feat!: remove neural networks and move to its own repo

.github/agents/orchestrator.agent.md

@@ -33,7 +33,7 @@ Triage requests and route to the right specialist. Do NOT implement or plan your
 
 ## Context to gather
 - Module: `analysis`, `windowing`, `control_analysis`, `controllers`, `dynamics`,
-  `estimators`, `filters`, `filters::passive`, `math`, `neural_network`,
+  `estimators`, `filters`, `filters::passive`, `math`,
   `optimization`, `regularization`, `solvers`
 - Existing patterns to follow?
 - Documentation update needed?

AGENTS.md

@@ -51,7 +51,7 @@ resource-constrained embedded systems. Real-time, deterministic, no heap.
 
 `analysis`, `windowing`, `control_analysis`, `controllers`, `estimators`,
 `filters` (active Kalman family — **not** `filters::active`), `filters::passive`, `math`,
-`neural_network`, `optimization`, `regularization`, `solvers`, and new:
+`optimization`, `regularization`, `solvers`, and new:
 `robust_control`, `nonlinear_control`.
 
 ## Math functions

CMakePresets.json

@@ -135,10 +135,6 @@
         "numerical.filters.active_test",
         "numerical.filters.passive_test",
         "numerical.math_test",
-        "numerical.neural_network.activation_test",
-        "numerical.neural_network.layer_test",
-        "numerical.neural_network.losses_test",
-        "numerical.neural_network.model_test",
         "numerical.nonlinear_control_test",
         "numerical.optimization_test",
         "numerical.regularization_test",
@@ -160,10 +156,6 @@
         "numerical.filters.active_test",
         "numerical.filters.passive_test",
         "numerical.math_test",
-        "numerical.neural_network.activation_test",
-        "numerical.neural_network.layer_test",
-        "numerical.neural_network.losses_test",
-        "numerical.neural_network.model_test",
         "numerical.nonlinear_control_test",
         "numerical.optimization_test",
         "numerical.regularization_test",

README.md

@@ -21,7 +21,6 @@ Refer to the documentation to quickly integrate and utilize the library's signal
 | [Controllers](doc/controllers/README.md)                           | Bang-Bang/Hysteresis, PID, LQR, LQI (Integral/Servo State Feedback), MPC, Saturation, Rate Limiter, Slew-Limited Saturation, Feedforward/2-DOF, Gain-Scheduled Controller, Lead-Lag Compensator, Luenberger Observer |
 | [Estimators](doc/estimators/README.md)                             | Linear Regression, Polynomial Fitting, Total Least Squares, Yule-Walker (offline), Recursive Least Squares, LMS / NLMS Adaptive Filter (online), Consistency Metrics / NEES / NIS |
 | [Filters](doc/filters/README.md)                                   | Kalman, Extended Kalman, Unscented Kalman, Square-Root Kalman, Alpha-Beta/Alpha-Beta-Gamma, FIR, IIR, Exponential Moving Average, Moving Average, Complementary, Median Filter, CIC (Cascaded Integrator-Comb), Notch/Comb Filter, Savitzky-Golay Filter, Biquad/Second-Order-Section Cascade, IIR Filter Design (Butterworth/Chebyshev-I + Bilinear Transform), Madgwick/Mahony AHRS |
-| [Neural Network](doc/neural_network/README.md)                     | Layers, activations, losses, model                                   |
 | [Optimization](doc/optimization/README.md)                         | Gradient Descent                                                     |
 | [Regularization](doc/regularization/README.md)                     | L1 (Lasso), L2 (Ridge)                                              |
 | [Math](doc/math/README.md)                                         | CORDIC, Quaternion, MatrixNorms, Step Response Metrics, MatrixExponential                             |

ROADMAP.md

@@ -43,7 +43,7 @@ magnitude/phase. The items below are the missing pieces. All are **float-only**,
 on bounded `math::Vector`/`math::Matrix` inputs; tests are `TEST_F` on `float`.
 
 > **Test-only helpers (not production components).** A ULP/relative-error comparator and a
-> finite-difference **gradient check** (for `neural_network/` and `optimization/`) are pure test
+> finite-difference **gradient check** (for `optimization/`) are pure test
 > utilities — add them to the `numerical.math_test_helper` INTERFACE library
 > ([`numerical/math/test_doubles/`](numerical/math/test_doubles/)), not to `numerical/` production code.
 

TESTING.md

@@ -61,7 +61,6 @@ Canonical rules still apply ([AGENTS.md](AGENTS.md), [testing.instructions.md](.
 | Optimization                                    | ●  |    |    |    | ●  | ●  |    |    |    |
 | Regularization                                  | ●  |    |    |    |    | ●  | ●  |    |    |
 | Solvers (linear / ODE / roots)                  | ●  |    | ○  | ●  | ●  | ●  | ●  |    | ●  |
-| Neural network                                  | ●  |    |    |    | ○  | ●  | ●  |    |    |
 | Math foundation                                 | ●  |    |    |    |    | ●  | ●  |    | ●  |
 
 ● primary   ○ situational
@@ -89,8 +88,6 @@ implementation against its own prior output.
 - **Transforms / filters** — known pairs (δ, sinusoid, DC); Butterworth/Chebyshev vs `scipy.signal`.
 - **Estimators / regression** — simulated known linear-Gaussian systems, AR processes with known
   coefficients, data on a known line/polynomial. Assert RMSE vs truth, NEES/NIS in χ² band, R².
-- **Neural network** — hand-computed forward/backward passes; finite-difference gradient checks.
-
 ### R2 — Unhappy flow (no exceptions here — errors are `std::optional`/status enums)
 Force **every** failure return and assert the *failure* value, not just success: non-SPD Cholesky,
 singular/rank-deficient solve, non-convergence at `maxIterations`, rank-deficient
@@ -257,18 +254,7 @@ final value.
   within the requested tolerance.
 - **M7 conservation** — energy drift bounded for a conservative system over many steps.
 
-### 10. Neural network — `neural_network/`
-`activation/*`, `layer/Dense`, `losses/*`, `model/Model`.
-
-- **M1 activation values** — reference points: `sigmoid(0)=0.5`, `tanh(0)=0`, `relu(−x)=0`,
-  `leaky_relu` slope; output range bounds; monotonicity where expected.
-- **M7 softmax** — outputs sum to 1 and are non-negative; shift-invariance.
-- **M1 loss values & gradients** — MSE of identical vectors = 0; loss non-negative; analytic gradient
-  matches finite-difference (M1 gradient check).
-- **M1 dense layer** — `output = W·x + b`; back-prop gradient check.
-- **Model (M6)** — forward pass is deterministic and equals the manual layer composition.
-
-### 11. Math foundation — `math/`
+### 10. Math foundation — `math/`
 `Matrix`, `ComplexNumber`, `Quaternion`, `Cordic`, `TrigonometricFunctions`, `HyperbolicFunctions`,
 `AdvancedFunctions`, `Statistics`, `LinearTimeInvariant`, `Toeplitz`, `QNumber`.
 

doc/estimators/LinearRegression.md

@@ -115,7 +115,7 @@ graph LR
 |-----------------------------------------------------------|--------------------------------------------------------------------------------------------|
 | [Gaussian Elimination](../solvers/GaussianElimination.md) | Used to solve the normal equation system                                                   |
 | [Yule-Walker](YuleWalker.md)                              | Structurally similar — also solves a linear system derived from correlations               |
-| [Neural Network](../neural_network/NeuralNetwork.md)      | A single-layer neural network with no activation and MSE loss reduces to linear regression |
+| Neural Network (neural-network-toobox-cpp)                | A single-layer neural network with no activation and MSE loss reduces to linear regression |
 
 ## References & Further Reading