/** * This creates the required output units. */ private void setupOutputs() throws Exception { m_outputs = new NeuralEnd[m_numClasses]; for (int noa = 0; noa < m_numClasses; noa++) { if (m_numeric) { m_outputs[noa] = new NeuralEnd(m_instances.classAttribute().name()); } else { m_outputs[noa] = new NeuralEnd(m_instances.classAttribute().value(noa)); } m_outputs[noa].setX(.9); m_outputs[noa].setY((noa + 1.0) / (m_numClasses + 1)); m_outputs[noa].setLink(false, noa); NeuralNode temp = new NeuralNode(String.valueOf(m_nextId), m_random, m_sigmoidUnit); m_nextId++; temp.setX(.75); temp.setY((noa + 1.0) / (m_numClasses + 1)); addNode(temp); NeuralConnection.connect(temp, m_outputs[noa]); } }
/** * This creates the required output units. */ private void setupOutputs() throws Exception { m_outputs = new NeuralEnd[m_numClasses]; for (int noa = 0; noa < m_numClasses; noa++) { if (m_numeric) { m_outputs[noa] = new NeuralEnd(m_instances.classAttribute().name()); } else { m_outputs[noa] = new NeuralEnd(m_instances.classAttribute().value(noa)); } m_outputs[noa].setX(.9); m_outputs[noa].setY((noa + 1.0) / (m_numClasses + 1)); m_outputs[noa].setLink(false, noa); NeuralNode temp = new NeuralNode(String.valueOf(m_nextId), m_random, m_sigmoidUnit); m_nextId++; temp.setX(.75); temp.setY((noa + 1.0) / (m_numClasses + 1)); addNode(temp); NeuralConnection.connect(temp, m_outputs[noa]); } }
/** * This creates the required input units. */ private void setupInputs() throws Exception { m_inputs = new NeuralEnd[m_numAttributes]; int now = 0; for (int noa = 0; noa < m_numAttributes + 1; noa++) { if (m_instances.classIndex() != noa) { m_inputs[noa - now] = new NeuralEnd(m_instances.attribute(noa).name()); m_inputs[noa - now].setX(.1); m_inputs[noa - now].setY((noa - now + 1.0) / (m_numAttributes + 1)); m_inputs[noa - now].setLink(true, noa); } else { now = 1; } } }
/** * This creates the required input units. */ private void setupInputs() throws Exception { m_inputs = new NeuralEnd[m_numAttributes]; int now = 0; for (int noa = 0; noa < m_numAttributes + 1; noa++) { if (m_instances.classIndex() != noa) { m_inputs[noa - now] = new NeuralEnd(m_instances.attribute(noa).name()); m_inputs[noa - now].setX(.1); m_inputs[noa - now].setY((noa - now + 1.0) / (m_numAttributes + 1)); m_inputs[noa - now].setLink(true, noa); } else { now = 1; } } }