+ fprintf(stderr, "New reference training points:");
+ for (unsigned i = 0; i < last_centers.size(); ++i) {
+ fprintf(stderr, " %.3f", last_centers[i]);
+ }
+ fprintf(stderr, "\n");
+}
+
+void spsa_train(const std::vector<float> &pcm, int sample_rate)
+{
+ float vals[NUM_SPSA_VALS] = { hysteresis_upper_limit, hysteresis_lower_limit, 1.0f }; // The rest is filled with 0.
+
+ float start_c = INITIAL_C;
+ double best_badness = HUGE_VAL;
+
+ for (int n = 1; n < NUM_ITER; ++n) {
+ float a = INITIAL_A * pow(n + A, -ALPHA);
+ float c = start_c * pow(n, -GAMMA);
+
+ // find a random perturbation
+ float p[NUM_SPSA_VALS];
+ float vals1[NUM_SPSA_VALS], vals2[NUM_SPSA_VALS];
+ for (int i = 0; i < NUM_SPSA_VALS; ++i) {
+ p[i] = (rand() % 2) ? 1.0 : -1.0;
+ vals1[i] = std::max(std::min(vals[i] - c * p[i], 1.0f), -1.0f);
+ vals2[i] = std::max(std::min(vals[i] + c * p[i], 1.0f), -1.0f);
+ }
+
+ std::vector<pulse> pulses1 = detect_pulses(do_fir_filter(pcm, vals1 + 2), vals1[0], vals1[1], sample_rate);
+ std::vector<pulse> pulses2 = detect_pulses(do_fir_filter(pcm, vals2 + 2), vals2[0], vals2[1], sample_rate);
+ float badness1 = eval_badness(pulses1, 1.0);
+ float badness2 = eval_badness(pulses2, 1.0);
+
+ // Find the gradient estimator
+ float g[NUM_SPSA_VALS];
+ for (int i = 0; i < NUM_SPSA_VALS; ++i) {
+ g[i] = (badness2 - badness1) / (2.0 * c * p[i]);
+ vals[i] -= a * g[i];
+ vals[i] = std::max(std::min(vals[i], 1.0f), -1.0f);
+ }
+ if (badness2 < badness1) {
+ std::swap(badness1, badness2);
+ std::swap(vals1, vals2);
+ std::swap(pulses1, pulses2);
+ }
+ if (badness1 < best_badness) {
+ fprintf(stderr, "\nNew best filter (badness=%f):", badness1);
+ for (int i = 0; i < NUM_FILTER_COEFF; ++i) {
+ fprintf(stderr, " %.5f", vals1[i + 2]);
+ }
+ fprintf(stderr, ", hysteresis limits = %f %f\n", vals1[0], vals1[1]);
+ best_badness = badness1;
+
+ find_kmeans(pulses1, 1.0, train_snap_points);
+
+ if (output_cycles_plot) {
+ output_cycle_plot(pulses1, 1.0);
+ }
+ }
+ fprintf(stderr, "%d ", n);
+ fflush(stderr);
+ }