From 1503fc83991237fa0cf6eb42b0ca1a4904cf8a01 Mon Sep 17 00:00:00 2001
From: jvech <jmvalenciae@unal.edu.co>
Date: Fri, 28 Jul 2023 09:29:18 -0500
Subject: add: network forward pass implemented

---
 src/main.c | 45 +++++++++++++++++++--------------------------
 src/nn.c   | 18 ++++++++++++++++++
 src/nn.h   |  3 +++
 3 files changed, 40 insertions(+), 26 deletions(-)

diff --git a/src/main.c b/src/main.c
index c348b76..a65591d 100644
--- a/src/main.c
+++ b/src/main.c
@@ -13,10 +13,6 @@ typedef struct Array {
 } Array;
 
 #define ARRAY_SIZE(x, type) sizeof(x) / sizeof(type)
-Layer neural[] = {
-    {.neurons = 5, .activation = relu},
-    {.neurons = 1, .activation = sigmoid},
-};
 
 static void json_read(const char *filepath,
                       Array *input, Array *out,
@@ -30,8 +26,8 @@ void json_read(const char *filepath,
                char *in_keys[],
                size_t n_input_keys)
 {
-    FILE *fp;
-    char *fp_buffer;
+    FILE *fp = NULL;
+    char *fp_buffer = NULL;
     size_t ret;
     int64_t fp_size;
 
@@ -90,34 +86,31 @@ json_read_error:
 }
 
 int main(void) {
+    Layer network[] = {
+        {.neurons = 5, .activation = relu},
+        {.neurons = 1, .activation = sigmoid},
+    };
     Array X, y;
     char *in_keys[] = {"area", "longitude", "latitude"};
     json_read("data/test.json", &X, &y, "price", in_keys, ARRAY_SIZE(in_keys, char *));
 
-    nn_layer_init_weights(neural, ARRAY_SIZE(neural, Layer), X.shape[1]);
-    double *out = nn_layer_forward(neural[0], X.data, X.shape);
+    size_t network_size = ARRAY_SIZE(network, Layer);
+    nn_network_init_weights(network, network_size, 3);
+    double **outputs = calloc(network_size, sizeof(double *));
 
-    printf("area\tlat\tlong\t| price\n");
-    for (size_t i = 0; i < X.shape[0]; i++) {
-        for (size_t j = 0; j < X.shape[1]; j++) {
-            size_t index = X.shape[1] * i + j;
-            printf("%.2lf\t", X.data[index]);
-        }
-        printf("| %.2lf\n", y.data[i]);
-    }
+    size_t out_rows = X.shape[0];//
+    for (size_t l = 0; l < network_size; l++) {//
+        outputs[l] = calloc(network[l].neurons * out_rows, sizeof(double));//
+    } //
 
-    printf("---\n");
-    for (size_t i = 0; i < X.shape[0]; i++) {
-        for (size_t j = 0; j < neural[0].neurons; j++) {
-            size_t index = neural[0].neurons * i + j;
-            printf("%.2lf\t", out[index]);
-        }
-        printf("\n");
-    }
+    nn_forward(outputs, X.data, X.shape, network, network_size);
 
+    for (size_t l = 0; l < network_size; l++) free(outputs[l]);
+    free(outputs);
 
-    nn_layer_free_weights(neural, ARRAY_SIZE(neural, Layer));
-    free(out);
+    nn_network_free_weights(network, network_size);
     free(X.data);
     free(y.data);
+
+    
 }
diff --git a/src/nn.c b/src/nn.c
index d773324..e47b56d 100644
--- a/src/nn.c
+++ b/src/nn.c
@@ -2,6 +2,24 @@
 
 static void fill_random_weights(double *weights, double *bias, size_t rows, size_t cols);
 
+void nn_forward(
+        double **out,
+        double *X, size_t X_shape[2],
+        Layer network[], size_t network_size)
+{
+    size_t in_shape[2] = {X_shape[0], X_shape[1]};
+    size_t out_shape[2];
+    out_shape[0] = X_shape[0];
+    double *input = X;
+
+    for (size_t l = 0; l < network_size; l++) {
+        out_shape[1] = network[l].neurons;
+        nn_layer_forward(network[l], out[l], out_shape, input, in_shape);
+        in_shape[1] = out_shape[1];
+        input = out[l];
+    }
+}
+
 void nn_layer_forward(Layer layer, double *out, size_t out_shape[2], double *input, size_t input_shape[2])
 {
     if (out_shape[0] != input_shape[0] || out_shape[1] != layer.neurons) {
diff --git a/src/nn.h b/src/nn.h
index d66fabc..a339dfc 100644
--- a/src/nn.h
+++ b/src/nn.h
@@ -24,4 +24,7 @@ void nn_layer_backward(Layer *layer, double *out, size_t out_shape[2]); //TODO
 double sigmoid(double x);
 double relu(double x);
 double identity(double x);
+
+
+void nn_forward(double **out, double *input, size_t input_shape[2], Layer network[], size_t network_size);
 #endif
-- 
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