List of usage examples for org.apache.commons.math.linear RealVector mapMultiply
RealVector mapMultiply(double d);
From source file:net.ostis.scpdev.scg.geometry.GraphLayout.java
private void calculateForces() { int n = nodes.size(); RealVector[] forces = new RealVector[n]; for (int i = 0; i < n; ++i) forces[i] = new ArrayRealVector(new double[] { 0, 0 }); Map<IFigure, Integer> obj_f = new HashMap<IFigure, Integer>(); Point[] o_pos = new Point[n]; ///*from w ww . ja va 2s . co m*/ // Calculation repulsion forces. // for (int idx = 0; idx < n; ++idx) { obj_f.put(nodes.get(idx), idx); Point p1 = nodes.get(idx).getBounds().getLocation(); RealVector p1v = new ArrayRealVector(2); p1v.setEntry(0, p1.x); p1v.setEntry(0, p1.y); double l = nullVector.getDistance(p1v); RealVector f = p1v.mapMultiply(gravity * (l - 3.0)); forces[idx].subtract(f); for (int jdx = idx + 1; jdx < n; ++jdx) { Point p2 = nodes.get(idx).getBounds().getLocation(); RealVector p2v = new ArrayRealVector(2); p2v.setEntry(0, p2.x); p2v.setEntry(0, p2.y); l = p1v.getDistance(p2v); if (l > max_rep_length) continue; if (l > 0.5) { f = p1v.subtract(p2v).mapMultiply(repulsion / l / l); } else { f = new ArrayRealVector(new double[] { Math.cos(0.17 * idx) * length * 7, Math.sin(0.17 * (idx + 1)) * length * 7 }); } forces[idx].add(f); forces[jdx].subtract(f); } } for (int idx = 0; idx < n; ++idx) { RealVector f = forces[idx]; f.mapMultiply(stepSize); nodes.get(idx).setLocation(new Point(f.getEntry(0), f.getEntry(1))); } }
From source file:net.ostis.scpdev.scg.geometry.LayoutRunnable.java
private void calculateForces() { int n = nodes.size(); RealVector[] forces = new RealVector[n]; for (int i = 0; i < n; ++i) forces[i] = new ArrayRealVector(nullVector); Map<IFigure, Integer> obj_f = new HashMap<IFigure, Integer>(); ///*from w w w . j av a 2 s . c o m*/ // Calculation repulsion forces. // for (int idx = 0; idx < n; ++idx) { SCgNodeShape obj = nodes.get(idx); obj_f.put(obj, idx); Point p1 = translateToCenter(obj.getBounds().getLocation()); RealVector p1v = new ArrayRealVector(2); p1v.setEntry(0, p1.x); p1v.setEntry(1, p1.y); double l = nullVector.getDistance(p1v); RealVector f = p1v.mapMultiply(gravity * (l - 3.0)); forces[idx] = forces[idx].subtract(f); for (int jdx = idx + 1; jdx < n; ++jdx) { Point p2 = translateToCenter(nodes.get(jdx).getBounds().getLocation()); RealVector p2v = new ArrayRealVector(2); p2v.setEntry(0, p2.x); p2v.setEntry(1, p2.y); l = p1v.getDistance(p2v) / 50; if (l > max_rep_length) continue; if (l > 0.5) { f = p1v.subtract(p2v).mapMultiply(repulsion / l / l); } else { f = new ArrayRealVector(new double[] { Math.cos(0.17 * idx) * length * 7, Math.sin(0.17 * (idx + 1)) * length * 7 }); } forces[idx] = forces[idx].add(f); forces[jdx] = forces[jdx].subtract(f); } } // // Calculation springs. // for (SCgPairConnection line : lines) { SCgPair pair = line.getModel(); SCgObject begin = pair.getBegin(); SCgObject end = pair.getEnd(); if (begin == null || end == null) continue; IFigure beginFigure = obj2figure.get(begin); IFigure endFigure = obj2figure.get(end); Point p1 = translateToCenter(beginFigure.getBounds().getLocation()); Point p2 = translateToCenter(endFigure.getBounds().getLocation()); RealVector p1v = new ArrayRealVector(2); p1v.setEntry(0, p1.x); p1v.setEntry(1, p1.y); RealVector p2v = new ArrayRealVector(2); p2v.setEntry(0, p2.x); p2v.setEntry(1, p2.y); double l = p1v.getDistance(p2v) / 50; RealVector f = null; if (l > 0) { RealVector pv = p2v.subtract(p1v); pv.unitize(); int cnt = begin.getInputCount() + end.getOutputCount(); f = pv.mapMultiply(rigidity * (l - Math.max(length, cnt / 3.0)) / l); if (nullVector.getDistance(f) > 10) f = pv.mapMultiply(rigidity / l); } else { f = new ArrayRealVector(nullVector); } if (obj_f.containsKey(beginFigure)) { int index = obj_f.get(beginFigure); forces[index] = forces[index].add(f); } if (obj_f.containsKey(endFigure)) { int index = obj_f.get(endFigure); forces[index] = forces[index].subtract(f); } } double maxf = 0.0; for (int idx = 0; idx < n; ++idx) { RealVector f = forces[idx]; f.mapMultiplyToSelf(stepSize); maxf = Math.max(maxf, nullVector.getDistance(f)); IFigure node = nodes.get(idx); Point location = translateToCenter(node.getBounds().getLocation()); location.x += f.getEntry(0); location.y += f.getEntry(1); node.setLocation(translateFromCenter(location)); } if (maxf > maxForce) { stepSize = stepMaxSize; } else { stepSize *= 0.97; } needLayout = stepSize > stepMinSize; }
From source file:eu.amidst.core.exponentialfamily.EF_Normal_NormalParents.java
/** * {@inheritDoc}/* w ww. j av a 2s . c om*/ */ @Override public void updateNaturalFromMomentParameters() { /* * First step: means and convariances */ CompoundVector globalMomentParam = (CompoundVector) this.momentParameters; double mean_X = globalMomentParam.getXYbaseMatrix().getEntry(0); RealVector mean_Y = globalMomentParam.getTheta_beta0BetaRV(); double cov_XX = globalMomentParam.getcovbaseMatrix().getEntry(0, 0) - mean_X * mean_X; RealMatrix cov_YY = globalMomentParam.getcovbaseMatrix().getSubMatrix(1, nOfParents, 1, nOfParents) .subtract(mean_Y.outerProduct(mean_Y)); RealVector cov_XY = globalMomentParam.getcovbaseMatrix().getSubMatrix(0, 0, 1, nOfParents).getRowVector(0) .subtract(mean_Y.mapMultiply(mean_X)); //RealVector cov_YX = cov_XY; //outerProduct transposes the vector automatically /* * Second step: betas and variance */ RealMatrix cov_YYInverse = new LUDecompositionImpl(cov_YY).getSolver().getInverse(); RealVector beta = cov_YYInverse.preMultiply(cov_XY); this.betas = new double[beta.getDimension()]; for (int i = 0; i < beta.getDimension(); i++) { this.betas[i] = beta.getEntry(i); } this.beta0 = mean_X - beta.dotProduct(mean_Y); this.variance = cov_XX - beta.dotProduct(cov_XY); }
From source file:eu.amidst.core.exponentialfamily.EF_Normal_NormalParents2.java
/** * {@inheritDoc}/* w ww .ja v a 2s .c om*/ */ @Override public void updateNaturalFromMomentParameters() { /* * First step: means and convariances */ CompoundVector globalMomentParam = (CompoundVector) this.momentParameters; double mean_X = globalMomentParam.getXYbaseMatrix().getEntry(0); RealVector mean_Y = globalMomentParam.getTheta_beta0BetaRV(); double cov_XX = globalMomentParam.getcovbaseMatrix().getEntry(0, 0) - mean_X * mean_X; RealMatrix cov_YY = globalMomentParam.getcovbaseMatrix().getSubMatrix(1, nOfParents, 1, nOfParents) .subtract(mean_Y.outerProduct(mean_Y)); RealVector cov_XY = globalMomentParam.getcovbaseMatrix().getSubMatrix(0, 0, 1, nOfParents).getRowVector(0) .subtract(mean_Y.mapMultiply(mean_X)); //RealVector cov_YX = cov_XY; //outerProduct transposes the vector automatically /* * Second step: betas and variance */ RealMatrix cov_YYInverse = new LUDecompositionImpl(cov_YY).getSolver().getInverse(); RealVector beta = cov_YYInverse.preMultiply(cov_XY); double beta_0 = mean_X - beta.dotProduct(mean_Y); double variance = cov_XX - beta.dotProduct(cov_XY); /* * Third step: natural parameters (5 in total) */ /* * 1) theta_0 */ double theta_0 = beta_0 / variance; double[] theta_0array = { theta_0 }; /* * 2) theta_0Theta */ double variance2Inv = 1.0 / (2 * variance); RealVector theta_0Theta = beta.mapMultiply(-beta_0 / variance); ((CompoundVector) this.naturalParameters) .setXYbaseVector(new ArrayRealVector(theta_0array, theta_0Theta.getData())); /* * 3) theta_Minus1 */ double theta_Minus1 = -variance2Inv; /* * 4) theta_beta */ RealVector theta_beta = beta.mapMultiply(variance2Inv); /* * 5) theta_betaBeta */ RealMatrix theta_betaBeta = beta.outerProduct(beta).scalarMultiply(-variance2Inv * 2); /* * Store natural parameters */ RealMatrix natural_XY = new Array2DRowRealMatrix(nOfParents + 1, nOfParents + 1); double[] theta_Minus1array = { theta_Minus1 }; RealVector covXY = new ArrayRealVector(theta_Minus1array, theta_beta.getData()); natural_XY.setColumnVector(0, covXY); natural_XY.setRowVector(0, covXY); natural_XY.setSubMatrix(theta_betaBeta.getData(), 1, 1); ((CompoundVector) this.naturalParameters).setcovbaseVector(natural_XY); }
From source file:rb.app.QNTransientRBSystem.java
/** * Perform online solve for current_params * with the N basis functions. Overload this * to solve the nonlinear RB system using * Newton's method.//from ww w . j a v a2 s. c om */ @Override public double RB_solve(int N) { current_N = N; _N_in_RB_solve = N; // Initialize tau_prod_k tau_prod_k = 1.; if (N > get_n_basis_functions()) { throw new RuntimeException( "ERROR: N cannot be larger than the number " + "of basis functions in RB_solve"); } if (N == 0) { throw new RuntimeException("ERROR: N must be greater than 0 in RB_solve"); } double dt = get_dt(); // First assemble the mass matrix RealMatrix RB_mass_matrix_N = new Array2DRowRealMatrix(N, N); for (int q_m = 0; q_m < get_Q_m(); q_m++) { RB_mass_matrix_N = RB_mass_matrix_N .add(RB_M_q_vector[q_m].getSubMatrix(0, N - 1, 0, N - 1).scalarMultiply(eval_theta_q_m(q_m))); } RealMatrix RB_RHS_Aq_matrix = new Array2DRowRealMatrix(N, N); RealMatrix Base_RB_LHS_matrix = RB_mass_matrix_N.scalarMultiply(1. / dt); for (int q_a = 0; q_a < get_Q_a(); q_a++) { double cached_theta_q_a = eval_theta_q_a(q_a); Base_RB_LHS_matrix = Base_RB_LHS_matrix.add(RB_A_q_vector[q_a].getSubMatrix(0, N - 1, 0, N - 1) .scalarMultiply(get_euler_theta() * cached_theta_q_a)); RB_RHS_Aq_matrix = RB_RHS_Aq_matrix .add(RB_A_q_vector[q_a].getSubMatrix(0, N - 1, 0, N - 1).scalarMultiply(-cached_theta_q_a)); } // Set system time level to 0 set_time_level(0); // Initialize a vector to store our current Newton iterate RealVector RB_u_bar = new ArrayRealVector(N); // and load the _k=0 data RB_solution = RB_u_bar; RB_temporal_solution_data[_k] = RB_u_bar; // Should use .copy() here! double error_bound_sum = 0.; // Set error bound at _k=0 error_bound_all_k[_k] = Math.sqrt(error_bound_sum); // Compute the outputs and associated error bounds at _k=0 for (int i = 0; i < get_n_outputs(); i++) { RB_outputs_all_k[i][_k] = 0.; RB_output_error_bounds_all_k[i][_k] = 0.; for (int q_l = 0; q_l < get_Q_l(i); q_l++) { RB_outputs_all_k[i][_k] += eval_theta_q_l(i, q_l) * (RB_output_vectors[i][q_l].getSubVector(0, N).dotProduct(RB_solution)); } RB_output_error_bounds_all_k[i][_k] = compute_output_dual_norm(i) * error_bound_all_k[_k]; } // Initialize a vector to store the solution from the old time-step RealVector RB_u_old = new ArrayRealVector(N); // Initialize a vector to store the Newton increment, RB_delta_u RealVector RB_delta_u = new ArrayRealVector(N); // Pre-compute eval_theta_c() double cached_theta_c = eval_theta_c(); for (int time_level = 1; time_level <= _K; time_level++) { set_time_level(time_level); // update the member variable _k // Set RB_u_old to be the result of the previous Newton loop RB_u_old = RB_u_bar.copy(); // Now we begin the nonlinear loop for (int l = 0; l < n_newton_steps; ++l) { // Get u_euler_theta = euler_theta*RB_u_bar + (1-euler_theta)*RB_u_old RealVector RB_u_euler_theta = RB_u_bar.mapMultiply(get_euler_theta()) .add(RB_u_old.mapMultiply(1. - get_euler_theta())); // Assemble the left-hand side for the RB linear system RealMatrix RB_LHS_matrix = Base_RB_LHS_matrix.copy(); // Add the trilinear term for (int i = 0; i < N; i++) { for (int j = 0; j < N; j++) { double new_entry = RB_LHS_matrix.getEntry(i, j); for (int n = 0; n < N; n++) { new_entry += cached_theta_c * get_euler_theta() * RB_u_euler_theta.getEntry(n) * (RB_trilinear_form[n][i][j] + RB_trilinear_form[j][i][n]); } RB_LHS_matrix.setEntry(i, j, new_entry); } } // Assemble the right-hand side for the RB linear system (the residual) // First add forcing terms RealVector RB_rhs = new ArrayRealVector(N); for (int q_f = 0; q_f < get_Q_f(); q_f++) { RB_rhs = RB_rhs.add(RB_F_q_vector[q_f].getSubVector(0, N).mapMultiply(eval_theta_q_f(q_f))); } // Now add -1./dt * M * (RB_u_bar - RB_u_old) RB_rhs = RB_rhs.add(RB_mass_matrix_N.operate(RB_u_bar).mapMultiply(-1. / dt)); RB_rhs = RB_rhs.add(RB_mass_matrix_N.operate(RB_u_old).mapMultiply(1. / dt)); // Now add the Aq stuff RB_rhs = RB_rhs.add(RB_RHS_Aq_matrix.operate(RB_u_euler_theta)); // Finally add the trilinear term for (int i = 0; i < N; i++) { double new_entry = RB_rhs.getEntry(i); for (int j = 0; j < N; j++) { double RB_u_euler_theta_j = RB_u_euler_theta.getEntry(j); for (int n = 0; n < N; n++) { new_entry -= cached_theta_c * RB_u_euler_theta.getEntry(n) * RB_u_euler_theta_j * RB_trilinear_form[n][i][j]; } } RB_rhs.setEntry(i, new_entry); } DecompositionSolver solver = new LUDecompositionImpl(RB_LHS_matrix).getSolver(); RB_delta_u = solver.solve(RB_rhs); // update the Newton iterate RB_u_bar = RB_u_bar.add(RB_delta_u); // Compute the l2 norm of RB_delta_u double RB_delta_u_norm = RB_delta_u.getNorm(); if (RB_delta_u_norm < nonlinear_tolerance) { break; } if ((l == (n_newton_steps - 1)) && (RB_delta_u_norm > nonlinear_tolerance)) { throw new RuntimeException("RB Newton loop did not converge"); } } // Load RB_solution into RB_solution_vector for residual computation RB_solution = RB_u_bar; old_RB_solution = RB_u_old; RB_temporal_solution_data[_k] = RB_u_bar; //should use copy here! double rho_LB = (mRbScmSystem == null) ? get_nominal_rho_LB() : get_SCM_lower_bound(); // Evaluate the dual norm of the residual for RB_solution_vector double epsilon_N = compute_residual_dual_norm(N); error_bound_sum += residual_scaling_numer(rho_LB) * Math.pow(epsilon_N, 2.); // store error bound at time-level _k error_bound_all_k[_k] = Math.sqrt(error_bound_sum / residual_scaling_denom(rho_LB)); // Now compute the outputs and associated error bounds for (int i = 0; i < get_n_outputs(); i++) { RB_outputs_all_k[i][_k] = 0.; RB_output_error_bounds_all_k[i][_k] = 0.; for (int q_l = 0; q_l < get_Q_l(i); q_l++) { RB_outputs_all_k[i][_k] += eval_theta_q_l(i, q_l) * (RB_output_vectors[i][q_l].getSubVector(0, N).dotProduct(RB_solution)); } RB_output_error_bounds_all_k[i][_k] = compute_output_dual_norm(i) * error_bound_all_k[_k]; } Log.d(DEBUG_TAG, "output = " + RB_outputs_all_k[0][_k] + ", bound=" + RB_output_error_bounds_all_k[0][_k]); } // Now compute the L2 norm of the RB solution at time-level _K // to normalize the error bound // We reuse RB_rhs here double final_RB_L2_norm = Math.sqrt(RB_mass_matrix_N.operate(RB_solution).dotProduct(RB_solution)); return (return_rel_error_bound ? error_bound_all_k[_K] / final_RB_L2_norm : error_bound_all_k[_K]); }