Completing network forward function

main.c

@@ -5,8 +5,8 @@
 #include "neurons.h"
 #include "network.h"
 #include "activations.h"
-#include "training.h"
 #include "preprocessing.h"
+#include "training.h"
 
 int main(int argc, char *argv[])
 {

training.c

@@ -5,20 +5,57 @@
 #include "activations.h"
 #include "neurons.h"
 #include "network.h"
+#include "preprocessing.h"
+#include "training.h"
 
 
 
-void forward(Network *network, float sample[])
+void forward(Network *network, Sample *sample)
+{
+    Feature *current_feature;
+    Neuron *current_neuron, *prev_layer_current_neuron;
+    Weight *current_weight;
+    int i;
+    for(i=0 ; i<network->n_layers ; i++)
+    {
+        if(i==0) //set first layer neurons' output equal to sample's features
+        {
+            current_feature = sample->first_feature;
+            current_neuron = network->layers_first_neurons[i];
+            while(current_neuron != NULL)
+            {
+                current_neuron->output = current_feature->value;
+                current_feature = current_feature->next_feature;
+                current_neuron = current_neuron->same_layer_next_neuron;
+            }
+        }else //when layer not first one, do dot product sum with bias
+        {
+            current_neuron = network->layers_first_neurons[i];
+            while(current_neuron != NULL)
+            {
+                prev_layer_current_neuron = network->layers_first_neurons[i-1];
+                current_neuron->output = current_neuron->bias;
+                current_weight = current_neuron->weights;
+                while(prev_layer_current_neuron != NULL)
+                {
+                    current_neuron->output += prev_layer_current_neuron->output*current_weight->value;
+                    current_weight = current_weight->next;
+                    prev_layer_current_neuron = prev_layer_current_neuron->same_layer_next_neuron;
+                }
+                current_neuron->output = current_neuron->activation( current_neuron->output ); //apply activation function
+                current_neuron = current_neuron->same_layer_next_neuron;
+            }
+        }
+    }
+}
+
+void errors_propagate(Network *network, Sample *sample)
 {
 
 }
 
-void errors_backpropagate(Network *network, float label[])
+void backpropagate(Network *network, float learning_rate)
 {
 
 }
 
-void apply_backpropagate(Network *network, float learning_rate)
-{
-
-}

training.h

@@ -1,8 +1,8 @@
 #ifndef TRAINING_H
 #define TRAINING_H
 
-void forward(Network *network, float sample[]);
+void forward(Network *network, Sample *sample);
-void errors_backpropagate(Network *network, float label[]);
+void errors_propagate(Network *network, Sample *sample);
-void apply_backpropagate(Network *network, float learning_rate);
+void backpropagate(Network *network, float learning_rate);
 
 #endif