Examples with MatrixDense gov.sandia.n2a.linear.MatrixDense used on opensource projects

Example 21 with MatrixDense

use of gov.sandia.n2a.linear.MatrixDense in project n2a by frothga.

the class Input method eval.

public Type eval(Instance context) {
    double line = Double.NEGATIVE_INFINITY;
    Type op1 = null;
    if (operands.length > 1)
        op1 = operands[1].eval(context);
    if (op1 instanceof Scalar)
        line = ((Scalar) op1).value;
    Holder H = getRow(context, line);
    if (H == null)
        return getType();
    int c = -1;
    boolean namedColumn = false;
    if (operands.length > 2) {
        Type columnSpec = operands[2].eval(context);
        if (columnSpec instanceof Text) {
            Integer columnMapping = H.columnMap.get(((Text) columnSpec).value);
            if (columnMapping == null)
                return new Scalar(0);
            c = columnMapping;
            namedColumn = true;
        } else // Otherwise, just assume it is a Scalar
        {
            c = (int) Math.round(((Scalar) columnSpec).value);
        }
    }
    int columns = H.currentValues.length;
    int lastColumn = columns - 1;
    if (H.smooth && line >= H.currentLine && Double.isFinite(H.currentLine) && !Double.isNaN(H.nextLine)) {
        // This should still work, even if line < H.currentLine.
        double b = (line - H.currentLine) / (H.nextLine - H.currentLine);
        double b1 = 1 - b;
        if (c >= 0) {
            // time column is not included in raw index
            if (H.time && !namedColumn && c >= H.timeColumn)
                c++;
            if (c >= columns)
                c = lastColumn;
            return new Scalar(b * H.nextValues[c] + b1 * H.currentValues[c]);
        } else {
            if (H.Alast == line)
                return H.A;
            // Create a new matrix
            if (columns > 1) {
                columns--;
                H.A = new MatrixDense(1, columns);
                int from = 0;
                for (int to = 0; to < columns; to++) {
                    if (from == H.timeColumn)
                        from++;
                    H.A.set(0, to, b * H.nextValues[from] + b1 * H.currentValues[from]);
                    from++;
                }
            } else // There is always at least 1 column, enforced by Holder.
            {
                H.A = new MatrixDense(1, 1);
                H.A.set(0, 0, b * H.nextValues[0] + b1 * H.currentValues[0]);
            }
            H.Alast = line;
            return H.A;
        }
    } else {
        if (c >= 0) {
            // time column is not included in raw index
            if (H.time && !namedColumn && c >= H.timeColumn)
                c++;
            if (c >= columns)
                c = lastColumn;
            return new Scalar(H.currentValues[c]);
        } else {
            if (H.Alast == H.currentLine)
                return H.A;
            // Create a new matrix
            if (H.time && columns > 1) {
                columns--;
                H.A = new MatrixDense(1, columns);
                int from = 0;
                for (int to = 0; to < columns; to++) {
                    if (from == H.timeColumn)
                        from++;
                    H.A.set(0, to, H.currentValues[from++]);
                }
            } else {
                H.A = new MatrixDense(H.currentValues, 0, 1, columns, columns, 1);
            }
            H.Alast = H.currentLine;
            return H.A;
        }
    }
}

Example 22 with MatrixDense

use of gov.sandia.n2a.linear.MatrixDense in project n2a by frothga.

the class Part method getXYZ.

public double[] getXYZ(Simulator simulator, boolean connect) {
    InternalBackendData bed = (InternalBackendData) equations.backendData;
    // default is ~[0,0,0]
    if (bed.xyz == null)
        return new double[3];
    // Either "constant" or stored
    if (!bed.xyz.hasAttribute("temporary"))
        return ((MatrixDense) get(bed.xyz)).getData();
    InstanceTemporaries temp;
    List<Variable> list;
    if (// evaluate in connect phase
    connect) {
        temp = new InstanceConnect(this, simulator);
        list = bed.XYZdependencies;
    } else // evaluate in live phase
    {
        temp = new InstanceTemporaries(this, simulator);
        list = bed.XYZdependenciesTemp;
    }
    for (Variable v : list) {
        Type result = v.eval(temp);
        if (result == null)
            temp.set(v, v.type);
        else
            temp.set(v, result);
    }
    Type result = bed.xyz.eval(temp);
    if (result == null)
        return new double[3];
    return ((MatrixDense) result).getData();
}

Example 23 with MatrixDense

use of gov.sandia.n2a.linear.MatrixDense in project n2a by frothga.

the class Sphere method simplify.

public Operator simplify(Variable from, boolean evalOnly) {
    if (operands.length == 0) {
        // No need to set from.changed
        operands = new Operator[1];
        Operator sigma = new Constant(new MatrixDense(3, 1, 1));
        sigma.parent = this;
        operands[0] = sigma;
        return this;
    }
    // Any number of operands >= 1. We only pay attention to first operand.
    operands[0] = operands[0].simplify(from, evalOnly);
    Operator sigma = operands[0];
    if (sigma.isScalar()) {
        if (sigma.getDouble() == 0) {
            // Needed because we are replacing ourself with a simpler constant.
            from.changed = true;
            sigma.parent = parent;
            return sigma;
        }
        // No need to set from.changed
        operands = new Operator[1];
        sigma = new Constant(new MatrixDense(3, 1, sigma.getDouble()));
        sigma.parent = this;
        operands[0] = sigma;
        return this;
    } else if (sigma instanceof Constant) {
        Constant cs = (Constant) sigma;
        if (cs.value instanceof Matrix) {
            // No need to set from.changed for any of the transforms below ...
            Matrix A = (Matrix) cs.value;
            int rows = A.rows();
            int cols = A.columns();
            if (rows == 1 && cols > 1) {
                cs.value = A = A.transpose();
                rows = cols;
                cols = 1;
            }
            if (// Check for zeroes in single column.
            cols == 1) {
                int rank = (int) A.norm(0);
                if (rank < rows) {
                    from.changed = true;
                    MatrixDense B = new MatrixDense(rows, rank);
                    cs.value = B;
                    int c = 0;
                    for (int r = 0; r < rows; r++) {
                        double value = A.get(r);
                        if (value != 0)
                            B.set(r, c++, value);
                    }
                }
            }
        // Don't bother checking for columns that are all zeroes.
        // Since sigma is a constant, this should be obvious to the user.
        }
    }
    return this;
}

Example 24 with MatrixDense

use of gov.sandia.n2a.linear.MatrixDense in project n2a by frothga.

the class Sphere method eval.

// This code assumes that simplify() has been run, so operand 0 is a matrix of correct form.
public Type eval(Instance context) throws EvaluationException {
    Random random;
    Simulator simulator = Simulator.instance.get();
    if (simulator == null)
        random = new Random();
    else
        random = simulator.random;
    Matrix sigma = (Matrix) operands[0].eval(context);
    int rows = sigma.rows();
    int cols = sigma.columns();
    int dimension = cols == 1 ? rows : cols;
    MatrixDense temp = new MatrixDense(dimension, 1);
    for (int r = 0; r < dimension; r++) temp.set(r, random.nextGaussian());
    temp = temp.multiply(new Scalar(Math.pow(random.nextDouble(), 1.0 / dimension) / temp.norm(2)));
    if (cols == 1)
        return sigma.multiplyElementwise(temp);
    return sigma.multiply(temp);
}

Example 25 with MatrixDense

use of gov.sandia.n2a.linear.MatrixDense in project n2a by frothga.

the class Uniform method eval.

public Type eval(Instance context) throws EvaluationException {
    Random random;
    Simulator simulator = Simulator.instance.get();
    if (simulator == null)
        random = new Random();
    else
        random = simulator.random;
    if (operands.length == 0)
        return new Scalar(random.nextDouble());
    Type sigma = operands[0].eval(context);
    if (sigma instanceof Scalar) {
        double u = random.nextDouble();
        double lo = ((Scalar) sigma).value;
        // Check if this is actually an interval form.
        if (operands.length > 1) {
            double hi = ((Scalar) operands[1].eval(context)).value;
            double step = 1;
            if (operands.length > 2)
                step = ((Scalar) operands[2].eval(context)).value;
            int steps = (int) Math.floor((hi - lo) / step) + 1;
            return new Scalar(lo + step * Math.floor(u * steps));
        }
        // Non-interval form. "lo" is really just "sigma"
        return new Scalar(u * lo);
    } else if (sigma instanceof Matrix) {
        Matrix scale = (Matrix) sigma;
        int rows = scale.rows();
        int columns = scale.columns();
        if (columns == 1) {
            Matrix result = new MatrixDense(rows, 1);
            for (int i = 0; i < rows; i++) result.set(i, random.nextDouble() * scale.get(i, 0));
            return result;
        } else if (rows == 1) {
            Matrix result = new MatrixDense(columns, 1);
            for (int i = 0; i < columns; i++) result.set(i, random.nextDouble() * scale.get(0, i));
            return result;
        } else {
            Matrix temp = new MatrixDense(columns, 1);
            for (int i = 0; i < columns; i++) temp.set(i, random.nextDouble());
            return sigma.multiply(temp);
        }
    } else {
        // We could throw an exception, but this is an adequate fallback.
        return new Scalar(random.nextDouble());
    }
}