preliminary tests with FFNN for freq estimation

freq_est.c

@@ -0,0 +1,65 @@
+#include <stdlib.h>
+#include <stdio.h>
+#include <math.h>
+#include <string.h>
+#include <stdbool.h>
+#include <assert.h>
+#include "signals.c"
+#include "osc.c"
+#include "dsm.c"
+#include "bitmath.c"
+#include "data.c"
+#include "kann.c"
+#include "kautodiff.c"
+
+int main(void) {
+
+    kad_node_t* t;
+    kann_t* ann;
+    size_t inputs = 1024;
+    size_t outputs = 1;
+    size_t num_layers = 1;
+    size_t neurons = 64;
+    size_t SR = 192000;
+
+    /* Create the neural network */
+    t = kann_layer_input(inputs);
+    for (size_t i = 0; i < num_layers; i++) {
+        t = kann_layer_dense(t, neurons);
+        t = kad_relu(t);
+    }
+    t = kann_layer_cost(t, outputs, KANN_C_MSE);
+    ann = kann_new(t, 0);
+
+    /* Create training data set */
+    size_t setsize = 8192;
+    Sig* x = malloc(sizeof(Sig));
+    Sig* y = malloc(sizeof(Sig));
+    sig_alloc(x, setsize, inputs, SR);
+    sig_alloc(y, setsize, outputs, SR);
+    freq_est_data(x, y);
+
+    /* Train the net. */
+    float lr = .001, frac_val = .1;
+    size_t mini_size = 64;
+    size_t max_epoch = 1000;
+    size_t max_drop_streak = 20;
+    kann_train_fnn1(ann, lr, mini_size, max_epoch, max_drop_streak, frac_val,
+        setsize, x->vec_space, y->vec_space);
+    sig_free(x);
+    sig_free(y);
+
+    /* Test the net. */
+    size_t testsize = 10;
+    x = malloc(sizeof(Sig));
+    y = malloc(sizeof(Sig));
+    sig_alloc(x, testsize, inputs, SR);
+    sig_alloc(y, testsize, outputs, SR);
+    freq_est_data(x, y);
+    const float* output;
+    for (size_t j = 0; j < testsize; j++) {
+        output = kann_apply1(ann, x->vec_space[j]);
+        printf("target: %.10f, ANN: %.10f, err: %.10f\n", 
+            y->vec_space[j][0], *output, y->vec_space[j][0] / *output);
+    }
+}