Examples of stallone.api.ints.IIntArray

stallone.api.ints.IIntArray
TODO: change this copy paste documentation This is a joint interface for all primitive double containers with a fixed order of elements, e.g. arrays, tables, lists. In principle real-valued tensors can also be handled by this object, although we currently provide no method to directly access more than two indexes in order to keep the interface small. Single-index based access should work on higher-order tensors as well. All IDoubleArray's have a sequence in which individual elements can be iterated. The implementing object defines this sequence. Index-based methods such as get(i), set(i, x), and the iterator operate based on this sequence. This interface is the standard way of exchanging real-valued double containers, in order to make all Classes operating with such objects compatible. This option was chosen in order to prefer flexibility and ease-to-use over strict type specificity Methods operating with double containers may return more specific objects, but should accept IDoubleArray (or IComplexArray, see below) as parameters If you want to operate with complex numbers, check out IComplexArray @author noe

    /**
       Drop all entries starting with the given pattern
    */
    public static String[] dropLeading(String[] arr, String pattern)
    {
  IIntArray droplist = Ints.create.array(0);
  for (int i=0; i<arr.length; i++)
      if (arr[i].startsWith(pattern))
    droplist = Ints.util.mergeToNew(droplist, i);
  return(drop(arr, droplist));
    }

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                C = alg.addWeightedToNew(0.5, C, 0.5, alg.transposeToNew(C));
            }


            if (arg.hasCommand("subset"))
            {
                IIntArray S = intseq.loadIntSequence(arg.getArgument("subset"));
                C = C.view(S.getArray(), S.getArray());
            }


            doubles.writeMatrixSparse(C, System.out);
        }
        if (arg.hasCommand("submatrix"))
        {
            IDoubleArray C = doublesNew.fromFile(arg.getArgument("submatrix", 0));
            IIntArray S = intseq.loadIntSequence(arg.getArgument("submatrix", 1));
            C = C.view(S.getArray(), S.getArray());
            doubles.writeMatrixSparse(C, System.out);
        }
        if (arg.hasCommand("populousSet"))
        {
            IDoubleArray C = doublesNew.fromFile(arg.getArgument("populousSet", 0));
            int n = C.rows();
            double minCount = arg.getIntArgument("populousSet",1);
            double minIn = arg.getIntArgument("populousSet",2);
            double minOut = arg.getIntArgument("populousSet",3);


            IDoubleArray counts = alg.rowSums(C);
            IDoubleArray ins = alg.columnSums(C);
            IDoubleArray outs = alg.rowSums(C);
            for (int i=0; i<n; i++)
            {
                ins.set(i,i, ins.get(i,i)-C.get(i,i));
                outs.set(i,i, outs.get(i,i)-C.get(i,i));
            }


            IIntArray iC = doubles.largeValueIndexes(counts, minCount);
            IIntArray iI = doubles.largeValueIndexes(ins, minIn);
            IIntArray iO = doubles.largeValueIndexes(outs, minOut);
            IIntArray I = ints.intersectionToNew(ints.intersectionToNew(iC, iI), iO);
            ints.print(I,"\n");
        }
    }

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        // define leaves
        int nClusters = _clusterMethodFull.getNumberOfClusters();
        for (int i=0; i<nClusters; i++)
        {
            IDoubleArray leafCenter = centers.get(i);
            IIntArray leafIndexes = ints.findAll(micro2macro, i);
            leaves.add(new Leaf(leafIndexes));
        }
    }

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        //System.out.println("Reassignment table: "+IntArrays.toString(reassign));


        // assignment 
        IDiscretization assignment = this.clusterMethodLeaves.getClusterAssignment();
        IDataSequence centers = this.clusterMethodLeaves.getClusterCenters();
        IIntArray micro2macroLeaf = cluster.discretize(subset, assignment);
        // update global assignment
        for (int i=0; i<leaf.indexes.size(); i++)
        {
            int globalIndex = leaf.indexes.get(i);
            int newClusterIndex = reassign[micro2macroLeaf.get(i)];
            micro2macroNew.set(globalIndex, newClusterIndex);
        }
        // create new leaves
        IIntArray[] newLeaveIndexes = new IIntArray[nPieces];
        for (int i=0; i<nPieces; i++)

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        micro2macroNew = micro2macro.copy();
        leavesNew = (ArrayList<Leaf>)leaves.clone();


        // start splitting
        boolean couldsplit = false;
        IIntArray I = ints.sortedIndexes(leafIndexes);
        for (int i=I.size()-1; i>=0; i--)
        {
            int splitIndex = leafIndexes.get(I.get(i));
            if (split(splitIndex))
                couldsplit = true;
        }
        return couldsplit;
    }

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        leavesNew.add(newLeaf);


        // refresh assignment
        for (int s=0; s<leavesNew.size(); s++)
        {
            IIntArray I = leavesNew.get(s).indexes;
            for (int i=0; i<I.size(); i++)
                micro2macroNew.set(I.get(i), s);
        }
    }

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        System.out.println("Strong Components: "+I.length);
        System.out.println("Lengths: "+intArrays.toString(I,", "));
        System.out.println("members: ");
        for (int i=0; i<I.length; i++)
        {
            IIntArray c = components.get(I[i]);
            System.out.println(ints.toString(c)+" ");
        }
        
        if (arg.hasCommand("oGiant"))
        {

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    public IDiscretization regularSelectionDiscretization(IDataSequence data,
        IMetric<IDoubleArray> metric, int k)
    {
        DataList clusterCenters = new DataList(k);
        IIntArray indexes = Ints.create.arrayRange(0, data.size(), data.size() / k);
        for (int i = 0; i < indexes.size(); i++)
        {
            clusterCenters.set(i, data.get(indexes.get(i)));
        }
        VoronoiDiscretization vd = new VoronoiDiscretization(clusterCenters, metric);
        return (vd);
    }

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    public IDiscretization randomSelectionDiscretization(IDataSequence data,
        IMetric<IDoubleArray> metric, int k)
    {
        DataList clusterCenters = new DataList(k);
        IIntArray indexes = Ints.create.arrayRandomIndexes(data.size(), k);
        for (int i = 0; i < indexes.size(); i++)
        {
            clusterCenters.add(data.get(indexes.get(i)));
        }


        VoronoiDiscretization vd = new VoronoiDiscretization(clusterCenters, metric);
        return (vd);
    }

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        return (perform(clusterNew.densitybased(data,N)));
    }


    public IIntArray discretize(IDataSequence data, IDiscretization disc)
    {
        IIntArray res = Ints.create.array(data.size());
        for (int i=0; i<data.size(); i++)
        {
            res.set(i, disc.assign(data.get(i)));
        }
        return(res);
    }

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