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#include "nnet.h"
static branch emptyBranch = {.data = 0.0, .weight = 0.0};
static neuron emptyNeuron = {.function = LINEAR, .size = 0, .branches = &emptyBranch, .bias = 0.0, .out = 0.0};
double drand(double high, double low){
srand(time(NULL));
return ((double)rand()*(high-low))/(double)RAND_MAX + low;
}
branch createBranch(double weight, double data){
branch b = {.weight = weight, .data = data};
return b;
}
neuron *createNeuron(double bias, functions function, int size, ...){
neuron *n = malloc(sizeof(neuron));
n->bias = bias;
n->function = function;
n->size = size;
n->branches = malloc(size*sizeof(branch));
va_list wargs;
va_start(wargs, size);
for(int i = 0; i < size; ++i)
n->branches[i] = createBranch(va_arg(wargs, double), 0.0);
n->out = 0;
va_end(wargs);
return n;
}
void addBranch(neuron *n, double weight){
branch *tmp = malloc((n->size+1)*sizeof(branch));
memmove(tmp, n->branches, n->size*sizeof(branch));
tmp[n->size] = createBranch(weight, 0.0);
n->branches = tmp;
n->size++;
}
void addBranches(neuron *n, int size, double *data){
for(int i = 0; i < size; ++i)
addBranch(n, data[i]);
}
void changeFunction(neuron *n, functions function){
n->function = function;
}
int changeWeight(neuron *n, int pos, double weight){
if(pos >= 0 && pos < n->size){
n->branches[pos].weight = weight;
return 0;
}
return -1;
}
int inputNeuron(neuron *n, int pos, double data){
if(pos >= 0 && pos < n->size){
n->branches[pos].data = data;
return 0;
}
return -1;
}
double rawValue(neuron *n){
double v = n->bias;
printf("0 ");
for(int i = 0; i < n->size; ++i){
printf("+ %.5lf + %.5lf*%.5lf ", n->bias, n->branches[i].data, n->branches[i].weight);
v += n->branches[i].data*n->branches[i].weight;
}
printf("= %.5lf\n", v);
return v;
}
double outputNeuron(neuron *n){
double x = rawValue(n);
switch(n->function){
case LINEAR:
n->out = x;
break;
case SIGMOID:
n->out = (double)(1.0/(1.0+exp(-x)));
}
return n->out;
}
layer createLayer(int size, ...){
layer l;
l.size = size;
l.neurons = malloc(size*sizeof(neuron*));
for(int i = 0; i < size; ++i)
l.neurons[i] = malloc(sizeof(neuron));
va_list nargs;
va_start(nargs, size);
int done = 0;
for(int i = 0; i < size; ++i){
neuron *a = va_arg(nargs, neuron*);
if(a == NULL)
done = 1;
if(done)
l.neurons[i] = &emptyNeuron;
else
l.neurons[i] = a;
}
va_end(nargs);
return l;
}
net createNet(int layers, ...){
net nt;
nt.layers = layers;
nt.head = NULL;
va_list largs;
va_start(largs, layers);
for(int i = 0; i < layers; ++i){
lnode *tmp = malloc(sizeof(lnode));
tmp->layer = va_arg(largs, layer);
tmp->next = NULL;
if(nt.head != NULL){
lnode *act = nt.head;
while(act->next != NULL)
act = act->next;
act->next = tmp;
}else{
nt.head = tmp;
}
}
va_end(largs);
return nt;
}
double *propagateLayer(layer *l, int size, double *data){
double *result = malloc(l->size*sizeof(double));
for(int i = 0; i < l->size; ++i){
if(l->neurons[i]->size < size){
int diff = size - l->neurons[i]->size;
printf("Creando %d mas\n", diff);
double *weights = malloc(diff*sizeof(double));
for(int j = 0; j < diff; ++j)
weights[j] = drand(2, -2);
addBranches(l->neurons[i], diff, weights);
}
for(int j = 0; j < size; ++j)
inputNeuron(l->neurons[i], j, data[j]);
result[i] = outputNeuron(l->neurons[i]);
}
return result;
}
double *propagate(net *nt, ...){
if(nt->head != NULL){
va_list ntargs;
va_start(ntargs, nt);
lnode *tmp = nt->head;
for(int i = 0; i < tmp->layer.size; ++i)
inputNeuron(tmp->layer.neurons[i], 0, va_arg(ntargs, double));
double *data = malloc(tmp->layer.size*sizeof(double));
for(int i = 0; i < tmp->layer.size; ++i)
data[i] = outputNeuron(tmp->layer.neurons[i]);
int size;
while(tmp->next != NULL){
size = tmp->layer.size;
double *tmpdata = propagateLayer(&tmp->next->layer, size, data);
data = tmpdata;
tmp = tmp->next;
}
return data;
}
return NULL;
}
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