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369 lines
11 KiB
369 lines
11 KiB
//==============================================================================
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//
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// Copyright (c) 2002-
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// Authors:
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// * Dave Parker <david.parker@comlab.ox.ac.uk> (University of Oxford, formerly University of Birmingham)
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//
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//------------------------------------------------------------------------------
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//
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// This file is part of PRISM.
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//
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// PRISM is free software; you can redistribute it and/or modify
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// it under the terms of the GNU General Public License as published by
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// the Free Software Foundation; either version 2 of the License, or
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// (at your option) any later version.
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//
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// PRISM is distributed in the hope that it will be useful,
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// but WITHOUT ANY WARRANTY; without even the implied warranty of
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// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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// GNU General Public License for more details.
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//
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// You should have received a copy of the GNU General Public License
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// along with PRISM; if not, write to the Free Software Foundation,
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// Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
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//
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//==============================================================================
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// includes
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#include "PrismSparse.h"
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#include <math.h>
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#include <util.h>
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#include <cudd.h>
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#include <dd.h>
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#include <odd.h>
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#include <dv.h>
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#include <prism.h>
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#include "sparse.h"
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#include "PrismSparseGlob.h"
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#include "jnipointer.h"
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#include "Measures.h"
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#include <new>
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//------------------------------------------------------------------------------
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JNIEXPORT jlong __jlongpointer JNICALL Java_sparse_PrismSparse_PS_1StochCumulReward
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(
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JNIEnv *env,
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jclass cls,
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jlong __jlongpointer tr, // trans matrix
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jlong __jlongpointer sr, // state rewards
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jlong __jlongpointer trr, // transition rewards
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jlong __jlongpointer od, // odd
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jlong __jlongpointer rv, // row vars
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jint num_rvars,
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jlong __jlongpointer cv, // col vars
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jint num_cvars,
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jdouble time // time bound
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)
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{
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// cast function parameters
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DdNode *trans = jlong_to_DdNode(tr); // trans matrix
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DdNode *state_rewards = jlong_to_DdNode(sr); // state rewards
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DdNode *trans_rewards = jlong_to_DdNode(trr); // transition rewards
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ODDNode *odd = jlong_to_ODDNode(od); // odd
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DdNode **rvars = jlong_to_DdNode_array(rv); // row vars
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DdNode **cvars = jlong_to_DdNode_array(cv); // col vars
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// mtbdds
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DdNode *tmp = NULL;
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// model stats
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int n;
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long nnz;
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// flags
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bool compact_tr, compact_d;
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// sparse matrix
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RMSparseMatrix *rmsm = NULL;
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CMSRSparseMatrix *cmsrsm = NULL;
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// vectors
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double *diags = NULL, *soln = NULL, *soln2 = NULL, *tmpsoln = NULL, *sum = NULL;
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DistVector *diags_dist = NULL;
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// fox glynn stuff
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FoxGlynnWeights fgw;
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// timing stuff
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long start1, start2, start3, stop;
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double time_taken, time_for_setup, time_for_iters;
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// misc
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bool done;
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int j, l, h;
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long i, iters, num_iters;
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double d, max_diag, weight, kb, kbt, unif, term_crit_param_unif;
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// measure for convergence termination check
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MeasureSupNorm measure(term_crit == TERM_CRIT_RELATIVE);
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// exception handling around whole function
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try {
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// start clocks
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start1 = start2 = util_cpu_time();
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// get number of states
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n = odd->eoff + odd->toff;
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// build sparse matrix
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PS_PrintToMainLog(env, "\nBuilding sparse matrix... ");
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// if requested, try and build a "compact" version
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compact_tr = true;
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cmsrsm = NULL;
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if (compact) cmsrsm = build_cmsr_sparse_matrix(ddman, trans, rvars, cvars, num_rvars, odd);
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if (cmsrsm != NULL) {
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nnz = cmsrsm->nnz;
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kb = cmsrsm->mem;
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}
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// if not or if it wasn't possible, built a normal one
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else {
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compact_tr = false;
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rmsm = build_rm_sparse_matrix(ddman, trans, rvars, cvars, num_rvars, odd);
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nnz = rmsm->nnz;
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kb = rmsm->mem;
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}
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kbt = kb;
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// print some info
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PS_PrintToMainLog(env, "[n=%d, nnz=%d%s] ", n, nnz, compact_tr?", compact":"");
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PS_PrintMemoryToMainLog(env, "[", kb, "]\n");
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// get vector of diagonals
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PS_PrintToMainLog(env, "Creating vector for diagonals... ");
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diags = compact_tr ? cmsr_negative_row_sums(cmsrsm) : rm_negative_row_sums(rmsm);
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// try and convert to compact form if required
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compact_d = false;
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if (compact) {
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if ((diags_dist = double_vector_to_dist(diags, n))) {
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compact_d = true;
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delete diags; diags = NULL;
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}
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}
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kb = (!compact_d) ? n*8.0/1024.0 : (diags_dist->num_dist*8.0+n*2.0)/1024.0;
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kbt += kb;
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if (compact_d) PS_PrintToMainLog(env, "[dist=%d, compact] ", diags_dist->num_dist);
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PS_PrintMemoryToMainLog(env, "[", kb, "]\n");
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// find max diagonal element
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if (!compact_d) {
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max_diag = diags[0];
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for (i = 1; i < n; i++) if (diags[i] < max_diag) max_diag = diags[i];
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} else {
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max_diag = diags_dist->dist[0];
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for (i = 1; i < diags_dist->num_dist; i++) if (diags_dist->dist[i] < max_diag) max_diag = diags_dist->dist[i];
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}
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max_diag = -max_diag;
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// constant for uniformization
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unif = 1.02*max_diag;
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// modify diagonals
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if (!compact_d) {
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for (i = 0; i < n; i++) diags[i] = diags[i] / unif + 1;
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} else {
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for (i = 0; i < diags_dist->num_dist; i++) diags_dist->dist[i] = diags_dist->dist[i] / unif + 1;
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}
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// uniformization
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if (!compact_tr) {
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for (i = 0; i < nnz; i++) rmsm->non_zeros[i] /= unif;
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} else {
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for (i = 0; i < cmsrsm->dist_num; i++) cmsrsm->dist[i] /= unif;
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}
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// combine state/transition rewards into a single vector
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// new state rewards = c + (R.C)1
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// first, multiply transition rates by transition rewards and sum rows
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// = (R.C)1
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Cudd_Ref(trans);
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Cudd_Ref(trans_rewards);
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tmp = DD_Apply(ddman, APPLY_TIMES, trans, trans_rewards);
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tmp = DD_SumAbstract(ddman, tmp, cvars, num_cvars);
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// then add state rewards
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// = c + (R.C)1
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Cudd_Ref(state_rewards);
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tmp = DD_Apply(ddman, APPLY_PLUS, tmp, state_rewards);
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soln = mtbdd_to_double_vector(ddman, tmp, rvars, num_rvars, odd);
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Cudd_RecursiveDeref(ddman, tmp);
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// create solution/iteration vectors
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PS_PrintToMainLog(env, "Allocating iteration vectors... ");
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// soln has already been created and initialised to rewards vector as required
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// need to create soln2 and sum
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soln2 = new double[n];
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sum = new double[n];
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kb = n*8.0/1024.0;
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kbt += 3*kb;
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PS_PrintMemoryToMainLog(env, "[3 x ", kb, "]\n");
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// print total memory usage
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PS_PrintMemoryToMainLog(env, "TOTAL: [", kbt, "]\n");
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// compute new termination criterion parameter (epsilon/8)
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term_crit_param_unif = term_crit_param / 8.0;
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// compute poisson probabilities (fox/glynn)
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PS_PrintToMainLog(env, "\nUniformisation: q.t = %f x %f = %f\n", unif, time, unif * time);
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fgw = fox_glynn(unif * time, 1.0e-300, 1.0e+300, term_crit_param_unif);
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if (fgw.right < 0) throw "Overflow in Fox-Glynn computation (time bound too big?)";
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for (i = fgw.left; i <= fgw.right; i++) {
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fgw.weights[i-fgw.left] /= fgw.total_weight;
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}
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PS_PrintToMainLog(env, "Fox-Glynn: left = %ld, right = %ld\n", fgw.left, fgw.right);
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// modify the poisson probabilities to what we need for this computation
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// first make the kth value equal to the sum of the values for 0...k
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for (i = fgw.left+1; i <= fgw.right; i++) {
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fgw.weights[i-fgw.left] += fgw.weights[i-1-fgw.left];
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}
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// then subtract from 1 and divide by uniformisation constant (q) to give mixed poisson probabilities
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for (i = fgw.left; i <= fgw.right; i++) {
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fgw.weights[i-fgw.left] = (1 - fgw.weights[i-fgw.left]) / unif;
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}
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// set up vectors
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for (i = 0; i < n; i++) {
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sum[i] = 0.0;
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}
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// get setup time
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stop = util_cpu_time();
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time_for_setup = (double)(stop - start2)/1000;
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start2 = stop;
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start3 = stop;
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// start transient analysis
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done = false;
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num_iters = -1;
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PS_PrintToMainLog(env, "\nStarting iterations...\n");
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// do 0th element of summation (doesn't require any matrix powers)
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if (fgw.left == 0) {
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for (i = 0; i < n; i++) sum[i] += fgw.weights[0] * soln[i];
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} else {
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for (i = 0; i < n; i++) sum[i] += soln[i] / unif;
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}
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// note that we ignore max_iters as we know how any iterations _should_ be performed
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for (iters = 1; (iters <= fgw.right) && !done; iters++) {
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// store local copies of stuff
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double *non_zeros;
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unsigned char *row_counts;
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int *row_starts;
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bool use_counts;
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unsigned int *cols;
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double *dist;
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int dist_shift;
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int dist_mask;
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if (!compact_tr) {
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non_zeros = rmsm->non_zeros;
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row_counts = rmsm->row_counts;
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row_starts = (int *)rmsm->row_counts;
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use_counts = rmsm->use_counts;
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cols = rmsm->cols;
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} else {
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row_counts = cmsrsm->row_counts;
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row_starts = (int *)cmsrsm->row_counts;
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use_counts = cmsrsm->use_counts;
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cols = cmsrsm->cols;
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dist = cmsrsm->dist;
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dist_shift = cmsrsm->dist_shift;
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dist_mask = cmsrsm->dist_mask;
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}
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// do matrix vector multiply bit
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h = 0;
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for (i = 0; i < n; i++) {
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d = (!compact_d) ? (diags[i] * soln[i]) : (diags_dist->dist[diags_dist->ptrs[i]] * soln[i]);
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if (!use_counts) { l = row_starts[i]; h = row_starts[i+1]; }
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else { l = h; h += row_counts[i]; }
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// "row major" version
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if (!compact_tr) {
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for (j = l; j < h; j++) {
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d += non_zeros[j] * soln[cols[j]];
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}
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// "compact msr" version
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} else {
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for (j = l; j < h; j++) {
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d += dist[(int)(cols[j] & dist_mask)] * soln[(int)(cols[j] >> dist_shift)];
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}
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}
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// set vector element
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soln2[i] = d;
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}
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// check for steady state convergence
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if (do_ss_detect) {
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measure.reset();
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measure.measure(soln, soln2, n);
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if (measure.value() < term_crit_param_unif) {
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done = true;
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}
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}
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// special case when finished early (steady-state detected)
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if (done) {
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// work out sum of remaining poisson probabilities
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if (iters <= fgw.left) {
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weight = time - iters/unif;
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} else {
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weight = 0.0;
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for (i = iters; i <= fgw.right; i++) {
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weight += fgw.weights[i-fgw.left];
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}
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}
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// add to sum
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for (i = 0; i < n; i++) sum[i] += weight * soln2[i];
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PS_PrintToMainLog(env, "\nSteady state detected at iteration %ld\n", iters);
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num_iters = iters;
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break;
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}
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// print occasional status update
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if ((util_cpu_time() - start3) > UPDATE_DELAY) {
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PS_PrintToMainLog(env, "Iteration %d (of %d): ", iters, fgw.right);
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if (do_ss_detect) PS_PrintToMainLog(env, "max %sdiff=%f, ", measure.isRelative()?"relative ":"", measure.value());
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PS_PrintToMainLog(env, "%.2f sec so far\n", ((double)(util_cpu_time() - start2)/1000));
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start3 = util_cpu_time();
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}
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// prepare for next iteration
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tmpsoln = soln;
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soln = soln2;
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soln2 = tmpsoln;
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// add to sum
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if (iters < fgw.left) {
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for (i = 0; i < n; i++) sum[i] += soln[i] / unif;
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} else {
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for (i = 0; i < n; i++) sum[i] += fgw.weights[iters-fgw.left] * soln[i];
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}
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}
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// stop clocks
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stop = util_cpu_time();
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time_for_iters = (double)(stop - start2)/1000;
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time_taken = (double)(stop - start1)/1000;
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// print iters/timing info
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if (num_iters == -1) num_iters = fgw.right;
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PS_PrintToMainLog(env, "\nIterative method: %ld iterations in %.2f seconds (average %.6f, setup %.2f)\n", num_iters, time_taken, time_for_iters/num_iters, time_for_setup);
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// catch exceptions: register error, free memory
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} catch (std::bad_alloc e) {
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PS_SetErrorMessage("Out of memory");
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if (sum) delete[] sum;
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sum = 0;
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} catch (const char *err) {
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PS_SetErrorMessage(err);
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if (sum) delete sum;
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sum = 0;
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}
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// free memory
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if (rmsm) delete rmsm;
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if (cmsrsm) delete cmsrsm;
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if (diags) delete[] diags;
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if (diags_dist) delete diags_dist;
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if (soln) delete[] soln;
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if (soln2) delete[] soln2;
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return ptr_to_jlong(sum);
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}
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//------------------------------------------------------------------------------
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