Exemple #1
0
        private void Graph(string[] args,
                           double[] x1, double[] y1,
                           double[] x2, double[] y2,
                           double[] x3, double[] y3)
        {
            string chartFileName = string.Empty;
            var    pl            = new PLStream();

            if (args.Length == 1 && args[0] == "svg")
            {
                chartFileName = @"Logistic.svg";
                pl.sdev("svg");
                pl.sfnam("Logistic.svg");
            }
            else
            {
                chartFileName = @"Logistic.png";
                pl.sdev("pngcairo");
                pl.sfnam("Logistic.png");
            }
            pl.spal0("cmap0_alternate.pal");
            pl.init();

            const int xMin = -4;
            const int xMax = 4;
            const int yMin = -5;
            const int yMax = 20;

            pl.env(xMin, xMax, yMin, yMax, AxesScale.Independent, AxisBox.BoxTicksLabels);
            // Set scaling for mail title text 125% size of default
            pl.lab("X1", "X2", "Title");
            pl.col0(3);
            pl.sym(x1, y1, (char)228);
            pl.col0(9);
            pl.sym(x2, y2, (char)228);
            pl.col0(2);
            pl.line(x3, y3);
            pl.eop();
            pl.gver(out var verText);
            var    p = new Process();
            string chartFileNamePath = @".\" + chartFileName;

            p.StartInfo = new ProcessStartInfo(chartFileNamePath)
            {
                UseShellExecute = true
            };
            p.Start();
        }
Exemple #2
0
        static void ProcessData(MLContext context, IDataView data)
        {
            var houses = context.Data.CreateEnumerable <HouseBlockData>(data, reuseRowObject: false).ToArray();
            // plot median house value by longitude
            var pl = new PLStream();

            pl.sdev("pngcairo");                // png rendering
            pl.sfnam("data.png");               // output filename
            pl.spal0("cmap0_alternate.pal");    // alternate color palette
            pl.init();
            pl.env(
                0, 10,                          // x-axis range
                0, 600000,                      // y-axis range
                AxesScale.Independent,          // scale x and y independently
                AxisBox.BoxTicksLabelsAxes);    // draw box, ticks, and num ticks
            pl.lab(
                "Median Income",                // x-axis label
                "Median House Value",           // y-axis label
                "House value by longitude");    // plot title
            pl.sym(
                houses.Select(h => (double)h.MedianIncome).ToArray(),
                houses.Select(h => (double)h.MedianHouseValue).ToArray(),
                (char)218
                );
            pl.eop();
        }
Exemple #3
0
        static void PlotData(MLContext context, IDataView data)
        {
            var transactions = context.Data.CreateEnumerable <CreditCardData>(data, reuseRowObject: false).ToArray();
            // plot median house value by longitude
            var pl = new PLStream();

            pl.sdev("pngcairo");                // png rendering
            pl.sfnam("data.png");               // output filename
            pl.spal0("cmap0_alternate.pal");    // alternate color palette
            pl.init();
            pl.env(
                0, 5000,                     // x-axis range
                0, 2500,                     // y-axis range
                AxesScale.Independent,       // scale x and y independently
                AxisBox.BoxTicksLabelsAxes); // draw box, ticks, and num ticks
            pl.lab(
                "Time",                      // x-axis label
                "Amount",                    // y-axis label
                "Transactions by Time");     // plot title
            pl.sym(
                transactions.Select(txn => (double)txn.Time).ToArray(),
                transactions.Select(txn => (double)txn.Amount).ToArray(),
                (char)218
                );
            pl.eop();
        }
Exemple #4
0
        private static void Graph(string[] args, double[] x, double[] y)
        {
            string chartFileName = string.Empty;
            var    pl            = new PLStream();

            if (args.Length == 1 && args[0] == "svg")
            {
                chartFileName = @"KNN.svg";
                pl.sdev("svg");
                pl.sfnam("KNN.svg");
            }
            else
            {
                chartFileName = @"KNN.png";
                pl.sdev("pngcairo");
                pl.sfnam("KNN.png");
            }
            pl.spal0("cmap0_alternate.pal");
            pl.init();
            const int xMin = 0;
            const int xMax = 1;
            const int yMin = 0;
            const int yMax = 1;

            pl.env(xMin, xMax, yMin, yMax, AxesScale.Independent, AxisBox.BoxTicksLabelsAxes);
            pl.schr(0, 0.75);
            pl.lab("X-axis", "Y-axis", "Title");
            pl.col0(3);
            pl.sym(x, y, (char)210);
            pl.eop();
            pl.gver(out var verText);
            var    p = new Process();
            string chartFileNamePath = @".\" + chartFileName;

            p.StartInfo = new ProcessStartInfo(chartFileNamePath)
            {
                UseShellExecute = true
            };
            p.Start();
        }
Exemple #5
0
        /// <summary>
        /// The main program entry point.
        /// </summary>
        /// <param name="args">The command line arguments</param>
        static void Main(string[] args)
        {
            // create the machine learning context
            var context = new MLContext();

            // load the dataset
            Console.WriteLine("Loading data...");
            var data = context.Data.LoadFromTextFile <HouseBlockData>(
                path: dataPath,
                hasHeader: true,
                separatorChar: ',');

            // keep only records with a median house value < 500,000
            data = context.Data.FilterRowsByColumn(
                data,
                "MedianHouseValue",
                upperBound: 499_999
                );

            // get an array of housing data
            var houses = context.Data.CreateEnumerable <HouseBlockData>(data, reuseRowObject: false).ToArray();

            // plot median house value by longitude
            var pl = new PLStream();

            pl.sdev("pngcairo");                // png rendering
            pl.sfnam("data.png");               // output filename
            pl.spal0("cmap0_alternate.pal");    // alternate color palette
            pl.init();
            pl.env(
                0, 10,                          // x-axis range
                0, 600000,                      // y-axis range
                AxesScale.Independent,          // scale x and y independently
                AxisBox.BoxTicksLabelsAxes);    // draw box, ticks, and num ticks
            pl.lab(
                "Median Income",                // x-axis label
                "Median House Value",           // y-axis label
                "House value by longitude");    // plot title
            pl.sym(
                houses.Select(h => (double)h.MedianIncome).ToArray(),
                houses.Select(h => (double)h.MedianHouseValue).ToArray(),
                (char)218
                );
            pl.eop();

            // build a data loading pipeline
            // step 1: divide the median house value by 1000
            var pipeline = context.Transforms.CustomMapping <HouseBlockData, ToMedianHouseValue>(
                (input, output) => { output.NormalizedMedianHouseValue = input.MedianHouseValue / 1000; },
                contractName: "MedianHouseValue"
                );

            // get a 10-record preview of the transformed data
            // var model = pipeline.Fit(data);
            // var transformedData = model.Transform(data);
            // var preview = transformedData.Preview(maxRows: 10);

            // show the preview
            // WritePreview(preview);

            // step 2: bin the longitude
            var pipeline2 = pipeline.Append(context.Transforms.NormalizeBinning(
                                                inputColumnName: "Longitude",
                                                outputColumnName: "BinnedLongitude",
                                                maximumBinCount: 10
                                                ))

                            // step 3: bin the latitude
                            .Append(context.Transforms.NormalizeBinning(
                                        inputColumnName: "Latitude",
                                        outputColumnName: "BinnedLatitude",
                                        maximumBinCount: 10
                                        ))

                            // step 4: one-hot encode the longitude
                            .Append(context.Transforms.Categorical.OneHotEncoding(
                                        inputColumnName: "BinnedLongitude",
                                        outputColumnName: "EncodedLongitude"
                                        ))

                            // step 5: one-hot encode the latitude
                            .Append(context.Transforms.Categorical.OneHotEncoding(
                                        inputColumnName: "BinnedLatitude",
                                        outputColumnName: "EncodedLatitude"
                                        ));

            // step 6: cross the two one-hot encoded columns
            var pipeline3 = pipeline2.Append(context.Transforms.CustomMapping <FromLocation, ToLocation>(
                                                 (input, output) =>
            {
                output.Location = new float[input.EncodedLongitude.Length * input.EncodedLatitude.Length];
                var index       = 0;
                for (var i = 0; i < input.EncodedLongitude.Length; i++)
                {
                    for (var j = 0; j < input.EncodedLatitude.Length; j++)
                    {
                        output.Location[index++] = input.EncodedLongitude[i] * input.EncodedLatitude[j];
                    }
                }
            },
                                                 contractName: "Location"
                                                 ))

                            // step 7: remove all the columns we don't need anymore
                            .Append(context.Transforms.DropColumns(
                                        "MedianHouseValue",
                                        "Longitude",
                                        "Latitude",
                                        "BinnedLongitude",
                                        "BinnedLatitude",
                                        "EncodedLongitude",
                                        "EncodedLatitude"
                                        ));

            // get a 10-record preview of the transformed data
            var model           = pipeline3.Fit(data);
            var transformedData = model.Transform(data);
            var preview         = transformedData.Preview(maxRows: 10);

            // show the location vector
            //WritePreview(preview);
            WritePreviewColumn(preview, "Location");
        }