Examples of weka.filters.unsupervised.attribute.NumericToNominal

Package weka.filters.unsupervised.attribute

Examples of weka.filters.unsupervised.attribute.NumericToNominal

weka.filters.unsupervised.attribute.NumericToNominal
A filter for turning numeric attributes into nominal ones. Unlike discretization, it just takes all numeric values and adds them to the list of nominal values of that attribute. Useful after CSV imports, to enforce certain attributes to become nominal, e.g., the class attribute, containing values from 1 to 5.
Valid options are:
```
 -R <col1,col2-col4,...> Specifies list of columns to Discretize. First and last are valid indexes. (default: first-last)
```
```
 -V Invert matching sense of column indexes.
```
@author fracpete (fracpete at waikato dot ac dot nz) @version $Revision: 5543 $

    //read from libsvm format files
    Instances instances = libsvm.getDataSet();
    
    //convert to nominal class argument to avoid 
    // exception in weka's smo :-/
    NumericToNominal filter = new NumericToNominal();
    filter.setInputFormat(instances);
    instances = Filter.useFilter(instances, filter);
    
    
    Random ran = new Random(System.currentTimeMillis());
    instances.randomize(ran);

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            try {
                this.splits[idx] = new Instances(data, offset, this.split_counts[idx]);
            
                // Apply NumericToNominal filter!
                NumericToNominal filter = new NumericToNominal();
                filter.setInputFormat(this.splits[idx]);
                this.splits[idx] = Filter.useFilter(this.splits[idx], filter);
                
            } catch (Exception ex) {
                throw new RuntimeException("Failed to split " + stype + " workload", ex);
            }

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            cluster_h.put(c);
        } // FOR
        System.err.println("Number of Elements: " + cluster_h.getValueCount());
        System.err.println(cluster_h);


        NumericToNominal filter = new NumericToNominal();
        filter.setInputFormat(newData);
        newData = Filter.useFilter(newData, filter);
        
        String output = this.catalog_proc.getName() + "-labeled.arff";
        FileUtil.writeStringToFile(output, newData.toString());
        LOG.info("Wrote labeled data set to " + output);

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            Map<Procedure, FeatureSet> fsets = new FeatureExtractor(catalogContext, p_estimator).calculate(workload);
            FeatureSet fset = fsets.get(catalog_proc);
            assertNotNull(fset);
            data = fset.export(catalog_proc.getName(), false);
            
            NumericToNominal weka_filter = new NumericToNominal();
            weka_filter.setInputFormat(data);
            data = Filter.useFilter(data, weka_filter);
        }
        assertNotNull(data);
        
        fclusterer = new FeatureClusterer(catalogContext, catalog_proc, workload, catalogContext.getAllPartitionIds());

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