void raise_layer(Block blockmap[][10]) {
int i, j;
for (i = 0; i < 20; i++) {
for (j = 9; j > 0; j-- ) {
blockmap[i][j].set(blockmap[i][j-1].check_type());
}
}
generate_layer(blockmap);
}
void NN::create_network(vector<int> layers) //create a new network
{
network.clear();
// srand(time(NULL));
for (unsigned int i=1; i<layers.size(); i++)
{
if(layers[i] == 0)
{
printf("You can't have a network layer with zero neurons, aborting!\n");
exit(0);
}
network.push_back(generate_layer(layers[i],layers[i-1])); //(out, in)
}
learning_rate = 0.001; //standard value - can be changed using the set_learning_rate method
linear_coef = 5;
}
void cubic( float x_size, float y_size, float z_size, const float3 & position, const float4 & color,
Vertex *res_vertices, Index *res_indices)
{
Index vertex = 0; // index of current vertex
DWORD index = 0; // index of current index
_ASSERT(res_vertices != NULL);
_ASSERT(res_indices != NULL);
float x_step = x_size / CUBIC_X_EDGES;
float y_step = y_size / CUBIC_Y_EDGES;
float z_step = z_size / CUBIC_Z_EDGES;
for(int i = 0; i <= CUBIC_Z_EDGES; ++i)
{
bool connect_with_previous_level = ( 0 != i);
bool inside = ( 0 != i && CUBIC_Z_EDGES != i);
generate_layer(x_step, y_step, position + float3(0, 0, i*z_step), color,
connect_with_previous_level, inside,
vertex, index, res_vertices, res_indices);
}
}
