public RunEpoch ( double input, double output ) : double | ||
input | double | Array of input vectors. |
output | double | Array of output vectors. |
Résultat | double |
public override ConfusionMatrix Execute() { //Create an network with one layer and one neuron in that layer var network = new ActivationNetwork(new ThresholdFunction(), 3, 1); //Bind the reference of the neuron var neuron = network.Layers[0].Neurons[0] as ActivationNeuron; //Create the Perceptron learning algorithm //Library perceptron implements a single layer linear classifier var teacher = new PerceptronLearning(network); teacher.LearningRate = 0.1; //Enrich the dimensions of the vectors, padding 1 to the end var richTraining = AlgorithmHelpers.PaddDimension(trainingSet); var richTesting = AlgorithmHelpers.PaddDimension(testSet); //Training the network until the error is small enough //or 500 hundred iterations have been computed int epochs = 0; while (true) { double error = teacher.RunEpoch(richTraining, trainingOutput);/// trainingSet.Length; ++epochs; if (error < 0.025 * trainingSet.Length || epochs == 500) break; } var predicted = richTesting .Select(x => neuron.Compute(x)) .Select(x => Convert.ToInt32(x)) .ToArray(); //Create a confusion matrix with the calculated parameters ConfusionMatrix cmatrix = new ConfusionMatrix(predicted, expected, POSITIVE, NEGATIVE); OnAlgorithmEnded(Enumerable.Repeat(neuron, 1), cmatrix); return cmatrix; }
// Worker thread void SearchSolution( ) { // prepare learning data double[][] input = new double[samples][]; double[][] output = new double[samples][]; for ( int i = 0; i < samples; i++ ) { input[i] = new double[variables]; output[i] = new double[1]; // copy input for ( int j = 0; j < variables; j++ ) input[i][j] = data[i, j]; // copy output output[i][0] = classes[i]; } // create perceptron ActivationNetwork network = new ActivationNetwork( new ThresholdFunction( ), variables, 1 ); ActivationNeuron neuron = network[0][0]; // create teacher PerceptronLearning teacher = new PerceptronLearning( network ); // set learning rate teacher.LearningRate = learningRate; // iterations int iteration = 1; // statistic files StreamWriter errorsFile = null; StreamWriter weightsFile = null; try { // check if we need to save statistics to files if ( saveStatisticsToFiles ) { // open files errorsFile = File.CreateText( "errors.csv" ); weightsFile = File.CreateText( "weights.csv" ); } // erros list ArrayList errorsList = new ArrayList( ); // loop while ( !needToStop ) { // save current weights if ( weightsFile != null ) { for ( int i = 0; i < variables; i++ ) { weightsFile.Write( neuron[i] + "," ); } weightsFile.WriteLine( neuron.Threshold ); } // run epoch of learning procedure double error = teacher.RunEpoch( input, output ); errorsList.Add( error ); // show current iteration SetText( iterationsBox, iteration.ToString( ) ); // save current error if ( errorsFile != null ) { errorsFile.WriteLine( error ); } // show classifier in the case of 2 dimensional data if ( ( neuron.InputsCount == 2 ) && ( neuron[1] != 0 ) ) { double k = - neuron[0] / neuron[1]; double b = - neuron.Threshold / neuron[1]; double[,] classifier = new double[2, 2] { { chart.RangeX.Min, chart.RangeX.Min * k + b }, { chart.RangeX.Max, chart.RangeX.Max * k + b } }; // update chart chart.UpdateDataSeries( "classifier", classifier ); } // stop if no error if ( error == 0 ) break; iteration++; } // show perceptron's weights ListViewItem item = null; ClearList( weightsList ); for ( int i = 0; i < variables; i++ ) { item = AddListItem( weightsList, string.Format( "Weight {0}", i + 1 ) ); AddListSubitem( item, neuron[i].ToString( "F6" ) ); } item = AddListItem( weightsList, "Threshold" ); AddListSubitem( item, neuron.Threshold.ToString( "F6" ) ); // show error's dynamics double[,] errors = new double[errorsList.Count, 2]; for ( int i = 0, n = errorsList.Count; i < n; i++ ) { errors[i, 0] = i; errors[i, 1] = (double) errorsList[i]; } errorChart.RangeX = new DoubleRange( 0, errorsList.Count - 1 ); errorChart.RangeY = new DoubleRange( 0, samples ); errorChart.UpdateDataSeries( "error", errors ); } catch ( IOException ) { MessageBox.Show( "Failed writing file", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error ); } finally { // close files if ( errorsFile != null ) errorsFile.Close( ); if ( weightsFile != null ) weightsFile.Close( ); } // enable settings controls EnableControls( true ); }
// Worker thread void SearchSolution( ) { // prepare learning data double[][] input = new double[samples][]; double[][] output = new double[samples][]; for ( int i = 0; i < samples; i++ ) { input[i] = new double[2]; output[i] = new double[classesCount]; // set input input[i][0] = data[i, 0]; input[i][1] = data[i, 1]; // set output output[i][classes[i]] = 1; } // create perceptron ActivationNetwork network = new ActivationNetwork( new ThresholdFunction( ), 2, classesCount ); ActivationLayer layer = network[0]; // create teacher PerceptronLearning teacher = new PerceptronLearning( network ); // set learning rate teacher.LearningRate = learningRate; // iterations int iteration = 1; // statistic files StreamWriter errorsFile = null; StreamWriter weightsFile = null; try { // check if we need to save statistics to files if ( saveStatisticsToFiles ) { // open files errorsFile = File.CreateText( "errors.csv" ); weightsFile = File.CreateText( "weights.csv" ); } // erros list ArrayList errorsList = new ArrayList( ); // loop while ( !needToStop ) { // save current weights if ( weightsFile != null ) { for ( int i = 0; i < classesCount; i++ ) { weightsFile.Write( "neuron" + i + ";" ); weightsFile.Write( layer[i][0] + ";" ); weightsFile.Write( layer[i][1] + ";" ); weightsFile.WriteLine( layer[i].Threshold ); } } // run epoch of learning procedure double error = teacher.RunEpoch( input, output ); errorsList.Add( error ); // save current error if ( errorsFile != null ) { errorsFile.WriteLine( error ); } // show current iteration iterationsBox.Text = iteration.ToString( ); // stop if no error if ( error == 0 ) break; // show classifiers for ( int j = 0; j < classesCount; j++ ) { double k = - layer[j][0] / layer[j][1]; double b = - layer[j].Threshold / layer[j][1]; double[,] classifier = new double[2, 2] { { chart.RangeX.Min, chart.RangeX.Min * k + b }, { chart.RangeX.Max, chart.RangeX.Max * k + b } }; // update chart chart.UpdateDataSeries( string.Format( "classifier" + j ), classifier ); } iteration++; } // show perceptron's weights weightsList.Items.Clear( ); for ( int i = 0; i < classesCount; i++ ) { string neuronName = string.Format( "Neuron {0}", i + 1 ); // weight 0 ListViewItem item = weightsList.Items.Add( neuronName ); item.SubItems.Add( "Weight 1" ); item.SubItems.Add( layer[i][0].ToString( "F6" ) ); // weight 1 item = weightsList.Items.Add( neuronName ); item.SubItems.Add( "Weight 2" ); item.SubItems.Add( layer[i][1].ToString( "F6" ) ); // threshold item = weightsList.Items.Add( neuronName ); item.SubItems.Add( "Threshold" ); item.SubItems.Add( layer[i].Threshold.ToString( "F6" ) ); } // show error's dynamics double[,] errors = new double[errorsList.Count, 2]; for ( int i = 0, n = errorsList.Count; i < n; i++ ) { errors[i, 0] = i; errors[i, 1] = (double) errorsList[i]; } errorChart.RangeX = new DoubleRange( 0, errorsList.Count - 1 ); errorChart.UpdateDataSeries( "error", errors ); } catch ( IOException ) { MessageBox.Show( "Failed writing file", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error ); } finally { // close files if ( errorsFile != null ) errorsFile.Close( ); if ( weightsFile != null ) weightsFile.Close( ); } // enable settings controls EnableControls( true ); }
public void LearnDemo() { ActivationNetwork network = new ActivationNetwork(new ThresholdFunction(), 2, 1);//Constants.StoneCount PerceptronLearning teacher = new PerceptronLearning(network); double[][] input = new double[4][]; double[][] output = new double[4][]; input[0] = new double[] { 0, 0 }; output[0] = new double[] { 0 }; input[1] = new double[] { 0, 1 }; output[1] = new double[] { 0 }; input[2] = new double[] { 1, 0 }; output[2] = new double[] { 0 }; input[3] = new double[] { 1, 1 }; output[3] = new double[] { 1 }; double error = 1.0; while (error > 0.001) { error = teacher.RunEpoch(input, output); } var k = network.Compute(new double[] { 0.9, 0.7 }); var o = network.Output; network.Save("a.txt"); }
//The Single Layer Perceptron classifier algorithm //It uses an Activation Network library object, with one layer and one neuron to the layer //To train the network it uses a PerceptronLearning library object private ConfusionMatrix RunPerceptron(Double[][] trainingSet, Double[][] trainingOutput, Double[][] testSet, int[] expected) { //Create an network with one layer and one neuron in that layer ActivationNetwork network = new ActivationNetwork(new ThresholdFunction(), 3, 1); //Bind the reference of the neuron ActivationNeuron neuron = network.Layers[0].Neurons[0] as ActivationNeuron; //Create the Perceptron learning algorithm //Library perceptron implements a single layer linear classifier PerceptronLearning teacher = new PerceptronLearning(network); teacher.LearningRate = 0.1; //Enrich the dimensions of the vectors, padding 1 to the end var richTraining = UtilityProvider.PaddDimension(trainingSet); var richTesting = UtilityProvider.PaddDimension(testSet); //Training the network until the error is small enough //or 500 hundred iterations have been computed int epochs = 0; while (true) { double error = teacher.RunEpoch(richTraining, trainingOutput);/// trainingSet.Length; ++epochs; if (error < 0.025 * trainingSet.Length || epochs == 500) break; // Console.Write("Iter: " + epochs + " " + error + "\n"); } var predicted = richTesting .Select(x => neuron.Compute(x)) .Select(x => Convert.ToInt32(x)) .ToArray(); // Calculate the coordinates of the classifier double k = (neuron.Weights[1] != 0) ? (-neuron.Weights[0] / neuron.Weights[1]) : 0; double b = (neuron.Weights[1] != 0) ? (-((ActivationNeuron)neuron).Threshold / neuron.Weights[1]) : 0; // Create the line and feed it to the data series double[,] classifier = new double[2, 2]{ { perceChart.RangeX.Min, perceChart.RangeX.Min * k + b}, { perceChart.RangeX.Max, perceChart.RangeX.Max * k + b} }; //For test, assume 1 as positive and 0 as negative int positive = 0; int negative = 1; //Create a confusion matrix with the calculated parameters ConfusionMatrix cmatrix = new ConfusionMatrix(predicted, expected, positive, negative); //Update the most accurate classification if (MostAccuratePerceptron == null || cmatrix.Accuracy > MostAccuratePerceptron.Item2.Accuracy) { MostAccuratePerceptron = Tuple.Create(classifier, cmatrix); } return cmatrix; }