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-rw-r--r--neural_ode.c1268
1 files changed, 0 insertions, 1268 deletions
diff --git a/neural_ode.c b/neural_ode.c
deleted file mode 100644
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--- a/neural_ode.c
+++ /dev/null
@@ -1,1268 +0,0 @@
-#include <stdio.h>
-#include <stdlib.h>
-#include <stdint.h>
-#include <string.h>
-#include <math.h>
-#include <time.h>
-
-/* ============================================================
- § Utilities: RNG, memory, vector/matrix ops
- ============================================================ */
-
-typedef struct { uint64_t state; } RNG;
-
-static RNG rng_init(uint64_t seed) {
- RNG r;
- r.state = seed ? seed : 1;
- return r;
-}
-
-static uint64_t rng_next(RNG *r) {
- uint64_t x = r->state;
- x ^= x << 13;
- x ^= x >> 7;
- x ^= x << 17;
- r->state = x;
- return x;
-}
-
-static double rng_uniform(RNG *r) {
- return (double)(rng_next(r) >> 11) / (double)(UINT64_C(1) << 53);
-}
-
-static double rng_normal(RNG *r) {
- double u1 = rng_uniform(r);
- double u2 = rng_uniform(r);
- if (u1 < 1e-300) u1 = 1e-300;
- return sqrt(-2.0 * log(u1)) * cos(2.0 * M_PI * u2);
-}
-
-static void *xmalloc(size_t n) {
- void *p = malloc(n);
- if (!p) { fprintf(stderr, "fatal: malloc(%zu) failed\n", n); abort(); }
- return p;
-}
-
-static void *xcalloc(size_t count, size_t size) {
- void *p = calloc(count, size);
- if (!p) { fprintf(stderr, "fatal: calloc(%zu, %zu) failed\n", count, size); abort(); }
- return p;
-}
-
-static double *vec_alloc(int n) { return (double *)xmalloc((size_t)n * sizeof(double)); }
-static double *vec_zeros(int n) { return (double *)xcalloc((size_t)n, sizeof(double)); }
-static void vec_zero(double *v, int n) { memset(v, 0, (size_t)n * sizeof(double)); }
-static void vec_copy(const double *src, double *dst, int n) { memcpy(dst, src, (size_t)n * sizeof(double)); }
-
-typedef struct {
- double *x; /* [D+1] for dynmlp_forward/vjp */
- double *h_pre; /* [H] for dynmlp_forward/vjp */
- double *h; /* [H] for dynmlp_forward/vjp */
- double *dh; /* [H] for dynmlp_vjp */
- double *dh_pre; /* [H] for dynmlp_vjp */
- double *dx; /* [D+1] for dynmlp_vjp */
- double *neg_a; /* [D] for adjoint_dynamics */
- double *vjp_z; /* [D] for adjoint_dynamics */
- double *vjp_theta; /* [nparams] for adjoint_dynamics */
-} Workspace;
-
-static Workspace workspace_alloc(int D, int H, int nparams) {
- Workspace ws;
- ws.x = vec_alloc(D + 1);
- ws.h_pre = vec_alloc(H);
- ws.h = vec_alloc(H);
- ws.dh = vec_alloc(H);
- ws.dh_pre = vec_alloc(H);
- ws.dx = vec_alloc(D + 1);
- ws.neg_a = vec_alloc(D);
- ws.vjp_z = vec_alloc(D);
- ws.vjp_theta = vec_alloc(nparams);
- return ws;
-}
-
-static void workspace_free(Workspace *ws) {
- free(ws->x);
- free(ws->h_pre);
- free(ws->h);
- free(ws->dh);
- free(ws->dh_pre);
- free(ws->dx);
- free(ws->neg_a);
- free(ws->vjp_z);
- free(ws->vjp_theta);
-}
-
-static void vec_add_scaled(double *dst, double alpha, const double *v, int n) {
- for (int i = 0; i < n; i++) dst[i] += alpha * v[i];
-}
-
-static double vec_dot(const double *a, const double *b, int n) {
- double s = 0.0;
- for (int i = 0; i < n; i++) s += a[i] * b[i];
- return s;
-}
-
-static void mat_vec(const double *M, const double *x, double *dst, int rows, int cols) {
- for (int i = 0; i < rows; i++) {
- double s = 0.0;
- for (int j = 0; j < cols; j++) s += M[i * cols + j] * x[j];
- dst[i] = s;
- }
-}
-
-static void mat_vec_T(const double *M, const double *v, double *dst, int rows, int cols) {
- for (int i = 0; i < rows; i++)
- for (int j = 0; j < cols; j++)
- dst[j] += M[i * cols + j] * v[i];
-}
-
-static void mat_outer_add(double *M, double alpha,
- const double *a, const double *b, int rows, int cols) {
- for (int i = 0; i < rows; i++)
- for (int j = 0; j < cols; j++)
- M[i * cols + j] += alpha * a[i] * b[j];
-}
-
-/* ============================================================
- § DynMLP: f(z, t, θ): R^(D+1) -> R^D
- ============================================================ */
-
-typedef struct {
- int D;
- int H;
- int nparams;
-} DynMLP;
-
-#define DYNMLP_W1(D, H) (0)
-#define DYNMLP_b1(D, H) ((D + 1) * (H))
-#define DYNMLP_W2(D, H) ((D + 1) * (H) + (H))
-#define DYNMLP_b2(D, H) ((D + 1) * (H) + (H) + (H) * (D))
-
-static int dynmlp_nparams(int D, int H) {
- return (D + 1) * H + H + H * D + D;
-}
-
-static void xavier_init(double *w, int fan_in, int fan_out, RNG *r) {
- double limit = sqrt(6.0 / (fan_in + fan_out));
- int n = fan_in * fan_out;
- for (int i = 0; i < n; i++)
- w[i] = (2.0 * rng_uniform(r) - 1.0) * limit;
-}
-
-static void dynmlp_init(DynMLP *net, int D, int H, double *theta, RNG *r) {
- net->D = D;
- net->H = H;
- net->nparams = dynmlp_nparams(D, H);
- xavier_init(theta + DYNMLP_W1(D, H), D + 1, H, r);
- vec_zero(theta + DYNMLP_b1(D, H), H);
- xavier_init(theta + DYNMLP_W2(D, H), H, D, r);
- vec_zero(theta + DYNMLP_b2(D, H), D);
-}
-
-static void dynmlp_forward(const DynMLP *net, const double *theta,
- const double *z, double t, double *out,
- Workspace *ws) {
- int D = net->D, H = net->H;
- const double *W1 = theta + DYNMLP_W1(D, H);
- const double *b1 = theta + DYNMLP_b1(D, H);
- const double *W2 = theta + DYNMLP_W2(D, H);
- const double *b2 = theta + DYNMLP_b2(D, H);
-
- double *x = ws->x;
- double *h_pre = ws->h_pre;
- double *h = ws->h;
-
- vec_copy(z, x, D);
- x[D] = t;
-
- mat_vec(W1, x, h_pre, H, D + 1);
- vec_add_scaled(h_pre, 1.0, b1, H);
-
- for (int i = 0; i < H; i++) h[i] = tanh(h_pre[i]);
-
- mat_vec(W2, h, out, D, H);
- vec_add_scaled(out, 1.0, b2, D);
-}
-
-/* Vector-Jacobian product: vjp_z = v^T (∂f/∂z), vjp_theta += v^T (∂f/∂θ)
- Note: vjp_theta is accumulated into, not overwritten. */
-static void dynmlp_vjp(const DynMLP *net, const double *theta,
- const double *z, double t, const double *v,
- double *vjp_z, double *vjp_theta,
- Workspace *ws) {
- int D = net->D;
- int H = net->H;
- const double *W1 = theta + DYNMLP_W1(D, H);
- const double *b1 = theta + DYNMLP_b1(D, H);
- const double *W2 = theta + DYNMLP_W2(D, H);
- double *dW1 = vjp_theta + DYNMLP_W1(D, H);
- double *db1 = vjp_theta + DYNMLP_b1(D, H);
- double *dW2 = vjp_theta + DYNMLP_W2(D, H);
- double *db2 = vjp_theta + DYNMLP_b2(D, H);
-
- double *x = ws->x;
- double *h_pre = ws->h_pre;
- double *h = ws->h;
- vec_copy(z, x, D);
- x[D] = t;
- mat_vec(W1, x, h_pre, H, D + 1);
- vec_add_scaled(h_pre, 1.0, b1, H);
-
- for (int i = 0; i < H; i++)
- h[i] = tanh(h_pre[i]);
-
- double *dh = ws->dh;
- double *dh_pre = ws->dh_pre;
- double *dx = ws->dx;
- vec_zero(dh, H);
- vec_zero(dx, D + 1);
-
- mat_vec_T(W2, v, dh, D, H);
- mat_outer_add(dW2, 1.0, v, h, D, H);
- vec_add_scaled(db2, 1.0, v, D);
-
- for (int i = 0; i < H; i++)
- dh_pre[i] = (1.0 - h[i] * h[i]) * dh[i];
-
- mat_vec_T(W1, dh_pre, dx, H, D + 1);
- mat_outer_add(dW1, 1.0, dh_pre, x, H, D + 1);
- vec_add_scaled(db1, 1.0, dh_pre, H);
-
- vec_copy(dx, vjp_z, D);
-}
-
-/* ============================================================
- § RK45 solver
- ============================================================ */
-
-typedef void (*ode_rhs_fn)(const double *state, double t, const double *params,
- int dim, double *out, void *ctx);
-
-typedef struct {
- double *y;
- int nfe; // number of fn evaluations
-} ODEResult;
-
-static const double dp_c[7] = { 0.0, 1.0/5.0, 3.0/10.0, 4.0/5.0, 8.0/9.0, 1.0, 1.0 };
-static const double dp_a2[1] = { 1.0/5.0 };
-static const double dp_a3[2] = { 3.0/40.0, 9.0/40.0 };
-static const double dp_a4[3] = { 44.0/45.0, -56.0/15.0, 32.0/9.0 };
-static const double dp_a5[4] = { 19372.0/6561.0, -25360.0/2187.0, 64448.0/6561.0, -212.0/729.0 };
-static const double dp_a6[5] = { 9017.0/3168.0, -355.0/33.0, 46732.0/5247.0, 49.0/176.0, -5103.0/18656.0 };
-static const double dp_b[7] = { 35.0/384.0, 0.0, 500.0/1113.0, 125.0/192.0, -2187.0/6784.0, 11.0/84.0, 0.0 };
-static const double dp_e[7] = {
- 35.0/384.0 - 5179.0/57600.0,
- 0.0,
- 500.0/1113.0 - 7571.0/16695.0,
- 125.0/192.0 - 393.0/640.0,
- -2187.0/6784.0 + 92097.0/339200.0,
- 11.0/84.0 - 187.0/2100.0,
- -1.0/40.0
-};
-
-ODEResult ode_solve(ode_rhs_fn f, const double *y0, double t0, double t1,
- const double *params, int dim, double atol, double rtol,
- void *ctx) {
- double **k = (double **)xmalloc(7 * sizeof(double *));
- for (int i = 0; i < 7; i++) k[i] = vec_alloc(dim);
- double *y = vec_alloc(dim);
- double *y5 = vec_alloc(dim);
- double *err = vec_alloc(dim);
- double *stg = vec_alloc(dim);
-
- ODEResult res = { vec_alloc(dim), 0 };
- vec_copy(y0, y, dim);
-
- double t = t0;
- double h = 0.01 * (t1 - t0);
- int k1_fresh = 0;
-
- for (int step = 0; step < 1000000; step++) {
- if (t1 > t0) {
- if (t >= t1) break;
- if (t + h > t1) h = t1 - t;
- } else {
- if (t <= t1) break;
- if (t + h < t1) h = t1 - t;
- }
-
- if (!k1_fresh) { f(y, t, params, dim, k[0], ctx); res.nfe++; k1_fresh = 1; }
-
- for (int i = 0; i < dim; i++)
- stg[i] = y[i] + h * dp_a2[0]*k[0][i];
- f(stg, t + dp_c[1]*h, params, dim, k[1], ctx); res.nfe++;
-
- for (int i = 0; i < dim; i++)
- stg[i] = y[i] + h * (dp_a3[0]*k[0][i] + dp_a3[1]*k[1][i]);
- f(stg, t + dp_c[2]*h, params, dim, k[2], ctx); res.nfe++;
-
- for (int i = 0; i < dim; i++)
- stg[i] = y[i] + h * (dp_a4[0]*k[0][i] + dp_a4[1]*k[1][i] + dp_a4[2]*k[2][i]);
- f(stg, t + dp_c[3]*h, params, dim, k[3], ctx); res.nfe++;
-
- for (int i = 0; i < dim; i++)
- stg[i] = y[i] + h * (dp_a5[0]*k[0][i] + dp_a5[1]*k[1][i]
- + dp_a5[2]*k[2][i] + dp_a5[3]*k[3][i]);
- f(stg, t + dp_c[4]*h, params, dim, k[4], ctx); res.nfe++;
-
- for (int i = 0; i < dim; i++)
- stg[i] = y[i] + h * (dp_a6[0]*k[0][i] + dp_a6[1]*k[1][i]
- + dp_a6[2]*k[2][i] + dp_a6[3]*k[3][i] + dp_a6[4]*k[4][i]);
- f(stg, t + dp_c[5]*h, params, dim, k[5], ctx); res.nfe++;
-
- for (int i = 0; i < dim; i++)
- y5[i] = y[i] + h * (dp_b[0]*k[0][i] + dp_b[2]*k[2][i]
- + dp_b[3]*k[3][i] + dp_b[4]*k[4][i] + dp_b[5]*k[5][i]);
- f(y5, t + h, params, dim, k[6], ctx); res.nfe++;
-
- for (int i = 0; i < dim; i++)
- err[i] = h * (dp_e[0]*k[0][i] + dp_e[2]*k[2][i] + dp_e[3]*k[3][i]
- + dp_e[4]*k[4][i] + dp_e[5]*k[5][i] + dp_e[6]*k[6][i]);
-
- double err_sq = 0.0;
- for (int i = 0; i < dim; i++) {
- double sc = atol + rtol * fmax(fabs(y[i]), fabs(y5[i]));
- double e = err[i] / sc;
- err_sq += e * e;
- }
- double err_norm = sqrt(err_sq / (double)dim);
-
- double factor;
- if (err_norm == 0.0) {
- factor = 5.0;
- } else {
- factor = 0.9 * pow(err_norm, -0.2);
- if (factor < 0.2) factor = 0.2;
- if (factor > 5.0) factor = 5.0;
- }
-
- if (err_norm <= 1.0) {
- vec_copy(y5, y, dim);
- t += h;
- double *tmp = k[0]; k[0] = k[6]; k[6] = tmp;
- h *= factor;
- } else {
- if (factor > 1.0) factor = 1.0;
- h *= factor;
- }
- }
-
- vec_copy(y, res.y, dim);
- for (int i = 0; i < 7; i++) free(k[i]);
-
- free(k);
- free(y);
- free(y5);
- free(err);
- free(stg);
-
- return res;
-}
-
-ODEResult ode_solve_times(ode_rhs_fn f, const double *y0, const double *times,
- int ntimes, const double *params, int dim,
- double atol, double rtol, void *ctx) {
- ODEResult res = { vec_alloc(dim * ntimes), 0 };
- vec_copy(y0, res.y, dim);
- for (int i = 1; i < ntimes; i++) {
- ODEResult seg = ode_solve(f, res.y + (i-1)*dim, times[i-1], times[i],
- params, dim, atol, rtol, ctx);
- vec_copy(seg.y, res.y + i * dim, dim);
- res.nfe += seg.nfe;
-
- free(seg.y);
- }
- return res;
-}
-
-/* ============================================================
- § Adjoint sensitivity method (Algorithm 1)
- ============================================================ */
-
-typedef struct {
- DynMLP net;
- const double *theta;
- int state_dim;
- int nparams;
- Workspace *ws;
-} AdjointCtx;
-
-static void neural_ode_rhs(const double *state, double t, const double *params,
- int dim, double *out, void *ctx) {
- (void)params;
- (void)dim;
-
- AdjointCtx *ac = (AdjointCtx *)ctx;
- dynmlp_forward(&ac->net, ac->theta, state, t, out, ac->ws);
-}
-
-static void adjoint_dynamics(const double *aug_state, double t, const double *params,
- int aug_dim, double *aug_out, void *ctx) {
- (void)params;
- (void)aug_dim;
-
- AdjointCtx *ac = (AdjointCtx *)ctx;
- int D = ac->state_dim;
- int nparams = ac->nparams;
- const double *z = aug_state;
- const double *a = aug_state + D;
-
- dynmlp_forward(&ac->net, ac->theta, z, t, aug_out, ac->ws);
-
- double *neg_a = ac->ws->neg_a;
- double *vjp_z = ac->ws->vjp_z;
- double *vjp_theta = ac->ws->vjp_theta;
- vec_zero(vjp_theta, nparams);
- for (int i = 0; i < D; i++) neg_a[i] = -a[i];
-
- dynmlp_vjp(&ac->net, ac->theta, z, t, neg_a, vjp_z, vjp_theta, ac->ws);
-
- vec_copy(vjp_z, aug_out + D, D);
- vec_copy(vjp_theta, aug_out + 2 * D, nparams);
-}
-
-typedef struct {
- double *dL_dz0;
- double *dL_dtheta;
- int nfe;
-} AdjointResult;
-
-typedef struct {
- int num_checkpoints;
- double *times; /* times[0..num_checkpoints], length num_checkpoints+1 */
- double **states; /* states[0..num_checkpoints], state at each checkpoint time */
- double *z1; /* separate copy of states[num_checkpoints] = z(t1) */
- int nfe;
-} ForwardResult;
-
-static void forward_result_free(ForwardResult *fr) {
- free(fr->times);
- for (int i = 0; i <= fr->num_checkpoints; i++)
- free(fr->states[i]);
- free(fr->states);
- free(fr->z1);
-}
-
-static ForwardResult forward_solve(const DynMLP *net, const double *theta,
- const double *z0, double t0, double t1,
- double atol, double rtol, int num_checkpoints) {
- int D = net->D;
- Workspace ws = workspace_alloc(D, net->H, net->nparams);
- AdjointCtx ac = { *net, theta, D, net->nparams, &ws };
-
- ForwardResult fr;
- fr.num_checkpoints = num_checkpoints;
- fr.nfe = 0;
-
- fr.times = vec_alloc(num_checkpoints + 1);
- fr.states = (double **)xmalloc((size_t)(num_checkpoints + 1) * sizeof(double *));
- for (int i = 0; i <= num_checkpoints; i++)
- fr.states[i] = vec_alloc(D);
-
- for (int i = 0; i <= num_checkpoints; i++)
- fr.times[i] = t0 + (t1 - t0) * (double)i / (double)num_checkpoints;
-
- vec_copy(z0, fr.states[0], D);
-
- for (int i = 0; i < num_checkpoints; i++) {
- ODEResult seg = ode_solve(neural_ode_rhs, fr.states[i],
- fr.times[i], fr.times[i + 1],
- NULL, D, atol, rtol, &ac);
- vec_copy(seg.y, fr.states[i + 1], D);
- fr.nfe += seg.nfe;
- free(seg.y);
- }
-
- fr.z1 = vec_alloc(D);
- vec_copy(fr.states[num_checkpoints], fr.z1, D);
-
- workspace_free(&ws);
- return fr;
-}
-
-static AdjointResult adjoint_solve(const DynMLP *net, const double *theta,
- const ForwardResult *fr, const double *dL_dz1,
- double atol, double rtol) {
- int D = net->D;
- int nparams = net->nparams;
- int aug_dim = 2 * D + nparams;
-
- double *aug = vec_zeros(aug_dim);
- vec_copy(fr->z1, aug, D);
- vec_copy(dL_dz1, aug + D, D);
-
- Workspace ws = workspace_alloc(D, net->H, nparams);
- AdjointCtx ac = { *net, theta, D, nparams, &ws };
- int total_nfe = 0;
-
- for (int k = fr->num_checkpoints; k >= 1; k--) {
- /* Replace z with stored checkpoint to prevent numerical drift */
- vec_copy(fr->states[k], aug, D);
-
- ODEResult seg = ode_solve(adjoint_dynamics, aug,
- fr->times[k], fr->times[k - 1],
- NULL, aug_dim, atol, rtol, &ac);
- vec_copy(seg.y, aug, aug_dim);
- total_nfe += seg.nfe;
- free(seg.y);
- }
-
- AdjointResult ar;
- ar.dL_dz0 = vec_alloc(D);
- ar.dL_dtheta = vec_alloc(nparams);
- ar.nfe = total_nfe;
- vec_copy(aug + D, ar.dL_dz0, D);
- vec_copy(aug + 2 * D, ar.dL_dtheta, nparams);
-
- workspace_free(&ws);
- free(aug);
- return ar;
-}
-
-typedef struct {
- double *z1;
- double *dL_dz0;
- double *dL_dtheta;
- int nfe_forward;
- int nfe_backward;
-} NeuralODEOutput;
-
-NeuralODEOutput neural_ode_forward_backward(const DynMLP *net, const double *theta,
- const double *z0, double t0, double t1,
- const double *target, double atol, double rtol,
- int num_checkpoints) {
- int D = net->D;
-
- ForwardResult fr = forward_solve(net, theta, z0, t0, t1, atol, rtol, num_checkpoints);
-
- double *dL_dz1 = vec_alloc(D);
- for (int i = 0; i < D; i++) dL_dz1[i] = fr.z1[i] - target[i];
-
- AdjointResult ar = adjoint_solve(net, theta, &fr, dL_dz1, atol, rtol);
-
- NeuralODEOutput out;
- out.z1 = vec_alloc(D);
- vec_copy(fr.z1, out.z1, D);
- out.dL_dz0 = ar.dL_dz0;
- out.dL_dtheta = ar.dL_dtheta;
- out.nfe_forward = fr.nfe;
- out.nfe_backward = ar.nfe;
-
- forward_result_free(&fr);
- free(dL_dz1);
- return out;
-}
-
-/* ============================================================
- § Multi-observation adjoint
- ============================================================ */
-
-typedef struct {
- double *dL_dz0;
- double *dL_dtheta;
- int nfe;
-} MultiObsAdjointResult;
-
-/* adjoint_solve_multi: backward pass with `kicks' at each observation time.
- z_traj[i*D .. i*D+D] = z(times[i]) from the forward pass.
- dL_dz_each[i*D .. i*D+D] = dL_i/dz(times[i]) for each observation. */
-static MultiObsAdjointResult adjoint_solve_multi(
- const DynMLP *net,
- const double *theta,
- const double *z_traj,
- const double *times,
- const double *dL_dz_each,
- int ntimes,
- double atol,
- double rtol)
-{
- int D = net->D;
- int nparams = net->nparams;
- int aug_dim = 2 * D + nparams;
-
- Workspace ws = workspace_alloc(D, net->H, nparams);
- AdjointCtx ac = { *net, theta, D, nparams, &ws };
-
- double *a = vec_alloc(D);
- double *dtheta = vec_zeros(nparams);
- double *aug = vec_alloc(aug_dim);
- int total_nfe = 0;
-
- vec_copy(dL_dz_each + (ntimes - 1) * D, a, D);
-
- for (int i = ntimes - 1; i >= 1; i--) {
- vec_copy(z_traj + i * D, aug, D);
- vec_copy(a, aug + D, D);
- vec_copy(dtheta, aug + 2 * D, nparams);
-
- ODEResult seg = ode_solve(adjoint_dynamics, aug,
- times[i], times[i - 1],
- NULL, aug_dim, atol, rtol, &ac);
- vec_copy(seg.y, aug, aug_dim);
- total_nfe += seg.nfe;
- free(seg.y);
-
- vec_copy(aug + D, a, D);
- vec_copy(aug + 2 * D, dtheta, nparams);
-
- /* Kick: add per-observation loss gradient at time t_{i-1} */
- vec_add_scaled(a, 1.0, dL_dz_each + (i - 1) * D, D);
- }
-
- MultiObsAdjointResult result;
- result.dL_dz0 = a;
- result.dL_dtheta = dtheta;
- result.nfe = total_nfe;
-
- workspace_free(&ws);
- free(aug);
- return result;
-}
-
-typedef struct {
- double *z_traj;
- double *dL_dz0;
- double *dL_dtheta;
- int nfe_forward;
- int nfe_backward;
-} MultiObsNeuralODEOutput;
-
-MultiObsNeuralODEOutput neural_ode_forward_backward_multi(
- const DynMLP *net,
- const double *theta,
- const double *z0,
- const double *times,
- const double *targets,
- int ntimes,
- double atol,
- double rtol)
-{
- int D = net->D;
- Workspace ws = workspace_alloc(D, net->H, net->nparams);
- AdjointCtx ac = { *net, theta, D, net->nparams, &ws };
-
- ODEResult fwd = ode_solve_times(neural_ode_rhs, z0, times, ntimes,
- NULL, D, atol, rtol, &ac);
- workspace_free(&ws);
-
- double *dL_dz_each = vec_alloc(ntimes * D);
- for (int i = 0; i < ntimes * D; i++)
- dL_dz_each[i] = fwd.y[i] - targets[i];
-
- MultiObsAdjointResult ar = adjoint_solve_multi(net, theta, fwd.y, times,
- dL_dz_each, ntimes, atol, rtol);
- free(dL_dz_each);
-
- MultiObsNeuralODEOutput out;
- out.z_traj = fwd.y;
- out.dL_dz0 = ar.dL_dz0;
- out.dL_dtheta = ar.dL_dtheta;
- out.nfe_forward = fwd.nfe;
- out.nfe_backward = ar.nfe;
- return out;
-}
-
-/* ============================================================
- § Training loop
- ============================================================ */
-
-
-typedef struct {
- double *m;
- double *v;
- int nparams;
- double lr;
- double beta1;
- double beta2;
- double eps;
- int t;
-} Adam;
-
-static Adam adam_init(int nparams, double lr, double beta1, double beta2, double eps) {
- Adam a;
- a.m = vec_zeros(nparams);
- a.v = vec_zeros(nparams);
- a.nparams = nparams;
- a.lr = lr;
- a.beta1 = beta1;
- a.beta2 = beta2;
- a.eps = eps;
- a.t = 0;
- return a;
-}
-
-static void adam_update(Adam *a, double *theta, const double *grad) {
- a->t++;
- double bc1 = 1.0 - pow(a->beta1, (double)a->t);
- double bc2 = 1.0 - pow(a->beta2, (double)a->t);
- double alpha = a->lr * sqrt(bc2) / bc1;
- for (int i = 0; i < a->nparams; i++) {
- a->m[i] = a->beta1 * a->m[i] + (1.0 - a->beta1) * grad[i];
- a->v[i] = a->beta2 * a->v[i] + (1.0 - a->beta2) * grad[i] * grad[i];
- theta[i] -= alpha * a->m[i] / (sqrt(a->v[i]) + a->eps);
- }
-}
-
-static void adam_free(Adam *a) {
- free(a->m);
- free(a->v);
-}
-
-static double train_one(const DynMLP *net, const double *theta,
- const double *z0, double t0, double t1,
- const double *target, double *grad_accum,
- double atol, double rtol, int num_checkpoints,
- int *nfe_fwd, int *nfe_bwd) {
- NeuralODEOutput out = neural_ode_forward_backward(net, theta, z0, t0, t1,
- target, atol, rtol, num_checkpoints);
- double loss = 0.0;
- int D = net->D;
- for (int i = 0; i < D; i++) {
- double d = out.z1[i] - target[i];
- loss += 0.5 * d * d;
- }
- for (int i = 0; i < net->nparams; i++) grad_accum[i] += out.dL_dtheta[i];
- *nfe_fwd += out.nfe_forward;
- *nfe_bwd += out.nfe_backward;
- free(out.z1);
- free(out.dL_dz0);
- free(out.dL_dtheta);
- return loss;
-}
-
-typedef struct {
- double loss;
- int nfe_fwd;
- int nfe_bwd;
-} TrainStepResult;
-
-static TrainStepResult train_step(const DynMLP *net, double *theta,
- const double **z0s, const double **targets,
- double t0, double t1, int batch_size,
- Adam *adam, double atol, double rtol, int num_checkpoints) {
- int nparams = net->nparams;
- double *grad_accum = vec_zeros(nparams);
- TrainStepResult res = { 0.0, 0, 0 };
-
- for (int b = 0; b < batch_size; b++) {
- res.loss += train_one(net, theta, z0s[b], t0, t1, targets[b],
- grad_accum, atol, rtol, num_checkpoints,
- &res.nfe_fwd, &res.nfe_bwd);
- }
- res.loss /= (double)batch_size;
- for (int i = 0; i < nparams; i++) grad_accum[i] /= (double)batch_size;
- adam_update(adam, theta, grad_accum);
- free(grad_accum);
- return res;
-}
-
-/* ============================================================
- § Spiral dataset
- ============================================================ */
-
-static void spiral_rhs(const double *state, double t, const double *params,
- int dim, double *out, void *ctx) {
- (void)t; (void)dim; (void)ctx;
- double alpha = params[0];
- out[0] = alpha * state[1];
- out[1] = -alpha * state[0];
-}
-
-typedef struct {
- double **z0;
- double **target;
- int num_samples;
-} Dataset;
-
-static Dataset generate_spiral_dataset(int num_samples, double t0, double t1,
- double noise_std, RNG *r) {
- Dataset ds;
- ds.num_samples = num_samples;
- ds.z0 = (double **)xmalloc(num_samples * sizeof(double *));
- ds.target = (double **)xmalloc(num_samples * sizeof(double *));
-
- for (int i = 0; i < num_samples; i++) {
- double alpha = 1.0;
- double angle = 2.0 * M_PI * rng_uniform(r);
- double radius = 0.5 + 1.0 * rng_uniform(r); /* uniform in [0.5, 1.5] */
-
- ds.z0[i] = vec_alloc(2);
- ds.target[i] = vec_alloc(2);
-
- ds.z0[i][0] = radius * cos(angle);
- ds.z0[i][1] = radius * sin(angle);
-
- ODEResult res = ode_solve(spiral_rhs, ds.z0[i], t0, t1,
- &alpha, 2, 1e-8, 1e-8, NULL);
- vec_copy(res.y, ds.target[i], 2);
- free(res.y);
-
- /* add noise to the initial observation */
- ds.z0[i][0] += noise_std * rng_normal(r);
- ds.z0[i][1] += noise_std * rng_normal(r);
- }
- return ds;
-}
-
-static void dataset_free(Dataset *ds) {
- for (int i = 0; i < ds->num_samples; i++) {
- free(ds->z0[i]);
- free(ds->target[i]);
- }
- free(ds->z0);
- free(ds->target);
-}
-
-static double evaluate(const DynMLP *net, const double *theta,
- const Dataset *ds, double t0, double t1,
- double atol, double rtol) {
- int D = net->D;
- Workspace ws = workspace_alloc(D, net->H, net->nparams);
- AdjointCtx ac = { *net, theta, D, net->nparams, &ws };
- double total_loss = 0.0;
- for (int i = 0; i < ds->num_samples; i++) {
- ODEResult fwd = ode_solve(neural_ode_rhs, ds->z0[i], t0, t1,
- NULL, D, atol, rtol, &ac);
- for (int j = 0; j < D; j++) {
- double d = fwd.y[j] - ds->target[i][j];
- total_loss += 0.5 * d * d;
- }
- free(fwd.y);
- }
- workspace_free(&ws);
- return total_loss / (double)ds->num_samples;
-}
-
-/* ============================================================
- § Tests
- ============================================================ */
-
-static void rhs_decay(const double *y, double t, const double *p, int d, double *out, void *ctx) {
- (void)t; (void)p; (void)d; (void)ctx;
- out[0] = -y[0];
-}
-
-static void rhs_rotation(const double *y, double t, const double *p, int d, double *out, void *ctx) {
- (void)t; (void)p; (void)d; (void)ctx;
- out[0] = -y[1]; out[1] = y[0];
-}
-
-static void test_ode_solver(void) {
- const double atol = 1e-8, rtol = 1e-8, tol = 1e-6;
-
- { double y0 = 1.0;
- ODEResult r = ode_solve(rhs_decay, &y0, 0.0, 1.0, NULL, 1, atol, rtol, NULL);
- double err = fabs(r.y[0] - exp(-1.0));
- printf("ODE test 1 (decay): err=%.2e nfe=%d %s\n", err, r.nfe, err < tol ? "PASS" : "FAIL");
- free(r.y); }
-
- { double y0[2] = {1.0, 0.0};
- ODEResult r = ode_solve(rhs_rotation, y0, 0.0, 2.0 * M_PI, NULL, 2, atol, rtol, NULL);
- double err = sqrt((r.y[0]-1.0)*(r.y[0]-1.0) + r.y[1]*r.y[1]);
- printf("ODE test 2 (rotation): err=%.2e nfe=%d %s\n", err, r.nfe, err < tol ? "PASS" : "FAIL");
- free(r.y); }
-
- { double y0 = exp(-1.0);
- ODEResult r = ode_solve(rhs_decay, &y0, 1.0, 0.0, NULL, 1, atol, rtol, NULL);
- double err = fabs(r.y[0] - 1.0);
- printf("ODE test 3 (backward): err=%.2e nfe=%d %s\n", err, r.nfe, err < tol ? "PASS" : "FAIL");
- free(r.y); }
-}
-
-static void test_dynmlp_gradients(RNG *r) {
- const int D = 3, H = 8;
- const double EPS = 1e-7, TOL = 1e-5;
-
- int np = dynmlp_nparams(D, H);
- double *theta = vec_alloc(np);
- double *z = vec_alloc(D);
- double *v = vec_alloc(D);
- double *out_p = vec_alloc(D);
- double *out_m = vec_alloc(D);
-
- DynMLP net;
- dynmlp_init(&net, D, H, theta, r);
- for (int i = 0; i < D; i++) z[i] = rng_normal(r);
- for (int i = 0; i < D; i++) v[i] = rng_normal(r);
- double t = rng_normal(r);
-
- Workspace ws = workspace_alloc(D, H, np);
-
- double *vjp_z = vec_zeros(D);
- double *vjp_theta = vec_zeros(np);
- dynmlp_vjp(&net, theta, z, t, v, vjp_z, vjp_theta, &ws);
-
- double *num_vjp_z = vec_alloc(D);
- for (int i = 0; i < D; i++) {
- double zi = z[i];
- z[i] = zi + EPS; dynmlp_forward(&net, theta, z, t, out_p, &ws);
- z[i] = zi - EPS; dynmlp_forward(&net, theta, z, t, out_m, &ws);
- z[i] = zi;
- num_vjp_z[i] = (vec_dot(v, out_p, D) - vec_dot(v, out_m, D)) / (2.0 * EPS);
- }
- double max_err_z = 0.0;
- for (int i = 0; i < D; i++) {
- double e = fabs(vjp_z[i] - num_vjp_z[i]);
- if (e > max_err_z) max_err_z = e;
- }
-
- double *num_vjp_theta = vec_alloc(np);
- for (int k = 0; k < np; k++) {
- double tk = theta[k];
- theta[k] = tk + EPS; dynmlp_forward(&net, theta, z, t, out_p, &ws);
- theta[k] = tk - EPS; dynmlp_forward(&net, theta, z, t, out_m, &ws);
- theta[k] = tk;
- num_vjp_theta[k] = (vec_dot(v, out_p, D) - vec_dot(v, out_m, D)) / (2.0 * EPS);
- }
- double max_err_theta = 0.0;
- for (int k = 0; k < np; k++) {
- double e = fabs(vjp_theta[k] - num_vjp_theta[k]);
- if (e > max_err_theta) max_err_theta = e;
- }
-
- printf("dL/dz: max_err=%.2e %s\n", max_err_z, max_err_z < TOL ? "PASS" : "FAIL");
- printf("dL/dtheta: max_err=%.2e %s\n", max_err_theta, max_err_theta < TOL ? "PASS" : "FAIL");
-
- workspace_free(&ws);
- free(theta); free(z); free(v); free(out_p); free(out_m);
- free(vjp_z); free(vjp_theta); free(num_vjp_z); free(num_vjp_theta);
-}
-
-static void test_adjoint_gradients(RNG *r) {
- const int D = 2, H = 8;
- const double EPS = 1e-5, atol = 1e-7, rtol = 1e-7;
- const double t0 = 0.0, t1 = 1.0;
-
- int np = dynmlp_nparams(D, H);
- double *theta = vec_alloc(np);
- double *z0 = vec_alloc(D);
- double *target = vec_alloc(D);
-
- DynMLP net;
- dynmlp_init(&net, D, H, theta, r);
- for (int i = 0; i < D; i++) z0[i] = rng_normal(r);
- for (int i = 0; i < D; i++) target[i] = rng_normal(r);
-
- NeuralODEOutput out = neural_ode_forward_backward(&net, theta, z0, t0, t1,
- target, atol, rtol, 10);
-
- Workspace ws = workspace_alloc(D, H, np);
-
-#define FWD_LOSS(z0_, theta_) ({ \
- AdjointCtx ac_ = { net, (theta_), D, np, &ws }; \
- ODEResult r_ = ode_solve(neural_ode_rhs, (z0_), t0, t1, NULL, D, atol, rtol, &ac_); \
- double l_ = 0.0; \
- for (int _i = 0; _i < D; _i++) { double _d = r_.y[_i] - target[_i]; l_ += 0.5*_d*_d; } \
- free(r_.y); l_; \
-})
-
- double *num_dL_dtheta = vec_alloc(np);
- for (int k = 0; k < np; k++) {
- double tk = theta[k];
- theta[k] = tk + EPS; double lp = FWD_LOSS(z0, theta);
- theta[k] = tk - EPS; double lm = FWD_LOSS(z0, theta);
- theta[k] = tk;
- num_dL_dtheta[k] = (lp - lm) / (2.0 * EPS);
- }
- double max_num_theta = 0.0;
- for (int k = 0; k < np; k++)
- if (fabs(num_dL_dtheta[k]) > max_num_theta) max_num_theta = fabs(num_dL_dtheta[k]);
- double max_err_theta = 0.0;
- for (int k = 0; k < np; k++) {
- double e = fabs(out.dL_dtheta[k] - num_dL_dtheta[k]);
- if (e > max_err_theta) max_err_theta = e;
- }
- double rel_theta = max_err_theta / (max_num_theta + 1e-8);
- printf("adjoint dL/dtheta: max_rel_err=%.2e nfe_fwd=%d nfe_bwd=%d %s\n",
- rel_theta, out.nfe_forward, out.nfe_backward, rel_theta < 1e-3 ? "PASS" : "FAIL");
-
- double *num_dL_dz0 = vec_alloc(D);
- for (int i = 0; i < D; i++) {
- double zi = z0[i];
- z0[i] = zi + EPS; double lp = FWD_LOSS(z0, theta);
- z0[i] = zi - EPS; double lm = FWD_LOSS(z0, theta);
- z0[i] = zi;
- num_dL_dz0[i] = (lp - lm) / (2.0 * EPS);
- }
- double max_num_z0 = 0.0;
- for (int i = 0; i < D; i++)
- if (fabs(num_dL_dz0[i]) > max_num_z0) max_num_z0 = fabs(num_dL_dz0[i]);
- double max_err_z0 = 0.0;
- for (int i = 0; i < D; i++) {
- double e = fabs(out.dL_dz0[i] - num_dL_dz0[i]);
- if (e > max_err_z0) max_err_z0 = e;
- }
- double rel_z0 = max_err_z0 / (max_num_z0 + 1e-8);
- printf("adjoint dL/dz0: max_rel_err=%.2e %s\n", rel_z0, rel_z0 < 1e-3 ? "PASS" : "FAIL");
-
-#undef FWD_LOSS
-
- workspace_free(&ws);
- free(out.z1); free(out.dL_dz0); free(out.dL_dtheta);
- free(num_dL_dtheta); free(num_dL_dz0);
- free(theta); free(z0); free(target);
-}
-
-static void test_multi_obs_adjoint(RNG *r) {
- const int D = 2, H = 8;
- const double EPS = 1e-5, atol = 1e-7, rtol = 1e-7;
- const int ntimes = 5;
-
- double times[5] = { 0.0, 0.5, 1.0, 1.5, 2.0 };
-
- int np = dynmlp_nparams(D, H);
- double *theta = vec_alloc(np);
- double *z0 = vec_alloc(D);
- double *targets = vec_alloc(ntimes * D);
-
- DynMLP net;
- dynmlp_init(&net, D, H, theta, r);
- for (int i = 0; i < D; i++) z0[i] = rng_normal(r);
- for (int i = 0; i < ntimes * D; i++) targets[i] = rng_normal(r);
-
- MultiObsNeuralODEOutput out = neural_ode_forward_backward_multi(
- &net, theta, z0, times, targets, ntimes, atol, rtol);
-
- Workspace ws = workspace_alloc(D, H, np);
- AdjointCtx ac = { net, theta, D, np, &ws };
-
- /* Numerical dL/dtheta */
- double *num_dL_dtheta = vec_alloc(np);
- for (int k = 0; k < np; k++) {
- double tk = theta[k];
-
- theta[k] = tk + EPS;
- ODEResult rp = ode_solve_times(neural_ode_rhs, z0, times, ntimes,
- NULL, D, atol, rtol, &ac);
- double lp = 0.0;
- for (int i = 0; i < ntimes * D; i++) {
- double d = rp.y[i] - targets[i]; lp += 0.5 * d * d;
- }
- free(rp.y);
-
- theta[k] = tk - EPS;
- ODEResult rm = ode_solve_times(neural_ode_rhs, z0, times, ntimes,
- NULL, D, atol, rtol, &ac);
- double lm = 0.0;
- for (int i = 0; i < ntimes * D; i++) {
- double d = rm.y[i] - targets[i]; lm += 0.5 * d * d;
- }
- free(rm.y);
-
- theta[k] = tk;
- num_dL_dtheta[k] = (lp - lm) / (2.0 * EPS);
- }
-
- double max_num_theta = 0.0;
- for (int k = 0; k < np; k++)
- if (fabs(num_dL_dtheta[k]) > max_num_theta) max_num_theta = fabs(num_dL_dtheta[k]);
- double max_err_theta = 0.0;
- for (int k = 0; k < np; k++) {
- double e = fabs(out.dL_dtheta[k] - num_dL_dtheta[k]);
- if (e > max_err_theta) max_err_theta = e;
- }
- double rel_theta = max_err_theta / (max_num_theta + 1e-8);
- printf("multi-obs adjoint dL/dtheta: max_rel_err=%.2e nfe_fwd=%d nfe_bwd=%d %s\n",
- rel_theta, out.nfe_forward, out.nfe_backward, rel_theta < 1e-3 ? "PASS" : "FAIL");
-
- /* Numerical dL/dz0 */
- double *num_dL_dz0 = vec_alloc(D);
- for (int i = 0; i < D; i++) {
- double zi = z0[i];
-
- z0[i] = zi + EPS;
- ODEResult rp = ode_solve_times(neural_ode_rhs, z0, times, ntimes,
- NULL, D, atol, rtol, &ac);
- double lp = 0.0;
- for (int j = 0; j < ntimes * D; j++) {
- double d = rp.y[j] - targets[j]; lp += 0.5 * d * d;
- }
- free(rp.y);
-
- z0[i] = zi - EPS;
- ODEResult rm = ode_solve_times(neural_ode_rhs, z0, times, ntimes,
- NULL, D, atol, rtol, &ac);
- double lm = 0.0;
- for (int j = 0; j < ntimes * D; j++) {
- double d = rm.y[j] - targets[j]; lm += 0.5 * d * d;
- }
- free(rm.y);
-
- z0[i] = zi;
- num_dL_dz0[i] = (lp - lm) / (2.0 * EPS);
- }
-
- double max_num_z0 = 0.0;
- for (int i = 0; i < D; i++)
- if (fabs(num_dL_dz0[i]) > max_num_z0) max_num_z0 = fabs(num_dL_dz0[i]);
- double max_err_z0 = 0.0;
- for (int i = 0; i < D; i++) {
- double e = fabs(out.dL_dz0[i] - num_dL_dz0[i]);
- if (e > max_err_z0) max_err_z0 = e;
- }
- double rel_z0 = max_err_z0 / (max_num_z0 + 1e-8);
- printf("multi-obs adjoint dL/dz0: max_rel_err=%.2e %s\n",
- rel_z0, rel_z0 < 1e-3 ? "PASS" : "FAIL");
-
- workspace_free(&ws);
- free(out.z_traj); free(out.dL_dz0); free(out.dL_dtheta);
- free(num_dL_dtheta); free(num_dL_dz0);
- free(theta); free(z0); free(targets);
-}
-
-static void test_training(RNG *r) {
- const int D = 2, H = 16;
- const int N = 50, BATCH = 10, ITERS = 300;
- const double t0 = 0.0, t1 = 1.0;
- const double atol = 1e-4, rtol = 1e-4;
-
- DynMLP net;
- int nparams = dynmlp_nparams(D, H);
- double *theta = vec_alloc(nparams);
- dynmlp_init(&net, D, H, theta, r);
- Adam adam = adam_init(nparams, 1e-3, 0.9, 0.999, 1e-8);
-
- double **z0s = (double **)xmalloc(N * sizeof(double *));
- double **targets = (double **)xmalloc(N * sizeof(double *));
- for (int i = 0; i < N; i++) {
- double angle = 2.0 * M_PI * rng_uniform(r);
- z0s[i] = vec_alloc(D);
- targets[i] = vec_alloc(D);
- z0s[i][0] = cos(angle);
- z0s[i][1] = sin(angle);
- targets[i][0] = -z0s[i][1];
- targets[i][1] = z0s[i][0];
- }
-
- const double **batch_z0 = (const double **)xmalloc(BATCH * sizeof(double *));
- const double **batch_tgt = (const double **)xmalloc(BATCH * sizeof(double *));
-
- printf("\n--- Training test (D=2, H=16, 90-deg rotation) ---\n");
- for (int iter = 0; iter < ITERS; iter++) {
- for (int b = 0; b < BATCH; b++) {
- int idx = (int)(rng_next(r) % (uint64_t)N);
- batch_z0[b] = z0s[idx];
- batch_tgt[b] = targets[idx];
- }
- TrainStepResult res = train_step(&net, theta, batch_z0, batch_tgt,
- t0, t1, BATCH, &adam, atol, rtol, 10);
- if ((iter + 1) % 50 == 0)
- printf("iter %3d loss=%.4f nfe_fwd=%d\n", iter + 1, res.loss, res.nfe_fwd);
- }
-
- Workspace ws = workspace_alloc(D, H, nparams);
- AdjointCtx ac = { net, theta, D, nparams, &ws };
- double final_loss = 0.0;
- for (int i = 0; i < N; i++) {
- ODEResult fwd = ode_solve(neural_ode_rhs, z0s[i], t0, t1, NULL, D, atol, rtol, &ac);
- for (int j = 0; j < D; j++) {
- double d = fwd.y[j] - targets[i][j];
- final_loss += 0.5 * d * d;
- }
- free(fwd.y);
- }
- final_loss /= (double)N;
- printf("Loss: %.4f\n", final_loss);
-
- workspace_free(&ws);
- adam_free(&adam);
- free(batch_z0); free(batch_tgt);
- for (int i = 0; i < N; i++) { free(z0s[i]); free(targets[i]); }
- free(z0s); free(targets);
- free(theta);
-}
-
-
-
-int main(void) {
- RNG r = rng_init(42);
-
- /* --- sanity checks --- */
- test_ode_solver();
- test_dynmlp_gradients(&r);
- test_adjoint_gradients(&r);
- test_multi_obs_adjoint(&r);
- test_training(&r);
-
- printf("\n--- Training demo (spiral) ---\n\n");
-
- r = rng_init((uint64_t)time(NULL));
-
- const double t0 = 0.0, t1 = 1.5;
- const double noise_std = 0.1;
- const double atol_train = 1e-3, rtol_train = 1e-3;
- const double atol_eval = 1e-5, rtol_eval = 1e-5;
- const int TRAIN_N = 200, TEST_N = 50;
- const int ITERS = 500, BATCH = 16, LOG_EVERY = 25;
-
- Dataset train_ds = generate_spiral_dataset(TRAIN_N, t0, t1, noise_std, &r);
- Dataset test_ds = generate_spiral_dataset(TEST_N, t0, t1, noise_std, &r);
-
- const int D = 2, H = 32;
- DynMLP net;
- int nparams = dynmlp_nparams(D, H);
- double *theta = vec_alloc(nparams);
- dynmlp_init(&net, D, H, theta, &r);
-
- Adam adam = adam_init(nparams, 1e-3, 0.9, 0.999, 1e-8);
-
- printf("%-6s %-12s %-12s %-10s %-10s",
- "Iter", "Train Loss", "Test Loss", "Fwd NFE", "Bwd NFE");
- printf("\n--------------------------------------------------\n");
-
- const double **batch_z0 = (const double **)xmalloc(BATCH * sizeof(double *));
- const double **batch_tgt = (const double **)xmalloc(BATCH * sizeof(double *));
-
- for (int iter = 1; iter <= ITERS; iter++) {
- for (int b = 0; b < BATCH; b++) {
- int idx = (int)(rng_next(&r) % (uint64_t)TRAIN_N);
- batch_z0[b] = train_ds.z0[idx];
- batch_tgt[b] = train_ds.target[idx];
- }
-
- TrainStepResult res = train_step(&net, theta, batch_z0, batch_tgt,
- t0, t1, BATCH, &adam,
- atol_train, rtol_train, 10);
-
- if (iter % LOG_EVERY == 0) {
- double test_loss = evaluate(&net, theta, &test_ds,
- t0, t1, atol_eval, rtol_eval);
- printf("%-6d %-12.6f %-12.6f %-10d %-10d\n",
- iter, res.loss, test_loss,
- res.nfe_fwd, res.nfe_bwd);
- fflush(stdout);
- }
- }
-
- free(batch_z0);
- free(batch_tgt);
-
- printf("\n--------------------------------------------------\n");
- double final_test_loss = evaluate(&net, theta, &test_ds,
- t0, t1, atol_eval, rtol_eval);
- printf("Final test loss : %.6f\n", final_test_loss);
- printf("Total parameters: %d\n", nparams);
-
- printf("\nSample predictions:\n");
- Workspace ws = workspace_alloc(D, H, nparams);
- AdjointCtx ac = { net, theta, D, nparams, &ws };
- for (int s = 0; s < 5; s++) {
- int idx = (int)(rng_next(&r) % (uint64_t)TEST_N);
- ODEResult fwd = ode_solve(neural_ode_rhs,
- test_ds.z0[idx], t0, t1,
- NULL, D, atol_eval, rtol_eval, &ac);
- printf(" z0=(%.4f, %.4f) predicted=(%.4f, %.4f) target=(%.4f, %.4f)\n",
- test_ds.z0[idx][0], test_ds.z0[idx][1],
- fwd.y[0], fwd.y[1],
- test_ds.target[idx][0], test_ds.target[idx][1]);
- free(fwd.y);
- }
- workspace_free(&ws);
-
- free(theta);
- adam_free(&adam);
- dataset_free(&train_ds);
- dataset_free(&test_ds);
-
- return 0;
-}
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