||
ii := ii + 1;
|
||||
UNTIL (ii>Number_of_seqs) OR (Found);
|
||||
{}
|
||||
|
||||
IF (Found) THEN {Return 1}
|
||||
BEGIN
|
||||
Classical_instructor := 1;
|
||||
END
|
||||
ELSE {Return 0}
|
||||
BEGIN
|
||||
Classical_instructor := 0;
|
||||
END;
|
||||
|
||||
END; {Classical_instructor}
|
||||
|
||||
BEGIN
|
||||
ii := 1;
|
||||
NEW(Seq_Table); {Allocate space for table}
|
||||
ASSIGN(Inf,'SEQUENCE.DAT'); {Set up to read data}
|
||||
RESET(Inf);
|
||||
WHILE (NOT EOF(Inf)) AND (ii <= Max_Seq) DO {Get data, put in table}
|
||||
BEGIN
|
||||
READLN(Inf,Seq_Table^[ii]);
|
||||
ii := ii + 1;
|
||||
END;
|
||||
CLOSE(Inf);
|
||||
Number_of_seqs := ii - 1;
|
||||
END.
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,379 @@
|
|||
.okidata9
|
||||
.ipr/ /
|
||||
.nopage break
|
||||
.page length 66
|
||||
.lpi6
|
||||
.above header 3
|
||||
.below header 3
|
||||
.above footer 3
|
||||
.below footer 3
|
||||
.page break
|
||||
.page number 1
|
||||
.pindent 15
|
||||
.pitch12
|
||||
.head//
|
||||
.foot/ #/
|
||||
.ipr/T/
|
||||
|
||||
|
||||
|
||||
|
||||
Competing Network Models and Problem-Solving
|
||||
|
||||
|
||||
(Poster Presentation at the First Annual Meeting of the
|
||||
International Neural Network Society, September 6-10, 1988.)
|
||||
|
||||
Diane J. Blackwood,
|
||||
Department of Biomedical Engineering,
|
||||
University of Texas at Arlington
|
||||
|
||||
Wesley R. Elsberry,
|
||||
Department of Computer Science,
|
||||
University of Texas at Arlington
|
||||
|
||||
and
|
||||
|
||||
Sam Leven,
|
||||
Neural Systems and Science,
|
||||
45 San Jacinto Way
|
||||
San Francisco, CA 94127
|
||||
|
||||
|
||||
ABSTRACT
|
||||
|
||||
Three of the most-often discussed neural networks models are
|
||||
analyzed and differentiated. The Hopfield, PDP, and ART models
|
||||
ask different questions, it is asserted -- and offer different
|
||||
answers for analyzing and construing complex environments. The
|
||||
three may not be competitors but, rather, complements. In fact,
|
||||
they may replicate different neural processes (Leven, 1987b). We
|
||||
seek to demonstrate the value of each model -- in a single case
|
||||
study.
|
||||
|
||||
The model offered by Hopfield (e.g., 1982) represents a
|
||||
fast-converging computable technique for analyzing highly limited
|
||||
classes of inputs. The PDP model (Rumelhart, et® al., 1986)
|
||||
offers the prospect of adoption of varied schemas, at the cost of
|
||||
a larger, more complex system. The ART model (e.g., Carpenter,
|
||||
et® al., 1987a) allows the greatest adaptability, including the
|
||||
capacity to vary vigilance levels and emulate many neural
|
||||
functions -- with the costs of much greater complexity and strain
|
||||
on system resources.
|
||||
|
||||
We present a single system, including analysis of different
|
||||
aspects of a problem by Hopfield, PDP, and ART networks, as an
|
||||
example of the potential for including many capabilities within
|
||||
the same environment.
|
||||
.start page
|
||||
|
||||
|
||||
While self-criticism in the neural network community is not
|
||||
unusual (eg., Rumelhart, et® al., 1986, Ch® 1; Grossberg, 1987a),
|
||||
we may find rapprochement among "competing paradigms" more
|
||||
effective than the occasional nastiness we encounter. Some
|
||||
problems, especially in complex controls on robotics, may be best
|
||||
addressed by a cooperative approach.
|
||||
|
||||
In fact, the three paradigms most often considered mutually
|
||||
exclusive (Hopfield, PDP, and ART) may actually represent
|
||||
different neural processes (Leven, 1987a). In any case, they
|
||||
clearly contemplate separate issues -- and may be best in
|
||||
approaching distinct problems.
|
||||
|
||||
Hopfield's model (Hopfield, 1982; Hopfield and Tank, 1986)
|
||||
represents a fast-converging computable technique for analyzing
|
||||
stereotyped or highly limited classes of inputs. Achieved minima
|
||||
have the virtue of remaining highly stable (representing
|
||||
permanent learning). This virtue has the accompanying cost, of
|
||||
course, of minimizing adaptability --recognizing new aspects of
|
||||
data is not seriously contemplated for a stable implementation.
|
||||
The model has a notable tolerance for data sets containing great
|
||||
amounts of simple noise; however, it tends to shrink from "multi
|
||||
flavored" problems, which require category or schema formation in
|
||||
an extensive environment.
|
||||
|
||||
The model of the Parallel Distributed Processing (PDP) group
|
||||
(Rumelhart, et® al., 1986) contemplates "schema formation",
|
||||
seeking to apply standard cognitive psychological insights to
|
||||
pattern recognition and category formation processes. They have
|
||||
sought to take minimal anatomies and build, following the work of
|
||||
Schank and Abelson (1977), basic semantic structures.
|
||||
|
||||
The PDP school has achieved notable successes in
|
||||
representing language (Sejnowski, 1986) and other areas with
|
||||
stable knowledge domains. Where "dynamic schemata" (Schank,
|
||||
1982) are generic to a problem -- where existing memory
|
||||
structures must be modified -- the strength of the simulated
|
||||
annealing algorithm becomes a weakness. Changing existing
|
||||
knowledge structures (by modification or replacement in the same
|
||||
state space) is well-nigh impossible (Yoon, et® al., 1988).
|
||||
|
||||
This weakness of the PDP, its stubbornness in resisting data
|
||||
that should produce restructured schemata, is also a strength.
|
||||
In certain environments, stable representations of higher-order
|
||||
structures (rules) coupled with the capacity to learn or be
|
||||
trained "up-front" may offer system designers desired control.
|
||||
Some systems should not be ENDLESSLY adaptive.
|
||||
.start page
|
||||
|
||||
Stephen Grossberg and his school (1987b & c, Carpenter et®
|
||||
al. 1987a & b) have suggested that the Adaptive Resonance (ART)
|
||||
model best represents higher-order neural functions. Equipped
|
||||
with representations for motivational processes and interactions
|
||||
between routines ("avalanches") and higher order structures (eg.,
|
||||
motivational dipoles and associated READ architectures), a full-
|
||||
blown ART system can model highly adaptive motor tasks and
|
||||
emulate higher-order behaviors (Levine, 1986; Leven, 1987a & b;
|
||||
and Ricart, 1988).
|
||||
|
||||
ART has the capacity to RECONSTRUE categories, based on
|
||||
continuing mismatches between data and existing higher order
|
||||
constructs and motivating environmental feedback. It also allows
|
||||
"masking fields" to eliminate from consideration whole segments
|
||||
of data which the system anticipates to be inappropriate or
|
||||
unnecessarily unsettling.
|
||||
|
||||
Under some circumstances, when using dipole structures to
|
||||
eliminate whole sets of competing representations (or rules), for
|
||||
example, ART can be faster -- and more effective -- than the
|
||||
alternatives we have presented. However, training an ART
|
||||
environment to perform highly routinized behaviors in which
|
||||
context has limited relevance has been considered more
|
||||
inefficient than using, say, the Hopfield model. Ordinarily, the
|
||||
powerful structures an ART modeler employs slow the learning
|
||||
process with error-checking routines which value fault-
|
||||
intolerance over speed. Yet, sometimes, in highly stable
|
||||
environments, designers may be uncomfortable with an ART system's
|
||||
capacity to "re-learn" essential skills they must employ.
|
||||
|
||||
Additionally, the rapid trainability and stability of a PDP
|
||||
environment may prove superior to ART, for many of the same
|
||||
reasons. Some higher-order rules (schemata) may be system-
|
||||
critical. In these cases, PROGRAMMERS SHOULD DESIGN SYSTEMS --
|
||||
NOT THE SYSTEMS DESIGNING THEMSELVES. Hence, some systems may
|
||||
require less-intrusive network engines (like PDP) --especially
|
||||
when these engines also provide greater speed.
|
||||
|
||||
Thus, the three models for neural network design may be
|
||||
COMPLEMENTARY in function: Hopfield offering speed and stability,
|
||||
PDP providing up-front learning and stable rule structures, and
|
||||
ART employing context- and environment-sensitive capabilities
|
||||
(see Figure 1). We demonstrate, below, that modelers ought to
|
||||
consider these qualities in developing extensive systems -- and
|
||||
utilize the many effective tools at our disposal.
|
||||
|
||||
.start page
|
||||
|
||||
EXAMPLE PROBLEM
|
||||
|
||||
BEETHOVEN is a "music composition" system (see Figure 2).
|
||||
It provides a three part neural network model. The system
|
||||
emulates fundamental compositional rules to generate and perform
|
||||
a musical sequence.
|
||||
|
||||
BACH is a Hopfield net provides a sequence of notes,
|
||||
emulating musical melodic performance. A single voice selects
|
||||
notes from within a single octave. Biases are provided -- as a
|
||||
composer has the innate tendency to choose certain intervals and
|
||||
to reject notes that tend to violate common rules of harmony
|
||||
(eg., Aldwell and Schachter, 1978).
|
||||
|
||||
This network of notes is output, in sequence, to a PDP back-
|
||||
propagation network named SALIERI, which has learned a set of
|
||||
standard, somewhat higher-order harmonic rules. The network
|
||||
judges the effectiveness of the sequence, note by note, based on
|
||||
the intervals involved and the absolute note values (eg., #7
|
||||
should precede #8 -- and, almost always, at the end of a phrase).
|
||||
These schemata, then, reject inappropriate sequences AND INHIBIT
|
||||
SOME INAPPROPRIATE NEXT NOTES. This "look-ahead" capability is
|
||||
unusual in a PDP environment, yet is fitting for the inhibitory
|
||||
role the network is playing and for the stability of the rule
|
||||
structure being employed.
|
||||
|
||||
The output from PDP flows, directly, to an ART network,
|
||||
BEETHOVEN. Employing a model of motivation (based on
|
||||
construction of category valuation and a healthy boredom at
|
||||
repetition), BEETHOVEN rejects "unaesthetic" sequences. As the
|
||||
number of phrases performed increases, the ART model develops
|
||||
intense biases, which it imposes on BACH and SALIERI.
|
||||
|
||||
One additional component of the environment is LOBES, the
|
||||
Context Manager. LOBES, loosely emulative of human frontal lobes
|
||||
(see Levine, 1986), maintains information about the processes
|
||||
being performed, mediates inter-model interaction, and provides
|
||||
for the final external output (sounding the speaker).
|
||||
|
||||
The model, then, utilizes the best capabilities of three
|
||||
distinctly different paradigms. Hopfield performs efficient
|
||||
routine processes, as would a "reptilian brain" (MacLean, 1970).
|
||||
PDP serves as an insistent schoolmarm, observing and enforcing
|
||||
higher-level rules, like a "neo-mammalian brain." ART provides a
|
||||
sense of fitness, an aesthetic fitting for models of the limbic
|
||||
system (or "mammalian brain").
|
||||
|
||||
Integration of many memory and processing functions in a
|
||||
three part model may be similar to human brain function (Leven,
|
||||
1987b). Regardless of its biological versimilitude, however,
|
||||
such an approach seems to offer unique combinations of speed,
|
||||
stability, and flexibility.
|
||||
|
||||
.start page
|
||||
.nopage break
|
||||
.page length 88
|
||||
.lpi8
|
||||
.page break
|
||||
.ipr//
|
||||
.pindent 12
|
||||
.ipr/ /
|
||||
.foot/ #/
|
||||
|
||||
REFERENCES
|
||||
|
||||
|
||||
Aldwell, E® & C® Schachter. 1978. Harmony and voice leading. Harcourt, Brace & Jovanovich, New York.
|
||||
|
||||
Carpenter, G.A® & S® Grossberg. 1987a. A massively parallel architecture for a self-organizing neural
|
||||
pattern recognition machine. Computer Vision, Graphics, and Image Processing 37:54-115.
|
||||
|
||||
Carpenter, G.A® & S® Grossberg. 1987b. ART 2: self-organization of stable category recognition codes for
|
||||
analog input patterns. Applied Optics 26(23):4919-4930.
|
||||
|
||||
Grossberg, S. 1987a® Competitive Learning: From interactive activation to adaptive resonance.
|
||||
Cognitive Science 11:23-63.
|
||||
|
||||
Grossberg, S., ed. 1987b & c. The Adaptive Brain. Vol. I and II. Elsevier/North-Holland, Amsterdam.
|
||||
|
||||
Hartley, R® and H® Szu. 1987. A comparison of the computational power of neural network models. IEEE Proc.
|
||||
ICNN III:15-22.
|
||||
|
||||
Hopfield, J.J. 1982. Neural networks and physical systems with emergent collective computational abilities.
|
||||
Proc. Natl. Acad. Sci. USA 79:2554-2558.
|
||||
|
||||
Hopfield, J.J® and D.W® Tank. 1985. "Neural" computation of decisions in optimization problems. Biol.
|
||||
Cybern. 52:141-152.
|
||||
|
||||
Hopfield, J.J® and D.W® Tank. 1986. Computing with neural circuits: A model. Science 233:625-633.
|
||||
|
||||
Leven, S. 1987a. Choice and neural process. Unpublished Ph.D. Dissertation, University of Texas at
|
||||
Arlington.
|
||||
|
||||
Leven, S. 1987b. S.A.M.: A triune extension to the ART model. Symposium on Neural Networks, North
|
||||
Texas State University. (Poster presentation)
|
||||
|
||||
Leven, S. 1988. Memory, helplessness, and the dynamics of hope. Presented at the Metroplex Institute for
|
||||
Neural Dynamics' Workshop on Motivation, Emotion, and Goal Direction in Neural Networks.
|
||||
|
||||
Levine, D.S. 1986. A neural network theory of frontal lobe function. In: The Proceedings of the Eighth
|
||||
Annual Conference of the Cognitive Science Society. Erlbaum.
|
||||
|
||||
MacLean, P. 1970. The triune brain, emotion, and scientific bias. In: F® Schmitt, ed. The
|
||||
Neurosciences: Second Study Program. Rockefeller University Press.
|
||||
|
||||
Ricart, R. 1988. Backward conditioning: A neural network model which exhibits both excitatory and inhibitory
|
||||
conditioning. Presented at the Metroplex Institute for Neural Dynamics' Workshop on Motivation, Emotion,
|
||||
and Goal Direction in Neural Networks.
|
||||
|
||||
Rumelhart, D® & J® McClelland. 1986. Parallel Distributed Processing. MIT Press.
|
||||
|
||||
Schank, R. 1982. Dynamic memory. Cambridge University Press.
|
||||
|
||||
Schank, R.C® & R.P® Abelson. 1977. Scripts, Plans, Goals, and Understanding. Erlbaum, Hillsdale, NJ.
|
||||
|
||||
Sejnowski, T.J® 1986. Open questions about computation in cerebral cortex. In: J.L. McClelland & D.E.
|
||||
Rumelhart, eds. Parallel Distributed Processing Volume 2. MIT Press.
|
||||
|
||||
Simpson, R. 1988. A review of artificial neural systems II: Paradigms, applications, and implementations.
|
||||
Prepublication copy of paper submitted to CRC Critical Reviews in Artificial Intelligence.
|
||||
|
||||
Tank, D.W® & J.J® Hopfield. 1986. Simple "neural" optimization networks: An A/D converter, signal decision
|
||||
circuit, and a linear programming circuit. IEEE Transaction on Circuits and Systems CAS-33(5):533-541
|
||||
|
||||
Yoon, Y®, L.L® Peterson, & P.R® Bergstrasser. 1988. A dermatology expert system using connectionist
|
||||
network. Unpublished poster presentation, IEEE ICNN.
|
||||
|
||||
.start page
|
||||
.nopage break
|
||||
.page length 88
|
||||
.ipr/ /
|
||||
.lpi8
|
||||
.page break
|
||||
.ipr/ /
|
||||
.pindent 12
|
||||
.foot/ #/
|
||||
|
||||
|
||||
|
||||
|
||||
Convergence Convergence Stability Feedback Category Mixed Data Category Computational
|
||||
Speed Likelihood Of Capability Formation (Complex Reconstruction Simplicity
|
||||
Network Environment)
|
||||
------- ------- ------- ------- ------- ------- ------- -------
|
||||
|
||||
Hopfield + + + - - - - +
|
||||
|
||||
PDP 0 0 +/0 + + 0 - 0
|
||||
|
||||
ART - - 0/- + ++ + + -
|
||||
|
||||
Where '+' indicates a relative advantage, '0' indicates no special advantage or disadvantage,
|
||||
and '-' indicates a relative disadvantage.
|
||||
|
||||
Figure 1. Comparative analysis of features of the Hopfield, PDP, and ART artificial neural network models
|
||||
|
||||
|
||||
.ipr//
|
||||
.pitch12
|
||||
.ipr/T/
|
||||
.pindent 10
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
+-----------------+
|
||||
| | (Match, Other Info)
|
||||
| Beethoven |---------------------+
|
||||
| | |
|
||||
| (ART 1) |<----------------+ |
|
||||
| | (Context) | |
|
||||
+-----------------+ | |
|
||||
^ | |
|
||||
| | |
|
||||
|(Approval) | |
|
||||
| | |
|
||||
| | V
|
||||
+-----------------+ +-----------------+
|
||||
| | | |
|
||||
| Salieri | (Approval) | Lobes |
|
||||
| +------------>| (Context |
|
||||
| (PDP) |<------------| Management) |
|
||||
| | (Silence!) | |
|
||||
+-----------------+ +-----------------+
|
||||
^ ^ | |
|
||||
| (Candidate note) | | |
|
||||
+-------------------------+ | |
|
||||
| | |
|
||||
| | |
|
||||
+-----------------+ | |
|
||||
| | | |
|
||||
| Bach | (Generate Note!) | |
|
||||
| |<-------------------+ |
|
||||
| (Hopfield) | |
|
||||
| | |
|
||||
+-----------------+ (New Note) |
|
||||
|
|
||||
V
|
||||
+-------------------+
|
||||
| |
|
||||
| |
|
||||
| Speaker |
|
||||
| |
|
||||
| |
|
||||
+-------------------+
|
||||
|
||||
|
||||
Figure 2. Structure of sample system utilizing Hopfield, PDP, and ART models.
|
||||
.start page
|
||||
.nopage break
|
||||
|
||||
|
|
@ -0,0 +1,180 @@
|
|||
|
||||
|
||||
UNIT Globals;
|
||||
{
|
||||
This unit provides a variety of constants and types used in the integrated
|
||||
ANN note generator and related programs.
|
||||
}
|
||||
{
|
||||
|
||||
Copyright 1989 by Wesley R. Elsberry. All rights reserved.
|
||||
|
||||
Commercial use of this software is prohibited without written consent of
|
||||
the author.
|
||||
|
||||
For information, bug reports, and updates contact
|
||||
|
||||
Wesley R. Elsberry
|
||||
528 Chambers Creek Drive South
|
||||
Everman, Texas 76140
|
||||
Telephone: (817) 551-7018
|
||||
|
||||
}
|
||||
|
||||
|
||||
INTERFACE
|
||||
|
||||
USES
|
||||
Misc1, DOS, ANSI_Z;
|
||||
|
||||
{PUBLIC DECLARATIONS}
|
||||
|
||||
CONST
|
||||
Pi = 3.141592653589793;
|
||||
Exp_Max = 80.0;
|
||||
Colon = ':';
|
||||
graphic_string = '0123456789';
|
||||
|
||||
{For Bach}
|
||||
v_len_in = 8;
|
||||
v_len_out = 5;
|
||||
|
||||
epsilon : REAL = 0.005;
|
||||
|
||||
HTN_co_res : REAL = 3.5;
|
||||
HTN_co_cap : REAL = 10.0;
|
||||
HTN_co_wt : REAL = 1.0;
|
||||
HTN_co_inp : REAL = 1.0;
|
||||
HTN_co_iter : REAL = 1.0;
|
||||
|
||||
ART_co_vigilance : REAL = 1.0;
|
||||
|
||||
global_resistance : REAL = 1.0;
|
||||
global_capacitance : REAL = 1.0;
|
||||
|
||||
(* System goes low, but then some nodes start getting higher activity...
|
||||
global_resistance = 5;
|
||||
global_capacitance = 1; *)
|
||||
|
||||
{For Play_note}
|
||||
N_C_mid = 264;
|
||||
N_D = 297;
|
||||
N_E = 330;
|
||||
N_F =352;
|
||||
N_G = 396;
|
||||
N_A = 440;
|
||||
N_B = 495;
|
||||
N_C_hi = 528;
|
||||
|
||||
{For Beethoven (ART 1)}
|
||||
Max_F2_nodes = 25;
|
||||
Max_F1_nodes = 41;
|
||||
Vector_length = Max_F1_nodes;
|
||||
|
||||
(* Max_F1_nodes = 16;
|
||||
Vector_length = 16; c. 6/18/89 *)
|
||||
|
||||
|
||||
TYPE
|
||||
REAL = SINGLE;
|
||||
|
||||
{For Beethoven (ART 1)}
|
||||
LTM_weights_ = ARRAY[1..Max_F1_nodes] OF REAL;
|
||||
|
||||
F1_node_ = RECORD
|
||||
Curr_A : REAL; {Value of node now}
|
||||
Last_A : REAL; {Value from last time step}
|
||||
END;
|
||||
|
||||
F1_layer_ptr_ = ^F1_layer_;
|
||||
F1_layer_ = ARRAY[1..Max_F1_nodes] OF F1_node_;
|
||||
|
||||
F2_node_ = RECORD
|
||||
Curr_B : REAL; {Value of node now}
|
||||
Last_B : REAL; {Value from last time step}
|
||||
Wup : LTM_weights_; {BU LTM weights}
|
||||
Wdn : LTM_weights_; {TD LTM weights}
|
||||
WIN : INTEGER; {0 if not winner, 1 if winner}
|
||||
Eligible : BOOLEAN; {TRUE if not rejected}
|
||||
Committed : BOOLEAN; {TRUE if represents a category}
|
||||
END;
|
||||
|
||||
F2_layer_ptr_ = ^F2_layer_;
|
||||
F2_layer_ = ARRAY[1..Max_F2_nodes] OF F2_node_;
|
||||
|
||||
{General}
|
||||
|
||||
file_string_ = STRING[127];
|
||||
Time_rec_ = RECORD
|
||||
h,m,s,f : INTEGER;
|
||||
END;
|
||||
Note_ = (Note_C_Lo,Note_D,Note_E,Note_F,Note_G,Note_A,Note_B,
|
||||
Note_C_Hi);
|
||||
Vector_ = ARRAY[1..Max_F1_nodes] OF BYTE;
|
||||
Notes_ = ARRAY[1..V_LEN_OUT] OF INTEGER;
|
||||
|
||||
Common_Area_ = RECORD
|
||||
Notes : Notes_;
|
||||
Delta_Vigilance : BOOLEAN;
|
||||
New_category : BOOLEAN;
|
||||
Is_Classical : BOOLEAN;
|
||||
Candidate_Note : INTEGER;
|
||||
END;
|
||||
note_record_ = RECORD
|
||||
n : ARRAY[1..500] OF BYTE;
|
||||
c : INTEGER;
|
||||
END;
|
||||
|
||||
PROCEDURE Dump_Common(cmn : Common_Area_);
|
||||
|
||||
|
||||
IMPLEMENTATION
|
||||
|
||||
{PRIVATE DECLARATIONS}
|
||||
|
||||
{IMPLEMENTATIONS OF PROCEDURES AND FUNCTIONS}
|
||||
|
||||
PROCEDURE Dump_Common(cmn : Common_Area_);
|
||||
{}
|
||||
|
||||
VAR
|
||||
ii : INTEGER;
|
||||
|
||||
BEGIN {}
|
||||
{ WRITELN('Dump of Common Area -----------');}
|
||||
ANSI_CUP(1,0);
|
||||
Write('Notes in sequence:');
|
||||
ANSI_CUP(1,27);
|
||||
FOR ii := 1 TO V_Len_Out DO BEGIN
|
||||
{}
|
||||
Write(cmn.Notes[ii],' ');
|
||||
END; {}
|
||||
{ WRITELN;}
|
||||
ANSI_CUP(3,0);
|
||||
Write('Change in vigilance:');
|
||||
ANSI_CUP(3,31);
|
||||
Write(cmn.Delta_Vigilance:5);
|
||||
ANSI_CUP(4,0);
|
||||
Write('New Category formed: ');
|
||||
ANSI_CUP(4,31);
|
||||
Write(cmn.New_category:5);
|
||||
ANSI_CUP(5,0);
|
||||
Write('Candidate sequence classical?:');
|
||||
ANSI_CUP(5,31);
|
||||
Write(cmn.Is_Classical:5);
|
||||
ANSI_CUP(6,0);
|
||||
Write('Candidate note:');
|
||||
ANSI_CUP(6,35);
|
||||
Write(cmn.Candidate_Note);
|
||||
ANSI_CUP(7,0);
|
||||
Write('------------------------------------');
|
||||
ANSI_CUP(23,0);
|
||||
END; {}
|
||||
|
||||
{----------------------------------------------------------}
|
||||
|
||||
|
||||
BEGIN {INITIALIZATION}
|
||||
END.
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
c:
|
||||
cd \
|
||||
md wre_thes
|
||||
cd wre_thes
|
||||
PAUSE Make sure diskette is in drive A:
|
||||
a:lharc e a:*.lhz
|
||||
|
||||
Binary file not shown.
|
|
@ -0,0 +1,212 @@
|
|||
|
||||
|
||||
PROGRAM HTN_data_build(Input,Output);
|
||||
|
||||
{
|
||||
|
||||
Copyright 1989 by Wesley R. Elsberry. All rights reserved.
|
||||
|
||||
Commercial use of this software is prohibited without written consent of
|
||||
the author.
|
||||
|
||||
For information, bug reports, and updates contact
|
||||
|
||||
Wesley R. Elsberry
|
||||
528 Chambers Creek Drive South
|
||||
Everman, Texas 76140
|
||||
Telephone: (817) 551-7018
|
||||
|
||||
}
|
||||
|
||||
|
||||
|
||||
USES
|
||||
CRT, misc1, DOS;
|
||||
|
||||
CONST
|
||||
row_inhibition : REAL = -0.08;
|
||||
col_inhibition : REAL = -0.08;
|
||||
seq_add : REAL = 0.0;
|
||||
|
||||
TYPE
|
||||
REAL = SINGLE;
|
||||
file_string_ = STRING[127];
|
||||
data_array_ = ARRAY[1..64,1..64] OF REAL;
|
||||
|
||||
VAR
|
||||
inf, outf : TEXT;
|
||||
outdatf : FILE OF data_array_;
|
||||
data : data_array_;
|
||||
ii, jj, kk, ll, mm, nn : INTEGER;
|
||||
inch : CHAR;
|
||||
line, value : file_string_;
|
||||
di1, di2 : INTEGER;
|
||||
note1, note2, posit1, posit2 : INTEGER;
|
||||
error : INTEGER;
|
||||
min, max, range : REAL;
|
||||
tii, tjj : INTEGER;
|
||||
tr, ts : REAL;
|
||||
sums : ARRAY[1..5,1..8] OF REAL;
|
||||
sumssum : REAL;
|
||||
|
||||
|
||||
PROCEDURE init_sums ;
|
||||
|
||||
VAR
|
||||
ii, jj : INTEGER;
|
||||
|
||||
BEGIN
|
||||
|
||||
FOR ii := 1 TO 5 DO
|
||||
FOR jj := 1 TO 8 DO sums[ii,jj] := 0;
|
||||
END;
|
||||
|
||||
FUNCTION maximum(r1,r2:REAL):REAL;
|
||||
|
||||
BEGIN
|
||||
IF r1 >= r2 THEN maximum := r1
|
||||
ELSE maximum := r2;
|
||||
END;
|
||||
|
||||
FUNCTION signum(x : REAL):REAL;
|
||||
|
||||
BEGIN
|
||||
IF (x >= 0.0) THEN BEGIN
|
||||
signum := 1;
|
||||
END
|
||||
ELSE BEGIN
|
||||
signum := -1;
|
||||
END;
|
||||
END;
|
||||
|
||||
PROCEDURE show_node_sums;
|
||||
|
||||
VAR
|
||||
ii, jj : INTEGER;
|
||||
|
||||
BEGIN
|
||||
init_sums;
|
||||
sumssum := 0;
|
||||
|
||||
FOR ii := 1 TO 5 DO
|
||||
FOR jj := 1 TO 8 DO BEGIN
|
||||
FOR kk := 1 TO 5 DO
|
||||
FOR ll := 1 TO 8 DO BEGIN
|
||||
sums[ii,jj] := sums[ii,jj] + data[(8*(ii-1)+jj),
|
||||
(8*(kk-1)+ll)];
|
||||
END;
|
||||
END;
|
||||
|
||||
FOR jj := 1 TO 8 DO BEGIN
|
||||
FOR ii := 1 TO 5 DO BEGIN
|
||||
WRITE(sums[ii,jj]:6:3,' ');
|
||||
sumssum := sumssum + sums[ii,jj];
|
||||
END;
|
||||
WRITELN;
|
||||
END;
|
||||
WRITELN (sumssum);
|
||||
WRITELN;
|
||||
|
||||
END;
|
||||
|
||||
PROCEDURE set_row_and_column_inhibition;
|
||||
|
||||
VAR
|
||||
ii, jj, kk, ll : INTEGER;
|
||||
|
||||
BEGIN
|
||||
|
||||
FOR note1 := 1 TO 8 DO
|
||||
FOR posit1 := 1 TO 5 DO BEGIN
|
||||
di1 := (8*(posit1-1)+note1);
|
||||
FOR ii := 1 TO 8 DO{increase column inhibition}
|
||||
BEGIN
|
||||
IF (note1 <> ii) THEN BEGIN
|
||||
di2 := (8*(posit1-1)+ii);
|
||||
data[di1,di2] := data[di1,di2] + col_inhibition;
|
||||
data[di2,di1] := data[di1,di2];
|
||||
END;
|
||||
END;
|
||||
FOR jj := 1 TO 5 DO BEGIN
|
||||
IF (posit1 <> jj) THEN BEGIN
|
||||
di2 := (8*(jj-1)+note1);
|
||||
data[di1,di2] := data[di1,di2] + row_inhibition;
|
||||
data[di2,di1] := data[di1,di2];
|
||||
END
|
||||
ELSE BEGIN
|
||||
END;
|
||||
END;
|
||||
END;
|
||||
|
||||
END;
|
||||
|
||||
PROCEDURE clear_diagonal;
|
||||
|
||||
VAR
|
||||
ii, jj, kk, ll : INTEGER;
|
||||
|
||||
BEGIN
|
||||
FOR ii := 1 TO 40 DO data[ii,ii] := 0.0;
|
||||
END;
|
||||
|
||||
|
||||
BEGIN
|
||||
col_inhibition := ((8.0+(8.0-5.0))/5.0) * row_inhibition;
|
||||
seq_add := -(row_inhibition/7.0);
|
||||
|
||||
init_sums;
|
||||
|
||||
NoSound;
|
||||
|
||||
FOR ii := 1 TO 40 DO
|
||||
FOR jj := 1 TO 40 DO data[ii,jj] := 0.0;
|
||||
|
||||
Assign(inf,'sequence.dat');
|
||||
Reset(inf);
|
||||
Assign(outdatf,'htn.dat');
|
||||
ReWRITE(outdatf);
|
||||
|
||||
WHILE NOT Eof(inf) DO BEGIN {get a line}
|
||||
Readln(inf,line);
|
||||
WRITELN(line); {increment connection values in the
|
||||
data array}
|
||||
FOR ii := 1 TO (Length(line)-1) DO BEGIN
|
||||
Val(Copy(line,ii,1),note1,error);
|
||||
Val(Copy(line,ii+1,1),note2,error);
|
||||
WRITELN(note1,',',note2);
|
||||
FOR posit1 := 1 TO 4 DO BEGIN
|
||||
di1 := (8*(posit1-1))+note1;
|
||||
di2 := (8*(posit1))+note2;
|
||||
data[di1,di2] := data[di1,di2] + seq_add;
|
||||
data[di2,di1] := data[di1,di2];
|
||||
{symmetric weights!}
|
||||
END;
|
||||
END;
|
||||
IF Length(line) >= 3 THEN
|
||||
FOR ii := 1 TO (Length(line)-2) DO BEGIN
|
||||
Val(Copy(line,ii,1),note1,error);
|
||||
Val(Copy(line,ii+2,1),note2,error);
|
||||
WRITELN(note1,',',note2);
|
||||
FOR posit1 := 1 TO 3 DO BEGIN
|
||||
di1 := (8*(posit1-1))+note1;
|
||||
di2 := (8*(posit1+1))+note2;
|
||||
data[di1,di2] := data[di1,di2] + seq_add;
|
||||
data[di2,di1] := data[di1,di2];
|
||||
END;
|
||||
END;
|
||||
END;
|
||||
show_node_sums;
|
||||
|
||||
set_row_and_column_inhibition;
|
||||
|
||||
show_node_sums;
|
||||
|
||||
clear_diagonal;
|
||||
show_node_sums;
|
||||
|
||||
WRITE(outdatf,data);
|
||||
Close(inf);
|
||||
Close(outdatf);
|
||||
END.
|
||||
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1 @@
|
|||
裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹<EFBFBD>
|
||||
|
|
@ -0,0 +1 @@
|
|||
裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹裹<EFBFBD>
|
||||
|
|
@ -0,0 +1,70 @@
|
|||
.okidata9
|
||||
.page length 66
|
||||
.lpi6
|
||||
.pitch12
|
||||
.pindent 10
|
||||
.above header 2
|
||||
.below header 2
|
||||
.above footer 2
|
||||
.below footer 2
|
||||
.page number 1
|
||||
.head//
|
||||
.foot//
|
||||
.page break
|
||||
|
||||
The Metroplex Institute for Neural Dynamics
|
||||
|
||||
100 Allentown Parkway, Suite 211, Allen, Texas 75002 (214) 422-4570
|
||||
|
||||
December 1989 Newsletter
|
||||
|
||||
DECEMBER MEETING ANNOUNCEMENT
|
||||
|
||||
DATE: Saturday, December 9, 1989
|
||||
TIME: 12:30 PM
|
||||
PLACE: McDermott Library, Room 2.406 (or adjacent room), the University of
|
||||
Texas at Dallas
|
||||
TOPIC: Isomorphic back-propagation networks
|
||||
SPEAKER: Mike Manry, Electrical Engineering, UTA
|
||||
|
||||
Dr. Manry will give an overview of research into methods of mapping signal
|
||||
processing algorithms onto back-propagation networks. The signal
|
||||
processing algorithms include Gaussian classifiers.
|
||||
|
||||
A new design technique for back-propagation networks is presented. In this
|
||||
technique, unit activation functions are approximated by power series.
|
||||
Basic building blocks such as monomial networks and multiplier networks are
|
||||
implemented with any desired degree of accuracy. Representation theorems
|
||||
for designing arbitrary continuous functions are also discussed.
|
||||
|
||||
These studies will give us a clearer understanding of the strengths,
|
||||
limitations, and uses of back-propagation networks. One specific
|
||||
application being used in this research is recognition of hand-printed
|
||||
numerals.
|
||||
|
||||
DIRECTIONS: UTD is on Floyd Road, north of Campbell Road in Richardson.
|
||||
Take Central Expressway to the Renner Road exit (north of Campbell Road).
|
||||
Take Renner Road west past the traffic light at Custer. You'll have to
|
||||
curve south as Renner ends at Floyd road. The main entrance to the UTD
|
||||
campus is at the (only) stop sign on Floyd between Renner and Campbell. Go
|
||||
west into the campus to the parking lot behind the guard station (it is
|
||||
usually open on Saturday afternoons). McDermott Library is the building on
|
||||
the south border of the lot. We meet in one of the classrooms on the left
|
||||
after you go in the main entrance which, according to UTD's unique
|
||||
numbering scheme, is on level two.
|
||||
|
||||
------------------------------------------------------------------------
|
||||
|
||||
November Meeting Notes
|
||||
|
||||
The presentation on transputers and the Occam language given by David Bye
|
||||
of INMOS was very informative. David Bye has complimentary literature
|
||||
available on transputers, systems, and the Occam language. If you are
|
||||
interested, contact him at the SGS Thomson (INMOS) facility at 1310
|
||||
Electronics Drive in Carrollton.
|
||||
|
||||
------------------------------------------------------------------------
|
||||
|
||||
For further information, contact Wesley Elsberry at (817) 551-7018.
|
||||
.start page
|
||||
|
||||
|
|
@ -0,0 +1,515 @@
|
|||
UNIT misc1;
|
||||
{
|
||||
This unit provides a number of functions of general utility.
|
||||
}
|
||||
|
||||
{
|
||||
|
||||
Copyright 1989 by Wesley R. Elsberry. All rights reserved.
|
||||
|
||||
Commercial use of this software is prohibited without written consent of
|
||||
the author.
|
||||
|
||||
For information, bug reports, and updates contact
|
||||
|
||||
Wesley R. Elsberry
|
||||
528 Chambers Creek Drive South
|
||||
Everman, Texas 76140
|
||||
Telephone: (817) 551-7018
|
||||
|
||||
}
|
||||
|
||||
|
||||
INTERFACE
|
||||
|
||||
USES DOS;
|
||||
|
||||
|
||||
CONST
|
||||
ASCII_NUL = #0;
|
||||
ASCII_SOH = #1;
|
||||
ASCII_STX = #2;
|
||||
ASCII_ETX = #3;
|
||||
ASCII_EOT = #4;
|
||||
ASCII_ENQ = #5;
|
||||
ASCII_ACK = #6;
|
||||
ASCII_BEL = #7;
|
||||
ASCII_BS = #8;
|
||||
ASCII_HT = #9;
|
||||
ASCII_LF = #10;
|
||||
ASCII_VT = #11;
|
||||
ASCII_FF = #12;
|
||||
ASCII_CR = #13;
|
||||
ASCII_SO = #14;
|
||||
ASCII_SI = #15;
|
||||
ASCII_DLE = #16;
|
||||
ASCII_DC1 = #17;
|
||||
ASCII_XON = #17;
|
||||
ASCII_DC2 = #18;
|
||||
ASCII_DC3 = #19;
|
||||
ASCII_XOFF = #19;
|
||||
ASCII_DC4 = #20;
|
||||
ASCII_NAK = #21;
|
||||
ASCII_SYN = #22;
|
||||
ASCII_ETB = #23;
|
||||
ASCII_CAN = #24;
|
||||
ASCII_EM = #25;
|
||||
ASCII_SUB = #26;
|
||||
ASCII_EOF = #26;
|
||||
ASCII_ESC = #27;
|
||||
ASCII_FS = #28;
|
||||
ASCII_GS = #29;
|
||||
ASCII_RS = #30;
|
||||
ASCII_US = #31;
|
||||
ASCII_SP = #32;
|
||||
ASCII_EXCL = #33;
|
||||
ASCII_DQUOTE = #34;
|
||||
ASCII_POUND = #35;
|
||||
ASCII_DOLLAR = #36;
|
||||
ASCII_PERCENT = #37;
|
||||
ASCII_AMPERSAND = #38;
|
||||
ASCII_SQUOTE = #39;
|
||||
ASCII_OPAREN = #40;
|
||||
ASCII_CPAREN = #41;
|
||||
ASCII_ASTERISK = #42;
|
||||
ASCII_PLUS = #43;
|
||||
ASCII_COMMA = #44;
|
||||
ASCII_DASH = #45;
|
||||
ASCII_PERIOD = #46;
|
||||
ASCII_SLASH = #47;
|
||||
ASCII_ZERO = #48;
|
||||
ASCII_ONE = #49;
|
||||
ASCII_TWO = #50;
|
||||
ASCII_THREE = #51;
|
||||
ASCII_FOUR = #52;
|
||||
ASCII_FIVE = #53;
|
||||
ASCII_SIX = #54;
|
||||
ASCII_SEVEN = #55;
|
||||
ASCII_EIGHT = #56;
|
||||
ASCII_NINE = #57;
|
||||
ASCII_COLON = #58;
|
||||
ASCII_SEMICOLON = #59;
|
||||
ASCII_LESSTHAN = #60;
|
||||
ASCII_EQUAL = #61;
|
||||
ASCII_GREATERTHAN = #62;
|
||||
ASCII_QMARK = #63;
|
||||
ASCII_AT = #64;
|
||||
ASCII_OBRACKET = #91;
|
||||
ASCII_BACKSLASH = #92;
|
||||
ASCII_CBRACKET = #93;
|
||||
ASCII_CARAT = #94;
|
||||
ASCII_UNDERLINE = #95;
|
||||
ASCII_BACKQUOTE = #96;
|
||||
ASCII_OBRACE = #123;
|
||||
ASCII_VLINE = #124;
|
||||
ASCII_CBRACE = #125;
|
||||
ASCII_TILDE = #126;
|
||||
ASCII_DEL = #127;
|
||||
|
||||
|
||||
|
||||
TYPE
|
||||
Time_rec_ = RECORD
|
||||
h,m,s,f : INTEGER;
|
||||
END;
|
||||
|
||||
|
||||
PROCEDURE Time(VAR TR : Time_rec_);
|
||||
{Gets system time from MS-DOS}
|
||||
|
||||
PROCEDURE Elapsed_time(VAR TR1, TR2 : Time_rec_);
|
||||
{Computes the difference between TR1 and TR2, returns result in TR1.
|
||||
TR1's previous value is destroyed.}
|
||||
|
||||
FUNCTION Convert_time_to_real(VAR CTR : Time_rec_):REAL;
|
||||
{}
|
||||
|
||||
PROCEDURE Convert_real_to_time(VAR RT : REAL; VAR CTR : Time_rec_);
|
||||
{}
|
||||
|
||||
PROCEDURE Trim(VAR alex : STRING;tchar : CHAR);
|
||||
{ This procedure trims a string variable of type STRING beginning
|
||||
|
||||
PROCEDURE StrUp(VAR strng : STRING);
|
||||
{ This procedure maps the characters of a string of type STRING to uppercase}
|
||||
|
||||
FUNCTION IsUpper(x : CHAR):BOOLEAN;
|
||||
{Returns true if x is an uppercase letter}
|
||||
|
||||
FUNCTION IsLower(x : CHAR):BOOLEAN;
|
||||
{Returns true if x is a lowercase letter}
|
||||
|
||||
PROCEDURE Error(msg : STRING);
|
||||
{ writes error message out to screen}
|
||||
|
||||
FUNCTION Gaussian(x,mu,sigma : REAL):REAL;
|
||||
{returns the gaussian density function of x, where mu is the}
|
||||
|
||||
FUNCTION Normal_Prob(x,mu,sigma : REAL):REAL;
|
||||
{uses a polynomial approximation to estimate
|
||||
the area under the normal curve}
|
||||
|
||||
FUNCTION Power(num,expon : REAL):REAL;
|
||||
{returns num^expon}
|
||||
|
||||
FUNCTION Slope(sumx,sumy,sumxy,sumx2,n :REAL):REAL;
|
||||
{returns linear regression determined slope of line}
|
||||
|
||||
FUNCTION Intercept(sumx,sumy,n,m : REAL):REAL;
|
||||
{returns linear regression determined intercept of line}
|
||||
|
||||
FUNCTION CorrCo(m,sigmax,sigmay : REAL):REAL;
|
||||
{returns correlation coefficient of x and y}
|
||||
|
||||
FUNCTION SD(sum,sum_sqrd,n : REAL):REAL;
|
||||
{returns standard deviation given the sum of values, the sum of
|
||||
the squares of values, and the number of values}
|
||||
|
||||
FUNCTION Map_Real(mapval, domain_min, domain_max,
|
||||
range_min, range_max : REAL): REAL;
|
||||
{ this functions maps the value passed to it into a new range }
|
||||
|
||||
FUNCTION Map_Int(mapval, domain_min, domain_max,
|
||||
range_min, range_max : INTEGER): INTEGER;
|
||||
{ this functions maps the value passed to it into a new range }
|
||||
{ must have MAP_REAL as above in program }
|
||||
|
||||
FUNCTION Map_Int_From_Real(mapval, domain_min, domain_max : REAL;
|
||||
range_min, range_max : INTEGER): INTEGER;
|
||||
{ this functions maps the value passed to it into a new range of type integer}
|
||||
|
||||
FUNCTION dir_console_IO (VAR ch :CHAR) : BOOLEAN;
|
||||
{Returns TRUE if a character has been captured at the keyboard, FALSE
|
||||
otherwise. If a character has been captured, CH contains it.}
|
||||
|
||||
FUNCTION check_kbd_status : BOOLEAN;
|
||||
{Returns TRUE if a key has been pressed, FALSE otherwise}
|
||||
|
||||
FUNCTION max_single(s1,s2 : SINGLE):SINGLE;
|
||||
{Returns the greater of two SINGLE type values}
|
||||
|
||||
FUNCTION min_single(s1,s2 : SINGLE):SINGLE;
|
||||
{Returns the lesser of two SINGLE type values}
|
||||
|
||||
|
||||
IMPLEMENTATION
|
||||
|
||||
|
||||
PROCEDURE Time(VAR TR : Time_rec_);
|
||||
{Gets system time from MS-DOS}
|
||||
|
||||
CONST
|
||||
lllama = 0;
|
||||
|
||||
VAR
|
||||
regs : registers;
|
||||
|
||||
BEGIN {Time}
|
||||
WITH regs DO BEGIN
|
||||
ax:=$2c00;
|
||||
MSDos(regs);
|
||||
TR.h := Hi(cx);
|
||||
TR.m := Lo(cx);
|
||||
TR.s := Hi(dx);
|
||||
TR.f := Lo(dx);
|
||||
END;
|
||||
END; {Time}
|
||||
|
||||
FUNCTION Convert_time_to_real(VAR CTR : Time_rec_):REAL;
|
||||
{}
|
||||
|
||||
VAR
|
||||
Tempr : REAL;
|
||||
|
||||
BEGIN {Convert_time_to_real}
|
||||
WITH CTR DO Tempr := f + (s*100.0) + (m*6000.0) + (h*360000.0);
|
||||
Convert_time_to_real := Tempr;
|
||||
END; {Convert_time_to_real}
|
||||
|
||||
PROCEDURE Convert_real_to_time(VAR RT : REAL;
|
||||
VAR CTR : Time_rec_);
|
||||
{}
|
||||
|
||||
VAR
|
||||
TempI : INTEGER;
|
||||
Tempr1, Tempr2 : REAL;
|
||||
|
||||
BEGIN {Convert_real_to_time}
|
||||
WITH CTR DO BEGIN
|
||||
Tempr2 := RT;
|
||||
Tempr1 := INT(Tempr2 / 360000.0);
|
||||
h := Trunc(Tempr1);
|
||||
Tempr2 := Tempr2 - (Tempr1 * 360000.0);
|
||||
Tempr1 := INT(Tempr2 /6000.0);
|
||||
m := Trunc(Tempr1);
|
||||
Tempr2 := Tempr2 - (Tempr1 * 6000.0);
|
||||
Tempr1 := INT(Tempr2 / 100);
|
||||
s := Trunc(Tempr1);
|
||||
Tempr2 := Tempr2 - (Tempr1 * 100);
|
||||
Tempr1 := INT(Tempr2);
|
||||
f := Trunc(Tempr1);
|
||||
END;
|
||||
END; {Convert_real_to_time}
|
||||
|
||||
|
||||
PROCEDURE Elapsed_time(VAR TR1, TR2 : Time_rec_);
|
||||
{Computes the difference between TR1 and TR2, returns result in TR1.
|
||||
TR1's previous value is destroyed.}
|
||||
|
||||
VAR
|
||||
Dif : TIme_rec_;
|
||||
T1 , T2 : REAL;
|
||||
|
||||
|
||||
BEGIN {Elapsed_time}
|
||||
Write('Time difference ',TR2.h:2,ascii_Colon,TR2.m:2,ascii_Colon,
|
||||
TR2.s:2,ascii_Colon,TR2.f:2, ' - ',TR1.h:2,ascii_Colon,TR1.m:
|
||||
2,ascii_Colon,TR1.s:2,ascii_Colon,TR1.f:2);
|
||||
T1 := Convert_time_to_real(TR1);
|
||||
T2 := Convert_time_to_real(TR2);
|
||||
IF (T2 < T1) THEN {}
|
||||
BEGIN
|
||||
T2 := T2 + 8640000.0;
|
||||
END
|
||||
ELSE {}
|
||||
BEGIN
|
||||
END;
|
||||
T1 := T2 - T1;
|
||||
Convert_real_to_time(T1,TR1);
|
||||
Writeln(' = ',TR1.h:2,ascii_Colon,TR1.m:2,ascii_Colon,TR1.s:2,
|
||||
ascii_Colon,TR1.f:2);
|
||||
END; {Elapsed_time}
|
||||
|
||||
|
||||
{$V-}
|
||||
PROCEDURE TRIM(VAR alex : STRING;
|
||||
tchar : CHAR);
|
||||
{ This procedure trims a string variable of type STRING beginning
|
||||
with the first occurrence of the character TCHAR}
|
||||
|
||||
VAR
|
||||
ii,jj :INTEGER;
|
||||
|
||||
BEGIN
|
||||
ii := Pos(tchar,alex);
|
||||
IF ii <> 0 THEN alex := Copy(alex,1,ii-1);
|
||||
END;
|
||||
{$V+}
|
||||
|
||||
{$V-}
|
||||
|
||||
PROCEDURE STRUP(VAR strng : STRING);
|
||||
{ This procedure maps the characters of a string of type STRING to
|
||||
uppercase}
|
||||
|
||||
VAR
|
||||
ii : INTEGER;
|
||||
|
||||
BEGIN
|
||||
FOR ii := 1 TO Length(strng) DO strng[ii] := UpCase(strng[ii]);
|
||||
END;
|
||||
{$V+}
|
||||
|
||||
|
||||
FUNCTION ISUPPER(x : CHAR):BOOLEAN;
|
||||
{Returns true if x is an uppercase letter}
|
||||
|
||||
BEGIN
|
||||
IF (x IN ['A'..'Z']) THEN isupper := TRUE
|
||||
ELSE isupper := FALSE;
|
||||
END;
|
||||
|
||||
FUNCTION ISLOWER(x : CHAR):BOOLEAN;
|
||||
{Returns true if x is a lowercase letter}
|
||||
|
||||
BEGIN
|
||||
IF (x IN ['a'..'z']) THEN islower := TRUE
|
||||
ELSE islower := FALSE;
|
||||
END;
|
||||
|
||||
{$V-}
|
||||
|
||||
PROCEDURE ERROR(msg : STRING);
|
||||
{ writes error message out to screen}
|
||||
|
||||
CONST
|
||||
bell = ^G;
|
||||
|
||||
BEGIN
|
||||
Write(bell,msg);
|
||||
END;
|
||||
{$V+}
|
||||
|
||||
|
||||
FUNCTION GAUSSIAN(x,mu,sigma : REAL):REAL;
|
||||
{returns the gaussian density function of x, where mu is the
|
||||
mean and sigma is the standard deviation}
|
||||
|
||||
BEGIN
|
||||
gaussian := (1/(sigma*Sqrt(2*Pi)))*Exp(-Sqr(x-mu)/(2*Sqr(sigma)));
|
||||
END;
|
||||
|
||||
FUNCTION NORMAL_PROB(x,mu,sigma : REAL):REAL;
|
||||
{uses a polynomial approximation to estimate
|
||||
the area under the normal curve}
|
||||
|
||||
CONST
|
||||
b1 = 0.319381530;
|
||||
b2 = -0.356563782;
|
||||
b3 = 1.781477937;
|
||||
b4 = -1.821255978;
|
||||
b5 = 1.330274429;
|
||||
p = 0.2316419;
|
||||
epsi = 7.5E-09;
|
||||
|
||||
VAR
|
||||
t, t2, t3, t4, t5, q, z : REAL;
|
||||
|
||||
BEGIN
|
||||
z := gaussian(x,mu,sigma) * ((x-mu)/sigma);
|
||||
t := 1/(1+p*x);
|
||||
t2 := t*t;
|
||||
t3 := t2*t;
|
||||
t4 := t3*t;
|
||||
t5 := t4*t;
|
||||
q := z * (b1*t + b2*t2 + b3*t3 + b4*t4 + b5*t5) + epsi;
|
||||
normal_prob := 1-q;
|
||||
END;
|
||||
|
||||
FUNCTION POWER(num,expon : REAL):REAL;
|
||||
{returns num^expon}
|
||||
|
||||
CONST
|
||||
Machine_infinity = 1E37;
|
||||
|
||||
VAR
|
||||
temp : REAL;
|
||||
|
||||
BEGIN
|
||||
temp := expon*Ln(num);
|
||||
IF temp >= Ln(machine_infinity) THEN power := machine_infinity
|
||||
ELSE power := Exp(temp);
|
||||
END;
|
||||
|
||||
FUNCTION SLOPE(sumx,sumy,sumxy,sumx2,n :REAL):REAL;
|
||||
{returns linear regression determined slope of line}
|
||||
|
||||
BEGIN
|
||||
slope := (sumxy-(sumx*sumy/n))/ (sumx2-(Sqr(sumx)/n));
|
||||
END;
|
||||
|
||||
FUNCTION INTERCEPT(sumx,sumy,n,m : REAL):REAL;
|
||||
{returns linear regression determined intercept of line}
|
||||
|
||||
BEGIN
|
||||
intercept := ((sumy-(m*sumx))/n);
|
||||
END;
|
||||
|
||||
FUNCTION CORRCO(m,sigmax,sigmay : REAL):REAL;
|
||||
{returns correlation coefficient of x and y}
|
||||
|
||||
BEGIN
|
||||
corrco := m*sigmax/sigmay;
|
||||
END;
|
||||
|
||||
FUNCTION SD(sum,sum_sqrd,n : REAL):REAL;
|
||||
{returns standard deviation given the sum of values, the sum of
|
||||
the squares of values, and the number of values}
|
||||
|
||||
BEGIN
|
||||
sd := Sqrt((sum_sqrd-(Sqr(sum)/n))/(n-1));
|
||||
END;
|
||||
|
||||
FUNCTION MAP_REAL(mapval, domain_min, domain_max, range_min, range_max :
|
||||
REAL): REAL;
|
||||
{ this functions maps the value passed to it into a new range }
|
||||
|
||||
BEGIN
|
||||
map_real := (((mapval - domain_min)/(domain_max - domain_min)) * (
|
||||
range_max - range_min)) + range_min;
|
||||
END;
|
||||
|
||||
FUNCTION MAP_INT(mapval, domain_min, domain_max, range_min, range_max :
|
||||
INTEGER): INTEGER;
|
||||
{ this functions maps the value passed to it into a new range }
|
||||
{ must have MAP_REAL as above in program }
|
||||
|
||||
VAR
|
||||
mv, dn, dx, rn, rx : REAL;
|
||||
|
||||
BEGIN
|
||||
mv := mapval;
|
||||
dn := domain_min;
|
||||
dx := domain_max;
|
||||
rn := range_min;
|
||||
rx := range_max;
|
||||
map_int := Round(map_real(mv,dn,dx,rn,rx));
|
||||
END;
|
||||
|
||||
FUNCTION MAP_INT_FROM_REAL(mapval, domain_min, domain_max : REAL;
|
||||
range_min, range_max : INTEGER): INTEGER;
|
||||
{ this functions maps the value passed to it into a new range of type
|
||||
integer}
|
||||
|
||||
BEGIN
|
||||
map_int_from_real := Round(map_real(mapval,domain_min,domain_max,
|
||||
range_min,range_max));
|
||||
|
||||
END;
|
||||
|
||||
|
||||
FUNCTION dir_console_IO (VAR ch :CHAR) : BOOLEAN;
|
||||
|
||||
VAR
|
||||
regs : registers; {From the DOS unit}
|
||||
|
||||
BEGIN
|
||||
regs.AH := $06;
|
||||
regs.DL := $FF;
|
||||
MSDos(regs);
|
||||
IF ((regs.flags AND FZERO) = 0) THEN BEGIN
|
||||
ch := Chr(regs.AL);
|
||||
dir_console_IO := TRUE;
|
||||
END
|
||||
ELSE BEGIN
|
||||
dir_console_IO := FALSE;
|
||||
END;
|
||||
END;
|
||||
|
||||
FUNCTION check_kbd_status : BOOLEAN;
|
||||
|
||||
VAR
|
||||
regs : registers; {From the DOS unit}
|
||||
|
||||
BEGIN
|
||||
regs.AH := $0B;
|
||||
MSDos(regs);
|
||||
IF (Ord(regs.AL) = $FF) THEN BEGIN
|
||||
check_kbd_status := TRUE;
|
||||
END
|
||||
ELSE BEGIN
|
||||
check_kbd_status := FALSE;
|
||||
END;
|
||||
END;
|
||||
|
||||
FUNCTION max_single(s1,s2 : SINGLE):SINGLE;
|
||||
{Returns the greater of two SINGLE type values}
|
||||
|
||||
BEGIN
|
||||
IF s1 >= s2 THEN max_single := s1
|
||||
ELSE max_single := s2;
|
||||
END;
|
||||
|
||||
|
||||
FUNCTION min_single(s1,s2 : SINGLE):SINGLE;
|
||||
{Returns the lesser of two SINGLE type values}
|
||||
|
||||
BEGIN
|
||||
IF s1 < s2 THEN min_single := s1
|
||||
ELSE min_single := s2;
|
||||
END;
|
||||
|
||||
BEGIN {initialize}
|
||||
END. {INITIALIZE}
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,111 @@
|
|||
|
||||
|
||||
|
||||
PROGRAM play_composition(Input,Output);
|
||||
{
|
||||
This program plays notess output by the integrated ANN note generator, the
|
||||
random composition program, and the classical composition program.
|
||||
}
|
||||
|
||||
{
|
||||
|
||||
Copyright 1989 by Wesley R. Elsberry. All rights reserved.
|
||||
|
||||
Commercial use of this software is prohibited without written consent of
|
||||
the author.
|
||||
|
||||
For information, bug reports, and updates contact
|
||||
|
||||
Wesley R. Elsberry
|
||||
528 Chambers Creek Drive South
|
||||
Everman, Texas 76140
|
||||
Telephone: (817) 551-7018
|
||||
|
||||
}
|
||||
|
||||
USES
|
||||
Dos, CRT, misc1;
|
||||
|
||||
CONST
|
||||
Pi = 3.141592653589793;
|
||||
Exp_Max = 80.0;
|
||||
Colon = ':';
|
||||
graphic_string = '0123456789';
|
||||
note_time = 1800;
|
||||
rest_time = 550;
|
||||
|
||||
{For Play_note}
|
||||
N_C_mid = 264;
|
||||
N_D = 297;
|
||||
N_E = 330;
|
||||
N_F =352;
|
||||
N_G = 396;
|
||||
N_A = 440;
|
||||
N_B = 495;
|
||||
N_C_hi = 528;
|
||||
|
||||
|
||||
TYPE
|
||||
REAL = SINGLE;
|
||||
|
||||
{General}
|
||||
|
||||
Note_ = (Note_C_Lo,Note_D,Note_E,Note_F,Note_G,Note_A,Note_B,
|
||||
Note_C_Hi);
|
||||
|
||||
VAR
|
||||
inf : TEXT; {Input file handle}
|
||||
instr : STRING;
|
||||
cnote : INTEGER;
|
||||
inch : CHAR;
|
||||
|
||||
{----------------------------------------------------------}
|
||||
|
||||
PROCEDURE play_a_note(cn : INTEGER);
|
||||
|
||||
VAR
|
||||
ii : INTEGER;
|
||||
|
||||
BEGIN
|
||||
|
||||
CASE cn OF
|
||||
1 : Sound(n_c_mid);
|
||||
2 : Sound(n_d);
|
||||
3 : Sound(n_e);
|
||||
4 : Sound(n_f);
|
||||
5 : Sound(n_g);
|
||||
6 : Sound(n_a);
|
||||
7 : Sound(n_b);
|
||||
8 : Sound(n_c_hi);
|
||||
ELSE
|
||||
NoSound;
|
||||
END;
|
||||
|
||||
Delay(note_time);
|
||||
NoSound;
|
||||
Delay(rest_time);
|
||||
END;
|
||||
|
||||
BEGIN {Main}
|
||||
{get filename}
|
||||
REPEAT
|
||||
Write ('File to play? ');
|
||||
Readln (instr);
|
||||
instr := FSearch(instr,GetEnv('PATH'));
|
||||
UNTIL (Length(instr) <> 0);
|
||||
Assign (inf,instr);
|
||||
Reset(inf);
|
||||
WHILE NOT Eof(inf) DO BEGIN
|
||||
Readln(inf,cnote);
|
||||
play_a_note(cnote);
|
||||
IF dir_console_IO(inch) THEN
|
||||
IF UpCase(inch) = 'Q' THEN BEGIN
|
||||
NoSound;
|
||||
EXIT;
|
||||
END;
|
||||
END;
|
||||
NoSound;
|
||||
END. {Main}
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,38 @@
|
|||
|
||||
|
||||
PROGRAM random_compositon (Input,Output);
|
||||
{
|
||||
This program outputs random notes.
|
||||
}
|
||||
|
||||
{
|
||||
|
||||
Copyright 1989 by Wesley R. Elsberry. All rights reserved.
|
||||
|
||||
Commercial use of this software is prohibited without written consent of
|
||||
the author.
|
||||
|
||||
For information, bug reports, and updates contact
|
||||
|
||||
Wesley R. Elsberry
|
||||
528 Chambers Creek Drive South
|
||||
Everman, Texas 76140
|
||||
Telephone: (817) 551-7018
|
||||
|
||||
}
|
||||
|
||||
VAR
|
||||
ii, jj : INTEGER;
|
||||
Outf : TEXT;
|
||||
|
||||
BEGIN
|
||||
Assign(outf,'rmus.mus');
|
||||
Rewrite(outf);
|
||||
Randomize;
|
||||
FOR ii := 1 TO 152 DO BEGIN
|
||||
Writeln(outf,(Random(8)+1):1);
|
||||
END;
|
||||
Close(outf);
|
||||
END.
|
||||
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,35 @@
|
|||
|
||||
|
||||
Integration and hybridization in neural network modelling
|
||||
|
||||
Copyright 1989 by Wesley R. Elsberry
|
||||
|
||||
A masters thesis presented to the University of Texas at Arlington.
|
||||
|
||||
|
||||
Files on this disk:
|
||||
|
||||
INT_ANN LZH
|
||||
{The text of the thesis, minus the figures.}
|
||||
|
||||
ANNEXE LZH
|
||||
{Simulation and support programs. ANNCOMP.EXE is the main simulation
|
||||
program.}
|
||||
|
||||
DATAF LZH
|
||||
{Contains data files necessary for running the simulations}
|
||||
|
||||
GO BAT
|
||||
{A batch file that creates a directory, WRE_THES, on the C: drive, and
|
||||
which unzips the compressed files into the new directory. Make sure
|
||||
that the diskette is in drive A: before invoking.}
|
||||
|
||||
ANNPRETT LZH
|
||||
{Pretty-printed Pascal source files.}
|
||||
|
||||
LHARC COM
|
||||
{A file decompression utility.}
|
||||
|
||||
and, of course, this file.
|
||||
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,14 @@
|
|||
Salieri net set up
|
||||
!L 0.5
|
||||
!A 0.5
|
||||
!I 40
|
||||
!H 20
|
||||
!O 1
|
||||
!T 1 set training_iterations
|
||||
!E 0.1
|
||||
!D s61.dat
|
||||
!R s61.out
|
||||
!W s61.wt
|
||||
!Z
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,64 @@
|
|||
!V 61
|
||||
!W 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
||||
!W 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
||||
!W 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
||||
!W 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
||||
!W 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
||||
!W 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
||||
!W 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
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|
||||
!W 0.4120 0.5625 -0.8810 0.7308 -0.8768 -0.2254 -0.3858 0.3594 0.6833 -0.1741 0.8853 0.6383 -0.2512 -0.6341 0.3679 -0.0086 0.4678 0.5472 -0.3544 0.0796 0.3187 0.8012 0.4148 -0.8477 -0.6299 -0.8463 -0.8339 0.7348 -0.9866 0.2152 0.8069 -0.0533 -0.7932 0.8631 -0.3095 -0.0156 0.4422 0.2448 0.1867 0.8851 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
||||
!W -0.6310 0.5604 0.5902 -0.3295 0.8722 -0.3304 0.5414 -0.2042 -1.0311 -0.5006 -0.6887 0.3324 0.1877 0.7726 -0.6233 -0.5918 0.4051 0.3512 -0.9627 0.0591 0.5596 -1.0250 0.0732 0.7424 0.1863 -0.2007 0.6890 0.8843 -0.1599 -0.6749 -0.1450 0.6026 -0.3409 -0.5392 0.4410 0.7942 -0.1104 -0.9241 0.2544 -0.5754 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
||||
!W 0.3154 0.7638 -0.5161 -0.6787 -0.6147 -0.4910 0.9554 -0.6002 -0.3040 -0.9965 -0.4748 -0.8161 -0.0513 -1.0038 -0.7551 -0.4040 0.8968 -0.5826 0.3153 0.9365 -0.4450 -0.4500 -0.3676 -0.5534 0.0291 -0.3440 0.6621 0.5321 0.5965 0.6141 -0.0184 -0.3625 -0.6506 -0.9503 -0.3996 -0.3161 0.4383 -0.4238 -0.7178 0.1013 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
||||
!W 0.5623 0.2054 0.0967 0.9602 0.3215 -0.2972 -0.2421 -0.2586 -0.8317 -0.8753 0.4790 0.4881 -0.6946 0.6584 -0.6727 0.2096 0.4446 0.6243 0.2529 -0.1499 -0.8026 0.9073 0.8276 0.9188 -0.1902 0.0255 0.3577 0.5839 -0.8137 -0.7689 -0.4078 0.5474 0.4189 -0.8387 -0.1737 0.3239 0.5573 0.1530 0.8796 -0.3542 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
||||
!W -0.2866 -1.0059 0.9690 0.7599 0.4782 -0.6084 -0.8412 0.5370 0.5231 -1.0303 -0.9897 -0.5671 0.8804 -0.4718 0.8907 -0.5204 0.8514 -0.4012 0.7465 -0.4825 -1.0175 0.8864 -0.1017 0.7829 0.3255 -0.4844 -0.1501 0.3590 0.2925 0.5096 0.9179 0.1356 -0.5691 -0.3420 -0.7553 -0.2266 0.0195 0.2677 -0.6291 0.3170 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
||||
!W -0.9237 -0.3537 0.1886 -0.5079 0.8179 -0.9842 -0.4943 -0.7417 -0.8436 -0.1618 0.1755 -0.0201 -0.2625 -0.4896 -0.4766 -0.9160 -0.3595 -0.8030 0.0743 0.1043 -0.4981 0.6396 0.8456 0.2367 -0.6813 -0.5641 -0.5740 -0.3468 0.4840 0.4694 0.1666 0.7121 -0.1332 -0.5978 0.1084 -0.7212 -1.1000 0.6436 0.0591 -0.2757 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
||||
!W -0.6575 -0.2055 0.0306 -0.3778 0.3174 0.2309 0.4669 0.8171 -0.6834 -0.3146 0.3023 -0.5447 -0.5955 0.1957 0.8378 0.3657 0.0583 -0.8839 0.3607 0.1423 0.8788 -0.9295 -0.7587 0.9530 -0.3602 0.8292 -0.8012 -0.9375 -0.0091 0.0446 0.5999 0.3758 0.0621 0.1505 -0.4968 0.0511 0.8480 0.7593 0.2344 0.0724 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
||||
!W -0.2137 -0.1663 0.1527 0.3831 -0.4444 -0.9529 0.2738 0.3726 -0.6549 0.7200 -0.9318 0.0797 0.8368 -0.4717 0.7169 0.6159 -0.4459 0.0119 0.0730 -0.1345 0.5315 0.3527 -0.7682 0.6127 0.4587 -0.1653 0.7051 0.5052 0.1339 -0.4084 -0.9209 -0.4960 0.2223 -0.4034 -0.3324 0.5219 0.6125 -0.3441 -0.3277 0.3082 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
||||
!W 0.1701 0.7238 -0.5399 0.4200 -0.5724 0.1927 0.6207 -0.7291 -0.5549 0.0121 0.2647 0.4297 -0.1209 0.9535 -0.7414 -0.4780 -0.5360 -0.4089 -0.5886 -0.2116 -0.1244 0.4154 0.1569 -0.1825 -0.9552 0.0146 -0.6584 -0.0751 -0.9857 -0.0007 -0.6749 -0.2210 0.8795 -0.4335 0.9668 -0.3744 -0.3040 -0.6058 0.6232 0.7091 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
||||
!W 0.7773 0.1475 -0.6726 0.2005 -0.2117 0.4589 0.8681 -0.2211 0.4067 -0.6055 0.7003 0.8207 -0.4224 -0.9159 0.5727 0.1668 0.0661 0.7136 -0.4574 0.5190 0.0826 0.2380 0.3364 0.8962 -0.3149 0.4892 -0.9789 0.0774 -0.8708 -0.8830 0.3458 0.0208 -0.0763 -0.3155 -0.4446 -0.2720 -0.9251 -0.5315 0.6031 0.9395 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
||||
!W -0.7416 0.4924 -0.4038 -0.6895 -0.8944 0.1152 -0.9205 -0.8366 0.6731 -0.0312 0.8193 0.8270 -0.8365 -0.2766 -0.0429 0.3934 0.3325 0.8114 0.8929 0.4366 0.9132 0.2455 -0.5544 0.7308 0.7297 -0.3294 0.1758 -0.7753 -0.2380 0.8242 -0.4577 -1.0306 -0.0554 -0.9811 0.3900 -0.7742 -0.1291 -0.2936 0.0148 -0.6617 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
||||
!W -0.0703 -0.3768 0.4144 0.2983 0.3524 -0.6137 0.2440 -0.8838 0.8980 -0.2774 -0.5914 -0.1437 -0.1138 -0.5522 -0.7996 -0.2161 0.7566 -0.1850 -0.8698 0.3928 0.1164 -0.3284 0.1758 -0.4813 -0.3258 -1.0765 -0.0610 0.1492 0.7963 0.7967 -0.6693 -0.9627 -0.0141 -0.2931 0.8108 -0.5054 -0.1443 -0.7649 0.8161 0.4256 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
||||
!W -0.3265 0.5221 0.5236 0.5709 -0.8379 -0.6252 -0.1016 0.9371 0.9186 0.7908 0.6904 0.2015 -0.3986 -0.6092 -0.6130 0.0180 0.2700 -0.8266 0.1088 -1.0348 -0.2726 0.0343 -0.4277 0.8879 -0.8953 -0.2478 0.2174 0.2613 -0.6738 -0.1412 0.2374 0.8069 -0.6075 -0.9603 0.2013 -0.5475 -0.5451 0.4215 0.7904 0.6294 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
||||
!W 0.6204 -0.2479 0.3610 -0.6442 0.2198 -0.1270 0.0529 -0.0496 0.7118 -0.3627 0.0039 -0.9271 0.5207 0.4132 -0.4368 0.7495 -0.3887 -0.8411 0.7995 -0.5429 -0.2067 0.5127 -0.9422 0.1268 0.2113 0.8595 0.6901 -1.0819 0.8435 0.0205 -0.6033 -0.6975 -0.0426 0.0301 -0.4844 -0.9754 0.4493 0.8814 0.0705 -0.3817 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
|
||||
!W 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.8465 -0.9480 -1.0240 0.8453 0.3406 -0.6085 -1.1087 0.7574 0.6576 -0.0049 0.1833 0.0483 -0.3441 1.3800 -0.4230 -0.8121 0.3001 -0.3075 -0.6494 -0.8333 0.0000
|
||||
!T 0.0230 -0.0624 -0.0387 -0.0249 -0.0592 0.0946 -0.0461 -0.0368 -0.0627 0.0138 -0.0085 -0.0554 -0.0227 0.1130 0.0041 -0.0466 -0.0653 0.0552 0.0170 -0.0734 -0.0908 -0.0234 -0.0130 -0.1462 -0.0006 -0.0003 -0.0064 -0.1354 -0.0098 -0.0589 -0.0142 -0.0955 0.0469 0.0579 -0.0568 0.0028 0.0167 0.0024 -0.0718 -0.1274 -0.1158 -0.2530 -0.3168 -0.2604 -0.6344 -0.1292 0.2879 -0.4527 0.0816 -0.2186 -0.4175 -0.3668 0.0683 -0.2972 -0.0510 0.2390 -0.0202 -0.3516 -0.0919 0.1575 -0.2976
|
||||
!Z
|
||||
|
|
@ -0,0 +1,252 @@
|
|||
|
||||
PROGRAM Salieri_network_training_program (Input,Output);
|
||||
|
||||
{
|
||||
|
||||
Copyright 1989 by Wesley R. Elsberry. All rights reserved.
|
||||
|
||||
Commercial use of this software is prohibited without written consent of
|
||||
the author.
|
||||
|
||||
For information, bug reports, and updates contact
|
||||
|
||||
Wesley R. Elsberry
|
||||
528 Chambers Creek Drive South
|
||||
Everman, Texas 76140
|
||||
Telephone: (817) 551-7018
|
||||
|
||||
}
|
||||
|
||||
USES
|
||||
DOS, struct, BP_unit, misc1, ANSI_Z, globals, clasinst;
|
||||
|
||||
{General}
|
||||
|
||||
TYPE
|
||||
REAL = SINGLE;
|
||||
bpnp_ = BP_node_ptr_;
|
||||
wnp_ = weight_node_ptr_;
|
||||
vnp_ = vector_node_ptr_;
|
||||
|
||||
seq_pop_rec_ = RECORD
|
||||
n : notes_;
|
||||
t : INTEGER;
|
||||
e : REAL;
|
||||
END;
|
||||
|
||||
seq_pop_ = ARRAY[0..99] OF seq_pop_rec_;
|
||||
|
||||
seq_pop_command_ = (init,replace);
|
||||
|
||||
VAR
|
||||
snet, s31net, s46net : BP_net_;
|
||||
ii, jj, kk : INTEGER;
|
||||
Done : BOOLEAN;
|
||||
cmn : common_area_;
|
||||
notes : notes_;
|
||||
tp1, tp2, tp3 : DVE_ptr_;
|
||||
error_m, tne, sum : ARRAY[1..3] OF REAL;
|
||||
ss : STRING;
|
||||
binsum : ARRAY[1..3] OF INTEGER;
|
||||
fpos, fneg : INTEGER;
|
||||
tr : REAL;
|
||||
sinch : CHAR;
|
||||
scon : STRING;
|
||||
sp : seq_pop_;
|
||||
|
||||
|
||||
PROCEDURE maintain_seq_pop (VAR sp1 : seq_pop_;
|
||||
spot : INTEGER;
|
||||
cmd : seq_pop_command_);
|
||||
|
||||
VAR
|
||||
ii, jj : INTEGER;
|
||||
|
||||
BEGIN
|
||||
CASE cmd OF
|
||||
init : BEGIN
|
||||
FOR ii := 0 TO 99 DO BEGIN
|
||||
FOR jj := 1 TO 5 DO BEGIN
|
||||
sp1[ii].n[jj] := 0;
|
||||
END;
|
||||
sp1[ii].t := 0;
|
||||
sp1[ii].e := 0.0;
|
||||
END; {FOR ii}
|
||||
END; {init}
|
||||
replace : BEGIN
|
||||
|
||||
REPEAT
|
||||
|
||||
FOR jj := 1 TO 3 DO BEGIN
|
||||
|
||||
IF (jj = 1) THEN BEGIN
|
||||
sp1[spot].n[jj] := Random(9);
|
||||
END
|
||||
ELSE BEGIN
|
||||
IF (sp1[spot].n[jj-1] = 0) THEN BEGIN
|
||||
sp1[spot].n[jj] := Random(9);
|
||||
END
|
||||
ELSE BEGIN
|
||||
sp1[spot].n[jj] := Random(8) + 1;
|
||||
END;
|
||||
END;
|
||||
END;
|
||||
FOR jj := 4 TO v_len_out DO BEGIN
|
||||
sp1[spot].n[jj] := Random(8) + 1;
|
||||
END; {FOR jj}
|
||||
sp1[spot].t := Classical_instructor(sp1[spot].n);
|
||||
UNTIL (Odd(spot)) OR (sp1[spot].t = 1);
|
||||
END; {replace}
|
||||
ELSE
|
||||
BEGIN
|
||||
END;
|
||||
END; {Case CMD}
|
||||
END;
|
||||
|
||||
PROCEDURE Set_input_vector_from_notes (vp : DVE_ptr_;
|
||||
n : notes_);
|
||||
|
||||
VAR
|
||||
ii : INTEGER;
|
||||
vpt : DVE_ptr_;
|
||||
vn : ARRAY[1..40] OF INTEGER;
|
||||
|
||||
BEGIN
|
||||
|
||||
FillChar (vn,SizeOf(vn),#0);
|
||||
{Blank the current vector}
|
||||
FOR ii := 1 TO 5 DO BEGIN{Notes subscript}
|
||||
IF n[ii] > 0 THEN vn [((ii-1)*8)+n[ii]] := 1;
|
||||
END; {For notes subscript}
|
||||
|
||||
vpt := vp;
|
||||
FOR ii := 1 TO 40 DO BEGIN
|
||||
vnp_(vpt^.dptr)^.v := vn[ii];
|
||||
vpt := vpt^.right;
|
||||
END; {FOR ii}
|
||||
END;
|
||||
|
||||
|
||||
BEGIN
|
||||
Done := FALSE;
|
||||
s46net.data_fname := 's61.dat';
|
||||
|
||||
ANSI_CUP(13,0);
|
||||
Writeln(MemAvail:8);
|
||||
|
||||
Writeln(s46net.data_fname);
|
||||
Setup_BP_net (s46net,s46net.data_fname);
|
||||
|
||||
Writeln;
|
||||
|
||||
Writeln(s46net.wt_fname);
|
||||
Set_BP_net_weights_from_file(s46net,s46net.wt_fname);
|
||||
|
||||
|
||||
ANSI_CLRSCR;
|
||||
Writeln(MemAvail:8);
|
||||
|
||||
maintain_seq_pop(sp,0,init);
|
||||
FOR ii := 1 TO 100 DO BEGIN
|
||||
maintain_seq_pop(sp,ii-1,replace);
|
||||
END;
|
||||
|
||||
REPEAT
|
||||
IF dir_console_IO (sinch) THEN BEGIN
|
||||
IF (UpCase(sinch) = 'Q') THEN BEGIN
|
||||
Close (s46net.out_f);
|
||||
EXIT;
|
||||
END;
|
||||
END;
|
||||
|
||||
FOR ii := 1 TO 3 DO BEGIN
|
||||
error_m[ii] := 0;
|
||||
sum[ii] := 0;
|
||||
binsum[ii] := 0;
|
||||
fpos := 0;
|
||||
fneg := 0;
|
||||
END;
|
||||
|
||||
FOR ii := 1 TO 100 DO BEGIN
|
||||
IF dir_console_IO (sinch) THEN BEGIN
|
||||
IF (UpCase(sinch) = 'Q') THEN BEGIN
|
||||
Close (s46net.out_f);
|
||||
EXIT;
|
||||
END;
|
||||
END;
|
||||
|
||||
|
||||
|
||||
Set_input_vector_from_notes (s46net.vi,sp[ii-1].n);
|
||||
vnp_(s46net.vts^.dptr)^.v := sp[ii-1].t;
|
||||
|
||||
|
||||
|
||||
BP_train_and_change (s46net);
|
||||
|
||||
tne[3] := ABS(BP_net_error(s46net));
|
||||
|
||||
sp[ii-1].e := tne[3];
|
||||
notes := sp[ii-1].n;
|
||||
|
||||
|
||||
FOR kk := 3 TO 3 DO BEGIN
|
||||
error_m[kk] := max_single(ABS(error_m[kk]),tne[kk]);
|
||||
END;
|
||||
|
||||
IF ((tne[3] > 0.50) AND (vnp_(s46net.vts^.dptr)^.v = 1.0)) OR
|
||||
((tne[3] >= 0.50) AND (vnp_(s46net.vts^.dptr)^.v = 0.0))
|
||||
THEN BEGIN
|
||||
INC(binsum[3]);
|
||||
IF ((tne[3] > 0.50) AND (vnp_(s46net.vts^.dptr)^.v = 1.0))
|
||||
THEN INC(fneg);
|
||||
IF ((tne[3] >= 0.50) AND (vnp_(s46net.vts^.dptr)^.v = 0.0)
|
||||
) THEN INC(fpos);
|
||||
Write (s46net.out_f,'I ');
|
||||
FOR kk := 1 TO 5 DO BEGIN
|
||||
Write (s46net.out_f,(notes[kk]/1.0):1:1,' ');
|
||||
END;
|
||||
Writeln (s46net.out_f);
|
||||
Writeln (s46net.out_f,'T ',vnp_(s46net.vts^.dptr)^.v:1:1);
|
||||
END;
|
||||
|
||||
ANSI_CUP(20,0);
|
||||
Write(ii:4,' Max Current Ave. Binary ');
|
||||
FOR kk := 1 TO 5 DO Write(notes[kk]:1);
|
||||
Write(' ',vnp_(s46net.vts^.dptr)^.v:2:1);
|
||||
|
||||
ANSI_CUP(24,17);
|
||||
FOR kk := 1 TO 5 DO Write(notes[kk]:1);
|
||||
|
||||
|
||||
FOR kk := 3 TO 3 DO BEGIN
|
||||
ANSI_CUP(20+kk,0);
|
||||
sum[kk] := sum[kk] + tne[kk];
|
||||
Write(kk:4,' ',error_m[kk]:5:3,' ',tne[kk]:5:3,
|
||||
' ',(sum[kk]/ii):5:3,' ',binsum[kk]:3);
|
||||
END;
|
||||
Write(' FPOS: ',fpos:3,' FNEG: ',fneg:3);
|
||||
|
||||
IF (sp[ii-1].e < Random) THEN BEGIN
|
||||
maintain_seq_pop(sp,ii-1,replace);
|
||||
END;
|
||||
|
||||
END; {FOR ii}
|
||||
|
||||
FOR kk := 3 TO 3 DO BEGIN
|
||||
ANSI_CUP(14+kk,0);
|
||||
Write(kk:4,' ',error_m[kk]:5:3,' ',(sum[kk]/100):5:3,' ',
|
||||
binsum[kk]:3);
|
||||
END;
|
||||
Write(' FPOS: ',fpos:3,' FNEG: ',fneg:3);
|
||||
|
||||
Done := (error_m[3] <= s46net.errtol);
|
||||
|
||||
Dump_BP_net_weights(s46net,s46net.wt_fname);
|
||||
|
||||
UNTIL (Done);
|
||||
Close (s46net.out_f);
|
||||
END.
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,15 @@
|
|||
154
|
||||
145
|
||||
15
|
||||
14
|
||||
51
|
||||
41
|
||||
146
|
||||
245
|
||||
135
|
||||
358
|
||||
78
|
||||
751
|
||||
251
|
||||
258
|
||||
|
||||
|
|
@ -0,0 +1,288 @@
|
|||
|
||||
|
||||
UNIT struct;
|
||||
{
|
||||
This unit provides a general linked-list seet of functions.
|
||||
}
|
||||
|
||||
{
|
||||
|
||||
Copyright 1989 by Wesley R. Elsberry. All rights reserved.
|
||||
|
||||
Commercial use of this software is prohibited without written consent of
|
||||
the author.
|
||||
|
||||
For information, bug reports, and updates contact
|
||||
|
||||
Wesley R. Elsberry
|
||||
528 Chambers Creek Drive South
|
||||
Everman, Texas 76140
|
||||
Telephone: (817) 551-7018
|
||||
|
||||
}
|
||||
|
||||
|
||||
INTERFACE
|
||||
|
||||
{PUBLIC DECLARATIONS}
|
||||
|
||||
|
||||
TYPE
|
||||
dve_ptr_ = ^dve_;
|
||||
dve_ = RECORD {terminology: DVE is diploid vector
|
||||
element}
|
||||
{Linkage can be both forward and
|
||||
backward}
|
||||
up, down, left, right : dve_ptr_;
|
||||
{Forward and backward links}
|
||||
dptr : POINTER; {Untyped pointer}
|
||||
END;
|
||||
|
||||
hve_ptr_ = ^hve_;
|
||||
hve_ = RECORD {terminology: HVE is haploid vector
|
||||
element}
|
||||
{Linkage is forward only}
|
||||
down, right : hve_ptr_; {Forward links}
|
||||
dptr : POINTER; {Untyped pointer}
|
||||
END;
|
||||
|
||||
|
||||
FUNCTION Create_DVE_vector (num_elem : WORD; size : WORD): POINTER;
|
||||
{This function takes a size for a pointer based structure which is based
|
||||
on the DVE_ type declaration and returns a pointer to a doubly linked
|
||||
list which has NUM_ELEM number of elements. The calling program should
|
||||
assign this pointer's value to a pointer of the type which SIZE
|
||||
describes. The head element's LEFT pointer is NIL, RIGHT points to the
|
||||
next element or is NIL, UP and DOWN are NIL. Elements beside the head
|
||||
element have non-NIL LEFT pointers. Calling routines should check for
|
||||
NIL returned pointers, as this indicates that not enough memory was
|
||||
available to allocate the vector.}
|
||||
|
||||
FUNCTION copy_DVE_vector (aptr : POINTER; num_elem : WORD; size : WORD): POINTER;
|
||||
{ This function takes a pointer to a DVE vector and creates a copy, then
|
||||
passes back a pointer to the copy vector.}
|
||||
|
||||
FUNCTION Create_HVE_vector (num_elem : WORD; size : WORD; rt : BOOLEAN): POINTER;
|
||||
{This function creates a linked list of HVE_ type, with space allocated
|
||||
based on size of a HVE_ based structure definition. If RT is TRUE, then
|
||||
the function allocates a vector using the RIGHT linkages, and DOWN
|
||||
pointers are set to NIL. Otherwise, the obverse is true. A NIL pointer
|
||||
returned as a result indicates that not enough memory was available for
|
||||
allocation.}
|
||||
|
||||
|
||||
FUNCTION Create_matrix (n_across, n_down : WORD; size : WORD): POINTER;
|
||||
{This function creates a doubly linked matrix of DVE vector elements,
|
||||
using the provided size.}
|
||||
|
||||
FUNCTION Find_element_DVE (num : WORD; pntr : POINTER): POINTER;
|
||||
|
||||
FUNCTION Find_element_HVE (num : WORD; pntr : POINTER): POINTER;
|
||||
|
||||
FUNCTION Find_element_matrix (n1, n2 : WORD; pntr : POINTER): POINTER;
|
||||
|
||||
IMPLEMENTATION
|
||||
|
||||
{PRIVATE DECLARATIONS}
|
||||
|
||||
{IMPLEMENTATIONS OF PROCEDURES AND FUNCTIONS}
|
||||
|
||||
FUNCTION Create_DVE_vector (num_elem : WORD;
|
||||
size : WORD): POINTER;
|
||||
{This function takes a size for a pointer based structure which is based
|
||||
on the DVE_ type declaration and returns a pointer to a doubly linked
|
||||
list which has NUM_ELEM number of elements. The calling program should
|
||||
assign this pointer's value to a pointer of the type which SIZE
|
||||
describes. The head element's LEFT pointer is NIL, RIGHT points to the
|
||||
next element or is NIL, UP and DOWN are NIL. Elements beside the head
|
||||
element have non-NIL LEFT pointers. Calling routines should check for
|
||||
NIL returned pointers, as this indicates that not enough memory was
|
||||
available to allocate the vector.}
|
||||
|
||||
VAR
|
||||
ii : INTEGER;
|
||||
TempStart, Temp : dve_ptr_;
|
||||
|
||||
BEGIN
|
||||
GetMem (TempStart,SizeOf(TempStart^));
|
||||
Temp := TempStart;
|
||||
Temp^.left := NIL;
|
||||
Temp^.up := NIL;
|
||||
Temp^.down := NIL;
|
||||
GetMem(Temp^.dptr,size);;
|
||||
FOR II := 2 TO num_elem DO BEGIN
|
||||
GetMem (Temp^.right,SizeOf(TempStart^));
|
||||
Temp^.right^.left := Temp;
|
||||
Temp := Temp^.right;
|
||||
Temp^.up := NIL;
|
||||
Temp^.down := NIL;
|
||||
GetMem(Temp^.dptr,size);
|
||||
END; {for}
|
||||
Temp^.right := NIL;
|
||||
Create_DVE_vector := TempStart;
|
||||
END;
|
||||
|
||||
FUNCTION copy_DVE_vector (aptr : POINTER;
|
||||
num_elem : WORD;
|
||||
size : WORD): POINTER;
|
||||
{ This function takes a pointer to a DVE vector and creates a copy, then
|
||||
passes back a pointer to the copy vector.}
|
||||
|
||||
VAR
|
||||
tptr, bptr, t2ptr : dve_ptr_;
|
||||
ii, jj : WORD;
|
||||
|
||||
BEGIN
|
||||
bptr := create_DVE_vector (num_elem,size);
|
||||
t2ptr := bptr;
|
||||
Tptr := aptr;
|
||||
ii := 1;
|
||||
WHILE (Tptr <> NIL) AND (ii < num_elem) DO BEGIN
|
||||
Move (bptr^.dptr^,tptr^.dptr^,size);
|
||||
Tptr := Tptr^.right;
|
||||
bptr := bptr^.right;
|
||||
INC(ii);
|
||||
END; {while}
|
||||
copy_DVE_vector := t2ptr;
|
||||
END;
|
||||
|
||||
FUNCTION Create_HVE_vector (num_elem : WORD;
|
||||
size : WORD;
|
||||
rt : BOOLEAN): POINTER;
|
||||
{This function creates a linked list of HVE_ type, with space allocated
|
||||
based on size of a HVE_ based structure definition and on the size of
|
||||
the
|
||||
user structure pointed to within each vector element. If RT is TRUE,
|
||||
then
|
||||
the function allocates a vector using the RIGHT linkages, and DOWN
|
||||
pointers
|
||||
are set to NIL. Otherwise, the obverse is true. A NIL pointer returned
|
||||
as
|
||||
a result indicates that not enough memory was available for allocation.}
|
||||
|
||||
VAR
|
||||
ii : INTEGER;
|
||||
TempStart, Temp : HVE_ptr_;
|
||||
|
||||
BEGIN
|
||||
IF rt THEN BEGIN
|
||||
GetMem (TempStart,SizeOf(TempStart^));
|
||||
Temp := TempStart;
|
||||
Temp^.down := NIL;
|
||||
GetMem(Temp^.dptr,size);;
|
||||
FOR II := 2 TO num_elem DO BEGIN
|
||||
GetMem (Temp^.right,SizeOf(TempStart^));
|
||||
Temp := Temp^.right;
|
||||
Temp^.down := NIL;
|
||||
GetMem(Temp^.dptr,size);
|
||||
END; {for}
|
||||
Temp^.right := NIL;
|
||||
END {if}
|
||||
ELSE BEGIN
|
||||
GetMem (TempStart,SizeOf(TempStart^));
|
||||
Temp := TempStart;
|
||||
Temp^.right := NIL;
|
||||
GetMem(Temp^.dptr,size);;
|
||||
FOR II := 2 TO num_elem DO BEGIN
|
||||
GetMem (Temp^.down,SizeOf(TempStart^));
|
||||
Temp := Temp^.down;
|
||||
Temp^.right := NIL;
|
||||
GetMem(Temp^.dptr,size);
|
||||
END; {for}
|
||||
Temp^.down := NIL;
|
||||
END; {else}
|
||||
Create_HVE_vector := TempStart;
|
||||
END;
|
||||
|
||||
FUNCTION Create_matrix (n_across, n_down : WORD;
|
||||
size : WORD): POINTER;
|
||||
{This function creates a doubly linked matrix of DVE vector elements,
|
||||
using the provided size for allocation of user space.}
|
||||
|
||||
VAR
|
||||
ii, jj : WORD;
|
||||
StartPtr, T1Ptr, T2Ptr : DVE_ptr_;
|
||||
|
||||
PROCEDURE Link_DVE_vectors (v1, v2 : DVE_ptr_);
|
||||
|
||||
BEGIN
|
||||
WHILE (v1 <> NIL) AND (v2 <> NIL) DO BEGIN
|
||||
v1^.down := v2;
|
||||
v2^.up := v1; {Next elements}
|
||||
v1 := v1^.right;
|
||||
v2 := v2^.right;
|
||||
END; {while}
|
||||
END; {Link_DVE_vectors}
|
||||
|
||||
BEGIN
|
||||
StartPtr := Create_DVE_vector (n_across, size);
|
||||
T1Ptr := StartPtr;
|
||||
FOR jj := 2 TO n_down DO BEGIN
|
||||
T2Ptr := Create_DVE_vector (n_across, size);
|
||||
Link_DVE_vectors (T1Ptr, T2Ptr);
|
||||
T1Ptr := T2Ptr;
|
||||
END; {for}
|
||||
Create_matrix := StartPtr;
|
||||
END; {Create_matrix}
|
||||
|
||||
FUNCTION Find_element_DVE (num : WORD;
|
||||
pntr : POINTER): POINTER;
|
||||
|
||||
VAR
|
||||
ii : WORD;
|
||||
T1 : DVE_ptr_;
|
||||
|
||||
BEGIN
|
||||
T1 := pntr;
|
||||
ii := 1;
|
||||
WHILE (T1 <> NIL) AND (ii < num) DO BEGIN
|
||||
T1 := T1^.right;
|
||||
INC(ii);
|
||||
END; {while}
|
||||
Find_element_DVE := T1;
|
||||
END; {}
|
||||
|
||||
FUNCTION Find_element_HVE (num : WORD;
|
||||
pntr : POINTER): POINTER;
|
||||
|
||||
VAR
|
||||
ii : WORD;
|
||||
T1 : HVE_ptr_;
|
||||
|
||||
BEGIN
|
||||
T1 := pntr;
|
||||
ii := 1;
|
||||
WHILE (T1 <> NIL) AND (ii < num) DO BEGIN
|
||||
T1 := T1^.right;
|
||||
INC(ii);
|
||||
END; {while}
|
||||
Find_element_HVE := T1;
|
||||
END; {}
|
||||
|
||||
FUNCTION Find_element_matrix (n1, n2 : WORD;
|
||||
pntr : POINTER): POINTER;
|
||||
|
||||
VAR
|
||||
ii, jj : WORD;
|
||||
T1 : DVE_ptr_;
|
||||
|
||||
BEGIN
|
||||
T1 := pntr;
|
||||
ii := 1;
|
||||
jj := 1;
|
||||
WHILE (T1 <> NIL) AND (jj < n2) DO BEGIN
|
||||
T1 := T1^.down;
|
||||
jj := jj + 1;
|
||||
END; {while}
|
||||
WHILE (T1 <> NIL) AND (ii < n1) DO BEGIN
|
||||
T1 := T1^.right;
|
||||
ii := ii + 1;
|
||||
END; {while}
|
||||
Find_element_matrix := T1;
|
||||
END; {}
|
||||
|
||||
|
||||
BEGIN {Initialization}
|
||||
END.
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,70 @@
|
|||
|
||||
|
||||
PROGRAM test_music (Input,Output);
|
||||
{
|
||||
This program compares the conformance of an input note sequence to the set
|
||||
of example sequences in 'SEQUENCE.DAT'.
|
||||
}
|
||||
|
||||
{
|
||||
|
||||
Copyright 1989 by Wesley R. Elsberry. All rights reserved.
|
||||
|
||||
Commercial use of this software is prohibited without written consent of
|
||||
the author.
|
||||
|
||||
For information, bug reports, and updates contact
|
||||
|
||||
Wesley R. Elsberry
|
||||
528 Chambers Creek Drive South
|
||||
Everman, Texas 76140
|
||||
Telephone: (817) 551-7018
|
||||
|
||||
}
|
||||
|
||||
USES
|
||||
DOS, misc1, ANSI_Z, globals, clasinst;
|
||||
|
||||
{General}
|
||||
|
||||
|
||||
VAR
|
||||
ii, jj, kk : INTEGER;
|
||||
notes : notes_;
|
||||
sinch : CHAR;
|
||||
scon,infname : STRING;
|
||||
inf : TEXT;
|
||||
cnt,success : INTEGER;
|
||||
|
||||
BEGIN
|
||||
Success := 0;
|
||||
Cnt := 0;
|
||||
|
||||
FOR ii := 1 TO 5 DO BEGIN
|
||||
notes[ii] := 0;
|
||||
END;
|
||||
|
||||
{Get filename to test}
|
||||
Write('Name of file to process: ');
|
||||
Readln(infname);
|
||||
{Open for input}
|
||||
Assign(inf,infname);
|
||||
Reset(inf);
|
||||
|
||||
REPEAT
|
||||
Readln(inf,notes[5]);
|
||||
IF (classical_instructor(notes) = 1) THEN BEGIN
|
||||
INC(success);
|
||||
END;
|
||||
INC(cnt);
|
||||
FOR ii := 1 TO 4 DO BEGIN
|
||||
notes[ii] := notes[ii+1];
|
||||
END;
|
||||
UNTIL (Eof(inf));
|
||||
|
||||
Close(inf);
|
||||
Writeln('Count = ',cnt:5,' Successes = ',success:5);
|
||||
|
||||
END.
|
||||
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
Binary file not shown.
|
|
@ -0,0 +1,186 @@
|
|||
|
||||
Revision date:
|
||||
Jun 07, 1988
|
||||
|
||||
|
||||
|
||||
Thesis proposal
|
||||
|
||||
|
||||
Wesley R. Elsberry
|
||||
Master's candidate, CSE
|
||||
|
||||
Committee:
|
||||
|
||||
Karan Briggs, CSE (Graduate Chairman)
|
||||
Daniel Levine, Mathematics
|
||||
Lynn Peterson, CSE
|
||||
|
||||
|
||||
Preliminary outline
|
||||
|
||||
|
||||
I. Introduction
|
||||
|
||||
II. Literature review
|
||||
|
||||
III. Topic proposal
|
||||
|
||||
a. Topic description
|
||||
|
||||
b. Topic verification (implementation)
|
||||
|
||||
i. Application proposal
|
||||
|
||||
ii. Description
|
||||
|
||||
iii. Resources needed for accomplishment
|
||||
|
||||
|
||||
************************************
|
||||
|
||||
I. Introduction
|
||||
|
||||
The field of artificial neural network research currently
|
||||
suffers from several misapprehensions on the part of researchers.
|
||||
First, communication continues to be sketchy and prone to
|
||||
misunderstanding, as no clearcut definitions have been attached
|
||||
to even the most commonly accepted terms and phrases that
|
||||
comprise ANN jargon. Researchers will ignore the
|
||||
interdisciplinary nature of ANN research to promote or denigrate
|
||||
ANN results in a specialized context. Often this is done in such
|
||||
a way that it is not clear that the comments or analysis are only
|
||||
valid in the specialized context. Second, the motivations for
|
||||
research vary wildly, and thus criticisms of models or data often
|
||||
are intiated on the basis of entirely different goal assumptions.
|
||||
Finally, much criticism and infighting occurs not because of any
|
||||
real research related causes, but because of politicking and the
|
||||
quest for personal power or recognition. While Kuhn [History of
|
||||
Scientific Revolutions] may revel in the unfolding byplay, it is
|
||||
a source of annoyance and an obstacle to good work for others
|
||||
engaged in this research. While these misapprehensions may not
|
||||
be conscious in nature, that does not lessen the negative impact
|
||||
of the misapprehensions.
|
||||
|
||||
One misapprehension which remains particularly pervasive is the
|
||||
idea that there exists one 'correct' model for artificial neural
|
||||
networks. The biological reality reflects a complex set of
|
||||
systems which accomplish diverse functions. No one has suggested
|
||||
that all biological neural systems operate in the same manner.
|
||||
Other, more easily apprehensible, biological systems reflect that
|
||||
variation arises both in structures and mechanisms that perform
|
||||
functional tasks. Spiders, insects, fish, birds, and mammals
|
||||
have all developed methods of flight, yet none are quite the
|
||||
same. Other examples can demonstrate that the same mechanism may
|
||||
be coopted for more than one purpose. Certainly the expectation
|
||||
should be that biological neural systems follow this pattern, yet
|
||||
the prevailing attitude in current ANN research denies this.
|
||||
|
||||
Different models reflect variation in an approach to a single
|
||||
function, or simply approaches to different functions.
|
||||
Comparisons which should account for this feature often do not.
|
||||
|
||||
Since various models will have features which make them
|
||||
preferable for classes of problems, solving problems which can be
|
||||
divided into subset problems may be best solved through
|
||||
integration and coordination of differing ANN models. This
|
||||
approach is expected to prove more tractable and productive than
|
||||
attempting to force a solution model to fit a specified problem
|
||||
complex (or changing the problem specification to fit the model).
|
||||
|
||||
|
||||
II. Literature review
|
||||
|
||||
Problem solving as McCulloch and Pitts envisioned it
|
||||
[from Levine 83]
|
||||
|
||||
As Rosenblatt redefined it
|
||||
[from Levine 83 and Rosenblatt ??]
|
||||
|
||||
What Hopfield says about Grossberg [this will be short]
|
||||
[from H-T 86]
|
||||
|
||||
What Rumelhart and McClelland say about Hopfield
|
||||
[from PDP]
|
||||
|
||||
What Rumelhart and McClelland say about Grossberg
|
||||
[from PDP]
|
||||
|
||||
What Grossberg says about everybody else [stated as briefly as
|
||||
possible]
|
||||
[from Applied Optics article, 87 Cognitive Science
|
||||
article]
|
||||
|
||||
Evidences for multi-model integration:
|
||||
|
||||
PDP Ch. 26, p 541: "A problem with the PDP models presented in
|
||||
this book is that they are too specialized, so concerned with
|
||||
solving the problem of the moment that they do not ask how the
|
||||
whole might fit together. The various chapters present us with
|
||||
different versions of a single, homogeneous structure,
|
||||
perfectly well-suited for doing its task, but not sufficient,
|
||||
in my opinion, at doing the whole task. One structure can't do
|
||||
the job: There have to be several parts to the system that do
|
||||
different things, sometimes communicating with each other,
|
||||
sometimes not."
|
||||
|
||||
Of course, McClelland here means to have several variants of
|
||||
the PDP model performing the functions, and is not per se
|
||||
referring to a multi-model approach. But the admission that a
|
||||
single instantiation of a model does not a solution make is
|
||||
very important.
|
||||
|
||||
|
||||
III. Topic proposal
|
||||
|
||||
a. Topic description
|
||||
Use the models of Hopfield, PDP, and Grossberg's ART in an
|
||||
integrated manner to solve a problem set that is a complex
|
||||
suite of problem classes. The purpose here is not to
|
||||
develop a general tool for such problems, but to demonstrate
|
||||
the desirability and applicability of using an integrative
|
||||
approach to ANN problem solving.
|
||||
|
||||
b. Topic verification (implementation)
|
||||
|
||||
i. Application proposal
|
||||
|
||||
Possible project 1: Cryptographic example. Small problem
|
||||
that involves transposition, pattern recognition, and
|
||||
feature detection and extraction. Models used as pre- and
|
||||
co- processors for problem-solving.
|
||||
|
||||
|
||||
ii. Description
|
||||
The data set generated for presentation to the solution
|
||||
system may have complex interdependencies which the ANN
|
||||
would have to extract.
|
||||
|
||||
iii. Resources needed for accomplishment
|
||||
|
||||
Computer:
|
||||
Available currently:
|
||||
Heathkit H-100, MS-DOS, 768K
|
||||
Heathkit H-158, MS-DOS (PC comp), 640K
|
||||
DEC PDP 11/23, RT-11, 256K
|
||||
|
||||
Languages:
|
||||
Available currently:
|
||||
Under MS-DOS:
|
||||
Turbo Pascal
|
||||
XLISP
|
||||
PD-Prolog
|
||||
Turbo C
|
||||
ECO-C88
|
||||
ICON
|
||||
MS-FORTRAN
|
||||
MASM
|
||||
|
||||
Under RT-11:
|
||||
MACRO-11 (assembler)
|
||||
DIBOL
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,136 @@
|
|||
|
||||
|
||||
PROGRAM trannote(Input,Output);
|
||||
{
|
||||
This program reads a file written by note_generator and converts it
|
||||
to a form usable by Music Transcription System (MTS).
|
||||
}
|
||||
|
||||
{
|
||||
|
||||
Copyright 1989 by Wesley R. Elsberry & Diane J. Blackwood.
|
||||
All rights reserved.
|
||||
|
||||
Commercial use of this software is prohibited without written consent of
|
||||
the authors.
|
||||
|
||||
For information, bug reports, and updates contact
|
||||
|
||||
Wesley R. Elsberry
|
||||
528 Chambers Creek Drive South
|
||||
Everman, Texas 76140
|
||||
Telephone: (817) 551-7018
|
||||
|
||||
}
|
||||
|
||||
USES
|
||||
Dos, CRT, ANN, struct;
|
||||
|
||||
CONST
|
||||
|
||||
{For file_note}
|
||||
fnote: ARRAY[1..8] OF INTEGER = (12,14,16,17,19,21,23,24);
|
||||
|
||||
|
||||
VAR
|
||||
cnote : INTEGER;
|
||||
hunt : STRING;
|
||||
inf : TEXT; {Input file handle}
|
||||
instr : STRING;
|
||||
line_ct : INTEGER;
|
||||
line : STRING; {String to hold line from temp file
|
||||
before writing to final file}
|
||||
outf : TEXT; {Output file handle}
|
||||
panel : INTEGER;
|
||||
|
||||
{----------------------------------------------------------}
|
||||
|
||||
PROCEDURE filenote(cn: INTEGER;
|
||||
VAR ln_ct: INTEGER);
|
||||
|
||||
VAR
|
||||
i : INTEGER;
|
||||
|
||||
BEGIN
|
||||
IF ( (ln_ct = 11) OR (SeekEof(inf)) ) THEN Write (outf,
|
||||
'0 1 4 40 0 ')
|
||||
ELSE Write (outf,'1 1 4 40 0 ');
|
||||
Write (outf,fnote[cn]);
|
||||
Writeln (outf,' -1 -1 ');
|
||||
IF (ln_ct = 1) THEN inc(panel);
|
||||
IF (ln_ct = 11) THEN BEGIN
|
||||
ln_ct := 0;
|
||||
Writeln (outf);
|
||||
Writeln (outf);
|
||||
END;
|
||||
END;
|
||||
|
||||
BEGIN {Main}
|
||||
|
||||
{initialize variables}
|
||||
line_ct := 1;
|
||||
panel := 0;
|
||||
|
||||
{get filename of input file}
|
||||
REPEAT
|
||||
Write ('File to translate? ');
|
||||
Readln (instr);
|
||||
instr := FSearch(instr,GetEnv('PATH'));
|
||||
UNTIL (Length(instr) <> 0);
|
||||
Assign (inf,instr);
|
||||
Reset(inf);
|
||||
|
||||
{get filename of output file}
|
||||
REPEAT
|
||||
Write ('File to store translated music? ');
|
||||
Readln (instr);
|
||||
hunt := FSearch(instr,GetEnv('PATH'));
|
||||
UNTIL (Length(hunt) = 0);
|
||||
Assign (outf,'temp.sng');
|
||||
Rewrite(outf);
|
||||
|
||||
{write header to mts file}
|
||||
Writeln(outf,'30');
|
||||
Writeln(outf,'10');
|
||||
Writeln(outf,'4 4 0 3 3 7 6 4 2 2 2 1 1 1 1 2 ');
|
||||
Writeln(outf,'0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ');
|
||||
|
||||
{process each note for use by mts}
|
||||
WHILE NOT Eof(inf) DO BEGIN
|
||||
Readln(inf,cnote);
|
||||
filenote(cnote,line_ct);
|
||||
line_ct := line_ct+1;
|
||||
END;
|
||||
|
||||
{close the files in use}
|
||||
Close(outf);
|
||||
Close(inf);
|
||||
|
||||
{Copy temp file to final file with the value for panel corrected}
|
||||
Assign (inf,'temp.sng');
|
||||
Reset (inf);
|
||||
Assign (outf,instr);
|
||||
Rewrite (outf);
|
||||
|
||||
{read first two header lines and use panel to correct the number of
|
||||
panels}
|
||||
Readln(inf,line);
|
||||
Writeln(outf,line);
|
||||
Readln(inf,line);
|
||||
Writeln(outf,panel);
|
||||
|
||||
{read the rest of the temp file and write to the final file}
|
||||
WHILE NOT Eof(inf) DO BEGIN
|
||||
Readln(inf,line);
|
||||
Writeln(outf,line);
|
||||
END;
|
||||
Writeln (outf);
|
||||
Writeln (outf);
|
||||
|
||||
{Close the files in use}
|
||||
Close(outf);
|
||||
Close(inf);
|
||||
END. {Main}
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,60 @@
|
|||
"""Compatibility-first Python port of the thesis composition system."""
|
||||
|
||||
from .analysis import CompositionAnalysis, analyze_composition
|
||||
from .art1 import ART1Category, ART1Network, ART1Params, ART1Result
|
||||
from .backprop import BackpropNetwork, BackpropResult
|
||||
from .beethoven import BeethovenCategorizer, BeethovenResult
|
||||
from .classical_rules import ClassicalInstructor
|
||||
from .encoding import encode_art_input, encode_note_sequence, encode_sequence_one_hot
|
||||
from .hopfield import (
|
||||
HopfieldNetworkState,
|
||||
HopfieldParams,
|
||||
HopfieldResult,
|
||||
HopfieldRunResult,
|
||||
generate_next_note,
|
||||
run_hopfield_network,
|
||||
)
|
||||
from .salieri import SalieriCritic, SalieriResult
|
||||
from .pipeline import CompositionPipeline, PipelineStep
|
||||
from .reporting import build_run_report, save_run_report_json
|
||||
from .types import (
|
||||
CompositionContext,
|
||||
CompositionRecord,
|
||||
CompositionRunReport,
|
||||
LegacyPaths,
|
||||
NoteSequence,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"CompositionAnalysis",
|
||||
"ART1Category",
|
||||
"ART1Network",
|
||||
"ART1Params",
|
||||
"ART1Result",
|
||||
"BackpropNetwork",
|
||||
"BackpropResult",
|
||||
"BeethovenCategorizer",
|
||||
"BeethovenResult",
|
||||
"ClassicalInstructor",
|
||||
"CompositionContext",
|
||||
"CompositionPipeline",
|
||||
"CompositionRecord",
|
||||
"CompositionRunReport",
|
||||
"HopfieldNetworkState",
|
||||
"HopfieldParams",
|
||||
"HopfieldResult",
|
||||
"HopfieldRunResult",
|
||||
"LegacyPaths",
|
||||
"NoteSequence",
|
||||
"PipelineStep",
|
||||
"SalieriCritic",
|
||||
"SalieriResult",
|
||||
"analyze_composition",
|
||||
"build_run_report",
|
||||
"encode_art_input",
|
||||
"encode_note_sequence",
|
||||
"encode_sequence_one_hot",
|
||||
"generate_next_note",
|
||||
"run_hopfield_network",
|
||||
"save_run_report_json",
|
||||
]
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
from .cli import main
|
||||
|
||||
|
||||
raise SystemExit(main())
|
||||
|
|
@ -0,0 +1,66 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from collections import Counter, defaultdict
|
||||
from dataclasses import dataclass
|
||||
import math
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class CompositionAnalysis:
|
||||
note_count: int
|
||||
alphabet_size: int
|
||||
unigram_entropy_bits: float
|
||||
conditional_entropy_bits: float
|
||||
normalized_entropy: float
|
||||
predictability: float
|
||||
redundancy: float
|
||||
|
||||
|
||||
def shannon_entropy(sequence: tuple[int, ...] | list[int]) -> float:
|
||||
if not sequence:
|
||||
return 0.0
|
||||
counts = Counter(sequence)
|
||||
total = len(sequence)
|
||||
return -sum((count / total) * math.log2(count / total) for count in counts.values())
|
||||
|
||||
|
||||
def first_order_conditional_entropy(sequence: tuple[int, ...] | list[int]) -> float:
|
||||
if len(sequence) < 2:
|
||||
return 0.0
|
||||
transitions: dict[int, Counter[int]] = defaultdict(Counter)
|
||||
source_counts = Counter(sequence[:-1])
|
||||
for left, right in zip(sequence[:-1], sequence[1:]):
|
||||
transitions[left][right] += 1
|
||||
total_transitions = len(sequence) - 1
|
||||
entropy = 0.0
|
||||
for source, next_counts in transitions.items():
|
||||
source_prob = source_counts[source] / total_transitions
|
||||
total = sum(next_counts.values())
|
||||
source_entropy = -sum(
|
||||
(count / total) * math.log2(count / total) for count in next_counts.values()
|
||||
)
|
||||
entropy += source_prob * source_entropy
|
||||
return entropy
|
||||
|
||||
|
||||
def analyze_composition(
|
||||
sequence: tuple[int, ...] | list[int],
|
||||
*,
|
||||
alphabet_size: int = 8,
|
||||
) -> CompositionAnalysis:
|
||||
notes = tuple(int(note) for note in sequence)
|
||||
unigram_entropy = shannon_entropy(notes)
|
||||
conditional_entropy = first_order_conditional_entropy(notes)
|
||||
max_entropy = math.log2(alphabet_size) if alphabet_size > 1 else 0.0
|
||||
normalized_entropy = unigram_entropy / max_entropy if max_entropy else 0.0
|
||||
predictability = 1.0 - (conditional_entropy / max_entropy if max_entropy else 0.0)
|
||||
redundancy = 1.0 - normalized_entropy
|
||||
return CompositionAnalysis(
|
||||
note_count=len(notes),
|
||||
alphabet_size=alphabet_size,
|
||||
unigram_entropy_bits=unigram_entropy,
|
||||
conditional_entropy_bits=conditional_entropy,
|
||||
normalized_entropy=normalized_entropy,
|
||||
predictability=predictability,
|
||||
redundancy=redundancy,
|
||||
)
|
||||
|
|
@ -0,0 +1,207 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass
|
||||
import json
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class ART1Params:
|
||||
max_categories: int
|
||||
input_length: int
|
||||
vigilance: float = 0.9
|
||||
initial_bottom_up: float = 0.1
|
||||
initial_top_down: float = 0.9
|
||||
vigilance_decay: float = 0.99
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class ART1Category:
|
||||
bottom_up: tuple[float, ...]
|
||||
top_down: tuple[float, ...]
|
||||
committed: bool
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class ART1Result:
|
||||
winner: int
|
||||
matched: bool
|
||||
new_category: bool
|
||||
delta_vigilance: bool
|
||||
committed_categories: int
|
||||
vigilance: float
|
||||
expected_vector: tuple[int, ...]
|
||||
|
||||
|
||||
class ART1Network:
|
||||
def __init__(self, params: ART1Params) -> None:
|
||||
self.params = params
|
||||
self.vigilance = params.vigilance
|
||||
self._categories = [
|
||||
{
|
||||
"bottom_up": [params.initial_bottom_up] * params.input_length,
|
||||
"top_down": [params.initial_top_down] * params.input_length,
|
||||
"committed": False,
|
||||
}
|
||||
for _ in range(params.max_categories)
|
||||
]
|
||||
|
||||
@property
|
||||
def committed_categories(self) -> int:
|
||||
return sum(1 for category in self._categories if category["committed"])
|
||||
|
||||
@property
|
||||
def categories(self) -> tuple[ART1Category, ...]:
|
||||
return tuple(
|
||||
ART1Category(
|
||||
bottom_up=tuple(category["bottom_up"]),
|
||||
top_down=tuple(category["top_down"]),
|
||||
committed=bool(category["committed"]),
|
||||
)
|
||||
for category in self._categories
|
||||
)
|
||||
|
||||
def categorize(self, input_vector: tuple[int, ...] | list[int]) -> ART1Result:
|
||||
vector = tuple(int(value) for value in input_vector)
|
||||
if len(vector) != self.params.input_length:
|
||||
raise ValueError(
|
||||
f"expected input length {self.params.input_length}, got {len(vector)}"
|
||||
)
|
||||
|
||||
eligible = {index for index, category in enumerate(self._categories) if category["committed"]}
|
||||
delta_vigilance = False
|
||||
|
||||
while True:
|
||||
if not eligible:
|
||||
if self.committed_categories < self.params.max_categories:
|
||||
winner = self.committed_categories
|
||||
self._commit_category(winner, vector)
|
||||
return ART1Result(
|
||||
winner=winner,
|
||||
matched=True,
|
||||
new_category=True,
|
||||
delta_vigilance=delta_vigilance,
|
||||
committed_categories=self.committed_categories,
|
||||
vigilance=self.vigilance,
|
||||
expected_vector=tuple(vector),
|
||||
)
|
||||
|
||||
self.vigilance *= self.params.vigilance_decay
|
||||
delta_vigilance = True
|
||||
eligible = {
|
||||
index for index, category in enumerate(self._categories) if category["committed"]
|
||||
}
|
||||
winner = self._choose_winner(vector, eligible)
|
||||
self._resonate(winner, vector)
|
||||
expected_vector = self._expected_vector(winner)
|
||||
return ART1Result(
|
||||
winner=winner,
|
||||
matched=True,
|
||||
new_category=False,
|
||||
delta_vigilance=True,
|
||||
committed_categories=self.committed_categories,
|
||||
vigilance=self.vigilance,
|
||||
expected_vector=expected_vector,
|
||||
)
|
||||
|
||||
winner = self._choose_winner(vector, eligible)
|
||||
expected_vector = self._expected_vector(winner)
|
||||
if self._match(vector, expected_vector):
|
||||
self._resonate(winner, vector)
|
||||
return ART1Result(
|
||||
winner=winner,
|
||||
matched=True,
|
||||
new_category=False,
|
||||
delta_vigilance=delta_vigilance,
|
||||
committed_categories=self.committed_categories,
|
||||
vigilance=self.vigilance,
|
||||
expected_vector=expected_vector,
|
||||
)
|
||||
|
||||
eligible.remove(winner)
|
||||
|
||||
def _choose_winner(self, vector: tuple[int, ...], eligible: set[int]) -> int:
|
||||
best_index = min(eligible)
|
||||
best_score = float("-inf")
|
||||
for index in sorted(eligible):
|
||||
category = self._categories[index]
|
||||
score = sum(
|
||||
vector[i] * category["bottom_up"][i] for i in range(self.params.input_length)
|
||||
)
|
||||
if score > best_score:
|
||||
best_score = score
|
||||
best_index = index
|
||||
return best_index
|
||||
|
||||
def _expected_vector(self, category_index: int) -> tuple[int, ...]:
|
||||
top_down = self._categories[category_index]["top_down"]
|
||||
threshold = sum(top_down) / self.params.input_length
|
||||
return tuple(1 if value >= threshold else 0 for value in top_down)
|
||||
|
||||
def _match(self, vector: tuple[int, ...], expected_vector: tuple[int, ...]) -> bool:
|
||||
ones_in_input = sum(vector)
|
||||
raw_match = sum(1 for left, right in zip(vector, expected_vector) if left == 1 and right == 1)
|
||||
if ones_in_input == 0:
|
||||
return raw_match > 0
|
||||
return (raw_match / ones_in_input) >= self.vigilance
|
||||
|
||||
def _commit_category(self, category_index: int, vector: tuple[int, ...]) -> None:
|
||||
category = self._categories[category_index]
|
||||
category["committed"] = True
|
||||
category["top_down"] = [float(value) for value in vector]
|
||||
ones = max(1, sum(vector))
|
||||
category["bottom_up"] = [float(value) / ones for value in vector]
|
||||
|
||||
def _resonate(self, category_index: int, vector: tuple[int, ...]) -> None:
|
||||
category = self._categories[category_index]
|
||||
intersected = [1 if category["top_down"][i] >= 0.5 and vector[i] == 1 else 0 for i in range(self.params.input_length)]
|
||||
category["top_down"] = [float(value) for value in intersected]
|
||||
ones = max(1, sum(intersected))
|
||||
category["bottom_up"] = [float(value) / ones for value in intersected]
|
||||
|
||||
def to_dict(self) -> dict[str, object]:
|
||||
return {
|
||||
"params": {
|
||||
"max_categories": self.params.max_categories,
|
||||
"input_length": self.params.input_length,
|
||||
"vigilance": self.params.vigilance,
|
||||
"initial_bottom_up": self.params.initial_bottom_up,
|
||||
"initial_top_down": self.params.initial_top_down,
|
||||
"vigilance_decay": self.params.vigilance_decay,
|
||||
},
|
||||
"vigilance": self.vigilance,
|
||||
"categories": self._categories,
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: dict[str, object]) -> "ART1Network":
|
||||
params_data = data["params"] # type: ignore[index]
|
||||
network = cls(
|
||||
ART1Params(
|
||||
max_categories=int(params_data["max_categories"]), # type: ignore[index]
|
||||
input_length=int(params_data["input_length"]), # type: ignore[index]
|
||||
vigilance=float(params_data["vigilance"]), # type: ignore[index]
|
||||
initial_bottom_up=float(params_data["initial_bottom_up"]), # type: ignore[index]
|
||||
initial_top_down=float(params_data["initial_top_down"]), # type: ignore[index]
|
||||
vigilance_decay=float(params_data["vigilance_decay"]), # type: ignore[index]
|
||||
)
|
||||
)
|
||||
network.vigilance = float(data["vigilance"])
|
||||
network._categories = [
|
||||
{
|
||||
"bottom_up": [float(value) for value in category["bottom_up"]],
|
||||
"top_down": [float(value) for value in category["top_down"]],
|
||||
"committed": bool(category["committed"]),
|
||||
}
|
||||
for category in data["categories"] # type: ignore[index]
|
||||
]
|
||||
return network
|
||||
|
||||
def save_json(self, path: str) -> None:
|
||||
with open(path, "w", encoding="utf-8") as handle:
|
||||
json.dump(self.to_dict(), handle, indent=2)
|
||||
|
||||
@classmethod
|
||||
def load_json(cls, path: str) -> "ART1Network":
|
||||
with open(path, "r", encoding="utf-8") as handle:
|
||||
data = json.load(handle)
|
||||
return cls.from_dict(data)
|
||||
|
|
@ -0,0 +1,282 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass
|
||||
import json
|
||||
import math
|
||||
import random
|
||||
from typing import Iterable
|
||||
|
||||
from .types import LegacyBPWeights, SalieriConfig
|
||||
|
||||
|
||||
def sigmoid(range_value: float, slope_mod: float, shift: float, x: float) -> float:
|
||||
temp = -(slope_mod * x)
|
||||
temp = max(min(temp, 80.0), -80.0)
|
||||
return (range_value / (1.0 + math.exp(temp))) - shift
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class BackpropNodeState:
|
||||
node_type: str
|
||||
net_input: float
|
||||
delta: float
|
||||
theta: float
|
||||
range_value: float = 1.0
|
||||
shift: float = 0.0
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class BackpropResult:
|
||||
outputs: tuple[float, ...]
|
||||
error: float
|
||||
node_states: tuple[BackpropNodeState, ...]
|
||||
|
||||
|
||||
class BackpropNetwork:
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
n_input: int,
|
||||
n_hidden: int,
|
||||
n_output: int,
|
||||
learning_rate: float,
|
||||
alpha: float,
|
||||
weights: list[list[float]],
|
||||
thetas: list[float],
|
||||
) -> None:
|
||||
self.n_input = n_input
|
||||
self.n_hidden = n_hidden
|
||||
self.n_output = n_output
|
||||
self.learning_rate = learning_rate
|
||||
self.alpha = alpha
|
||||
self.node_count = n_input + n_hidden + n_output
|
||||
self.weights = weights
|
||||
self.thetas = thetas
|
||||
self.last_weight_updates = [
|
||||
[0.0 for _ in range(self.node_count)] for _ in range(self.node_count)
|
||||
]
|
||||
self.last_theta_updates = [0.0 for _ in range(self.node_count)]
|
||||
self.node_types = self._build_node_types()
|
||||
self.connectivity = self._build_connectivity()
|
||||
|
||||
@classmethod
|
||||
def random(
|
||||
cls,
|
||||
*,
|
||||
n_input: int,
|
||||
n_hidden: int,
|
||||
n_output: int,
|
||||
learning_rate: float = 0.5,
|
||||
alpha: float = 0.5,
|
||||
rng: random.Random | None = None,
|
||||
) -> "BackpropNetwork":
|
||||
generator = rng or random.Random()
|
||||
node_count = n_input + n_hidden + n_output
|
||||
weights = [
|
||||
[generator.uniform(-1.0, 1.0) for _ in range(node_count)]
|
||||
for _ in range(node_count)
|
||||
]
|
||||
thetas = [0.0] * n_input + [generator.gauss(0.0, 0.25) for _ in range(n_hidden + n_output)]
|
||||
return cls(
|
||||
n_input=n_input,
|
||||
n_hidden=n_hidden,
|
||||
n_output=n_output,
|
||||
learning_rate=learning_rate,
|
||||
alpha=alpha,
|
||||
weights=weights,
|
||||
thetas=thetas,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def from_legacy(
|
||||
cls,
|
||||
*,
|
||||
config: SalieriConfig,
|
||||
legacy_weights: LegacyBPWeights,
|
||||
) -> "BackpropNetwork":
|
||||
return cls(
|
||||
n_input=config.n_input,
|
||||
n_hidden=config.n_hidden,
|
||||
n_output=config.n_output,
|
||||
learning_rate=config.learning_rate,
|
||||
alpha=config.alpha,
|
||||
weights=[list(row) for row in legacy_weights.weights],
|
||||
thetas=list(legacy_weights.thetas),
|
||||
)
|
||||
|
||||
def predict(self, inputs: Iterable[float]) -> BackpropResult:
|
||||
input_values = tuple(float(value) for value in inputs)
|
||||
if len(input_values) != self.n_input:
|
||||
raise ValueError(f"expected {self.n_input} inputs, got {len(input_values)}")
|
||||
|
||||
net_inputs = [0.0 for _ in range(self.node_count)]
|
||||
activations = [0.0 for _ in range(self.node_count)]
|
||||
|
||||
for idx in range(self.node_count):
|
||||
if self.node_types[idx] == "input":
|
||||
net_inputs[idx] = input_values[idx]
|
||||
activations[idx] = input_values[idx]
|
||||
continue
|
||||
|
||||
total = 0.0
|
||||
for src in range(self.node_count):
|
||||
if not self.connectivity[idx][src]:
|
||||
continue
|
||||
if self.node_types[src] == "input":
|
||||
total += net_inputs[src] * self.weights[idx][src]
|
||||
else:
|
||||
total += sigmoid(1.0, 1.0, 0.0, net_inputs[src] + self.thetas[src]) * self.weights[idx][src]
|
||||
net_inputs[idx] = total
|
||||
activations[idx] = sigmoid(1.0, 1.0, 0.0, total + self.thetas[idx])
|
||||
|
||||
outputs = tuple(activations[self.n_input + self.n_hidden :])
|
||||
node_states = tuple(
|
||||
BackpropNodeState(
|
||||
node_type=self.node_types[idx],
|
||||
net_input=net_inputs[idx],
|
||||
delta=0.0,
|
||||
theta=self.thetas[idx],
|
||||
)
|
||||
for idx in range(self.node_count)
|
||||
)
|
||||
return BackpropResult(outputs=outputs, error=0.0, node_states=node_states)
|
||||
|
||||
def train_step(self, inputs: Iterable[float], targets: Iterable[float]) -> BackpropResult:
|
||||
input_values = tuple(float(value) for value in inputs)
|
||||
target_values = tuple(float(value) for value in targets)
|
||||
if len(target_values) != self.n_output:
|
||||
raise ValueError(f"expected {self.n_output} targets, got {len(target_values)}")
|
||||
|
||||
net_inputs = [0.0 for _ in range(self.node_count)]
|
||||
activations = [0.0 for _ in range(self.node_count)]
|
||||
|
||||
for idx in range(self.node_count):
|
||||
if self.node_types[idx] == "input":
|
||||
net_inputs[idx] = input_values[idx]
|
||||
activations[idx] = input_values[idx]
|
||||
continue
|
||||
|
||||
total = 0.0
|
||||
for src in range(self.node_count):
|
||||
if not self.connectivity[idx][src]:
|
||||
continue
|
||||
source_activation = (
|
||||
net_inputs[src]
|
||||
if self.node_types[src] == "input"
|
||||
else sigmoid(1.0, 1.0, 0.0, net_inputs[src] + self.thetas[src])
|
||||
)
|
||||
total += source_activation * self.weights[idx][src]
|
||||
net_inputs[idx] = total
|
||||
activations[idx] = sigmoid(1.0, 1.0, 0.0, total + self.thetas[idx])
|
||||
|
||||
deltas = [0.0 for _ in range(self.node_count)]
|
||||
output_start = self.n_input + self.n_hidden
|
||||
max_error = 0.0
|
||||
|
||||
for idx in range(self.node_count - 1, -1, -1):
|
||||
activation = activations[idx]
|
||||
if self.node_types[idx] == "output":
|
||||
target = target_values[idx - output_start]
|
||||
raw_error = target - activation
|
||||
max_error = max(max_error, abs(raw_error))
|
||||
deltas[idx] = raw_error * activation * (1.0 - activation)
|
||||
elif self.node_types[idx] == "hidden":
|
||||
downstream = 0.0
|
||||
for dst in range(self.node_count):
|
||||
if self.connectivity[dst][idx]:
|
||||
downstream += deltas[dst] * self.weights[dst][idx]
|
||||
deltas[idx] = activation * (1.0 - activation) * downstream
|
||||
|
||||
for idx in range(self.node_count):
|
||||
theta_update = self.learning_rate * deltas[idx] + self.alpha * self.last_theta_updates[idx]
|
||||
self.last_theta_updates[idx] = theta_update
|
||||
self.thetas[idx] += theta_update
|
||||
|
||||
for dst in range(self.node_count):
|
||||
destination_activation = (
|
||||
net_inputs[dst]
|
||||
if self.node_types[dst] == "input"
|
||||
else activations[dst]
|
||||
)
|
||||
for src in range(self.node_count):
|
||||
if not self.connectivity[dst][src]:
|
||||
continue
|
||||
update = self.learning_rate * (deltas[src] * destination_activation)
|
||||
update += self.alpha * self.last_weight_updates[dst][src]
|
||||
self.last_weight_updates[dst][src] = update
|
||||
self.weights[dst][src] += update
|
||||
|
||||
outputs = tuple(activations[output_start:])
|
||||
node_states = tuple(
|
||||
BackpropNodeState(
|
||||
node_type=self.node_types[idx],
|
||||
net_input=net_inputs[idx],
|
||||
delta=deltas[idx],
|
||||
theta=self.thetas[idx],
|
||||
)
|
||||
for idx in range(self.node_count)
|
||||
)
|
||||
return BackpropResult(outputs=outputs, error=max_error, node_states=node_states)
|
||||
|
||||
def _build_node_types(self) -> list[str]:
|
||||
return (
|
||||
["input"] * self.n_input
|
||||
+ ["hidden"] * self.n_hidden
|
||||
+ ["output"] * self.n_output
|
||||
)
|
||||
|
||||
def _build_connectivity(self) -> list[list[bool]]:
|
||||
connectivity = [[False for _ in range(self.node_count)] for _ in range(self.node_count)]
|
||||
hidden_start = self.n_input
|
||||
output_start = self.n_input + self.n_hidden
|
||||
for dst in range(hidden_start, output_start):
|
||||
for src in range(self.n_input):
|
||||
connectivity[dst][src] = True
|
||||
for dst in range(output_start, self.node_count):
|
||||
for src in range(hidden_start, output_start):
|
||||
connectivity[dst][src] = True
|
||||
return connectivity
|
||||
|
||||
def to_dict(self) -> dict[str, object]:
|
||||
return {
|
||||
"n_input": self.n_input,
|
||||
"n_hidden": self.n_hidden,
|
||||
"n_output": self.n_output,
|
||||
"learning_rate": self.learning_rate,
|
||||
"alpha": self.alpha,
|
||||
"weights": self.weights,
|
||||
"thetas": self.thetas,
|
||||
"last_weight_updates": self.last_weight_updates,
|
||||
"last_theta_updates": self.last_theta_updates,
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: dict[str, object]) -> "BackpropNetwork":
|
||||
network = cls(
|
||||
n_input=int(data["n_input"]),
|
||||
n_hidden=int(data["n_hidden"]),
|
||||
n_output=int(data["n_output"]),
|
||||
learning_rate=float(data["learning_rate"]),
|
||||
alpha=float(data["alpha"]),
|
||||
weights=[[float(value) for value in row] for row in data["weights"]], # type: ignore[index]
|
||||
thetas=[float(value) for value in data["thetas"]], # type: ignore[index]
|
||||
)
|
||||
network.last_weight_updates = [
|
||||
[float(value) for value in row]
|
||||
for row in data.get("last_weight_updates", network.last_weight_updates) # type: ignore[arg-type]
|
||||
]
|
||||
network.last_theta_updates = [
|
||||
float(value)
|
||||
for value in data.get("last_theta_updates", network.last_theta_updates) # type: ignore[arg-type]
|
||||
]
|
||||
return network
|
||||
|
||||
def save_json(self, path: str) -> None:
|
||||
with open(path, "w", encoding="utf-8") as handle:
|
||||
json.dump(self.to_dict(), handle, indent=2)
|
||||
|
||||
@classmethod
|
||||
def load_json(cls, path: str) -> "BackpropNetwork":
|
||||
with open(path, "r", encoding="utf-8") as handle:
|
||||
data = json.load(handle)
|
||||
return cls.from_dict(data)
|
||||
|
|
@ -0,0 +1,77 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass
|
||||
import json
|
||||
|
||||
from .art1 import ART1Network, ART1Params, ART1Result
|
||||
from .encoding import encode_art_input, encode_note_sequence
|
||||
from .types import ART_CATEGORY_LIMIT, ART_INPUT_LENGTH, NoteSequence
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class BeethovenResult:
|
||||
notes: NoteSequence
|
||||
is_classical: bool
|
||||
art_result: ART1Result
|
||||
|
||||
|
||||
class BeethovenCategorizer:
|
||||
def __init__(self, network: ART1Network | None = None) -> None:
|
||||
self.network = network or ART1Network(
|
||||
ART1Params(
|
||||
max_categories=ART_CATEGORY_LIMIT,
|
||||
input_length=ART_INPUT_LENGTH,
|
||||
)
|
||||
)
|
||||
|
||||
def categorize(
|
||||
self,
|
||||
notes: list[int] | tuple[int, ...],
|
||||
*,
|
||||
is_classical: bool,
|
||||
) -> BeethovenResult:
|
||||
sequence = encode_note_sequence(notes)
|
||||
input_vector = encode_art_input(sequence, is_classical=is_classical)
|
||||
art_result = self.network.categorize(input_vector)
|
||||
return BeethovenResult(
|
||||
notes=sequence,
|
||||
is_classical=is_classical,
|
||||
art_result=art_result,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def with_params(
|
||||
cls,
|
||||
*,
|
||||
max_categories: int = ART_CATEGORY_LIMIT,
|
||||
input_length: int = ART_INPUT_LENGTH,
|
||||
vigilance: float = 0.9,
|
||||
vigilance_decay: float = 0.99,
|
||||
) -> "BeethovenCategorizer":
|
||||
return cls(
|
||||
network=ART1Network(
|
||||
ART1Params(
|
||||
max_categories=max_categories,
|
||||
input_length=input_length,
|
||||
vigilance=vigilance,
|
||||
vigilance_decay=vigilance_decay,
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
def to_dict(self) -> dict[str, object]:
|
||||
return {"network": self.network.to_dict()}
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: dict[str, object]) -> "BeethovenCategorizer":
|
||||
return cls(network=ART1Network.from_dict(data["network"])) # type: ignore[arg-type]
|
||||
|
||||
def save_json(self, path: str) -> None:
|
||||
with open(path, "w", encoding="utf-8") as handle:
|
||||
json.dump(self.to_dict(), handle, indent=2)
|
||||
|
||||
@classmethod
|
||||
def load_json(cls, path: str) -> "BeethovenCategorizer":
|
||||
with open(path, "r", encoding="utf-8") as handle:
|
||||
data = json.load(handle)
|
||||
return cls.from_dict(data)
|
||||
|
|
@ -0,0 +1,33 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass
|
||||
from pathlib import Path
|
||||
|
||||
from .encoding import encode_note_sequence
|
||||
from .types import NoteSequence
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class ClassicalInstructor:
|
||||
sequences: tuple[str, ...]
|
||||
|
||||
@classmethod
|
||||
def from_sequence_file(cls, path: str | Path) -> "ClassicalInstructor":
|
||||
sequence_path = Path(path)
|
||||
sequences = tuple(
|
||||
line.strip().rstrip("\x1a")
|
||||
for line in sequence_path.read_text(encoding="ascii").splitlines()
|
||||
if line.strip().rstrip("\x1a")
|
||||
)
|
||||
return cls(sequences=sequences)
|
||||
|
||||
def classify(self, notes: list[int] | tuple[int, ...]) -> int:
|
||||
target = "".join(str(note) for note in encode_note_sequence(notes))
|
||||
for candidate in self.sequences:
|
||||
candidate_len = len(candidate)
|
||||
if target[-candidate_len:] == candidate:
|
||||
return 1
|
||||
return 0
|
||||
|
||||
def __call__(self, notes: list[int] | tuple[int, ...]) -> int:
|
||||
return self.classify(notes)
|
||||
|
|
@ -0,0 +1,81 @@
|
|||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
from pathlib import Path
|
||||
|
||||
from .beethoven import BeethovenCategorizer
|
||||
from .pipeline import CompositionPipeline
|
||||
from .reporting import build_run_report, save_run_report_json
|
||||
from .salieri import SalieriCritic
|
||||
|
||||
|
||||
def build_parser() -> argparse.ArgumentParser:
|
||||
parser = argparse.ArgumentParser(prog="triune-cadence")
|
||||
parser.add_argument("--thes-root", default="THES")
|
||||
parser.add_argument("--notes", type=int, default=16)
|
||||
parser.add_argument("--object-threshold", type=int, default=3)
|
||||
parser.add_argument("--max-attempts-per-note", type=int, default=500)
|
||||
parser.add_argument("--art-vigilance", type=float, default=0.9)
|
||||
parser.add_argument("--art-vigilance-decay", type=float, default=0.99)
|
||||
parser.add_argument("--save-salieri")
|
||||
parser.add_argument("--save-beethoven")
|
||||
parser.add_argument("--load-salieri")
|
||||
parser.add_argument("--load-beethoven")
|
||||
parser.add_argument("--save-report")
|
||||
return parser
|
||||
|
||||
|
||||
def main() -> int:
|
||||
args = build_parser().parse_args()
|
||||
root = Path(args.thes_root)
|
||||
pipeline = CompositionPipeline.from_legacy_data_with_options(
|
||||
root,
|
||||
object_threshold=args.object_threshold,
|
||||
art_vigilance=args.art_vigilance,
|
||||
art_vigilance_decay=args.art_vigilance_decay,
|
||||
)
|
||||
|
||||
if args.load_salieri:
|
||||
pipeline.salieri = SalieriCritic.load_json(args.load_salieri)
|
||||
if args.load_beethoven:
|
||||
pipeline.beethoven = BeethovenCategorizer.load_json(args.load_beethoven)
|
||||
|
||||
record = pipeline.compose(
|
||||
max_notes=args.notes,
|
||||
max_attempts_per_note=args.max_attempts_per_note,
|
||||
)
|
||||
report = build_run_report(
|
||||
record,
|
||||
parameters={
|
||||
"thes_root": str(root),
|
||||
"notes_requested": args.notes,
|
||||
"object_threshold": args.object_threshold,
|
||||
"max_attempts_per_note": args.max_attempts_per_note,
|
||||
"art_vigilance": args.art_vigilance,
|
||||
"art_vigilance_decay": args.art_vigilance_decay,
|
||||
},
|
||||
)
|
||||
|
||||
print("notes:", " ".join(str(note) for note in report.notes))
|
||||
print(
|
||||
"per_note_seconds:",
|
||||
" ".join(f"{elapsed:.6f}" for elapsed in report.per_note_seconds),
|
||||
)
|
||||
print(f"total_seconds: {report.total_seconds:.6f}")
|
||||
if report.per_note_seconds:
|
||||
mean_seconds = sum(report.per_note_seconds) / len(report.per_note_seconds)
|
||||
print(f"mean_note_seconds: {mean_seconds:.6f}")
|
||||
print(f"unigram_entropy_bits: {report.unigram_entropy_bits:.4f}")
|
||||
print(f"conditional_entropy_bits: {report.conditional_entropy_bits:.4f}")
|
||||
print(f"normalized_entropy: {report.normalized_entropy:.4f}")
|
||||
print(f"predictability: {report.predictability:.4f}")
|
||||
print(f"redundancy: {report.redundancy:.4f}")
|
||||
|
||||
if args.save_salieri:
|
||||
pipeline.salieri.save_json(args.save_salieri)
|
||||
if args.save_beethoven:
|
||||
pipeline.beethoven.save_json(args.save_beethoven)
|
||||
if args.save_report:
|
||||
save_run_report_json(report, args.save_report)
|
||||
|
||||
return 0
|
||||
|
|
@ -0,0 +1,34 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from .types import ART_INPUT_LENGTH, NOTE_VOCABULARY_SIZE, NoteSequence, SEQUENCE_LENGTH
|
||||
|
||||
|
||||
def encode_note_sequence(notes: list[int] | tuple[int, ...]) -> NoteSequence:
|
||||
if len(notes) != SEQUENCE_LENGTH:
|
||||
raise ValueError(f"expected {SEQUENCE_LENGTH} notes, got {len(notes)}")
|
||||
encoded = tuple(int(note) for note in notes)
|
||||
for note in encoded:
|
||||
if not 0 <= note <= NOTE_VOCABULARY_SIZE:
|
||||
raise ValueError(f"note out of range: {note}")
|
||||
return encoded
|
||||
|
||||
|
||||
def encode_sequence_one_hot(notes: list[int] | tuple[int, ...]) -> tuple[int, ...]:
|
||||
encoded = encode_note_sequence(notes)
|
||||
vector = [0] * (SEQUENCE_LENGTH * NOTE_VOCABULARY_SIZE)
|
||||
for index, note in enumerate(encoded):
|
||||
if note > 0:
|
||||
vector[index * NOTE_VOCABULARY_SIZE + (note - 1)] = 1
|
||||
return tuple(vector)
|
||||
|
||||
|
||||
def encode_art_input(
|
||||
notes: list[int] | tuple[int, ...],
|
||||
*,
|
||||
is_classical: bool,
|
||||
) -> tuple[int, ...]:
|
||||
vector = list(encode_sequence_one_hot(notes))
|
||||
vector.append(1 if is_classical else 0)
|
||||
if len(vector) != ART_INPUT_LENGTH:
|
||||
raise AssertionError(f"unexpected ART input length: {len(vector)}")
|
||||
return tuple(vector)
|
||||
|
|
@ -0,0 +1,62 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from dataclasses import asdict
|
||||
import csv
|
||||
from pathlib import Path
|
||||
|
||||
from .pipeline import CompositionPipeline
|
||||
from .reporting import build_run_report, save_run_report_json
|
||||
from .types import CompositionRunReport
|
||||
|
||||
|
||||
def run_parameter_sweep(
|
||||
*,
|
||||
thes_root: str | Path,
|
||||
output_dir: str | Path,
|
||||
notes: int,
|
||||
parameter_sets: list[dict[str, object]],
|
||||
) -> list[CompositionRunReport]:
|
||||
root = Path(thes_root)
|
||||
destination = Path(output_dir)
|
||||
destination.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
reports: list[CompositionRunReport] = []
|
||||
for index, params in enumerate(parameter_sets, start=1):
|
||||
pipeline = CompositionPipeline.from_legacy_data_with_options(
|
||||
root,
|
||||
object_threshold=int(params.get("object_threshold", 3)),
|
||||
art_vigilance=float(params.get("art_vigilance", 0.9)),
|
||||
art_vigilance_decay=float(params.get("art_vigilance_decay", 0.99)),
|
||||
)
|
||||
max_attempts = int(params.get("max_attempts_per_note", 500))
|
||||
record = pipeline.compose(max_notes=notes, max_attempts_per_note=max_attempts)
|
||||
report = build_run_report(
|
||||
record,
|
||||
parameters={
|
||||
"notes_requested": notes,
|
||||
**params,
|
||||
},
|
||||
)
|
||||
save_run_report_json(report, str(destination / f"run_{index:03d}.json"))
|
||||
reports.append(report)
|
||||
|
||||
_write_summary_csv(destination / "summary.csv", reports)
|
||||
return reports
|
||||
|
||||
|
||||
def _write_summary_csv(path: Path, reports: list[CompositionRunReport]) -> None:
|
||||
if not reports:
|
||||
return
|
||||
rows = []
|
||||
for report in reports:
|
||||
row = asdict(report)
|
||||
row["notes"] = " ".join(str(note) for note in report.notes)
|
||||
row["per_note_seconds"] = " ".join(f"{value:.6f}" for value in report.per_note_seconds)
|
||||
row["parameters"] = str(report.parameters)
|
||||
rows.append(row)
|
||||
|
||||
fieldnames = list(rows[0].keys())
|
||||
with path.open("w", encoding="utf-8", newline="") as handle:
|
||||
writer = csv.DictWriter(handle, fieldnames=fieldnames)
|
||||
writer.writeheader()
|
||||
writer.writerows(rows)
|
||||
|
|
@ -0,0 +1,274 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass
|
||||
import math
|
||||
import random
|
||||
from typing import Callable
|
||||
|
||||
from .encoding import encode_note_sequence
|
||||
from .types import NOTE_VOCABULARY_SIZE, SEQUENCE_LENGTH
|
||||
|
||||
NoiseFn = Callable[[float, float], float]
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class HopfieldParams:
|
||||
epsilon: float = 0.005
|
||||
resistance_scale: float = 3.5
|
||||
capacitance_scale: float = 10.0
|
||||
weight_scale: float = 1.0
|
||||
input_scale: float = 1.0
|
||||
iteration_scale: float = 1.0
|
||||
global_resistance: float = 1.0
|
||||
global_capacitance: float = 1.0
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class HopfieldResult:
|
||||
input_notes: tuple[int, ...]
|
||||
output_notes: tuple[int, ...]
|
||||
candidate_note: int
|
||||
iterations: int
|
||||
activations: tuple[tuple[float, ...], ...]
|
||||
outputs: tuple[tuple[float, ...], ...]
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class HopfieldNetworkState:
|
||||
activations: tuple[tuple[float, ...], ...]
|
||||
outputs: tuple[tuple[float, ...], ...]
|
||||
external_inputs: tuple[tuple[float, ...], ...]
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class HopfieldRunResult:
|
||||
state: HopfieldNetworkState
|
||||
iterations: int
|
||||
|
||||
|
||||
def make_gaussian_noise(rng: random.Random | None = None) -> NoiseFn:
|
||||
generator = rng or random.Random()
|
||||
|
||||
def gaussian_noise(mean: float, variance: float) -> float:
|
||||
u1 = generator.random()
|
||||
u2 = generator.random()
|
||||
# Match the Pascal Box-Muller form and avoid ln(0).
|
||||
u1 = max(u1, 1e-12)
|
||||
x = math.sqrt(-2.0 * math.log(u1)) * math.cos(2.0 * math.pi * u2)
|
||||
return variance * x + mean
|
||||
|
||||
return gaussian_noise
|
||||
|
||||
|
||||
def tanh_clamped(value: float, exp_max: float = 80.0) -> float:
|
||||
value = max(min(value, exp_max), -exp_max)
|
||||
return (math.exp(value) - math.exp(-value)) / (math.exp(value) + math.exp(-value))
|
||||
|
||||
|
||||
def run_hopfield_network(
|
||||
external_inputs: tuple[tuple[float, ...], ...],
|
||||
weight_matrix: tuple[tuple[float, ...], ...],
|
||||
*,
|
||||
params: HopfieldParams | None = None,
|
||||
initial_activations: tuple[tuple[float, ...], ...] | None = None,
|
||||
) -> HopfieldRunResult:
|
||||
model = params or HopfieldParams()
|
||||
row_count = len(external_inputs)
|
||||
if row_count == 0:
|
||||
raise ValueError("external_inputs cannot be empty")
|
||||
column_count = len(external_inputs[0])
|
||||
if any(len(row) != column_count for row in external_inputs):
|
||||
raise ValueError("external_inputs rows must be the same length")
|
||||
_validate_weight_matrix(weight_matrix, active_size=row_count * column_count)
|
||||
|
||||
base_activations = initial_activations or tuple(
|
||||
tuple(0.5 for _ in range(column_count)) for _ in range(row_count)
|
||||
)
|
||||
if len(base_activations) != row_count or any(len(row) != column_count for row in base_activations):
|
||||
raise ValueError("initial_activations shape must match external_inputs")
|
||||
|
||||
activations = [
|
||||
[list(row) for row in base_activations],
|
||||
[list(row) for row in base_activations],
|
||||
]
|
||||
outputs = [
|
||||
[[0.0 for _ in range(column_count)] for _ in range(row_count)],
|
||||
[[0.0 for _ in range(column_count)] for _ in range(row_count)],
|
||||
]
|
||||
inputs = [
|
||||
[list(row) for row in external_inputs],
|
||||
[list(row) for row in external_inputs],
|
||||
]
|
||||
|
||||
outputs[1][0][0] = 20.0
|
||||
time_step = 0
|
||||
_update_outputs(activations, outputs, time_step, model, row_count, column_count)
|
||||
iterations = 0
|
||||
|
||||
while not _done(outputs, model.epsilon, row_count, column_count):
|
||||
time_step = time_step % 2
|
||||
next_time = (time_step + 1) % 2
|
||||
_update_outputs(activations, outputs, time_step, model, row_count, column_count)
|
||||
for row_index in range(row_count):
|
||||
for column_index in range(column_count):
|
||||
delta = _delta_neuron_activation(
|
||||
row_index=row_index,
|
||||
column_index=column_index,
|
||||
row_count=row_count,
|
||||
column_count=column_count,
|
||||
time_step=time_step,
|
||||
activations=activations,
|
||||
outputs=outputs,
|
||||
inputs=inputs,
|
||||
weight_matrix=weight_matrix,
|
||||
params=model,
|
||||
)
|
||||
activations[next_time][row_index][column_index] = (
|
||||
activations[time_step][row_index][column_index]
|
||||
+ model.iteration_scale * delta
|
||||
)
|
||||
time_step += 1
|
||||
iterations += 1
|
||||
|
||||
final_slot = time_step % 2
|
||||
state = HopfieldNetworkState(
|
||||
activations=tuple(tuple(row) for row in activations[final_slot]),
|
||||
outputs=tuple(tuple(row) for row in outputs[final_slot]),
|
||||
external_inputs=tuple(tuple(row) for row in external_inputs),
|
||||
)
|
||||
return HopfieldRunResult(state=state, iterations=iterations)
|
||||
|
||||
|
||||
def generate_next_note(
|
||||
notes: list[int] | tuple[int, ...],
|
||||
weight_matrix: tuple[tuple[float, ...], ...],
|
||||
*,
|
||||
params: HopfieldParams | None = None,
|
||||
noise: NoiseFn | None = None,
|
||||
) -> HopfieldResult:
|
||||
model = params or HopfieldParams()
|
||||
gaussian_noise = noise or make_gaussian_noise()
|
||||
input_notes = encode_note_sequence(notes)
|
||||
inputs = [[0.0 for _ in range(SEQUENCE_LENGTH)] for _ in range(NOTE_VOCABULARY_SIZE)]
|
||||
|
||||
for note_index in range(NOTE_VOCABULARY_SIZE):
|
||||
for position in range(SEQUENCE_LENGTH):
|
||||
note_value = input_notes[position]
|
||||
if note_value == 0:
|
||||
current_input = gaussian_noise(0.5, 0.25)
|
||||
elif note_value == note_index + 1:
|
||||
current_input = 0.67 + gaussian_noise(0.0, 0.1)
|
||||
else:
|
||||
current_input = 0.33 + gaussian_noise(0.0, 0.1)
|
||||
inputs[note_index][position] = current_input
|
||||
|
||||
output_notes = list(input_notes)
|
||||
run_result = run_hopfield_network(
|
||||
tuple(tuple(row) for row in inputs),
|
||||
weight_matrix,
|
||||
params=model,
|
||||
)
|
||||
for position in range(SEQUENCE_LENGTH):
|
||||
output_notes[position] = _max_cell_in_column(run_result.state.outputs, position)
|
||||
|
||||
return HopfieldResult(
|
||||
input_notes=input_notes,
|
||||
output_notes=tuple(output_notes),
|
||||
candidate_note=output_notes[-1],
|
||||
iterations=run_result.iterations,
|
||||
activations=run_result.state.activations,
|
||||
outputs=run_result.state.outputs,
|
||||
)
|
||||
|
||||
|
||||
def _validate_weight_matrix(
|
||||
weight_matrix: tuple[tuple[float, ...], ...],
|
||||
*,
|
||||
active_size: int,
|
||||
) -> None:
|
||||
if len(weight_matrix) < active_size:
|
||||
raise ValueError(f"weight matrix needs at least {active_size} rows")
|
||||
if any(len(row) < active_size for row in weight_matrix[:active_size]):
|
||||
raise ValueError(f"weight matrix needs at least {active_size} columns")
|
||||
|
||||
|
||||
def _update_outputs(
|
||||
activations: list[list[list[float]]],
|
||||
outputs: list[list[list[float]]],
|
||||
time_step: int,
|
||||
params: HopfieldParams,
|
||||
row_count: int,
|
||||
column_count: int,
|
||||
) -> None:
|
||||
for row_index in range(row_count):
|
||||
for column_index in range(column_count):
|
||||
outputs[time_step][row_index][column_index] = 0.5 * (
|
||||
1.0
|
||||
+ tanh_clamped(
|
||||
activations[time_step][row_index][column_index] / params.global_capacitance
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def _done(
|
||||
outputs: list[list[list[float]]],
|
||||
epsilon: float,
|
||||
row_count: int,
|
||||
column_count: int,
|
||||
) -> bool:
|
||||
for row_index in range(row_count):
|
||||
for column_index in range(column_count):
|
||||
if abs(outputs[0][row_index][column_index] - outputs[1][row_index][column_index]) > epsilon:
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def _weight_coord(row_index: int, column_index: int, row_count: int) -> int:
|
||||
return row_count * column_index + row_index
|
||||
|
||||
|
||||
def _delta_neuron_activation(
|
||||
*,
|
||||
row_index: int,
|
||||
column_index: int,
|
||||
row_count: int,
|
||||
column_count: int,
|
||||
time_step: int,
|
||||
activations: list[list[list[float]]],
|
||||
outputs: list[list[list[float]]],
|
||||
inputs: list[list[list[float]]],
|
||||
weight_matrix: tuple[tuple[float, ...], ...],
|
||||
params: HopfieldParams,
|
||||
) -> float:
|
||||
weight_sum = 0.0
|
||||
current_index = _weight_coord(row_index, column_index, row_count)
|
||||
for other_row in range(row_count):
|
||||
for other_column in range(column_count):
|
||||
other_index = _weight_coord(other_row, other_column, row_count)
|
||||
weight_sum += (
|
||||
weight_matrix[current_index][other_index]
|
||||
* params.weight_scale
|
||||
* outputs[time_step][other_row][other_column]
|
||||
)
|
||||
activation = activations[time_step][row_index][column_index]
|
||||
neuron_input = inputs[time_step][row_index][column_index]
|
||||
numerator = (
|
||||
-(activation / (params.global_resistance * params.resistance_scale))
|
||||
+ (neuron_input * params.input_scale)
|
||||
+ weight_sum
|
||||
)
|
||||
return numerator / (params.global_capacitance * params.capacitance_scale)
|
||||
|
||||
|
||||
def _max_cell_in_column(
|
||||
output_grid: list[list[float]] | tuple[tuple[float, ...], ...],
|
||||
position: int,
|
||||
) -> int:
|
||||
max_value = 0.0
|
||||
max_note = 1
|
||||
for note_index in range(NOTE_VOCABULARY_SIZE):
|
||||
output = output_grid[note_index][position]
|
||||
if output > max_value:
|
||||
max_value = output
|
||||
max_note = note_index + 1
|
||||
return max_note
|
||||
|
|
@ -0,0 +1,15 @@
|
|||
from .legacy_files import (
|
||||
load_hopfield_weight_matrix,
|
||||
load_legacy_paths,
|
||||
load_salieri_config,
|
||||
load_salieri_weights,
|
||||
load_sequence_table,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
"load_hopfield_weight_matrix",
|
||||
"load_legacy_paths",
|
||||
"load_salieri_config",
|
||||
"load_salieri_weights",
|
||||
"load_sequence_table",
|
||||
]
|
||||
|
|
@ -0,0 +1,108 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from pathlib import Path
|
||||
import struct
|
||||
|
||||
from composer_ans.types import (
|
||||
HOPFIELD_WEIGHT_DIMENSION,
|
||||
LegacyBPWeights,
|
||||
LegacyPaths,
|
||||
SALIERI_NODE_COUNT,
|
||||
SalieriConfig,
|
||||
)
|
||||
|
||||
|
||||
def load_legacy_paths(root: str | Path) -> LegacyPaths:
|
||||
return LegacyPaths(root=Path(root))
|
||||
|
||||
|
||||
def load_sequence_table(path: str | Path) -> tuple[str, ...]:
|
||||
sequence_path = Path(path)
|
||||
return tuple(
|
||||
line.strip().rstrip("\x1a")
|
||||
for line in sequence_path.read_text(encoding="ascii").splitlines()
|
||||
if line.strip().rstrip("\x1a")
|
||||
)
|
||||
|
||||
|
||||
def load_salieri_config(path: str | Path) -> SalieriConfig:
|
||||
values: dict[str, str] = {}
|
||||
for raw_line in Path(path).read_text(encoding="ascii").splitlines():
|
||||
line = raw_line.strip()
|
||||
if not line.startswith("!"):
|
||||
continue
|
||||
code = line[1:2].upper()
|
||||
if code == "Z":
|
||||
break
|
||||
payload = line[2:].strip()
|
||||
values[code] = payload
|
||||
return SalieriConfig(
|
||||
learning_rate=float(values["L"]),
|
||||
alpha=float(values["A"]),
|
||||
n_input=int(values["I"]),
|
||||
n_hidden=int(values["H"]),
|
||||
n_output=int(values["O"]),
|
||||
training_iterations=int(values["T"].split()[0]),
|
||||
error_tolerance=float(values["E"]),
|
||||
data_file=values["D"],
|
||||
report_file=values["R"],
|
||||
weight_file=values["W"],
|
||||
)
|
||||
|
||||
|
||||
def load_salieri_weights(path: str | Path) -> LegacyBPWeights:
|
||||
vector_length = None
|
||||
weights: list[tuple[float, ...]] = []
|
||||
thetas: tuple[float, ...] | None = None
|
||||
for raw_line in Path(path).read_text(encoding="ascii").splitlines():
|
||||
line = raw_line.strip()
|
||||
if not line.startswith("!"):
|
||||
continue
|
||||
code = line[1:2].upper()
|
||||
payload = line[2:].strip()
|
||||
if code == "V":
|
||||
vector_length = int(payload)
|
||||
elif code == "W":
|
||||
row = tuple(float(item) for item in payload.split())
|
||||
weights.append(row)
|
||||
elif code == "T":
|
||||
thetas = tuple(float(item) for item in payload.split())
|
||||
elif code == "Z":
|
||||
break
|
||||
if vector_length is None:
|
||||
raise ValueError("missing !V in weight file")
|
||||
if len(weights) != vector_length:
|
||||
raise ValueError(f"expected {vector_length} weight rows, got {len(weights)}")
|
||||
if any(len(row) != vector_length for row in weights):
|
||||
raise ValueError("weight matrix is not square")
|
||||
if thetas is None:
|
||||
raise ValueError("missing !T in weight file")
|
||||
if len(thetas) != vector_length:
|
||||
raise ValueError(f"expected {vector_length} theta values, got {len(thetas)}")
|
||||
return LegacyBPWeights(
|
||||
vector_length=vector_length,
|
||||
weights=tuple(weights),
|
||||
thetas=thetas,
|
||||
)
|
||||
|
||||
|
||||
def load_hopfield_weight_matrix(path: str | Path) -> tuple[tuple[float, ...], ...]:
|
||||
data = Path(path).read_bytes()
|
||||
expected_size = HOPFIELD_WEIGHT_DIMENSION * HOPFIELD_WEIGHT_DIMENSION * 4
|
||||
if len(data) != expected_size:
|
||||
raise ValueError(f"expected {expected_size} bytes, got {len(data)}")
|
||||
values = struct.unpack(
|
||||
f"<{HOPFIELD_WEIGHT_DIMENSION * HOPFIELD_WEIGHT_DIMENSION}f",
|
||||
data,
|
||||
)
|
||||
rows = []
|
||||
for offset in range(0, len(values), HOPFIELD_WEIGHT_DIMENSION):
|
||||
rows.append(tuple(values[offset : offset + HOPFIELD_WEIGHT_DIMENSION]))
|
||||
return tuple(rows)
|
||||
|
||||
|
||||
def extract_active_hopfield_submatrix(
|
||||
matrix: tuple[tuple[float, ...], ...],
|
||||
) -> tuple[tuple[float, ...], ...]:
|
||||
active_size = SALIERI_NODE_COUNT - 21
|
||||
return tuple(tuple(row[:active_size]) for row in matrix[:active_size])
|
||||
|
|
@ -0,0 +1,157 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass
|
||||
from pathlib import Path
|
||||
import time
|
||||
|
||||
from .beethoven import BeethovenCategorizer, BeethovenResult
|
||||
from .hopfield import HopfieldResult, generate_next_note
|
||||
from .io.legacy_files import extract_active_hopfield_submatrix, load_hopfield_weight_matrix
|
||||
from .salieri import SalieriCritic, SalieriResult
|
||||
from .types import CompositionContext, CompositionRecord, LegacyPaths
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class PipelineStep:
|
||||
context: CompositionContext
|
||||
hopfield: HopfieldResult
|
||||
salieri: SalieriResult
|
||||
beethoven: BeethovenResult
|
||||
accepted: bool
|
||||
objects: bool
|
||||
elapsed_seconds: float
|
||||
|
||||
|
||||
class CompositionPipeline:
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
hopfield_weights: tuple[tuple[float, ...], ...],
|
||||
salieri: SalieriCritic,
|
||||
beethoven: BeethovenCategorizer,
|
||||
object_threshold: int = 3,
|
||||
) -> None:
|
||||
self.hopfield_weights = hopfield_weights
|
||||
self.salieri = salieri
|
||||
self.beethoven = beethoven
|
||||
self.object_threshold = object_threshold
|
||||
|
||||
@classmethod
|
||||
def from_legacy_data(cls, root: str | Path) -> "CompositionPipeline":
|
||||
return cls.from_legacy_data_with_options(root)
|
||||
|
||||
@classmethod
|
||||
def from_legacy_data_with_options(
|
||||
cls,
|
||||
root: str | Path,
|
||||
*,
|
||||
object_threshold: int = 3,
|
||||
art_vigilance: float = 0.9,
|
||||
art_vigilance_decay: float = 0.99,
|
||||
) -> "CompositionPipeline":
|
||||
paths = LegacyPaths(root=Path(root))
|
||||
hopfield_weights = extract_active_hopfield_submatrix(
|
||||
load_hopfield_weight_matrix(paths.hopfield_weights)
|
||||
)
|
||||
salieri = SalieriCritic.from_legacy_paths(paths.root)
|
||||
beethoven = BeethovenCategorizer.with_params(
|
||||
vigilance=art_vigilance,
|
||||
vigilance_decay=art_vigilance_decay,
|
||||
)
|
||||
return cls(
|
||||
hopfield_weights=hopfield_weights,
|
||||
salieri=salieri,
|
||||
beethoven=beethoven,
|
||||
object_threshold=object_threshold,
|
||||
)
|
||||
|
||||
def neural_step(self, context: CompositionContext) -> PipelineStep:
|
||||
start_time = time.perf_counter()
|
||||
hopfield = generate_next_note(context.notes, self.hopfield_weights)
|
||||
salieri = self.salieri.evaluate_and_train(hopfield.output_notes)
|
||||
beethoven = self.beethoven.categorize(
|
||||
hopfield.output_notes,
|
||||
is_classical=salieri.is_classical,
|
||||
)
|
||||
|
||||
since_novelty = 0 if (beethoven.art_result.delta_vigilance or beethoven.art_result.new_category) else context.since_novelty + 1
|
||||
frustration = context.frustration + 1 if beethoven.art_result.delta_vigilance else context.frustration
|
||||
objects = since_novelty >= self.object_threshold
|
||||
if objects:
|
||||
since_novelty = 0
|
||||
|
||||
accepted = not (
|
||||
(objects and salieri.is_classical) or ((not objects) and (not salieri.is_classical))
|
||||
)
|
||||
|
||||
if accepted:
|
||||
next_context = CompositionContext(
|
||||
notes=hopfield.output_notes,
|
||||
delta_vigilance=beethoven.art_result.delta_vigilance,
|
||||
new_category=beethoven.art_result.new_category,
|
||||
is_classical=salieri.is_classical,
|
||||
candidate_note=hopfield.candidate_note,
|
||||
since_novelty=since_novelty,
|
||||
frustration=0,
|
||||
note_count=context.note_count + 1,
|
||||
)
|
||||
else:
|
||||
reset_notes = list(hopfield.output_notes)
|
||||
reset_notes[-1] = 0
|
||||
next_context = CompositionContext(
|
||||
notes=tuple(reset_notes),
|
||||
delta_vigilance=beethoven.art_result.delta_vigilance,
|
||||
new_category=beethoven.art_result.new_category,
|
||||
is_classical=salieri.is_classical,
|
||||
candidate_note=hopfield.candidate_note,
|
||||
since_novelty=since_novelty,
|
||||
frustration=frustration + 1,
|
||||
note_count=context.note_count,
|
||||
)
|
||||
|
||||
return PipelineStep(
|
||||
context=next_context,
|
||||
hopfield=hopfield,
|
||||
salieri=salieri,
|
||||
beethoven=beethoven,
|
||||
accepted=accepted,
|
||||
objects=objects,
|
||||
elapsed_seconds=time.perf_counter() - start_time,
|
||||
)
|
||||
|
||||
def step_until_accepted(
|
||||
self,
|
||||
context: CompositionContext,
|
||||
*,
|
||||
max_attempts: int = 500,
|
||||
) -> PipelineStep:
|
||||
current = context
|
||||
for _ in range(max_attempts):
|
||||
step = self.neural_step(current)
|
||||
if step.accepted:
|
||||
return step
|
||||
current = step.context
|
||||
raise RuntimeError("failed to accept a note within max_attempts")
|
||||
|
||||
def compose(
|
||||
self,
|
||||
*,
|
||||
max_notes: int,
|
||||
initial_context: CompositionContext | None = None,
|
||||
max_attempts_per_note: int = 500,
|
||||
) -> CompositionRecord:
|
||||
compose_start = time.perf_counter()
|
||||
context = initial_context or CompositionContext()
|
||||
accepted_notes: list[int] = []
|
||||
per_note_seconds: list[float] = []
|
||||
for _ in range(max_notes):
|
||||
note_start = time.perf_counter()
|
||||
step = self.step_until_accepted(context, max_attempts=max_attempts_per_note)
|
||||
accepted_notes.append(step.context.notes[-1])
|
||||
per_note_seconds.append(time.perf_counter() - note_start)
|
||||
context = step.context
|
||||
return CompositionRecord(
|
||||
notes=tuple(accepted_notes),
|
||||
per_note_seconds=tuple(per_note_seconds),
|
||||
total_seconds=time.perf_counter() - compose_start,
|
||||
)
|
||||
|
|
@ -0,0 +1,34 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from dataclasses import asdict
|
||||
import json
|
||||
|
||||
from .analysis import CompositionAnalysis, analyze_composition
|
||||
from .types import CompositionRecord, CompositionRunReport
|
||||
|
||||
|
||||
def build_run_report(
|
||||
record: CompositionRecord,
|
||||
*,
|
||||
alphabet_size: int = 8,
|
||||
parameters: dict[str, object] | None = None,
|
||||
) -> CompositionRunReport:
|
||||
analysis = analyze_composition(record.notes, alphabet_size=alphabet_size)
|
||||
return CompositionRunReport(
|
||||
notes=record.notes,
|
||||
per_note_seconds=record.per_note_seconds,
|
||||
total_seconds=record.total_seconds,
|
||||
parameters=parameters or {},
|
||||
note_count=analysis.note_count,
|
||||
alphabet_size=analysis.alphabet_size,
|
||||
unigram_entropy_bits=analysis.unigram_entropy_bits,
|
||||
conditional_entropy_bits=analysis.conditional_entropy_bits,
|
||||
normalized_entropy=analysis.normalized_entropy,
|
||||
predictability=analysis.predictability,
|
||||
redundancy=analysis.redundancy,
|
||||
)
|
||||
|
||||
|
||||
def save_run_report_json(report: CompositionRunReport, path: str) -> None:
|
||||
with open(path, "w", encoding="utf-8") as handle:
|
||||
json.dump(asdict(report), handle, indent=2)
|
||||
|
|
@ -0,0 +1,78 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
from .backprop import BackpropNetwork, BackpropResult
|
||||
from .classical_rules import ClassicalInstructor
|
||||
from .encoding import encode_note_sequence, encode_sequence_one_hot
|
||||
from .io.legacy_files import load_salieri_config, load_salieri_weights
|
||||
from .types import LegacyPaths, NoteSequence
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class SalieriResult:
|
||||
notes: NoteSequence
|
||||
target: int
|
||||
raw_output: float
|
||||
is_classical: bool
|
||||
error: float
|
||||
network_result: BackpropResult
|
||||
|
||||
|
||||
class SalieriCritic:
|
||||
def __init__(self, *, network: BackpropNetwork, instructor: ClassicalInstructor) -> None:
|
||||
self.network = network
|
||||
self.instructor = instructor
|
||||
|
||||
@classmethod
|
||||
def from_legacy_paths(cls, root: str | Path) -> "SalieriCritic":
|
||||
paths = LegacyPaths(root=Path(root))
|
||||
config = load_salieri_config(paths.salieri_config)
|
||||
weights = load_salieri_weights(paths.salieri_weights)
|
||||
instructor = ClassicalInstructor.from_sequence_file(paths.sequence_data)
|
||||
network = BackpropNetwork.from_legacy(config=config, legacy_weights=weights)
|
||||
return cls(network=network, instructor=instructor)
|
||||
|
||||
def evaluate_and_train(
|
||||
self,
|
||||
notes: list[int] | tuple[int, ...],
|
||||
*,
|
||||
target: int | None = None,
|
||||
) -> SalieriResult:
|
||||
sequence = encode_note_sequence(notes)
|
||||
encoded = tuple(float(value) for value in encode_sequence_one_hot(sequence))
|
||||
training_target = self.instructor(sequence) if target is None else int(target)
|
||||
network_result = self.network.train_step(encoded, (float(training_target),))
|
||||
raw_output = network_result.outputs[0]
|
||||
return SalieriResult(
|
||||
notes=sequence,
|
||||
target=training_target,
|
||||
raw_output=raw_output,
|
||||
is_classical=raw_output > 0.5,
|
||||
error=network_result.error,
|
||||
network_result=network_result,
|
||||
)
|
||||
|
||||
def to_dict(self) -> dict[str, object]:
|
||||
return {
|
||||
"network": self.network.to_dict(),
|
||||
"sequences": list(self.instructor.sequences),
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: dict[str, object]) -> "SalieriCritic":
|
||||
network = BackpropNetwork.from_dict(data["network"]) # type: ignore[arg-type]
|
||||
instructor = ClassicalInstructor(sequences=tuple(data["sequences"])) # type: ignore[arg-type]
|
||||
return cls(network=network, instructor=instructor)
|
||||
|
||||
def save_json(self, path: str) -> None:
|
||||
with open(path, "w", encoding="utf-8") as handle:
|
||||
json.dump(self.to_dict(), handle, indent=2)
|
||||
|
||||
@classmethod
|
||||
def load_json(cls, path: str) -> "SalieriCritic":
|
||||
with open(path, "r", encoding="utf-8") as handle:
|
||||
data = json.load(handle)
|
||||
return cls.from_dict(data)
|
||||
|
|
@ -0,0 +1,89 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass
|
||||
from pathlib import Path
|
||||
|
||||
NOTE_VOCABULARY_SIZE = 8
|
||||
SEQUENCE_LENGTH = 5
|
||||
ART_INPUT_LENGTH = 41
|
||||
ART_CATEGORY_LIMIT = 25
|
||||
HOPFIELD_WEIGHT_DIMENSION = 64
|
||||
SALIERI_NODE_COUNT = 61
|
||||
|
||||
NoteSequence = tuple[int, ...]
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class LegacyPaths:
|
||||
root: Path
|
||||
|
||||
@property
|
||||
def sequence_data(self) -> Path:
|
||||
return self.root / "SEQUENCE.DAT"
|
||||
|
||||
@property
|
||||
def salieri_config(self) -> Path:
|
||||
return self.root / "S61.DAT"
|
||||
|
||||
@property
|
||||
def salieri_weights(self) -> Path:
|
||||
return self.root / "S61.WT"
|
||||
|
||||
@property
|
||||
def hopfield_weights(self) -> Path:
|
||||
return self.root / "HTN.DAT"
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class SalieriConfig:
|
||||
learning_rate: float
|
||||
alpha: float
|
||||
n_input: int
|
||||
n_hidden: int
|
||||
n_output: int
|
||||
training_iterations: int
|
||||
error_tolerance: float
|
||||
data_file: str
|
||||
report_file: str
|
||||
weight_file: str
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class LegacyBPWeights:
|
||||
vector_length: int
|
||||
weights: tuple[tuple[float, ...], ...]
|
||||
thetas: tuple[float, ...]
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class CompositionContext:
|
||||
notes: NoteSequence = (0, 0, 0, 0, 0)
|
||||
delta_vigilance: bool = False
|
||||
new_category: bool = False
|
||||
is_classical: bool = False
|
||||
candidate_note: int = 0
|
||||
since_novelty: int = 0
|
||||
frustration: int = 0
|
||||
note_count: int = 0
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class CompositionRecord:
|
||||
notes: tuple[int, ...]
|
||||
per_note_seconds: tuple[float, ...] = ()
|
||||
total_seconds: float = 0.0
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class CompositionRunReport:
|
||||
notes: tuple[int, ...]
|
||||
per_note_seconds: tuple[float, ...]
|
||||
total_seconds: float
|
||||
parameters: dict[str, object]
|
||||
note_count: int
|
||||
alphabet_size: int
|
||||
unigram_entropy_bits: float
|
||||
conditional_entropy_bits: float
|
||||
normalized_entropy: float
|
||||
predictability: float
|
||||
redundancy: float
|
||||
Binary file not shown.
|
|
@ -0,0 +1,189 @@
|
|||
\documentclass[12pt]{article}
|
||||
\usepackage[utf8]{inputenc}
|
||||
\usepackage[T1]{fontenc}
|
||||
\usepackage{geometry}
|
||||
\geometry{margin=1in}
|
||||
\title{Competing Network Models and Problem-Solving}
|
||||
\author{Converted from legacy plain-text source}
|
||||
\date{}
|
||||
\begin{document}
|
||||
\maketitle
|
||||
\begin{flushleft}
|
||||
% This is a conservative automated conversion from the legacy text file.
|
||||
% Manual cleanup will still be necessary for figures, references, footnotes, and formatting.
|
||||
|
||||
% raw formatter directive: .okidata9
|
||||
% raw formatter directive: .ipr//
|
||||
% raw formatter directive: .nopage break
|
||||
% raw formatter directive: .page length 66
|
||||
% raw formatter directive: .lpi6
|
||||
% raw formatter directive: .above header 3
|
||||
% raw formatter directive: .below header 3
|
||||
% raw formatter directive: .above footer 3
|
||||
% raw formatter directive: .below footer 3
|
||||
% raw formatter directive: .page break
|
||||
% raw formatter directive: .page number 1
|
||||
% raw formatter directive: .pindent 15
|
||||
% raw formatter directive: .pitch12
|
||||
% raw formatter directive: .head//
|
||||
% raw formatter directive: .foot/ \#/
|
||||
% raw formatter directive: .ipr/T/
|
||||
Competing Network Models and Problem-Solving
|
||||
|
||||
(Poster Presentation at the First Annual Meeting of the International Neural Network Society, September 6-10, 1988.)
|
||||
|
||||
Diane J. Blackwood, Department of Biomedical Engineering, University of Texas at Arlington
|
||||
|
||||
Wesley R. Elsberry, Department of Computer Science, University of Texas at Arlington
|
||||
|
||||
and
|
||||
|
||||
Sam Leven, Neural Systems and Science, 45 San Jacinto Way San Francisco, CA 94127
|
||||
|
||||
\section*{ABSTRACT}
|
||||
Three of the most-often discussed neural networks models are analyzed and differentiated. The Hopfield, PDP, and ART models ask different questions, it is asserted -- and offer different answers for analyzing and construing complex environments. The three may not be competitors but, rather, complements. In fact, they may replicate different neural processes (Leven, 1987b). We seek to demonstrate the value of each model -- in a single case study.
|
||||
|
||||
The model offered by Hopfield (e.g., 1982) represents a fast-converging computable technique for analyzing highly limited classes of inputs. The PDP model (Rumelhart, et' al., 1986) offers the prospect of adoption of varied schemas, at the cost of a larger, more complex system. The ART model (e.g., Carpenter, et' al., 1987a) allows the greatest adaptability, including the capacity to vary vigilance levels and emulate many neural functions -- with the costs of much greater complexity and strain on system resources.
|
||||
|
||||
We present a single system, including analysis of different aspects of a problem by Hopfield, PDP, and ART networks, as an example of the potential for including many capabilities within the same environment.
|
||||
|
||||
% raw formatter directive: .start page
|
||||
While self-criticism in the neural network community is not unusual (eg., Rumelhart, et' al., 1986, Ch' 1; Grossberg, 1987a), we may find rapprochement among "competing paradigms" more effective than the occasional nastiness we encounter. Some problems, especially in complex controls on robotics, may be best addressed by a cooperative approach.
|
||||
|
||||
In fact, the three paradigms most often considered mutually exclusive (Hopfield, PDP, and ART) may actually represent different neural processes (Leven, 1987a). In any case, they clearly contemplate separate issues -- and may be best in approaching distinct problems.
|
||||
|
||||
Hopfield's model (Hopfield, 1982; Hopfield and Tank, 1986) represents a fast-converging computable technique for analyzing stereotyped or highly limited classes of inputs. Achieved minima have the virtue of remaining highly stable (representing permanent learning). This virtue has the accompanying cost, of course, of minimizing adaptability --recognizing new aspects of data is not seriously contemplated for a stable implementation. The model has a notable tolerance for data sets containing great amounts of simple noise; however, it tends to shrink from "multi flavored" problems, which require category or schema formation in an extensive environment.
|
||||
|
||||
The model of the Parallel Distributed Processing (PDP) group (Rumelhart, et' al., 1986) contemplates "schema formation", seeking to apply standard cognitive psychological insights to pattern recognition and category formation processes. They have sought to take minimal anatomies and build, following the work of Schank and Abelson (1977), basic semantic structures.
|
||||
|
||||
The PDP school has achieved notable successes in representing language (Sejnowski, 1986) and other areas with stable knowledge domains. Where "dynamic schemata" (Schank, 1982) are generic to a problem -- where existing memory structures must be modified -- the strength of the simulated annealing algorithm becomes a weakness. Changing existing knowledge structures (by modification or replacement in the same state space) is well-nigh impossible (Yoon, et' al., 1988).
|
||||
|
||||
This weakness of the PDP, its stubbornness in resisting data that should produce restructured schemata, is also a strength. In certain environments, stable representations of higher-order structures (rules) coupled with the capacity to learn or be trained "up-front" may offer system designers desired control. Some systems should not be ENDLESSLY adaptive.
|
||||
|
||||
% raw formatter directive: .start page
|
||||
Stephen Grossberg and his school (1987b \& c, Carpenter et' al. 1987a \& b) have suggested that the Adaptive Resonance (ART) model best represents higher-order neural functions. Equipped with representations for motivational processes and interactions between routines ("avalanches") and higher order structures (eg., motivational dipoles and associated READ architectures), a full- blown ART system can model highly adaptive motor tasks and emulate higher-order behaviors (Levine, 1986; Leven, 1987a \& b; and Ricart, 1988).
|
||||
|
||||
ART has the capacity to RECONSTRUE categories, based on continuing mismatches between data and existing higher order constructs and motivating environmental feedback. It also allows "masking fields" to eliminate from consideration whole segments of data which the system anticipates to be inappropriate or unnecessarily unsettling.
|
||||
|
||||
Under some circumstances, when using dipole structures to eliminate whole sets of competing representations (or rules), for example, ART can be faster -- and more effective -- than the alternatives we have presented. However, training an ART environment to perform highly routinized behaviors in which context has limited relevance has been considered more inefficient than using, say, the Hopfield model. Ordinarily, the powerful structures an ART modeler employs slow the learning process with error-checking routines which value fault- intolerance over speed. Yet, sometimes, in highly stable environments, designers may be uncomfortable with an ART system's capacity to "re-learn" essential skills they must employ.
|
||||
|
||||
Additionally, the rapid trainability and stability of a PDP environment may prove superior to ART, for many of the same reasons. Some higher-order rules (schemata) may be system- critical. In these cases, PROGRAMMERS SHOULD DESIGN SYSTEMS -- NOT THE SYSTEMS DESIGNING THEMSELVES. Hence, some systems may require less-intrusive network engines (like PDP) --especially when these engines also provide greater speed.
|
||||
|
||||
Thus, the three models for neural network design may be COMPLEMENTARY in function: Hopfield offering speed and stability, PDP providing up-front learning and stable rule structures, and ART employing context- and environment-sensitive capabilities (see Figure 1). We demonstrate, below, that modelers ought to consider these qualities in developing extensive systems -- and utilize the many effective tools at our disposal.
|
||||
|
||||
% raw formatter directive: .start page
|
||||
\section*{EXAMPLE PROBLEM}
|
||||
BEETHOVEN is a "music composition" system (see Figure 2). It provides a three part neural network model. The system emulates fundamental compositional rules to generate and perform a musical sequence.
|
||||
|
||||
BACH is a Hopfield net provides a sequence of notes, emulating musical melodic performance. A single voice selects notes from within a single octave. Biases are provided -- as a composer has the innate tendency to choose certain intervals and to reject notes that tend to violate common rules of harmony (eg., Aldwell and Schachter, 1978).
|
||||
|
||||
This network of notes is output, in sequence, to a PDP back- propagation network named SALIERI, which has learned a set of standard, somewhat higher-order harmonic rules. The network judges the effectiveness of the sequence, note by note, based on the intervals involved and the absolute note values (eg., \#7 should precede \#8 -- and, almost always, at the end of a phrase). These schemata, then, reject inappropriate sequences AND INHIBIT SOME INAPPROPRIATE NEXT NOTES. This "look-ahead" capability is unusual in a PDP environment, yet is fitting for the inhibitory role the network is playing and for the stability of the rule structure being employed.
|
||||
|
||||
The output from PDP flows, directly, to an ART network, BEETHOVEN. Employing a model of motivation (based on construction of category valuation and a healthy boredom at repetition), BEETHOVEN rejects "unaesthetic" sequences. As the number of phrases performed increases, the ART model develops intense biases, which it imposes on BACH and SALIERI.
|
||||
|
||||
One additional component of the environment is LOBES, the Context Manager. LOBES, loosely emulative of human frontal lobes (see Levine, 1986), maintains information about the processes being performed, mediates inter-model interaction, and provides for the final external output (sounding the speaker).
|
||||
|
||||
The model, then, utilizes the best capabilities of three distinctly different paradigms. Hopfield performs efficient routine processes, as would a "reptilian brain" (MacLean, 1970). PDP serves as an insistent schoolmarm, observing and enforcing higher-level rules, like a "neo-mammalian brain." ART provides a sense of fitness, an aesthetic fitting for models of the limbic system (or "mammalian brain").
|
||||
|
||||
Integration of many memory and processing functions in a three part model may be similar to human brain function (Leven, 1987b). Regardless of its biological versimilitude, however, such an approach seems to offer unique combinations of speed, stability, and flexibility.
|
||||
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
% raw formatter directive: .pindent 12
|
||||
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|
||||
/
|
||||
|
||||
% raw formatter directive: .foot/
|
||||
\#/
|
||||
|
||||
\section*{REFERENCES}
|
||||
Aldwell, E' \& C' Schachter. 1978. Harmony and voice leading. Harcourt, Brace \& Jovanovich, New York.
|
||||
|
||||
Carpenter, G.A' \& S' Grossberg. 1987a. A massively parallel architecture for a self-organizing neural pattern recognition machine. Computer Vision, Graphics, and Image Processing 37:54-115.
|
||||
|
||||
Carpenter, G.A' \& S' Grossberg. 1987b. ART 2: self-organization of stable category recognition codes for analog input patterns. Applied Optics 26(23):4919-4930.
|
||||
|
||||
Grossberg, S. 1987a' Competitive Learning: From interactive activation to adaptive resonance. Cognitive Science 11:23-63.
|
||||
|
||||
Grossberg, S., ed. 1987b \& c. The Adaptive Brain. Vol. I and II. Elsevier/North-Holland, Amsterdam.
|
||||
|
||||
Hartley, R' and H' Szu. 1987. A comparison of the computational power of neural network models. IEEE Proc.
|
||||
|
||||
\section*{ICNN III:15-22.}
|
||||
Hopfield, J.J. 1982. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. USA 79:2554-2558.
|
||||
|
||||
Hopfield, J.J' and D.W' Tank. 1985. "Neural" computation of decisions in optimization problems. Biol. Cybern. 52:141-152.
|
||||
|
||||
Hopfield, J.J' and D.W' Tank. 1986. Computing with neural circuits: A model. Science 233:625-633.
|
||||
|
||||
Leven, S. 1987a. Choice and neural process. Unpublished Ph.D. Dissertation, University of Texas at Arlington.
|
||||
|
||||
Leven, S. 1987b. S.A.M.: A triune extension to the ART model. Symposium on Neural Networks, North Texas State University. (Poster presentation)
|
||||
|
||||
Leven, S. 1988. Memory, helplessness, and the dynamics of hope. Presented at the Metroplex Institute for Neural Dynamics' Workshop on Motivation, Emotion, and Goal Direction in Neural Networks.
|
||||
|
||||
Levine, D.S. 1986. A neural network theory of frontal lobe function. In: The Proceedings of the Eighth Annual Conference of the Cognitive Science Society. Erlbaum.
|
||||
|
||||
MacLean, P. 1970. The triune brain, emotion, and scientific bias. In: F' Schmitt, ed. The Neurosciences: Second Study Program. Rockefeller University Press.
|
||||
|
||||
Ricart, R. 1988. Backward conditioning: A neural network model which exhibits both excitatory and inhibitory conditioning. Presented at the Metroplex Institute for Neural Dynamics' Workshop on Motivation, Emotion, and Goal Direction in Neural Networks.
|
||||
|
||||
Rumelhart, D' \& J' McClelland. 1986. Parallel Distributed Processing. MIT Press.
|
||||
|
||||
Schank, R. 1982. Dynamic memory. Cambridge University Press.
|
||||
|
||||
Schank, R.C' \& R.P' Abelson. 1977. Scripts, Plans, Goals, and Understanding. Erlbaum, Hillsdale, NJ.
|
||||
|
||||
Sejnowski, T.J' 1986. Open questions about computation in cerebral cortex. In: J.L. McClelland \& D.E. Rumelhart, eds. Parallel Distributed Processing Volume 2. MIT Press.
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||||
|
||||
Simpson, R. 1988. A review of artificial neural systems II: Paradigms, applications, and implementations. Prepublication copy of paper submitted to CRC Critical Reviews in Artificial Intelligence.
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||||
|
||||
Tank, D.W' \& J.J' Hopfield. 1986. Simple "neural" optimization networks: An A/D converter, signal decision circuit, and a linear programming circuit. IEEE Transaction on Circuits and Systems CAS-33(5):533-541
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||||
|
||||
Yoon, Y', L.L' Peterson, \& P.R' Bergstrasser. 1988. A dermatology expert system using connectionist network. Unpublished poster presentation, IEEE ICNN.
|
||||
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
/
|
||||
|
||||
% raw formatter directive: .pindent 12
|
||||
% raw formatter directive: .foot/ \#/
|
||||
Convergence Convergence Stability Feedback Category Mixed Data Category Computational Speed Likelihood Of Capability Formation (Complex Reconstruction Simplicity Network Environment) ------- ------- ------- ------- ------- ------- ------- -------
|
||||
|
||||
Hopfield + + + - - - - +
|
||||
|
||||
\section*{PDP 0 0 +/0 + + 0 - 0}
|
||||
\section*{ART - - 0/- + ++ + + -}
|
||||
Where '+' indicates a relative advantage, '0' indicates no special advantage or disadvantage, and '-' indicates a relative disadvantage.
|
||||
|
||||
Figure 1. Comparative analysis of features of the Hopfield, PDP, and ART artificial neural network models
|
||||
|
||||
% raw formatter directive: .ipr//
|
||||
% raw formatter directive: .pitch12
|
||||
% raw formatter directive: .ipr/T/
|
||||
% raw formatter directive: .pindent 10
|
||||
+-----------------+ | | (Match, Other Info) | Beethoven |---------------------+ | | |
|
||||
|
||||
\section*{| (ART 1) |<----------------+ |}
|
||||
| | (Context) | | +-----------------+ | | \textasciicircum{} | | | | | |(Approval) | | | | |
|
||||
|
||||
\section*{| | V}
|
||||
+-----------------+ +-----------------+ | | | | | Salieri | (Approval) | Lobes | | +------------>| (Context | | (PDP) |<------------| Management) | | | (Silence!) | | +-----------------+ +-----------------+ \textasciicircum{} \textasciicircum{} | | | (Candidate note) | | | +-------------------------+ | | | | | | | | +-----------------+ | | | | | | | Bach | (Generate Note!) | | | |<-------------------+ | | (Hopfield) | | | | | +-----------------+ (New Note) | |
|
||||
|
||||
+-------------------+ | | | | | Speaker | | | | | +-------------------+
|
||||
|
||||
Figure 2. Structure of sample system utilizing Hopfield, PDP, and ART models.
|
||||
|
||||
% raw formatter directive: .start page
|
||||
% raw formatter directive: .nopage break
|
||||
\end{flushleft}
|
||||
\end{document}
|
||||
|
|
@ -0,0 +1,436 @@
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||||
author = {Levine, D},
|
||||
year = {1986},
|
||||
title = {A neural network theory of frontal lobe function},
|
||||
booktitle = {Proceedings of the Eighth Annual Conference of the Cognitive Science Society, Erlbaum, Hillsdale, New Jersey},
|
||||
pages = {716-727}
|
||||
}
|
||||
|
||||
@unpublished{Levine1990,
|
||||
author = {Levine, D},
|
||||
year = {1990},
|
||||
title = {Integration, disintegration and the frontal lobes},
|
||||
note = {To appear in Motivation, Emotion, and Goal Direction in Neural Networks, D. Levine and S. Leven, eds., Erlbaum, Hillsdale, New Jersey.}
|
||||
}
|
||||
|
||||
@article{Levine1989,
|
||||
author = {Levine, D., and Prueitt, P},
|
||||
year = {1989},
|
||||
title = {Modeling some effects of frontal lobe damage: novelty and perseveration},
|
||||
journal = {Neural Networks},
|
||||
volume = {2},
|
||||
pages = {103-116}
|
||||
}
|
||||
|
||||
@article{Lippmann1987,
|
||||
author = {Lippmann, R. P},
|
||||
year = {1987},
|
||||
title = {An introduction to computing with neural nets},
|
||||
journal = {IEEE ASSP Magazine},
|
||||
pages = {4-22},
|
||||
month = {apr}
|
||||
}
|
||||
|
||||
@article{MacCulloch1943,
|
||||
author = {MacCulloch, W. S., and Pitts, W},
|
||||
year = {1943},
|
||||
title = {A logical calculus of the ideas immanent in nervous activity},
|
||||
journal = {Bull. Math. Biophys},
|
||||
volume = {5},
|
||||
pages = {115-133}
|
||||
}
|
||||
|
||||
@incollection{MacLean1970,
|
||||
author = {MacLean, P. D},
|
||||
year = {1970},
|
||||
title = {The triune brain, emotion, and scientific bias},
|
||||
booktitle = {The Neurosciences Second Study Program},
|
||||
editor = {F. O. Schmitt},
|
||||
publisher = {Rockefeller University Press},
|
||||
address = {New York},
|
||||
pages = {486-494}
|
||||
}
|
||||
|
||||
@inproceedings{Matsuoka1989,
|
||||
author = {Matsuoka, T. and Hamada, H. and Nakatsu, R.},
|
||||
year = {1989},
|
||||
title = {Syllable recognition using integrated neural networks}
|
||||
}
|
||||
|
||||
@techreport{Neuroscience1988,
|
||||
author = {{Metroplex Study Group on Computational Neuroscience}},
|
||||
year = {1988},
|
||||
title = {Computational neuroscience: an opportunity for technology leadership for the Metroplex},
|
||||
institution = {North Texas Commission Regional Technology Program},
|
||||
note = {Report to the North Texas Commission Regional Technology Program.}
|
||||
}
|
||||
|
||||
@article{Newell1976,
|
||||
author = {Newell, A., and Simon, H. A},
|
||||
year = {1976},
|
||||
title = {Computer science as empirical inquiry: symbols and search},
|
||||
journal = {Communications of the ACM},
|
||||
volume = {19},
|
||||
number = {3},
|
||||
pages = {113-126}
|
||||
}
|
||||
|
||||
@article{Nottebohm1989,
|
||||
author = {Nottebohm, F},
|
||||
year = {1989},
|
||||
title = {From bird song to neurogenesis},
|
||||
journal = {Scientific American},
|
||||
pages = {74-79},
|
||||
month = {feb}
|
||||
}
|
||||
|
||||
@book{Pao1989,
|
||||
author = {Pao,Y.-H},
|
||||
year = {1989},
|
||||
title = {Adaptive Pattern Recognition and Neural Networks},
|
||||
publisher = {Addison-Wesley},
|
||||
address = {Reading, Massachusetts}
|
||||
}
|
||||
|
||||
@misc{Paris1989,
|
||||
author = {Paris, M},
|
||||
year = {1989},
|
||||
title = {Personal Communication}
|
||||
}
|
||||
|
||||
@techreport{Parker1985,
|
||||
author = {Parker, D. B},
|
||||
year = {1985},
|
||||
title = {Learning-logic},
|
||||
institution = {Massachusetts Institute of Technology, Center for Computational Research in Economics and Management Science},
|
||||
address = {Cambridge, Massachusetts},
|
||||
number = {TR-47}
|
||||
}
|
||||
|
||||
@inproceedings{Rabelo1989,
|
||||
author = {Rabelo, L. C., and Alptekin, S},
|
||||
year = {1989},
|
||||
title = {Using hybrid neural networks and expert systems},
|
||||
booktitle = {Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (IJCNN-89) Vol. II}
|
||||
}
|
||||
|
||||
@inproceedings{Reddy1973,
|
||||
author = {Reddy, D. R., Erman, L. D., Fennell, R. D., and Neely, R. B},
|
||||
year = {1973},
|
||||
title = {The Hearsay speech understanding system: an example of the recognition process},
|
||||
booktitle = {Proceedings of the International Conference on Artificial Intelligence},
|
||||
pages = {185-194}
|
||||
}
|
||||
|
||||
@incollection{Rumelhart1986,
|
||||
author = {Rumelhart, D., Hinton, G., and Williams, R},
|
||||
year = {1986},
|
||||
title = {Learning internal representations by back propagation},
|
||||
booktitle = {Parallel Distributed Processing},
|
||||
editor = {D. Rumelhart and J. McClelland and the PDP Research Group},
|
||||
publisher = {MIT Press},
|
||||
address = {Cambridge, Massachusetts},
|
||||
volume = {1},
|
||||
pages = {365-422}
|
||||
}
|
||||
|
||||
@article{Shannon1948,
|
||||
author = {Shannon, C},
|
||||
year = {1948},
|
||||
title = {A mathematical theory of communication},
|
||||
journal = {Bell System Technical Journal},
|
||||
volume = {27},
|
||||
pages = {379-423}
|
||||
}
|
||||
|
||||
@unpublished{Simpson1988,
|
||||
author = {Simpson, P},
|
||||
year = {1988},
|
||||
title = {A review of artificial neural systems, Parts 1 and 2},
|
||||
note = {Submitted to CRC Critical Reviews in Artificial Intelligence.}
|
||||
}
|
||||
|
||||
@inproceedings{Sontag1989,
|
||||
author = {Sontag, E. D., and Sussman, H. J},
|
||||
year = {1989},
|
||||
title = {Back-propagation separates when perceptrons do},
|
||||
booktitle = {Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (IJCNN-89) Vol. I},
|
||||
pages = {639-642}
|
||||
}
|
||||
|
||||
@inproceedings{Szu1989,
|
||||
author = {Szu, H},
|
||||
year = {1989},
|
||||
title = {Reconfigurable neural nets by energy convergence learning principle based on extended McCullch-Pitts neurons and synapses},
|
||||
booktitle = {Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (IJCNN-89) Vol. I},
|
||||
pages = {485-496}
|
||||
}
|
||||
|
||||
@inproceedings{Tsutsumi1989,
|
||||
author = {Tsutsumi, K},
|
||||
year = {1989},
|
||||
title = {A multi-layered neural network composed of backprop. and Hopfield nets and internal space representation},
|
||||
booktitle = {Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (IJCNN-89) Vol. I},
|
||||
pages = {507-512}
|
||||
}
|
||||
|
||||
@phdthesis{Werbos1974,
|
||||
author = {Werbos, P. J},
|
||||
year = {1974},
|
||||
title = {Beyond regression: new tools for prediction and analysis in the behavioral sciences},
|
||||
school = {Unpublished Ph. D. dissertation, Harvard University}
|
||||
}
|
||||
|
||||
@inproceedings{Widrow1987,
|
||||
author = {Widrow, B},
|
||||
year = {1987},
|
||||
title = {ADALINE and MADALINE - 1963},
|
||||
booktitle = {Proceedings of the IEEE International Joint Conference on Neural Networks (ICNN-87) Vol. I},
|
||||
pages = {143-158}
|
||||
}
|
||||
|
||||
@article{Widrow1961,
|
||||
author = {Widrow, B., Pierce, W. H., and Angell, J. B},
|
||||
year = {1961},
|
||||
title = {Birth, life, and death in microelectronic systems},
|
||||
journal = {IRE Transactions on Military Electronics},
|
||||
volume = {4},
|
||||
pages = {191-201}
|
||||
}
|
||||
|
||||
@article{Widrow1988,
|
||||
author = {Widrow, B. and Winter, R},
|
||||
year = {1988},
|
||||
title = {Neural nets for adaptive filtering and adaptive pattern recognition},
|
||||
journal = {IEEE Computer},
|
||||
volume = {21},
|
||||
number = {3},
|
||||
pages = {25-39}
|
||||
}
|
||||
Binary file not shown.
File diff suppressed because it is too large
Load Diff
Binary file not shown.
|
|
@ -0,0 +1,99 @@
|
|||
\documentclass[12pt]{article}
|
||||
\usepackage[utf8]{inputenc}
|
||||
\usepackage[T1]{fontenc}
|
||||
\usepackage{geometry}
|
||||
\geometry{margin=1in}
|
||||
\title{Thesis Proposal}
|
||||
\author{Converted from legacy plain-text source}
|
||||
\date{}
|
||||
\begin{document}
|
||||
\maketitle
|
||||
\begin{flushleft}
|
||||
% This is a conservative automated conversion from the legacy text file.
|
||||
% Manual cleanup will still be necessary for figures, references, footnotes, and formatting.
|
||||
|
||||
Revision date: Jun 07, 1988
|
||||
|
||||
Thesis proposal
|
||||
|
||||
Wesley R. Elsberry Master's candidate, CSE
|
||||
|
||||
Committee:
|
||||
|
||||
Karan Briggs, CSE (Graduate Chairman) Daniel Levine, Mathematics Lynn Peterson, CSE
|
||||
|
||||
Preliminary outline
|
||||
|
||||
\section*{I. Introduction}
|
||||
\section*{II. Literature review}
|
||||
\section*{III. Topic proposal}
|
||||
a. Topic description
|
||||
|
||||
b. Topic verification (implementation)
|
||||
|
||||
i. Application proposal
|
||||
|
||||
ii. Description
|
||||
|
||||
iii. Resources needed for accomplishment
|
||||
|
||||
************************************
|
||||
|
||||
\section*{I. Introduction}
|
||||
The field of artificial neural network research currently suffers from several misapprehensions on the part of researchers. First, communication continues to be sketchy and prone to misunderstanding, as no clearcut definitions have been attached to even the most commonly accepted terms and phrases that comprise ANN jargon. Researchers will ignore the interdisciplinary nature of ANN research to promote or denigrate ANN results in a specialized context. Often this is done in such a way that it is not clear that the comments or analysis are only valid in the specialized context. Second, the motivations for research vary wildly, and thus criticisms of models or data often are intiated on the basis of entirely different goal assumptions. Finally, much criticism and infighting occurs not because of any real research related causes, but because of politicking and the quest for personal power or recognition. While Kuhn [History of Scientific Revolutions] may revel in the unfolding byplay, it is a source of annoyance and an obstacle to good work for others engaged in this research. While these misapprehensions may not be conscious in nature, that does not lessen the negative impact of the misapprehensions.
|
||||
|
||||
One misapprehension which remains particularly pervasive is the idea that there exists one 'correct' model for artificial neural networks. The biological reality reflects a complex set of systems which accomplish diverse functions. No one has suggested that all biological neural systems operate in the same manner. Other, more easily apprehensible, biological systems reflect that variation arises both in structures and mechanisms that perform functional tasks. Spiders, insects, fish, birds, and mammals have all developed methods of flight, yet none are quite the same. Other examples can demonstrate that the same mechanism may be coopted for more than one purpose. Certainly the expectation should be that biological neural systems follow this pattern, yet the prevailing attitude in current ANN research denies this.
|
||||
|
||||
Different models reflect variation in an approach to a single function, or simply approaches to different functions. Comparisons which should account for this feature often do not.
|
||||
|
||||
Since various models will have features which make them preferable for classes of problems, solving problems which can be divided into subset problems may be best solved through integration and coordination of differing ANN models. This approach is expected to prove more tractable and productive than attempting to force a solution model to fit a specified problem complex (or changing the problem specification to fit the model).
|
||||
|
||||
\section*{II. Literature review}
|
||||
Problem solving as McCulloch and Pitts envisioned it [from Levine 83]
|
||||
|
||||
As Rosenblatt redefined it [from Levine 83 and Rosenblatt ??]
|
||||
|
||||
What Hopfield says about Grossberg [this will be short] [from H-T 86]
|
||||
|
||||
What Rumelhart and McClelland say about Hopfield [from PDP]
|
||||
|
||||
What Rumelhart and McClelland say about Grossberg [from PDP]
|
||||
|
||||
What Grossberg says about everybody else [stated as briefly as possible] [from Applied Optics article, 87 Cognitive Science article]
|
||||
|
||||
Evidences for multi-model integration:
|
||||
|
||||
PDP Ch. 26, p 541: "A problem with the PDP models presented in this book is that they are too specialized, so concerned with solving the problem of the moment that they do not ask how the whole might fit together. The various chapters present us with different versions of a single, homogeneous structure, perfectly well-suited for doing its task, but not sufficient, in my opinion, at doing the whole task. One structure can't do the job: There have to be several parts to the system that do different things, sometimes communicating with each other, sometimes not."
|
||||
|
||||
Of course, McClelland here means to have several variants of the PDP model performing the functions, and is not per se referring to a multi-model approach. But the admission that a single instantiation of a model does not a solution make is very important.
|
||||
|
||||
\section*{III. Topic proposal}
|
||||
a. Topic description Use the models of Hopfield, PDP, and Grossberg's ART in an integrated manner to solve a problem set that is a complex suite of problem classes. The purpose here is not to develop a general tool for such problems, but to demonstrate the desirability and applicability of using an integrative approach to ANN problem solving.
|
||||
|
||||
b. Topic verification (implementation)
|
||||
|
||||
i. Application proposal
|
||||
|
||||
Possible project 1: Cryptographic example. Small problem that involves transposition, pattern recognition, and feature detection and extraction. Models used as pre- and co- processors for problem-solving.
|
||||
|
||||
ii. Description The data set generated for presentation to the solution system may have complex interdependencies which the ANN would have to extract.
|
||||
|
||||
iii. Resources needed for accomplishment
|
||||
|
||||
Computer: Available currently: Heathkit H-100, MS-DOS, 768K Heathkit H-158, MS-DOS (PC comp), 640K
|
||||
|
||||
\section*{DEC PDP 11/23, RT-11, 256K}
|
||||
Languages: Available currently: Under MS-DOS: Turbo Pascal
|
||||
|
||||
\section*{XLISP}
|
||||
PD-Prolog Turbo C
|
||||
|
||||
\section*{ECO-C88}
|
||||
\section*{ICON}
|
||||
\section*{MS-FORTRAN}
|
||||
\section*{MASM}
|
||||
Under RT-11: MACRO-11 (assembler)
|
||||
|
||||
\section*{DIBOL}
|
||||
\end{flushleft}
|
||||
\end{document}
|
||||
|
|
@ -0,0 +1,17 @@
|
|||
[build-system]
|
||||
requires = ["setuptools>=61"]
|
||||
build-backend = "setuptools.build_meta"
|
||||
|
||||
[project]
|
||||
name = "triune-cadence"
|
||||
version = "0.1.0"
|
||||
description = "TriuneCadence: a modular neural music composition system inspired by a 1989 thesis"
|
||||
requires-python = ">=3.10"
|
||||
|
||||
[project.scripts]
|
||||
triune-cadence = "composer_ans.cli:main"
|
||||
composer-ans = "composer_ans.cli:main"
|
||||
|
||||
[tool.pytest.ini_options]
|
||||
testpaths = ["tests"]
|
||||
pythonpath = ["."]
|
||||
|
|
@ -0,0 +1,9 @@
|
|||
from __future__ import annotations
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
ROOT = Path(__file__).resolve().parents[1]
|
||||
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.insert(0, str(ROOT))
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
from composer_ans.analysis import analyze_composition
|
||||
|
||||
|
||||
def test_entropy_distinguishes_constant_and_varied_sequences() -> None:
|
||||
constant = analyze_composition((1, 1, 1, 1))
|
||||
varied = analyze_composition((1, 2, 3, 4))
|
||||
|
||||
assert constant.unigram_entropy_bits == 0.0
|
||||
assert varied.unigram_entropy_bits > constant.unigram_entropy_bits
|
||||
|
||||
|
||||
def test_analysis_reports_predictability_for_repeating_pattern() -> None:
|
||||
repeating = analyze_composition((1, 2, 1, 2, 1, 2, 1, 2))
|
||||
|
||||
assert repeating.conditional_entropy_bits < repeating.unigram_entropy_bits
|
||||
assert 0.0 <= repeating.predictability <= 1.0
|
||||
|
|
@ -0,0 +1,23 @@
|
|||
from composer_ans.art1 import ART1Network, ART1Params
|
||||
|
||||
|
||||
def test_generic_art1_commits_first_category() -> None:
|
||||
network = ART1Network(ART1Params(max_categories=3, input_length=4, vigilance=0.9))
|
||||
|
||||
result = network.categorize((1, 0, 1, 0))
|
||||
|
||||
assert result.winner == 0
|
||||
assert result.new_category is True
|
||||
assert result.committed_categories == 1
|
||||
assert result.expected_vector == (1, 0, 1, 0)
|
||||
|
||||
|
||||
def test_generic_art1_reuses_matching_category() -> None:
|
||||
network = ART1Network(ART1Params(max_categories=3, input_length=4, vigilance=0.9))
|
||||
network.categorize((1, 0, 1, 0))
|
||||
|
||||
result = network.categorize((1, 0, 1, 0))
|
||||
|
||||
assert result.winner == 0
|
||||
assert result.new_category is False
|
||||
assert result.committed_categories == 1
|
||||
|
|
@ -0,0 +1,49 @@
|
|||
from pathlib import Path
|
||||
|
||||
from composer_ans.backprop import BackpropNetwork
|
||||
from composer_ans.io.legacy_files import load_salieri_config, load_salieri_weights
|
||||
from composer_ans.salieri import SalieriCritic
|
||||
|
||||
|
||||
THES = Path(__file__).resolve().parents[1] / "THES"
|
||||
|
||||
|
||||
def test_generic_backprop_predict_and_train_step() -> None:
|
||||
network = BackpropNetwork.random(
|
||||
n_input=2,
|
||||
n_hidden=2,
|
||||
n_output=1,
|
||||
learning_rate=0.5,
|
||||
alpha=0.1,
|
||||
)
|
||||
|
||||
predicted = network.predict((0.0, 1.0))
|
||||
trained = network.train_step((0.0, 1.0), (1.0,))
|
||||
|
||||
assert len(predicted.outputs) == 1
|
||||
assert len(trained.outputs) == 1
|
||||
assert 0.0 <= trained.outputs[0] <= 1.0
|
||||
assert trained.error >= 0.0
|
||||
assert any(state.delta != 0.0 for state in trained.node_states if state.node_type != "input")
|
||||
|
||||
|
||||
def test_backprop_loads_legacy_salieri_network() -> None:
|
||||
config = load_salieri_config(THES / "S61.DAT")
|
||||
weights = load_salieri_weights(THES / "S61.WT")
|
||||
network = BackpropNetwork.from_legacy(config=config, legacy_weights=weights)
|
||||
|
||||
result = network.predict(tuple(0.0 for _ in range(config.n_input)))
|
||||
|
||||
assert network.node_count == 61
|
||||
assert len(result.outputs) == 1
|
||||
assert 0.0 <= result.outputs[0] <= 1.0
|
||||
|
||||
|
||||
def test_salieri_wrapper_runs_on_thesis_sequence_window() -> None:
|
||||
critic = SalieriCritic.from_legacy_paths(THES)
|
||||
|
||||
result = critic.evaluate_and_train((1, 4, 5, 1, 0))
|
||||
|
||||
assert result.target in (0, 1)
|
||||
assert 0.0 <= result.raw_output <= 1.0
|
||||
assert isinstance(result.is_classical, bool)
|
||||
|
|
@ -0,0 +1,12 @@
|
|||
from composer_ans.beethoven import BeethovenCategorizer
|
||||
|
||||
|
||||
def test_beethoven_wrapper_encodes_notes_and_classicality() -> None:
|
||||
beethoven = BeethovenCategorizer()
|
||||
|
||||
first = beethoven.categorize((1, 4, 5, 1, 0), is_classical=True)
|
||||
second = beethoven.categorize((1, 4, 5, 1, 0), is_classical=True)
|
||||
|
||||
assert first.art_result.committed_categories >= 1
|
||||
assert second.art_result.winner == first.art_result.winner
|
||||
assert second.art_result.new_category is False
|
||||
|
|
@ -0,0 +1,21 @@
|
|||
from pathlib import Path
|
||||
|
||||
from composer_ans.classical_rules import ClassicalInstructor
|
||||
|
||||
|
||||
THES = Path(__file__).resolve().parents[1] / "THES"
|
||||
|
||||
|
||||
def test_classical_instructor_matches_legacy_suffix_behavior() -> None:
|
||||
instructor = ClassicalInstructor.from_sequence_file(THES / "SEQUENCE.DAT")
|
||||
|
||||
assert instructor([0, 0, 1, 5, 4]) == 1
|
||||
assert instructor([0, 1, 4, 5, 1]) == 1
|
||||
assert instructor([1, 2, 3, 4, 5]) == 0
|
||||
|
||||
|
||||
def test_classical_instructor_loads_all_sequences() -> None:
|
||||
instructor = ClassicalInstructor.from_sequence_file(THES / "SEQUENCE.DAT")
|
||||
|
||||
assert len(instructor.sequences) == 14
|
||||
assert instructor.sequences[:3] == ("154", "145", "15")
|
||||
|
|
@ -0,0 +1,46 @@
|
|||
from pathlib import Path
|
||||
import subprocess
|
||||
import sys
|
||||
|
||||
|
||||
ROOT = Path(__file__).resolve().parents[1]
|
||||
|
||||
|
||||
def test_cli_runs_and_reports_metrics(tmp_path: Path) -> None:
|
||||
result = subprocess.run(
|
||||
[
|
||||
sys.executable,
|
||||
"-m",
|
||||
"composer_ans",
|
||||
"--thes-root",
|
||||
str(ROOT / "THES"),
|
||||
"--notes",
|
||||
"4",
|
||||
"--object-threshold",
|
||||
"2",
|
||||
"--max-attempts-per-note",
|
||||
"600",
|
||||
"--art-vigilance",
|
||||
"0.85",
|
||||
"--art-vigilance-decay",
|
||||
"0.98",
|
||||
"--save-salieri",
|
||||
str(tmp_path / "salieri.json"),
|
||||
"--save-beethoven",
|
||||
str(tmp_path / "beethoven.json"),
|
||||
"--save-report",
|
||||
str(tmp_path / "report.json"),
|
||||
],
|
||||
cwd=ROOT,
|
||||
text=True,
|
||||
capture_output=True,
|
||||
check=True,
|
||||
)
|
||||
|
||||
assert "notes:" in result.stdout
|
||||
assert "per_note_seconds:" in result.stdout
|
||||
assert "total_seconds:" in result.stdout
|
||||
assert "unigram_entropy_bits:" in result.stdout
|
||||
assert (tmp_path / "salieri.json").exists()
|
||||
assert (tmp_path / "beethoven.json").exists()
|
||||
assert (tmp_path / "report.json").exists()
|
||||
|
|
@ -0,0 +1,21 @@
|
|||
from composer_ans.encoding import encode_art_input, encode_note_sequence, encode_sequence_one_hot
|
||||
|
||||
|
||||
def test_encode_note_sequence_validates_shape_and_range() -> None:
|
||||
assert encode_note_sequence([1, 2, 3, 4, 5]) == (1, 2, 3, 4, 5)
|
||||
|
||||
|
||||
def test_encode_sequence_one_hot_matches_pascal_layout() -> None:
|
||||
vector = encode_sequence_one_hot([1, 0, 8, 2, 0])
|
||||
assert len(vector) == 40
|
||||
assert vector[:8] == (1, 0, 0, 0, 0, 0, 0, 0)
|
||||
assert vector[8:16] == (0, 0, 0, 0, 0, 0, 0, 0)
|
||||
assert vector[16:24] == (0, 0, 0, 0, 0, 0, 0, 1)
|
||||
assert vector[24:32] == (0, 1, 0, 0, 0, 0, 0, 0)
|
||||
assert vector[32:40] == (0, 0, 0, 0, 0, 0, 0, 0)
|
||||
|
||||
|
||||
def test_encode_art_input_appends_classicality_bit() -> None:
|
||||
vector = encode_art_input([1, 2, 3, 4, 5], is_classical=True)
|
||||
assert len(vector) == 41
|
||||
assert vector[-1] == 1
|
||||
|
|
@ -0,0 +1,28 @@
|
|||
import csv
|
||||
from pathlib import Path
|
||||
|
||||
from composer_ans.experiments import run_parameter_sweep
|
||||
|
||||
|
||||
THES = Path(__file__).resolve().parents[1] / "THES"
|
||||
|
||||
|
||||
def test_run_parameter_sweep_writes_reports_and_summary(tmp_path: Path) -> None:
|
||||
reports = run_parameter_sweep(
|
||||
thes_root=THES,
|
||||
output_dir=tmp_path,
|
||||
notes=2,
|
||||
parameter_sets=[
|
||||
{"object_threshold": 2, "art_vigilance": 0.85},
|
||||
{"object_threshold": 3, "art_vigilance": 0.9},
|
||||
],
|
||||
)
|
||||
|
||||
assert len(reports) == 2
|
||||
assert (tmp_path / "run_001.json").exists()
|
||||
assert (tmp_path / "run_002.json").exists()
|
||||
assert (tmp_path / "summary.csv").exists()
|
||||
|
||||
with (tmp_path / "summary.csv").open("r", encoding="utf-8", newline="") as handle:
|
||||
rows = list(csv.DictReader(handle))
|
||||
assert len(rows) == 2
|
||||
|
|
@ -0,0 +1,59 @@
|
|||
from pathlib import Path
|
||||
|
||||
from composer_ans.hopfield import HopfieldParams, generate_next_note, run_hopfield_network
|
||||
from composer_ans.io.legacy_files import extract_active_hopfield_submatrix, load_hopfield_weight_matrix
|
||||
|
||||
|
||||
THES = Path(__file__).resolve().parents[1] / "THES"
|
||||
|
||||
|
||||
def deterministic_noise(mean: float, variance: float) -> float:
|
||||
return mean
|
||||
|
||||
|
||||
def test_hopfield_zero_matrix_converges_to_first_note_for_blank_column() -> None:
|
||||
zero_matrix = tuple(tuple(0.0 for _ in range(40)) for _ in range(40))
|
||||
|
||||
result = generate_next_note(
|
||||
[1, 2, 3, 4, 0],
|
||||
zero_matrix,
|
||||
noise=deterministic_noise,
|
||||
)
|
||||
|
||||
assert result.output_notes[:4] == (1, 2, 3, 4)
|
||||
assert result.candidate_note == 1
|
||||
assert result.iterations > 0
|
||||
|
||||
|
||||
def test_generic_hopfield_core_runs_on_arbitrary_grid_shape() -> None:
|
||||
inputs = (
|
||||
(0.8, 0.2),
|
||||
(0.1, 0.9),
|
||||
(0.4, 0.3),
|
||||
)
|
||||
size = len(inputs) * len(inputs[0])
|
||||
weights = tuple(tuple(0.0 for _ in range(size)) for _ in range(size))
|
||||
|
||||
result = run_hopfield_network(inputs, weights)
|
||||
|
||||
assert result.iterations > 0
|
||||
assert len(result.state.outputs) == 3
|
||||
assert len(result.state.outputs[0]) == 2
|
||||
|
||||
|
||||
def test_hopfield_legacy_matrix_runs_with_deterministic_noise() -> None:
|
||||
matrix = extract_active_hopfield_submatrix(load_hopfield_weight_matrix(THES / "HTN.DAT"))
|
||||
|
||||
result = generate_next_note(
|
||||
[1, 4, 5, 1, 0],
|
||||
matrix,
|
||||
params=HopfieldParams(),
|
||||
noise=deterministic_noise,
|
||||
)
|
||||
|
||||
assert len(result.output_notes) == 5
|
||||
assert all(1 <= note <= 8 for note in result.output_notes)
|
||||
assert 1 <= result.candidate_note <= 8
|
||||
assert result.iterations > 0
|
||||
assert len(result.outputs) == 8
|
||||
assert len(result.outputs[0]) == 5
|
||||
|
|
@ -0,0 +1,51 @@
|
|||
from pathlib import Path
|
||||
|
||||
from composer_ans.io.legacy_files import (
|
||||
extract_active_hopfield_submatrix,
|
||||
load_hopfield_weight_matrix,
|
||||
load_salieri_config,
|
||||
load_salieri_weights,
|
||||
load_sequence_table,
|
||||
)
|
||||
|
||||
|
||||
THES = Path(__file__).resolve().parents[1] / "THES"
|
||||
|
||||
|
||||
def test_load_sequence_table() -> None:
|
||||
sequences = load_sequence_table(THES / "SEQUENCE.DAT")
|
||||
assert len(sequences) == 14
|
||||
assert sequences[-2:] == ("251", "258")
|
||||
|
||||
|
||||
def test_load_salieri_config() -> None:
|
||||
config = load_salieri_config(THES / "S61.DAT")
|
||||
assert config.learning_rate == 0.5
|
||||
assert config.alpha == 0.5
|
||||
assert config.n_input == 40
|
||||
assert config.n_hidden == 20
|
||||
assert config.n_output == 1
|
||||
assert config.training_iterations == 1
|
||||
assert config.error_tolerance == 0.1
|
||||
assert config.data_file == "s61.dat"
|
||||
assert config.weight_file == "s61.wt"
|
||||
|
||||
|
||||
def test_load_salieri_weights() -> None:
|
||||
weights = load_salieri_weights(THES / "S61.WT")
|
||||
assert weights.vector_length == 61
|
||||
assert len(weights.weights) == 61
|
||||
assert len(weights.weights[0]) == 61
|
||||
assert len(weights.thetas) == 61
|
||||
assert weights.weights[0][:5] == (0.0, 0.0, 0.0, 0.0, 0.0)
|
||||
|
||||
|
||||
def test_load_hopfield_weight_matrix() -> None:
|
||||
matrix = load_hopfield_weight_matrix(THES / "HTN.DAT")
|
||||
active = extract_active_hopfield_submatrix(matrix)
|
||||
assert len(matrix) == 64
|
||||
assert len(matrix[0]) == 64
|
||||
assert len(active) == 40
|
||||
assert len(active[0]) == 40
|
||||
assert matrix[0][0] == 0.0
|
||||
assert abs(matrix[0][1] - (-0.35199999809265137)) < 1e-7
|
||||
|
|
@ -0,0 +1,75 @@
|
|||
from __future__ import annotations
|
||||
|
||||
import importlib.util
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
ROOT = Path(__file__).resolve().parents[1]
|
||||
|
||||
|
||||
def _load_module(name: str, relative_path: str):
|
||||
module_path = ROOT / relative_path
|
||||
spec = importlib.util.spec_from_file_location(name, module_path)
|
||||
module = importlib.util.module_from_spec(spec)
|
||||
assert spec.loader is not None
|
||||
spec.loader.exec_module(module)
|
||||
return module
|
||||
|
||||
|
||||
def test_thesis_conversion_separates_appendix_titles(tmp_path) -> None:
|
||||
converter = _load_module("convert_thes_to_latex", "tools/convert_thes_to_latex.py")
|
||||
source = ROOT / "THES" / "INT_ANN.TXT"
|
||||
destination = tmp_path / "thesis.tex"
|
||||
|
||||
converter.convert_thesis_file(
|
||||
source,
|
||||
destination,
|
||||
"Integration and Hybridization in Neural Network Modelling",
|
||||
)
|
||||
|
||||
tex = destination.read_text(encoding="utf-8")
|
||||
assert (
|
||||
r"\chapter{Data File Listing: Classical Sequences Data File}" in tex
|
||||
)
|
||||
assert (
|
||||
r"\chapter{Data File Listing: Back-Propagation Network Data File}" in tex
|
||||
)
|
||||
assert "Classical Sequences Data File Appendix 16" not in tex
|
||||
|
||||
|
||||
def test_bibtex_export_preserves_year_suffix_and_extracts_proceedings_fields() -> None:
|
||||
extractor = _load_module("extract_thesis_bibtex", "tools/extract_thesis_bibtex.py")
|
||||
entry = (
|
||||
"Carpenter, G., and Grossberg, S. 1987a. "
|
||||
"A massively parallel architecture for a self-organizing neural pattern recognition machine. "
|
||||
"Computer Vision, Graphics, and Image Processing 37, 54-115."
|
||||
)
|
||||
bibtex = extractor.to_bibtex(entry, 1)
|
||||
|
||||
assert "@article{Carpenter1987a" in bibtex
|
||||
assert "year = {1987a}" in bibtex
|
||||
assert "journal = {Computer Vision, Graphics, and Image Processing}" in bibtex
|
||||
assert "volume = {37}" in bibtex
|
||||
assert "pages = {54-115}" in bibtex
|
||||
|
||||
|
||||
def test_bibtex_export_applies_manual_normalization_overrides() -> None:
|
||||
extractor = _load_module("extract_thesis_bibtex", "tools/extract_thesis_bibtex.py")
|
||||
|
||||
hopfield_entry = (
|
||||
"Hopfield, J. J. 1982. Neural networks and physical systems with emergent "
|
||||
"collective computational abilities. Proceedings of the National Academy "
|
||||
"of Sciences 79, 2554-2558."
|
||||
)
|
||||
hopfield_bibtex = extractor.to_bibtex(hopfield_entry, 1)
|
||||
assert "@article{Hopfield1982" in hopfield_bibtex
|
||||
assert "journal = {Proceedings of the National Academy of Sciences}" in hopfield_bibtex
|
||||
assert "volume = {79}" in hopfield_bibtex
|
||||
|
||||
leven_entry = (
|
||||
"Leven, S. 1987b. S.A.M.: a triune extension to the ART model. "
|
||||
"'North Texas State University Symposium on Neural Networks' Poster Presentation."
|
||||
)
|
||||
leven_bibtex = extractor.to_bibtex(leven_entry, 2)
|
||||
assert "@inproceedings{Leven1987b" in leven_bibtex
|
||||
assert "title = {S.A.M.: a triune extension to the ART model}" in leven_bibtex
|
||||
|
|
@ -0,0 +1,40 @@
|
|||
from pathlib import Path
|
||||
|
||||
from composer_ans.pipeline import CompositionPipeline
|
||||
from composer_ans.types import CompositionContext
|
||||
|
||||
|
||||
THES = Path(__file__).resolve().parents[1] / "THES"
|
||||
|
||||
|
||||
def test_pipeline_single_neural_step_returns_structured_result() -> None:
|
||||
pipeline = CompositionPipeline.from_legacy_data(THES)
|
||||
|
||||
step = pipeline.neural_step(CompositionContext())
|
||||
|
||||
assert len(step.context.notes) == 5
|
||||
assert 0 <= step.context.candidate_note <= 8
|
||||
assert isinstance(step.accepted, bool)
|
||||
assert isinstance(step.objects, bool)
|
||||
assert step.elapsed_seconds >= 0.0
|
||||
|
||||
|
||||
def test_pipeline_step_until_accepted_accepts_note() -> None:
|
||||
pipeline = CompositionPipeline.from_legacy_data(THES)
|
||||
|
||||
step = pipeline.step_until_accepted(CompositionContext(), max_attempts=500)
|
||||
|
||||
assert step.accepted is True
|
||||
assert 1 <= step.context.notes[-1] <= 8
|
||||
assert step.context.note_count == 1
|
||||
|
||||
|
||||
def test_pipeline_compose_returns_requested_number_of_notes() -> None:
|
||||
pipeline = CompositionPipeline.from_legacy_data(THES)
|
||||
|
||||
record = pipeline.compose(max_notes=3)
|
||||
|
||||
assert len(record.notes) == 3
|
||||
assert len(record.per_note_seconds) == 3
|
||||
assert record.total_seconds >= 0.0
|
||||
assert all(1 <= note <= 8 for note in record.notes)
|
||||
|
|
@ -0,0 +1,33 @@
|
|||
import json
|
||||
from pathlib import Path
|
||||
|
||||
from composer_ans.reporting import build_run_report, save_run_report_json
|
||||
from composer_ans.types import CompositionRecord
|
||||
|
||||
|
||||
def test_build_run_report_combines_timing_and_analysis() -> None:
|
||||
record = CompositionRecord(
|
||||
notes=(1, 2, 1, 2),
|
||||
per_note_seconds=(0.1, 0.2, 0.3, 0.4),
|
||||
total_seconds=1.0,
|
||||
)
|
||||
|
||||
report = build_run_report(record, parameters={"object_threshold": 3})
|
||||
|
||||
assert report.note_count == 4
|
||||
assert report.total_seconds == 1.0
|
||||
assert len(report.per_note_seconds) == 4
|
||||
assert report.parameters["object_threshold"] == 3
|
||||
assert 0.0 <= report.predictability <= 1.0
|
||||
|
||||
|
||||
def test_save_run_report_json_writes_expected_fields(tmp_path: Path) -> None:
|
||||
record = CompositionRecord(notes=(1, 2, 3), per_note_seconds=(0.1, 0.2, 0.3), total_seconds=0.6)
|
||||
report = build_run_report(record)
|
||||
path = tmp_path / "report.json"
|
||||
|
||||
save_run_report_json(report, str(path))
|
||||
data = json.loads(path.read_text(encoding="utf-8"))
|
||||
|
||||
assert data["notes"] == [1, 2, 3]
|
||||
assert data["total_seconds"] == 0.6
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
from pathlib import Path
|
||||
|
||||
from composer_ans.salieri import SalieriCritic
|
||||
|
||||
|
||||
THES = Path(__file__).resolve().parents[1] / "THES"
|
||||
|
||||
|
||||
def test_salieri_uses_classical_instructor_when_target_omitted() -> None:
|
||||
critic = SalieriCritic.from_legacy_paths(THES)
|
||||
|
||||
positive = critic.evaluate_and_train((0, 0, 1, 5, 4))
|
||||
negative = critic.evaluate_and_train((1, 2, 3, 4, 5))
|
||||
|
||||
assert positive.target == 1
|
||||
assert negative.target == 0
|
||||
|
|
@ -0,0 +1,30 @@
|
|||
from pathlib import Path
|
||||
|
||||
from composer_ans.beethoven import BeethovenCategorizer
|
||||
from composer_ans.salieri import SalieriCritic
|
||||
|
||||
|
||||
THES = Path(__file__).resolve().parents[1] / "THES"
|
||||
|
||||
|
||||
def test_salieri_round_trip_json(tmp_path: Path) -> None:
|
||||
critic = SalieriCritic.from_legacy_paths(THES)
|
||||
critic.evaluate_and_train((1, 4, 5, 1, 0))
|
||||
path = tmp_path / "salieri.json"
|
||||
|
||||
critic.save_json(str(path))
|
||||
restored = SalieriCritic.load_json(str(path))
|
||||
|
||||
result = restored.evaluate_and_train((1, 4, 5, 1, 0))
|
||||
assert 0.0 <= result.raw_output <= 1.0
|
||||
|
||||
|
||||
def test_beethoven_round_trip_json(tmp_path: Path) -> None:
|
||||
beethoven = BeethovenCategorizer()
|
||||
beethoven.categorize((1, 4, 5, 1, 0), is_classical=True)
|
||||
path = tmp_path / "beethoven.json"
|
||||
|
||||
beethoven.save_json(str(path))
|
||||
restored = BeethovenCategorizer.load_json(str(path))
|
||||
result = restored.categorize((1, 4, 5, 1, 0), is_classical=True)
|
||||
assert result.art_result.committed_categories >= 1
|
||||
|
|
@ -0,0 +1,356 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from pathlib import Path
|
||||
import re
|
||||
|
||||
|
||||
ROOT = Path(__file__).resolve().parents[1]
|
||||
THES = ROOT / "THES"
|
||||
OUT = ROOT / "latex"
|
||||
|
||||
FILES = {
|
||||
"INT_ANN.TXT": {
|
||||
"target": "integration_and_hybridization_in_neural_network_modelling",
|
||||
"title": "Integration and Hybridization in Neural Network Modelling",
|
||||
"mode": "thesis",
|
||||
},
|
||||
"COMPCOOP.TXT": {
|
||||
"target": "competing_network_models_and_problem_solving",
|
||||
"title": "Competing Network Models and Problem-Solving",
|
||||
"mode": "generic",
|
||||
},
|
||||
"THPROPOS.TXT": {
|
||||
"target": "thesis_proposal",
|
||||
"title": "Thesis Proposal",
|
||||
"mode": "generic",
|
||||
},
|
||||
}
|
||||
|
||||
FRONT_MATTER_HEADINGS = {
|
||||
"ACKNOWLEDGEMENTS",
|
||||
"ABSTRACT",
|
||||
"TABLE OF CONTENTS",
|
||||
"LIST OF ILLUSTRATIONS",
|
||||
"LIST OF TABLES",
|
||||
}
|
||||
|
||||
|
||||
def escape_latex(text: str) -> str:
|
||||
replacements = {
|
||||
"\\": r"\textbackslash{}",
|
||||
"&": r"\&",
|
||||
"%": r"\%",
|
||||
"$": r"\$",
|
||||
"#": r"\#",
|
||||
"_": r"\_",
|
||||
"{": r"\{",
|
||||
"}": r"\}",
|
||||
"~": r"\textasciitilde{}",
|
||||
"^": r"\textasciicircum{}",
|
||||
}
|
||||
for src, dst in replacements.items():
|
||||
text = text.replace(src, dst)
|
||||
return text
|
||||
|
||||
|
||||
def clean_line(line: str) -> str:
|
||||
line = re.sub(r"[\x00-\x08\x0b-\x1f]", "", line)
|
||||
line = line.replace("\x1a", "").replace("\x17", "").replace("\ufeff", "")
|
||||
line = line.replace("<EFBFBD>", "'").replace("®", "'").replace("", "'")
|
||||
line = re.sub(r"\s+", " ", line)
|
||||
return line.rstrip()
|
||||
|
||||
|
||||
def looks_like_heading(line: str) -> bool:
|
||||
stripped = line.strip()
|
||||
if not stripped:
|
||||
return False
|
||||
if len(stripped) > 80:
|
||||
return False
|
||||
if stripped.startswith("."):
|
||||
return False
|
||||
if stripped in FRONT_MATTER_HEADINGS:
|
||||
return True
|
||||
if re.fullmatch(r"[ivxlcdmIVXLCDM]+", stripped):
|
||||
return False
|
||||
if re.fullmatch(r"\d+", stripped):
|
||||
return False
|
||||
if re.fullmatch(r"[A-Z][A-Z\s\-]{2,}", stripped) and len(stripped.split()) <= 10:
|
||||
return True
|
||||
if re.fullmatch(r"[IVX]+\.\s+.*", stripped):
|
||||
return True
|
||||
if re.fullmatch(r"\d+\.\s+.*", stripped):
|
||||
return True
|
||||
if stripped.isupper() and len(stripped.split()) <= 12 and len(stripped) >= 4:
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def is_page_artifact(line: str) -> bool:
|
||||
stripped = line.strip()
|
||||
if not stripped:
|
||||
return False
|
||||
if re.fullmatch(r"[ivxlcdmIVXLCDM]+", stripped):
|
||||
return True
|
||||
if re.fullmatch(r"\d+", stripped):
|
||||
return True
|
||||
if stripped == "Publication No." or re.fullmatch(r"_+", stripped):
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def heading_command(line: str) -> str:
|
||||
stripped = line.strip()
|
||||
if stripped in FRONT_MATTER_HEADINGS:
|
||||
return "section*"
|
||||
if re.fullmatch(r"[IVX]+\.\s+.*", stripped):
|
||||
return "section*"
|
||||
if re.fullmatch(r"\d+\.\s+.*", stripped):
|
||||
return "section*"
|
||||
return "section*"
|
||||
|
||||
|
||||
def convert_text_file(source: Path, destination: Path, title: str) -> None:
|
||||
lines = [clean_line(line) for line in source.read_text(encoding="latin-1").splitlines()]
|
||||
body: list[str] = []
|
||||
paragraph: list[str] = []
|
||||
|
||||
def flush_paragraph() -> None:
|
||||
if paragraph:
|
||||
body.append(escape_latex(" ".join(part.strip() for part in paragraph if part.strip())))
|
||||
body.append("")
|
||||
paragraph.clear()
|
||||
|
||||
for line in lines:
|
||||
stripped = line.strip()
|
||||
if not stripped:
|
||||
flush_paragraph()
|
||||
continue
|
||||
if is_page_artifact(stripped):
|
||||
flush_paragraph()
|
||||
continue
|
||||
if stripped.startswith("."):
|
||||
flush_paragraph()
|
||||
body.append(f"% raw formatter directive: {escape_latex(stripped)}")
|
||||
continue
|
||||
if looks_like_heading(stripped):
|
||||
flush_paragraph()
|
||||
body.append(rf"\{heading_command(stripped)}{{{escape_latex(stripped)}}}")
|
||||
continue
|
||||
paragraph.append(stripped)
|
||||
|
||||
flush_paragraph()
|
||||
|
||||
tex = [
|
||||
r"\documentclass[12pt]{article}",
|
||||
r"\usepackage[utf8]{inputenc}",
|
||||
r"\usepackage[T1]{fontenc}",
|
||||
r"\usepackage{geometry}",
|
||||
r"\geometry{margin=1in}",
|
||||
r"\title{" + escape_latex(title) + "}",
|
||||
r"\author{Converted from legacy plain-text source}",
|
||||
r"\date{}",
|
||||
r"\begin{document}",
|
||||
r"\maketitle",
|
||||
r"\begin{flushleft}",
|
||||
"% This is a conservative automated conversion from the legacy text file.",
|
||||
"% Manual cleanup will still be necessary for figures, references, footnotes, and formatting.",
|
||||
"",
|
||||
*body,
|
||||
r"\end{flushleft}",
|
||||
r"\end{document}",
|
||||
"",
|
||||
]
|
||||
destination.write_text("\n".join(tex), encoding="utf-8")
|
||||
|
||||
|
||||
def _clean_lines(source: Path) -> list[str]:
|
||||
return [clean_line(line) for line in source.read_text(encoding="latin-1").splitlines()]
|
||||
|
||||
|
||||
def _find_line_index(lines: list[str], needle: str, start: int = 0) -> int:
|
||||
for idx in range(start, len(lines)):
|
||||
if lines[idx].strip() == needle:
|
||||
return idx
|
||||
raise ValueError(f"could not find line: {needle}")
|
||||
|
||||
|
||||
def _collect_paragraphs(lines: list[str]) -> list[str]:
|
||||
paragraphs: list[str] = []
|
||||
current: list[str] = []
|
||||
for raw in lines:
|
||||
stripped = raw.strip()
|
||||
if not stripped or is_page_artifact(stripped):
|
||||
if current:
|
||||
paragraphs.append(escape_latex(" ".join(current)))
|
||||
current.clear()
|
||||
continue
|
||||
current.append(stripped)
|
||||
if current:
|
||||
paragraphs.append(escape_latex(" ".join(current)))
|
||||
return paragraphs
|
||||
|
||||
|
||||
def _collect_uppercase_heading_lines(lines: list[str], start_idx: int) -> tuple[list[str], int]:
|
||||
heading_lines: list[str] = []
|
||||
idx = start_idx
|
||||
while idx < len(lines):
|
||||
candidate = lines[idx].strip()
|
||||
idx += 1
|
||||
if not candidate or is_page_artifact(candidate):
|
||||
continue
|
||||
if re.fullmatch(r"(CHAPTER|APPENDIX)\s+\d+", candidate):
|
||||
idx -= 1
|
||||
break
|
||||
if candidate.isupper() and len(candidate) <= 120:
|
||||
heading_lines.append(candidate)
|
||||
continue
|
||||
idx -= 1
|
||||
break
|
||||
return heading_lines, idx
|
||||
|
||||
|
||||
def convert_thesis_file(source: Path, destination: Path, title: str) -> None:
|
||||
lines = _clean_lines(source)
|
||||
ack_idx = _find_line_index(lines, "ACKNOWLEDGEMENTS")
|
||||
abs_idx = _find_line_index(lines, "ABSTRACT", ack_idx + 1)
|
||||
toc_idx = _find_line_index(lines, "TABLE OF CONTENTS", abs_idx + 1)
|
||||
chap1_idx = _find_line_index(lines, "CHAPTER 1", abs_idx + 1)
|
||||
bib_idx = _find_line_index(lines, "BIBLIOGRAPHY", chap1_idx + 1)
|
||||
|
||||
acknowledgements = _collect_paragraphs(lines[ack_idx + 1 : abs_idx])
|
||||
abstract_lines = []
|
||||
for line in lines[abs_idx + 1 : toc_idx]:
|
||||
stripped = line.strip()
|
||||
if not stripped or is_page_artifact(stripped):
|
||||
abstract_lines.append("")
|
||||
continue
|
||||
if stripped in {
|
||||
"INTEGRATION AND HYBRIDIZATION IN NEURAL NETWORK MODELLING",
|
||||
"Wesley Royce Elsberry, M.S.",
|
||||
"The University of Texas at Arlington, 1989",
|
||||
"Supervising Professor: Karan Briggs",
|
||||
}:
|
||||
continue
|
||||
if ". . ." in stripped:
|
||||
continue
|
||||
abstract_lines.append(stripped)
|
||||
abstract = _collect_paragraphs(abstract_lines)
|
||||
|
||||
body = _convert_thesis_body(lines[chap1_idx:bib_idx])
|
||||
tex = [
|
||||
r"\documentclass[12pt]{report}",
|
||||
r"\usepackage[utf8]{inputenc}",
|
||||
r"\usepackage[T1]{fontenc}",
|
||||
r"\usepackage{geometry}",
|
||||
r"\geometry{margin=1in}",
|
||||
r"\title{" + escape_latex(title) + "}",
|
||||
r"\author{Wesley Royce Elsberry}",
|
||||
r"\date{August 1989}",
|
||||
r"\begin{document}",
|
||||
r"\begin{titlepage}",
|
||||
r"\centering",
|
||||
r"{\Large " + escape_latex(title) + r"\par}",
|
||||
r"\vspace{1.5cm}",
|
||||
r"{\large Wesley Royce Elsberry\par}",
|
||||
r"\vspace{1cm}",
|
||||
r"Presented to the Faculty of the Graduate School of\par",
|
||||
r"The University of Texas at Arlington in Partial Fulfillment\par",
|
||||
r"of the Requirements for the Degree of\par",
|
||||
r"\vspace{0.5cm}",
|
||||
r"{\large Master of Science in Computer Science\par}",
|
||||
r"\vfill",
|
||||
r"The University of Texas at Arlington\par",
|
||||
r"August 1989\par",
|
||||
r"\end{titlepage}",
|
||||
r"\chapter*{Acknowledgements}",
|
||||
*[p + "\n" for p in acknowledgements],
|
||||
r"\chapter*{Abstract}",
|
||||
*[p + "\n" for p in abstract],
|
||||
r"\tableofcontents",
|
||||
*body,
|
||||
r"\nocite{*}",
|
||||
r"\bibliographystyle{plain}",
|
||||
r"\bibliography{integration_and_hybridization_in_neural_network_modelling}",
|
||||
r"\end{document}",
|
||||
"",
|
||||
]
|
||||
destination.write_text("\n".join(tex), encoding="utf-8")
|
||||
|
||||
|
||||
def _convert_thesis_body(lines: list[str]) -> list[str]:
|
||||
body: list[str] = []
|
||||
paragraph: list[str] = []
|
||||
idx = 0
|
||||
in_appendix = False
|
||||
appendix_mode = "normal"
|
||||
|
||||
def flush_paragraph() -> None:
|
||||
if paragraph:
|
||||
body.append(escape_latex(" ".join(paragraph)))
|
||||
body.append("")
|
||||
paragraph.clear()
|
||||
|
||||
while idx < len(lines):
|
||||
stripped = lines[idx].strip()
|
||||
idx += 1
|
||||
if not stripped or is_page_artifact(stripped):
|
||||
flush_paragraph()
|
||||
continue
|
||||
if stripped.startswith("."):
|
||||
flush_paragraph()
|
||||
body.append(f"% raw formatter directive: {escape_latex(stripped)}")
|
||||
continue
|
||||
if re.fullmatch(r"CHAPTER\s+\d+", stripped):
|
||||
flush_paragraph()
|
||||
heading_lines, idx = _collect_uppercase_heading_lines(lines, idx)
|
||||
title = " ".join(heading_lines) if heading_lines else stripped
|
||||
body.append(rf"\chapter{{{escape_latex(title.title())}}}")
|
||||
continue
|
||||
if re.fullmatch(r"APPENDIX\s+\d+", stripped):
|
||||
flush_paragraph()
|
||||
if not in_appendix:
|
||||
body.append(r"\appendix")
|
||||
in_appendix = True
|
||||
heading_lines, idx = _collect_uppercase_heading_lines(lines, idx)
|
||||
title = " ".join(heading_lines) if heading_lines else stripped
|
||||
body.append(rf"\chapter{{{escape_latex(title.title())}}}")
|
||||
if (
|
||||
"PROGRAM SOURCE LISTING" in title
|
||||
or "DATA FILE LISTING" in title
|
||||
):
|
||||
appendix_mode = "listing"
|
||||
body.append(
|
||||
"This appendix is represented in the repository by the legacy source and data files in \\texttt{THES/}. "
|
||||
"The automated thesis conversion suppresses the full listing here to keep the document manageable."
|
||||
)
|
||||
body.append("")
|
||||
else:
|
||||
appendix_mode = "normal"
|
||||
continue
|
||||
if in_appendix and appendix_mode == "listing":
|
||||
continue
|
||||
if stripped.isupper() and len(stripped.split()) <= 10 and len(stripped) > 4:
|
||||
flush_paragraph()
|
||||
body.append(rf"\section*{{{escape_latex(stripped.title())}}}")
|
||||
continue
|
||||
paragraph.append(stripped)
|
||||
|
||||
flush_paragraph()
|
||||
return body
|
||||
|
||||
|
||||
def main() -> int:
|
||||
OUT.mkdir(parents=True, exist_ok=True)
|
||||
for source_name, config in FILES.items():
|
||||
source = THES / source_name
|
||||
destination = OUT / f"{config['target']}.tex"
|
||||
if config["mode"] == "thesis":
|
||||
convert_thesis_file(source, destination, config["title"])
|
||||
else:
|
||||
convert_text_file(source, destination, config["title"])
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
|
|
@ -0,0 +1,437 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from pathlib import Path
|
||||
import re
|
||||
|
||||
|
||||
ROOT = Path(__file__).resolve().parents[1]
|
||||
THES = ROOT / "THES" / "INT_ANN.TXT"
|
||||
OUT = ROOT / "latex" / "integration_and_hybridization_in_neural_network_modelling.bib"
|
||||
|
||||
MANUAL_OVERRIDES = {
|
||||
"Farhat1986": {
|
||||
"kind": "incollection",
|
||||
"title": "Neural net models and optical computing: an overview",
|
||||
"booktitle": "Hybrid and Optical Computing",
|
||||
"editor": "Harold Szu",
|
||||
"publisher": "SPIE",
|
||||
"address": "Bellingham, Washington",
|
||||
"volume": "634",
|
||||
"pages": "277-306",
|
||||
},
|
||||
"Harmon1970": {
|
||||
"kind": "incollection",
|
||||
"title": "Neural subsystems: an interpretive summary",
|
||||
"booktitle": "The Neurosciences Second Study Program",
|
||||
"editor": "F. O. Schmitt",
|
||||
"publisher": "Rockefeller University Press",
|
||||
"address": "New York",
|
||||
"pages": "486-494",
|
||||
},
|
||||
"HechtNielsen1986": {
|
||||
"kind": "incollection",
|
||||
"title": "Performance limits of optical, electro-optical, and electronic neurocomputers",
|
||||
"booktitle": "Hybrid and Optical Computing",
|
||||
"editor": "H. Szu",
|
||||
"publisher": "SPIE",
|
||||
"address": "Bellingham, Washington",
|
||||
"volume": "634",
|
||||
"pages": "277-306",
|
||||
},
|
||||
"Hopfield1982": {
|
||||
"kind": "article",
|
||||
"journal": "Proceedings of the National Academy of Sciences",
|
||||
"volume": "79",
|
||||
"pages": "2554-2558",
|
||||
},
|
||||
"Leven1987a": {
|
||||
"kind": "phdthesis",
|
||||
"title": "Choice and Neural process: A dissertation",
|
||||
"school": "University of Texas at Arlington",
|
||||
"note": "Chapter 5: Neural process and form -- mathematics and meaning.",
|
||||
},
|
||||
"Leven1987b": {
|
||||
"kind": "inproceedings",
|
||||
"title": "S.A.M.: a triune extension to the ART model",
|
||||
"note": "Poster presentation at the North Texas State University Symposium on Neural Networks.",
|
||||
},
|
||||
"Levine1990": {
|
||||
"kind": "unpublished",
|
||||
"note": "To appear in Motivation, Emotion, and Goal Direction in Neural Networks, D. Levine and S. Leven, eds., Erlbaum, Hillsdale, New Jersey.",
|
||||
},
|
||||
"Lippmann1987": {
|
||||
"kind": "article",
|
||||
"journal": "IEEE ASSP Magazine",
|
||||
"month": "apr",
|
||||
"pages": "4-22",
|
||||
},
|
||||
"MacLean1970": {
|
||||
"kind": "incollection",
|
||||
"title": "The triune brain, emotion, and scientific bias",
|
||||
"booktitle": "The Neurosciences Second Study Program",
|
||||
"editor": "F. O. Schmitt",
|
||||
"publisher": "Rockefeller University Press",
|
||||
"address": "New York",
|
||||
"pages": "486-494",
|
||||
},
|
||||
"Matsuoka1989": {
|
||||
"author": "Matsuoka, T. and Hamada, H. and Nakatsu, R.",
|
||||
},
|
||||
"Neuroscience1988": {
|
||||
"kind": "techreport",
|
||||
"author": "{Metroplex Study Group on Computational Neuroscience}",
|
||||
"institution": "North Texas Commission Regional Technology Program",
|
||||
"note": "Report to the North Texas Commission Regional Technology Program.",
|
||||
},
|
||||
"Newell1976": {
|
||||
"kind": "article",
|
||||
"journal": "Communications of the ACM",
|
||||
"volume": "19",
|
||||
"number": "3",
|
||||
"pages": "113-126",
|
||||
},
|
||||
"Nottebohm1989": {
|
||||
"kind": "article",
|
||||
"journal": "Scientific American",
|
||||
"month": "feb",
|
||||
"pages": "74-79",
|
||||
},
|
||||
"Pao1989": {
|
||||
"kind": "book",
|
||||
"publisher": "Addison-Wesley",
|
||||
"address": "Reading, Massachusetts",
|
||||
},
|
||||
"Parker1985": {
|
||||
"kind": "techreport",
|
||||
"institution": "Massachusetts Institute of Technology, Center for Computational Research in Economics and Management Science",
|
||||
"address": "Cambridge, Massachusetts",
|
||||
"number": "TR-47",
|
||||
"title": "Learning-logic",
|
||||
},
|
||||
"Rumelhart1986": {
|
||||
"kind": "incollection",
|
||||
"title": "Learning internal representations by back propagation",
|
||||
"booktitle": "Parallel Distributed Processing",
|
||||
"editor": "D. Rumelhart and J. McClelland and the PDP Research Group",
|
||||
"publisher": "MIT Press",
|
||||
"address": "Cambridge, Massachusetts",
|
||||
"volume": "1",
|
||||
"pages": "365-422",
|
||||
},
|
||||
"Simpson1988": {
|
||||
"kind": "unpublished",
|
||||
"note": "Submitted to CRC Critical Reviews in Artificial Intelligence.",
|
||||
},
|
||||
"Sontag1989": {
|
||||
"kind": "inproceedings",
|
||||
"title": "Back-propagation separates when perceptrons do",
|
||||
"booktitle": "Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (IJCNN-89) Vol. I",
|
||||
"pages": "639-642",
|
||||
},
|
||||
"Tsutsumi1989": {
|
||||
"kind": "inproceedings",
|
||||
"title": "A multi-layered neural network composed of backprop. and Hopfield nets and internal space representation",
|
||||
"booktitle": "Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (IJCNN-89) Vol. I",
|
||||
"pages": "507-512",
|
||||
},
|
||||
"Hewitt1985": {
|
||||
"kind": "article",
|
||||
"journal": "Byte",
|
||||
"volume": "10",
|
||||
"number": "4",
|
||||
"pages": "223-242",
|
||||
},
|
||||
"Widrow1988": {
|
||||
"kind": "article",
|
||||
"journal": "IEEE Computer",
|
||||
"volume": "21",
|
||||
"number": "3",
|
||||
"pages": "25-39",
|
||||
},
|
||||
"Charniak1985": {
|
||||
"kind": "book",
|
||||
"publisher": "Addison-Wesley",
|
||||
"address": "Reading, Massachusetts",
|
||||
"note": "701 pp.",
|
||||
},
|
||||
"Hebb1949": {
|
||||
"kind": "book",
|
||||
"publisher": "Wiley",
|
||||
"address": "New York",
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def clean_line(line: str) -> str:
|
||||
line = re.sub(r"[\x00-\x1f]", "", line)
|
||||
line = (
|
||||
line.replace("<EFBFBD>", "'")
|
||||
.replace("®", "'")
|
||||
.replace("÷", "-")
|
||||
.replace("`", "'")
|
||||
)
|
||||
return re.sub(r"\s+", " ", line).strip()
|
||||
|
||||
|
||||
def load_entries() -> list[str]:
|
||||
lines = THES.read_text(encoding="latin-1").splitlines()
|
||||
start = next(i for i, line in enumerate(lines) if line.strip() == "BIBLIOGRAPHY")
|
||||
chunks: list[list[str]] = []
|
||||
current: list[str] = []
|
||||
for raw in lines[start + 1 :]:
|
||||
line = clean_line(raw)
|
||||
if not line or re.fullmatch(r"[ivxlcdmIVXLCDM]+|\d+", line):
|
||||
if current:
|
||||
chunks.append(current)
|
||||
current = []
|
||||
continue
|
||||
current.append(line)
|
||||
if current:
|
||||
chunks.append(current)
|
||||
return [" ".join(chunk) for chunk in chunks]
|
||||
|
||||
|
||||
def bib_key(entry: str, index: int) -> str:
|
||||
match = re.match(r"([A-Za-z][A-Za-z\-\.\s,&']+?)\s+(\d{4}[a-z]?)\.", entry)
|
||||
if match:
|
||||
surname = re.sub(r"[^A-Za-z]", "", match.group(1).split(",")[0].split()[-1])
|
||||
year = match.group(2)
|
||||
return f"{surname}{year}"
|
||||
return f"elsberryRef{index:03d}"
|
||||
|
||||
|
||||
def entry_type(entry: str) -> str:
|
||||
lowered = entry.lower()
|
||||
if "dissertation" in lowered:
|
||||
return "phdthesis"
|
||||
if "personal communication" in lowered:
|
||||
return "misc"
|
||||
if "proceedings" in lowered or "conference" in lowered or "poster presentation" in lowered:
|
||||
return "inproceedings"
|
||||
if re.search(r"\b\d+\s*,\s*\d+\s*-\s*\d+\.?$", entry):
|
||||
return "article"
|
||||
if "press" in lowered or "books" in lowered or "wiley" in lowered or "addison-wesley" in lowered:
|
||||
return "book"
|
||||
if "journal" in lowered or "magazine" in lowered or "cybernetics" in lowered or "biosciences" in lowered:
|
||||
return "article"
|
||||
return "misc"
|
||||
|
||||
|
||||
def split_author_year(entry: str) -> tuple[str, str, str]:
|
||||
match = re.match(r"(.+?)\s+(\d{4}[a-z]?)\.\s+(.*)$", entry)
|
||||
if not match:
|
||||
return "Unknown", "0000", entry
|
||||
return match.group(1).strip(), match.group(2), match.group(3).strip()
|
||||
|
||||
|
||||
def split_title_note(rest: str) -> tuple[str, str]:
|
||||
cues = (
|
||||
"Proceedings",
|
||||
"Proc.",
|
||||
"In ",
|
||||
"American ",
|
||||
"Computer ",
|
||||
"Applied ",
|
||||
"Mathematical ",
|
||||
"Studies ",
|
||||
"International ",
|
||||
"Rockefeller ",
|
||||
"Bantam ",
|
||||
"Wiley",
|
||||
"SPIE",
|
||||
"Biological ",
|
||||
"Byte ",
|
||||
"Communications ",
|
||||
"Scientific ",
|
||||
"Bell ",
|
||||
"IEEE ",
|
||||
"IRE ",
|
||||
"Neural Networks ",
|
||||
"Bull.",
|
||||
"Addison-Wesley",
|
||||
"Massachusetts Institute",
|
||||
"Report ",
|
||||
"Submitted ",
|
||||
"To appear ",
|
||||
"Unpublished ",
|
||||
"University ",
|
||||
"Poster ",
|
||||
"'North",
|
||||
)
|
||||
for cue in cues:
|
||||
pattern = rf"^(?P<title>.+?)\.\s+(?P<note>{re.escape(cue)}.*)$"
|
||||
match = re.match(pattern, rest)
|
||||
if match:
|
||||
return match.group("title").strip(), match.group("note").strip()
|
||||
title = rest.split(".")[0].strip()
|
||||
note = rest[len(title):].strip().lstrip(".").strip()
|
||||
return title, note
|
||||
|
||||
|
||||
def normalize_author(author: str) -> str:
|
||||
author = re.sub(r"\s+", " ", author.strip().rstrip("."))
|
||||
author = author.replace("Foo,Y.", "Foo, Y.")
|
||||
author = author.replace("Pao,Y.-H.", "Pao, Y.-H.")
|
||||
author = author.replace("F. O. Scmitt", "F. O. Schmitt")
|
||||
return author
|
||||
|
||||
|
||||
def _field(name: str, value: str) -> str:
|
||||
return f" {name} = {{{value}}}"
|
||||
|
||||
|
||||
def _extract_inproceedings_fields(note: str) -> list[str]:
|
||||
fields: list[str] = []
|
||||
proceedings_match = re.search(
|
||||
r"(Proceedings of .*?(?:\(.*?\))?(?:\s+Vol\.\s*[IVX0-9]+)?)"
|
||||
r"(?:\.\s*|,\s*(?:pp\.|\d)|$)",
|
||||
note,
|
||||
flags=re.IGNORECASE,
|
||||
)
|
||||
if proceedings_match:
|
||||
booktitle = proceedings_match.group(1).rstrip(" .,;")
|
||||
fields.append(_field("booktitle", booktitle))
|
||||
pages_match = re.search(r"(\d+\s*-\s*\d+)", note)
|
||||
if pages_match:
|
||||
fields.append(_field("pages", pages_match.group(1).replace(" ", "")))
|
||||
return fields
|
||||
|
||||
|
||||
def _extract_article_fields(note: str) -> list[str]:
|
||||
fields: list[str] = []
|
||||
journal_match = re.match(r"(.+?)\s+(\d+),\s*(\d+\s*-\s*\d+)\.?\s*$", note)
|
||||
if journal_match:
|
||||
fields.append(_field("journal", journal_match.group(1).rstrip(" .,;")))
|
||||
fields.append(_field("volume", journal_match.group(2)))
|
||||
fields.append(_field("pages", journal_match.group(3).replace(" ", "")))
|
||||
return fields
|
||||
journal_match = re.match(r"(.+?)\s+(\d+)\s*,\s*(\d+\s*-\s*\d+)\.?\s*$", note)
|
||||
if journal_match:
|
||||
fields.append(_field("journal", journal_match.group(1).rstrip(" .,;")))
|
||||
fields.append(_field("volume", journal_match.group(2)))
|
||||
fields.append(_field("pages", journal_match.group(3).replace(" ", "")))
|
||||
return fields
|
||||
|
||||
|
||||
def _extract_book_fields(note: str) -> list[str]:
|
||||
fields: list[str] = []
|
||||
publisher_match = re.match(r"([^,.]+(?:Press|Books|Wiley|Addison-Wesley|SPIE|University Press))[,\.]\s*(.*)$", note)
|
||||
if publisher_match:
|
||||
fields.append(_field("publisher", publisher_match.group(1).strip()))
|
||||
if publisher_match.group(2).strip():
|
||||
fields.append(_field("address", publisher_match.group(2).strip(" .")))
|
||||
return fields
|
||||
if note:
|
||||
fields.append(_field("note", note))
|
||||
return fields
|
||||
|
||||
|
||||
def _extract_phdthesis_fields(note: str) -> list[str]:
|
||||
fields: list[str] = []
|
||||
school_match = re.search(r"(The University of .*?|.*?University.*?)\.", note)
|
||||
if school_match:
|
||||
fields.append(_field("school", school_match.group(1).strip()))
|
||||
remainder = note.replace(school_match.group(0), "", 1).strip(" .")
|
||||
if remainder:
|
||||
fields.append(_field("note", remainder))
|
||||
return fields
|
||||
if note:
|
||||
fields.append(_field("note", note))
|
||||
return fields
|
||||
|
||||
|
||||
def _extra_fields(kind: str, note: str) -> list[str]:
|
||||
normalized = note.replace("In '", "In ").replace(",'", ",")
|
||||
if not normalized:
|
||||
return []
|
||||
if kind == "inproceedings":
|
||||
fields = _extract_inproceedings_fields(normalized)
|
||||
if fields:
|
||||
return fields
|
||||
if kind == "article":
|
||||
fields = _extract_article_fields(normalized)
|
||||
if fields:
|
||||
return fields
|
||||
if kind == "book":
|
||||
return _extract_book_fields(normalized)
|
||||
if kind == "phdthesis":
|
||||
return _extract_phdthesis_fields(normalized)
|
||||
return [_field("note", normalized)]
|
||||
|
||||
|
||||
def apply_override(
|
||||
key: str,
|
||||
kind: str,
|
||||
author: str,
|
||||
year: str,
|
||||
title: str,
|
||||
note: str,
|
||||
fields: list[str],
|
||||
) -> tuple[str, str, str, str, list[str]]:
|
||||
override = MANUAL_OVERRIDES.get(key)
|
||||
if not override:
|
||||
return kind, author, year, title, fields
|
||||
|
||||
kind = override.get("kind", kind)
|
||||
author = override.get("author", author)
|
||||
title = override.get("title", title)
|
||||
|
||||
ordered_fields = [
|
||||
_field("author", author),
|
||||
_field("year", year),
|
||||
_field("title", title),
|
||||
]
|
||||
for name in (
|
||||
"journal",
|
||||
"booktitle",
|
||||
"editor",
|
||||
"publisher",
|
||||
"institution",
|
||||
"school",
|
||||
"address",
|
||||
"volume",
|
||||
"number",
|
||||
"pages",
|
||||
"month",
|
||||
"note",
|
||||
):
|
||||
value = override.get(name)
|
||||
if value:
|
||||
ordered_fields.append(_field(name, value))
|
||||
return kind, author, year, title, ordered_fields
|
||||
|
||||
|
||||
def to_bibtex(entry: str, index: int) -> str:
|
||||
key = bib_key(entry, index)
|
||||
kind = entry_type(entry)
|
||||
author, year, rest = split_author_year(entry)
|
||||
author = normalize_author(author)
|
||||
title, note = split_title_note(rest)
|
||||
fields = [
|
||||
_field("author", author),
|
||||
_field("year", year),
|
||||
_field("title", title),
|
||||
]
|
||||
fields.extend(_extra_fields(kind, note))
|
||||
kind, author, year, title, fields = apply_override(
|
||||
key, kind, author, year, title, note, fields
|
||||
)
|
||||
return "@{kind}{{{key},\n{fields}\n}}".format(
|
||||
kind=kind,
|
||||
key=key,
|
||||
fields=",\n".join(fields),
|
||||
)
|
||||
|
||||
|
||||
def main() -> int:
|
||||
entries = load_entries()
|
||||
OUT.parent.mkdir(parents=True, exist_ok=True)
|
||||
OUT.write_text(
|
||||
"\n\n".join(to_bibtex(entry, idx) for idx, entry in enumerate(entries, start=1)) + "\n",
|
||||
encoding="utf-8",
|
||||
)
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
Loading…
Reference in New Issue