28 lines
5.1 KiB
Java
28 lines
5.1 KiB
Java
package org.gcube.dataanalysis.wps.statisticalmanager.synchserver.mappedclasses.clusterers;
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import java.io.File;
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import java.net.URL;
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import java.io.IOException;
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import java.io.InputStream;
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import java.util.LinkedHashMap;
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import java.io.StringWriter;
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import org.apache.commons.io.IOUtils;
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import org.apache.xmlbeans.XmlObject;
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import org.gcube.dataanalysis.wps.statisticalmanager.synchserver.bindings.*;
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import org.n52.wps.algorithm.annotation.*;
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import org.n52.wps.io.data.*;
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import org.n52.wps.io.data.binding.complex.*;
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import org.n52.wps.io.data.binding.literal.*;
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import org.n52.wps.server.*;import org.gcube.dataanalysis.wps.statisticalmanager.synchserver.mapping.AbstractEcologicalEngineMapper;import org.n52.wps.server.*;import org.gcube.dataanalysis.wps.statisticalmanager.synchserver.mappedclasses.*;
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@Algorithm(statusSupported=true, title="KMEANS", abstrakt="A clustering algorithm for real valued vectors that relies on the k-means algorithm, i.e. a method aiming to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. A Maximum of 4000 points is allowed.", identifier="org.gcube.dataanalysis.wps.statisticalmanager.synchserver.mappedclasses.clusterers.KMEANS", version = "1.1.0")
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public class KMEANS extends AbstractEcologicalEngineMapper implements IClusterer{
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@ComplexDataInput(abstrakt="Name of the parameter: OccurrencePointsTable. Occurrence Points Table. Max 4000 points [a http link to a table in UTF-8 encoding following this template: (GENERIC) A generic comma separated csv file in UTF-8 encoding]", title="Occurrence Points Table. Max 4000 points [a http link to a table in UTF-8 encoding following this template: (GENERIC) A generic comma separated csv file in UTF-8 encoding]", maxOccurs=1, minOccurs=1, identifier = "OccurrencePointsTable", binding = GenericFileDataBinding.class) public void setOccurrencePointsTable(GenericFileData file) {inputs.put("OccurrencePointsTable",file);}
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@LiteralDataInput(abstrakt="Name of the parameter: FeaturesColumnNames. column Names for the features [a sequence of names of columns from OccurrencePointsTable separated by | ]", defaultValue="", title="column Names for the features [a sequence of names of columns from OccurrencePointsTable separated by | ]", identifier = "FeaturesColumnNames", maxOccurs=1, minOccurs=1, binding = LiteralStringBinding.class) public void setFeaturesColumnNames(String data) {inputs.put("FeaturesColumnNames",data);}
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@LiteralDataInput(abstrakt="Name of the parameter: OccurrencePointsClusterLabel. table name of the resulting distribution", defaultValue="OccCluster_", title="table name of the resulting distribution", identifier = "OccurrencePointsClusterLabel", maxOccurs=1, minOccurs=1, binding = LiteralStringBinding.class) public void setOccurrencePointsClusterLabel(String data) {inputs.put("OccurrencePointsClusterLabel",data);}
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@LiteralDataInput(abstrakt="Name of the parameter: k. expected Number of Clusters", defaultValue="3", title="expected Number of Clusters", identifier = "k", maxOccurs=1, minOccurs=1, binding = LiteralIntBinding.class) public void setk(Integer data) {inputs.put("k",""+data);}
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@LiteralDataInput(abstrakt="Name of the parameter: max_runs. max runs of the clustering procedure", defaultValue="10", title="max runs of the clustering procedure", identifier = "max_runs", maxOccurs=1, minOccurs=1, binding = LiteralIntBinding.class) public void setmax_runs(Integer data) {inputs.put("max_runs",""+data);}
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@LiteralDataInput(abstrakt="Name of the parameter: max_optimization_steps. max number of internal optimization steps", defaultValue="5", title="max number of internal optimization steps", identifier = "max_optimization_steps", maxOccurs=1, minOccurs=1, binding = LiteralIntBinding.class) public void setmax_optimization_steps(Integer data) {inputs.put("max_optimization_steps",""+data);}
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@LiteralDataInput(abstrakt="Name of the parameter: min_points. number of points which define an outlier set", defaultValue="2", title="number of points which define an outlier set", identifier = "min_points", maxOccurs=1, minOccurs=1, binding = LiteralIntBinding.class) public void setmin_points(Integer data) {inputs.put("min_points",""+data);}
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@ComplexDataOutput(abstrakt="Name of the parameter: OutputTable. Output cluster table [a http link to a table in UTF-8 ecoding following this template: (CLUSTER) http://goo.gl/PnKhhb]", title="Output cluster table [a http link to a table in UTF-8 ecoding following this template: (CLUSTER) http://goo.gl/PnKhhb]", identifier = "OutputTable", binding = CsvFileDataBinding.class) public GenericFileData getOutputTable() {URL url=null;try {url = new URL((String) outputs.get("OutputTable")); return new GenericFileData(url.openStream(),"text/csv");} catch (Exception e) {e.printStackTrace();return null;}}
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@ComplexDataOutput(abstrakt="Output that is not predetermined", title="NonDeterministicOutput", identifier = "non_deterministic_output", binding = GenericXMLDataBinding.class)
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public XmlObject getNon_deterministic_output() {return (XmlObject) outputs.get("non_deterministic_output");}
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@Execute public void run() throws Exception { super.run(); } } |