FANN_EXTERNAL void FANN_API fann_get_bias_array(struct fann *ann, unsigned int *bias)
{
struct fann_layer *layer_it;
for (layer_it = ann->first_layer; layer_it != ann->last_layer; ++layer_it, ++bias) {
switch (fann_get_network_type(ann)) {
case FANN_NETTYPE_LAYER: {
/* Report one bias in each layer except the last */
if (layer_it != ann->last_layer-1)
*bias = 1;
else
*bias = 0;
break;
}
case FANN_NETTYPE_SHORTCUT: {
/* The bias in the first layer is reused for all layers */
if (layer_it == ann->first_layer)
*bias = 1;
else
*bias = 0;
break;
}
default: {
/* Unknown network type, assume no bias present */
*bias = 0;
break;
}
}
}
}
bool ViFann::loadFromFile(const QString &path)
{
QFile file(path);
if(!file.exists()) return false;
clear();
mNetwork = fann_create_from_file(path.toLatin1().data());
if(mNetwork == NULL) return false;
fann_nettype_enum type = fann_get_network_type(mNetwork);
//TODO: get type
int layerCount = fann_get_num_layers(mNetwork);
unsigned int layers[layerCount];
fann_get_layer_array(mNetwork, layers);
for(int i = 0; i < layerCount; ++i)
{
mNeurons.append(layers[i]);
}
mInputCount = mNeurons.first();
mOutputCount = mNeurons.last();
if(mInput == NULL) delete [] mInput;
if(mOutput == NULL) delete [] mOutput;
mInput = new float[mInputCount];
mOutput = new float[mOutputCount];
mConnectionRate = fann_get_connection_rate(mNetwork);
//TODO: get weights
return true;
}
FANN_EXTERNAL void FANN_API fann_get_layer_array(struct fann *ann, unsigned int *layers)
{
struct fann_layer *layer_it;
for (layer_it = ann->first_layer; layer_it != ann->last_layer; layer_it++) {
unsigned int count = layer_it->last_neuron - layer_it->first_neuron;
/* Remove the bias from the count of neurons. */
switch (fann_get_network_type(ann)) {
case FANN_NETTYPE_LAYER: {
--count;
break;
}
case FANN_NETTYPE_SHORTCUT: {
/* The bias in the first layer is reused for all layers */
if (layer_it == ann->first_layer)
--count;
break;
}
default: {
/* Unknown network type, assume no bias present */
break;
}
}
*layers++ = count;
}
}
