ArrayList<NeuralConnection> tmp = new ArrayList<NeuralConnection>(4); for (int noa = 0; noa < m_numAttributes; noa++) { if (m_inputs[noa].onUnit(g, x, y, w, h)) { tmp.add(m_inputs[noa]); selection( if (m_outputs[noa].onUnit(g, x, y, w, h)) { tmp.add(m_outputs[noa]); selection( ArrayList<NeuralConnection> tmp = new ArrayList<NeuralConnection>(4); for (int noa = 0; noa < m_numAttributes; noa++) { if (m_inputs[noa].onUnit(g, x, y, w, h)) { tmp.add(m_inputs[noa]); selection( if (m_outputs[noa].onUnit(g, x, y, w, h)) { tmp.add(m_outputs[noa]); selection(
ArrayList<NeuralConnection> tmp = new ArrayList<NeuralConnection>(4); for (int noa = 0; noa < m_numAttributes; noa++) { if (m_inputs[noa].onUnit(g, x, y, w, h)) { tmp.add(m_inputs[noa]); selection( if (m_outputs[noa].onUnit(g, x, y, w, h)) { tmp.add(m_outputs[noa]); selection( ArrayList<NeuralConnection> tmp = new ArrayList<NeuralConnection>(4); for (int noa = 0; noa < m_numAttributes; noa++) { if (m_inputs[noa].onUnit(g, x, y, w, h)) { tmp.add(m_inputs[noa]); selection( if (m_outputs[noa].onUnit(g, x, y, w, h)) { tmp.add(m_outputs[noa]); selection(