コード例 #1
0
ファイル: Program.cs プロジェクト: tomas962/pubgML
        static void Main(string[] args)
        {
            if (usePca)
            {
                //Application.EnableVisualStyles();
                //Application.Run(new Diagrams());


                (List <List <double> > data, _) = ReadDataAsList(dataFile, ';', columnsToIgnore, targetColumns, dataStartLine, dataEndLine);

                KNN(data);

                var pca = PCA.Compute(data, 5);
                //read data from file
                (double[][] dataArray, double[][] targetArray) = ReadDataAsArray(dataFile, ';', columnsToIgnore, targetColumns, dataStartLine, dataEndLine);
                double[][] pcaData = List2DToArray2D(pca);
                //normalize all data values (scale between 0 and 1)
                (double[][] normalizedPcaData, _, _) = NormalizeData(pcaData);
                (double[][] normalizedTargets, double[] min, double[] max) = NormalizeData(targetArray);

                //split data into two sets - training and testing = 80% and 20%
                (double[][] trainDataArray, double[][] trainTargetArray, double[][] testDataArray, double[][] testTargetArray) = SplitData(normalizedPcaData, normalizedTargets, dataSplitPercentage);

                //var nn2 = new NeuralNetwork2();
                //nn2.Run(trainDataArray, trainTargetArray, testDataArray, testTargetArray);
                //create network
                var nn = new BPNeuralNetwork(normalizedPcaData[0].Length, hiddenNeuronCount, outputNeuronCount, min, max, testDataArray, testTargetArray);
                //var nn = new BPNeuralNetwork(normalizedPcaData[0].Length, hiddenNeuronCount, outputNeuronCount, min, max);
                //nn.CrossValidation(trainDataArray, trainTargetArray, trainingEpochCount, learningRate, learningMomentum, nn);
                //train network
                (double[] trainErrors, double[] validationErrors, double[] testErrors) = nn.Train(trainDataArray, trainTargetArray, trainingEpochCount, learningRate, learningMomentum);

                PlotValidationChart(trainErrors, validationErrors, testErrors);

                //string[] propertyNames = ReadPropertyNames("../../Data/property_names.csv", ',');
                //for (int i = 0; i < propertyNames.Length; i++)
                //    Console.WriteLine(i + " | " + propertyNames[i]);

                //Test the network
                for (int i = 0; i < testDataArray.Length; i++)
                {
                    nn.ComputeOutputs(testDataArray[i], testTargetArray[i]);
                }
            }
            else
            {
                //read data from file
                (double[][] dataArray, double[][] targetArray) = ReadDataAsArray(dataFile, ';', columnsToIgnore, targetColumns, dataStartLine, dataEndLine);
                //normalize all data values (scale between 0 and 1)
                (double[][] normalizedData, _, _) = NormalizeData(dataArray);
                (double[][] normalizedTargets, double[] min, double[] max) = NormalizeData(targetArray);

                //split data into two sets - training and testing = 80% and 20%
                (double[][] trainDataArray, double[][] trainTargetArray, double[][] testDataArray, double[][] testTargetArray) = SplitData(normalizedData, normalizedTargets, dataSplitPercentage);
                //(double[][] trainDataArray, double[][] trainTargetArray, double[][] testDataArray, double[][] testTargetArray) = SplitData(dataArray, targetArray, 0.8);

                //create network
                var nn = new BPNeuralNetwork(dataArray[0].Length, hiddenNeuronCount, outputNeuronCount, min, max, testDataArray, testTargetArray);

                //train network
                (double[] trainErrors, double[] validationErrors, double[] testErrors) = nn.Train(trainDataArray, trainTargetArray, trainingEpochCount, learningRate, learningMomentum);

                PlotValidationChart(trainErrors, validationErrors, testErrors);
                //string[] propertyNames = ReadPropertyNames("../../Data/property_names.csv", ',');
                //for (int i = 0; i < propertyNames.Length; i++)
                //    Console.WriteLine(i + " | " + propertyNames[i]);

                //Test the network
                for (int i = 0; i < testDataArray.Length; i++)
                {
                    nn.ComputeOutputs(testDataArray[i], testTargetArray[i]);
                }
            }
            Console.WriteLine();
            Console.ReadLine();
        }
コード例 #2
0
ファイル: Program.cs プロジェクト: tomas962/pubgML
        static void KNN(List <List <double> > data)
        {
            Console.WriteLine("-------------------------------------");
            Console.WriteLine("K-Nearest Neighbour method");
            Console.WriteLine("-------------------------------------\n");
            var normalized = NormalizeData(data);
            var pca        = PCA.Compute(normalized, 2);

            (double[][] dataArray, double[][] targetArray) = ReadDataAsArray(dataFile, ';', columnsToIgnore, targetColumns, dataStartLine, dataEndLine);
            double[][] normalizedPcaData = List2DToArray2D(pca);
            (double[][] normalizedTargets, double[] min, double[] max) = NormalizeData(targetArray);

            //(var data1, var data2) = CrossValidationSplitData(normalizedPcaData, normalizedTargets, 10);
            (double[][] trainDataArray, double[][] trainTargetArray, double[][] testDataArray, double[][] testTargetArray) = SplitData(normalizedPcaData, normalizedTargets, dataSplitPercentage);

            //Application.EnableVisualStyles();
            //Application.Run(new Diagrams(trainDataArray, trainTargetArray, min[0], max[0]));

            ChartValues <ObservablePoint> points1      = new ChartValues <ObservablePoint>();
            ChartValues <ObservablePoint> points2      = new ChartValues <ObservablePoint>();
            ChartValues <ObservablePoint> points3      = new ChartValues <ObservablePoint>();
            ChartValues <ObservablePoint> points1Wrong = new ChartValues <ObservablePoint>();
            ChartValues <ObservablePoint> points2Wrong = new ChartValues <ObservablePoint>();
            ChartValues <ObservablePoint> points3Wrong = new ChartValues <ObservablePoint>();
            int K       = 131;
            int N       = 200;
            int correct = 0;
            int wrong   = 0;

            for (int i = 0; i < N; i++)//testDataArray.Length
            {
                int classIdx = KNearestNeighbor.Classify(testDataArray[i], trainDataArray, trainTargetArray, K, min[0], max[0]);
                Console.WriteLine("Classified: {0}", classIdx == 1?"0-50": classIdx == 2 ? "50-150" : ">150");
                var actual = testTargetArray[i][0] * (max[0] - min[0]) + min[0];
                Console.WriteLine("Actual: {0}\n", actual);
                if (classIdx == 1)
                {
                    if (actual < 50)
                    {
                        correct++;
                        points1.Add(new ObservablePoint
                        {
                            X = testDataArray[i][0],
                            Y = testDataArray[i][1]
                        });
                    }
                    else
                    {
                        wrong++;
                        points1Wrong.Add(new ObservablePoint
                        {
                            X = testDataArray[i][0],
                            Y = testDataArray[i][1]
                        });
                    }
                }
                else if (classIdx == 2)
                {
                    if (actual > 150)
                    {
                        correct++;
                        points2.Add(new ObservablePoint
                        {
                            X = testDataArray[i][0],
                            Y = testDataArray[i][1]
                        });
                    }
                    else
                    {
                        wrong++;
                        points2Wrong.Add(new ObservablePoint
                        {
                            X = testDataArray[i][0],
                            Y = testDataArray[i][1]
                        });
                    }
                }
                else
                {
                    if (actual > 50 && actual < 150)
                    {
                        correct++;
                        points3.Add(new ObservablePoint
                        {
                            X = testDataArray[i][0],
                            Y = testDataArray[i][1]
                        });
                    }
                    else
                    {
                        wrong++;
                        points3Wrong.Add(new ObservablePoint
                        {
                            X = testDataArray[i][0],
                            Y = testDataArray[i][1]
                        });
                    }
                }
            }
            Console.WriteLine("Accuracy: {0} %", (double)correct / N * 100);
            Application.EnableVisualStyles();
            Application.Run(new Diagrams(points1, points2, points3, points1Wrong, points2Wrong, points3Wrong));

            Console.WriteLine("-------------------------------------\n");
        }