Does anyone know how to create a java snippet node which can output a table or list? all I can see is java snippet which can append table and that snippet only apples to each row of the table? I want to be able within that block to use weka forcast library. Currently I am generating a list and I have no idea how to use this code within Knime since the code runs for each row of the input table and I only can replace values for each row but not replacing the whole row.
here is my code.
// system imports
import org.knime.base.node.jsnippet.expression.AbstractJSnippet;
import org.knime.base.node.jsnippet.expression.Abort;
import org.knime.base.node.jsnippet.expression.Cell;
import org.knime.base.node.jsnippet.expression.ColumnException;
import org.knime.base.node.jsnippet.expression.TypeException;
import static org.knime.base.node.jsnippet.expression.Type.*;
import java.util.Date;
import java.util.Calendar;
import org.w3c.dom.Document;
// Your custom imports:
import java.io.*;
import java.nio.charset.Charset;
import java.nio.charset.StandardCharsets;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.ArrayList;
import java.util.List;
import weka.core.Instances;
import weka.classifiers.functions.LinearRegression;
import weka.classifiers.evaluation.NumericPrediction;
import weka.classifiers.timeseries.WekaForecaster;
import org.knime.core.node.NodeLogger;
// system variables
public class JSnippet extends AbstractJSnippet {
// Fields for output columns
/** Output column: "Test" */
public Double out_Test;
// Your custom variables:
NodeLogger logger = NodeLogger.getLogger(AbstractJSnippet.class);
// expression start
public void snippet() throws TypeException, ColumnException, Abort {
// Enter your code here:
try {
// path to the Australian wine data included with the time series
// forecasting
// package
logger.warn("got here");
String path = "C:\\data\\dataset\\dataset.arff";
Path filepath = Paths.get(path);
Charset charset = StandardCharsets.UTF_8;
String content = new String(Files.readAllBytes(filepath), charset);
content = content.replaceAll("@ATTRIBUTE DateTime STRING", "@ATTRIBUTE DateTime Date");
Files.write(filepath, content.getBytes(charset));
FileReader fr = new FileReader(path);
Instances value = new Instances(new BufferedReader(fr));
// new forecaster
WekaForecaster forecaster = new WekaForecaster();
// set the targets we want to forecast. This method calls
// setFieldsToLag() on the lag maker object for us
forecaster.setFieldsToForecast("Amp");
// default underlying classifier is SMOreg (SVM) - we'll use
// gaussian processes for regression instead
forecaster.setBaseForecaster(new LinearRegression());
forecaster.getTSLagMaker().setTimeStampField("DateTime"); // date
forecaster.getTSLagMaker().setMinLag(1);
forecaster.getTSLagMaker().setMaxLag(10); // monthly data
// add a month of the year indicator field
forecaster.getTSLagMaker().setAddMonthOfYear(true);
// add a quarter of the year indicator field
forecaster.getTSLagMaker().setAddQuarterOfYear(true);
// build the model
logger.warn("about to forcast");
forecaster.buildForecaster(value, System.out);
// prime the forecaster with enough recent historical data
// to cover up to the maximum lag. In our case, we could just supply
// the 12 most recent historical instances, as this covers our
// maximum
// lag period
forecaster.primeForecaster(value);
// forecast for 12 units (months) beyond the end of the
// training data
List<List<NumericPrediction>> forecast = forecaster.forecast(10, System.out);
// output the predictions. Outer list is over the steps; inner list
// is over
// the targets
List<Double> list = new ArrayList<Double>();
logger.warn("got here");
for (int i = 0; i < 10; i++) {
List<NumericPrediction> predsAtStep = forecast.get(i);
NumericPrediction predForTarget = predsAtStep.get(0);
if(ROWINDEX == i){
list.add(predForTarget.predicted());
}
}
} catch (Exception ex) {
ex.printStackTrace();
}
// expression end
}
}