Univariate_linearRegression.java

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package univariate_linearregression;
import java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStreamReader;
import java.sql.Driver;
import java.util.stream.IntStream;
import javafx.application.Application;
import javafx.application.Platform;
import javafx.scene.Scene;
import javafx.scene.chart.LineChart;
import javafx.scene.chart.NumberAxis;
import javafx.scene.chart.ScatterChart;
import javafx.scene.chart.XYChart;
import javafx.scene.layout.Pane;
import javafx.stage.Stage;

/**
 *
 * @author admin
 */
public class Univariate_linearRegression extends Application {

    static double[][][] TRAINING_DATA = 

  // SEQUENTIALLY CLUSTERED DATASET    
    {{{1.0,400},{800}},
     {{1.0,450},{820}},
     {{1.0,500},{980}},
     {{1.0,550},{990}},
     {{1.0,600},{920}},
     {{1.0,650},{930}},
     {{1.0,700},{1250}},
     {{1.0,750},{1280}},
     {{1.0,800},{1400}},
     {{1.0,850},{1350}},
     {{1.0,900},{1430}},
     {{1.0,950},{1400}},
     {{1.0,1000},{1500}},
     {{1.0,1050},{1550}},
     {{1.0,1100},{1600}},
     {{1.0,1150},{1700}},
     {{1.0,1200},{1800}},
     {{1.0,1250},{1900}},
     {{1.0,1300},{2100}}};
            
  /* RANDOMIZED CLUSTERED DATASET    
    {{{1.0,400},{800}},
     {{1.0,450},{820}},
     {{1.0,500},{980}},
     {{1.0,1150},{1700}},
     {{1.0,1200},{1800}},
     {{1.0,1250},{1900}},
     {{1.0,1300},{2100}},
     {{1.0,550},{990}},
     {{1.0,850},{1350}},
     {{1.0,900},{1430}},
     {{1.0,950},{1400}},
     {{1.0,1000},{1500}},
     {{1.0,1050},{1550}},
     {{1.0,600},{920}},
     {{1.0,650},{930}},
     {{1.0,700},{1250}},
     {{1.0,750},{1280}},
     {{1.0,800},{1400}},
     {{1.0,1100},{1600}}};
   */
            
   /* IRREGULAR SCATTERED DATASET
   {{{1.0,400},{800}},
     {{1.0,450},{820}},
     {{1.0,500},{980}},
     {{1.0,1050},{3700}},
     {{1.0,1200},{800}},
     {{1.0,1250},{1900}},
     {{1.0,1300},{100}},
     {{1.0,550},{90}},
     {{1.0,9850},{350}},
     {{1.0,900},{430}},
     {{1.0,950},{1030}},
     {{1.0,1000},{500}},
     {{1.0,1050},{1350}},
     {{1.0,600},{920}},
     {{1.0,650},{9303}},
     {{1.0,700},{1250}},
     {{1.0,150},{180}},
     {{1.0,820},{1100}},
     {{1.0,1300},{600}}};
     */       
           
               
    
    static logicClass lc;
    public static void main(String[] args) throws Exception {
        

        double[][] xArray = new double[TRAINING_DATA.length][TRAINING_DATA[0][0].length];
        double[][] yArray = new double[TRAINING_DATA.length][1];
        
        IntStream.range(0, TRAINING_DATA.length).forEach(i ->{
            IntStream.range(0, TRAINING_DATA[0][0].length).forEach(j-> xArray[i][j]= TRAINING_DATA[i][0][j]);
            yArray[i][0]=TRAINING_DATA[i][1][0];
        });
        
        lc = new logicClass(xArray, yArray);
        launch();
        
    }
    static void  handleCommandLine() throws IOException {
        BufferedReader bufferedReader = new BufferedReader(new InputStreamReader(System.in));
        while(true)
        {
            System.out.println("> To estimate rent, enter apt size (in square feet), or exit:");
            try{
                String entry = bufferedReader.readLine();
                if(!entry.equals("exit"))
                    System.out.print("Estimated rent: $"+ lc.estimateRent(entry));
                else System.exit(0);
            }catch (Exception e)
            {
                System.out.println("Invalid input");
            }
        }
    }
    public void start(Stage stage) throws Exception{
        Platform.setImplicitExit(false);
        XYChart.Series<Number,Number> series1 = new XYChart.Series<Number, Number>();
        XYChart.Series<Number,Number> series2 = new XYChart.Series<Number, Number>();
        
        IntStream.range(0, Univariate_linearRegression.TRAINING_DATA.length).forEach( i-> 
            series1.getData().add(
                    new XYChart.Data<Number,Number>(
                            Univariate_linearRegression.TRAINING_DATA[i][0][1],Univariate_linearRegression.TRAINING_DATA[i][1][0])));
        
        IntStream.range(0, Univariate_linearRegression.TRAINING_DATA.length).forEach( i-> 
            series2.getData().add(
                    new XYChart.Data<Number,Number>(
                            Univariate_linearRegression.TRAINING_DATA[i][0][1], lc.getEstimate().getEntry(i, 0))));
    
        NumberAxis xAxis = new NumberAxis(0,1500,400);
        xAxis.setLabel("Size(in square feet)");
        
        NumberAxis yAxis = new NumberAxis(0,2200,700);
        yAxis.setLabel("Rent(in USD)");
    
        ScatterChart<Number,Number> scatterChart = new ScatterChart<Number,Number>(xAxis,yAxis);
        scatterChart.getData().add(series1);
        LineChart<Number,Number> lineChart = new LineChart<Number,Number>(xAxis,yAxis);
        lineChart.getData().add(series2);
        
        lineChart.setOpacity(0.4);
        
        Pane pane = new Pane();
        pane.getChildren().addAll(scatterChart,lineChart);
        stage.setScene(new Scene(pane,580,370));
        
        stage.setOnHidden(e -> { try{
            handleCommandLine();
        }
        catch(Exception e1)
        {
                e1.printStackTrace();
        }});
        
        System.out.println("Close display window to proceed");
        stage.setTitle("Univariate linear regression");
        stage.show();
    }
    
}