Example usage for org.apache.commons.math3.linear Array2DRowRealMatrix Array2DRowRealMatrix

List of usage examples for org.apache.commons.math3.linear Array2DRowRealMatrix Array2DRowRealMatrix

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Introduction

In this page you can find the example usage for org.apache.commons.math3.linear Array2DRowRealMatrix Array2DRowRealMatrix.

Prototype

public Array2DRowRealMatrix()

Source Link

Document

Creates a matrix with no data

Usage

From source file:edu.cmu.tetrad.util.ApacheTetradMatrix.java

private ApacheTetradMatrix(double[][] apacheData) {
    if (apacheData.length == 0) {
        this.apacheData = new Array2DRowRealMatrix();
    } else {/*w  w w . j a  va 2 s.co  m*/
        this.apacheData = new BlockRealMatrix(apacheData);
    }
}

From source file:edu.cmu.tetrad.util.TetradMatrix.java

public TetradMatrix(double[][] data) {
    if (data.length == 0) {
        this.apacheData = new Array2DRowRealMatrix();
    } else {/*from   w  ww .j av  a  2s  .c o m*/
        //            this.apacheData = new OpenMapRealMatrix(data.length, data[0].length);
        //
        //            for (int i = 0; i < data.length; i++) {
        //                for (int j = 0; j < data[0].length; j++) {
        //                    apacheData.setEntry(i, j, data[i][j]);
        //                }
        //            }
        this.apacheData = new BlockRealMatrix(data);
    }

    this.m = data.length;
    this.n = m == 0 ? 0 : data[0].length;
}

From source file:edu.cmu.tetrad.util.ApacheTetradMatrix.java

private ApacheTetradMatrix(int m, int n) {
    if (m == 0) {
        this.apacheData = new Array2DRowRealMatrix();
    } else {/*from  w w w.  j  a  va 2s .c om*/
        this.apacheData = new BlockRealMatrix(m, n);
    }
}

From source file:edu.cmu.tetrad.util.TetradMatrix1.java

public TetradMatrix1(double[][] data) {
    if (data.length == 0) {
        this.apacheData = new Array2DRowRealMatrix();
    } else {/*from   w  w w  . ja  v  a 2 s  .  c o m*/
        //            this.apacheData = new OpenMapRealMatrix(data.length, data[0].length);
        //
        //            for (int i = 0; i < data.length; i++) {
        //                for (int j = 0; j < data[0].length; j++) {
        //                    apacheData.setEntry(i, j, data[i][j]);
        //                }
        //            }
        this.apacheData = new BlockRealMatrix(data);
    }

    this.m = data.length;
    this.n = m == 0 ? 0 : data[0].length;
}

From source file:edu.stanford.cfuller.imageanalysistools.clustering.GaussianLikelihoodObjectiveFunction.java

/**
 * Constructs a new GaussianLikelihoodObjectiveFunction that will determine the likelihood of having observed the
 * supplied ClusterObjects at their locations.
 *
 * @param objects   A Vector containing the observed ClusterObjects (with locations already determined and assigned).
 *///from   ww w  .ja v a 2 s  .  co m
public GaussianLikelihoodObjectiveFunction(java.util.Vector<ClusterObject> objects) {
    mean = new ArrayRealVector(numDim);
    x = new ArrayRealVector(numDim);
    pk = new ArrayRealVector();
    clusterProbs = new Array2DRowRealMatrix();
    abdMatrices = new java.util.Vector<RealMatrix>();
    det = new ArrayRealVector();
    this.objects = objects;

}

From source file:edu.cmu.tetrad.util.TetradMatrix.java

public TetradMatrix(int m, int n) {
    if (m == 0 || n == 0) {
        this.apacheData = new Array2DRowRealMatrix();
    } else {// ww w. jav  a  2 s  .  c o  m
        //            this.apacheData = new OpenMapRealMatrix(m, n);
        this.apacheData = new BlockRealMatrix(m, n);
    }

    this.m = m;
    this.n = n;
}

From source file:edu.cmu.tetrad.util.TetradMatrix1.java

public TetradMatrix1(int m, int n) {
    if (m == 0 || n == 0) {
        this.apacheData = new Array2DRowRealMatrix();
    } else {/*ww w.j  av a2 s  .  c  o  m*/
        //            this.apacheData = new OpenMapRealMatrix(m, n);
        this.apacheData = new BlockRealMatrix(m, n);
    }

    this.m = m;
    this.n = n;
}

From source file:com.bolatu.gezkoncsvlogger.GyroOrientation.RotationKalmanFilter.java

/**
 * Creates a new Kalman filter with the given process and measurement
 * models.//from w ww  . j av a  2  s.  com
 *
 * @param process
 *            the model defining the underlying process dynamics
 * @param measurement
 *            the model defining the given measurement characteristics
 * @throws NullArgumentException
 *             if any of the given inputs is null (except for the control
 *             matrix)
 * @throws NonSquareMatrixException
 *             if the transition matrix is non square
 * @throws DimensionMismatchException
 *             if the column dimension of the transition matrix does not
 *             match the dimension of the initial state estimation vector
 * @throws MatrixDimensionMismatchException
 *             if the matrix dimensions do not fit together
 */
public RotationKalmanFilter(final ProcessModel process, final MeasurementModel measurement)
        throws NullArgumentException, NonSquareMatrixException, DimensionMismatchException,
        MatrixDimensionMismatchException {

    MathUtils.checkNotNull(process);
    MathUtils.checkNotNull(measurement);

    this.processModel = process;
    this.measurementModel = measurement;

    transitionMatrix = processModel.getStateTransitionMatrix();
    MathUtils.checkNotNull(transitionMatrix);
    transitionMatrixT = transitionMatrix.transpose();

    // create an empty matrix if no control matrix was given
    if (processModel.getControlMatrix() == null) {
        controlMatrix = new Array2DRowRealMatrix();
    } else {
        controlMatrix = processModel.getControlMatrix();
    }

    measurementMatrix = measurementModel.getMeasurementMatrix();
    MathUtils.checkNotNull(measurementMatrix);
    measurementMatrixT = measurementMatrix.transpose();

    // check that the process and measurement noise matrices are not null
    // they will be directly accessed from the model as they may change
    // over time
    RealMatrix processNoise = processModel.getProcessNoise();
    MathUtils.checkNotNull(processNoise);
    RealMatrix measNoise = measurementModel.getMeasurementNoise();
    MathUtils.checkNotNull(measNoise);

    // set the initial state estimate to a zero vector if it is not
    // available from the process model
    if (processModel.getInitialStateEstimate() == null) {
        stateEstimation = new ArrayRealVector(transitionMatrix.getColumnDimension());
    } else {
        stateEstimation = processModel.getInitialStateEstimate();
    }

    if (transitionMatrix.getColumnDimension() != stateEstimation.getDimension()) {
        throw new DimensionMismatchException(transitionMatrix.getColumnDimension(),
                stateEstimation.getDimension());
    }

    // initialize the error covariance to the process noise if it is not
    // available from the process model
    if (processModel.getInitialErrorCovariance() == null) {
        errorCovariance = processNoise.copy();
    } else {
        errorCovariance = processModel.getInitialErrorCovariance();
    }

    // sanity checks, the control matrix B may be null

    // A must be a square matrix
    if (!transitionMatrix.isSquare()) {
        throw new NonSquareMatrixException(transitionMatrix.getRowDimension(),
                transitionMatrix.getColumnDimension());
    }

    // row dimension of B must be equal to A
    // if no control matrix is available, the row and column dimension will
    // be 0
    if (controlMatrix != null && controlMatrix.getRowDimension() > 0 && controlMatrix.getColumnDimension() > 0
            && controlMatrix.getRowDimension() != transitionMatrix.getRowDimension()) {
        throw new MatrixDimensionMismatchException(controlMatrix.getRowDimension(),
                controlMatrix.getColumnDimension(), transitionMatrix.getRowDimension(),
                controlMatrix.getColumnDimension());
    }

    // Q must be equal to A
    MatrixUtils.checkAdditionCompatible(transitionMatrix, processNoise);

    // column dimension of H must be equal to row dimension of A
    if (measurementMatrix.getColumnDimension() != transitionMatrix.getRowDimension()) {
        throw new MatrixDimensionMismatchException(measurementMatrix.getRowDimension(),
                measurementMatrix.getColumnDimension(), measurementMatrix.getRowDimension(),
                transitionMatrix.getRowDimension());
    }

    // row dimension of R must be equal to row dimension of H
    if (measNoise.getRowDimension() != measurementMatrix.getRowDimension()) {
        throw new MatrixDimensionMismatchException(measNoise.getRowDimension(), measNoise.getColumnDimension(),
                measurementMatrix.getRowDimension(), measNoise.getColumnDimension());
    }
}

From source file:edu.cmu.tetrad.util.ApacheTetradMatrix.java

/**
 * Adds semantic checks to the default deserialization method. This method
 * must have the standard signature for a readObject method, and the body of
 * the method must begin with "s.defaultReadObject();". Other than that, any
 * semantic checks can be specified and do not need to stay the same from
 * version to version. A readObject method of this form may be added to any
 * class, even if Tetrad sessions were previously saved out using a version
 * of the class that didn't include it. (That's what the
 * "s.defaultReadObject();" is for. See J. Bloch, Effective Java, for help.
 *
 * @throws java.io.IOException/*from w  w w. ja va  2s  . co  m*/
 * @throws ClassNotFoundException
 */
private void readObject(ObjectInputStream s) throws IOException, ClassNotFoundException {
    s.defaultReadObject();

    if (this.data != null) {
        double[][] d = data.toArray();

        if (d.length == 0) {
            this.apacheData = new Array2DRowRealMatrix();
        } else {
            this.apacheData = new BlockRealMatrix(d);
        }

        this.data = null;
    }
}

From source file:i5.las2peer.services.servicePackage.TemplateService.java

@GET
@Path("/graphs/termmatrix")
@Produces(MediaType.APPLICATION_JSON)//from  w w  w.java2 s .c  om
public HttpResponse getTermMatrix() {
    Connection conn = null;
    PreparedStatement stmnt = null;
    ResultSet rs = null;
    JSONArray json = new JSONArray();
    EntityManagement em = new EntityManagement();
    WordConverter wordConv = new WordConverter();
    Array2DRowRealMatrix matrix = new Array2DRowRealMatrix();
    ToJSON converter = new ToJSON();
    Algorithms alg = new Algorithms();

    try {
        // get connection from connection pool
        conn = dbm.getConnection();

        // prepare statement
        stmnt = conn.prepareStatement("SELECT id,content,author FROM urch order by author;");

        // retrieve result set
        rs = stmnt.executeQuery();

        //LinkedList<Node> nodes = em.listNodes(rs);   // listing nodes and textpreprocessing of content
        em.createNodeTable(conn);
        em.createGraphTable(conn);

        //persist graph built form dataset
        Graph graph = em.persistNewGraph(rs, conn);

        Termmatrix termMat = wordConv.convertTFIDF(graph.getNodes());
        //matrix = wordConv.convertTFIDF(nodes);      // creating term matrix, computing tf- idf, converting to json array for visualization   

        Clustering clust = alg.optCosts(termMat, 2);
        em.createCommunityTables(conn);
        em.createCoverTable(conn);
        em.persistCover(clust, conn);
        //json = converter.termmatrixToJson(termMat);
        //json = converter.termmatrixToJson(matrix);
        return new HttpResponse("New Cover created from dataset urch and persisted", HttpURLConnection.HTTP_OK);

    } catch (Exception e) {

        return new HttpResponse("Internal error: " + e.getMessage(), HttpURLConnection.HTTP_INTERNAL_ERROR);

    } finally {
        // free resources if exception or not
        if (rs != null) {
            try {
                rs.close();
            } catch (Exception e) {
                Context.logError(this, e.getMessage());

                // return HTTP Response on error
                return new HttpResponse("Internal error: " + e.getMessage(),
                        HttpURLConnection.HTTP_INTERNAL_ERROR);
            }
        }
        if (stmnt != null) {
            try {
                stmnt.close();
            } catch (Exception e) {
                Context.logError(this, e.getMessage());

                // return HTTP Response on error
                return new HttpResponse("Internal error: " + e.getMessage(),
                        HttpURLConnection.HTTP_INTERNAL_ERROR);
            }
        }
        if (conn != null) {
            try {
                conn.close();
            } catch (Exception e) {
                Context.logError(this, e.getMessage());

                // return HTTP Response on error
                return new HttpResponse("Internal error: " + e.getMessage(),
                        HttpURLConnection.HTTP_INTERNAL_ERROR);
            }
        }
    }
}