/************************************************************************* Copyright (c) 2008, Sergey Bochkanov (ALGLIB project). >>> SOURCE LICENSE >>> This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation (www.fsf.org); either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. A copy of the GNU General Public License is available at http://www.fsf.org/licensing/licenses >>> END OF LICENSE >>> *************************************************************************/ using System; namespace alglib { public class logit { public struct logitmodel { public double[] w; }; public struct logitmcstate { public bool brackt; public bool stage1; public int infoc; public double dg; public double dgm; public double dginit; public double dgtest; public double dgx; public double dgxm; public double dgy; public double dgym; public double finit; public double ftest1; public double fm; public double fx; public double fxm; public double fy; public double fym; public double stx; public double sty; public double stmin; public double stmax; public double width; public double width1; public double xtrapf; }; /************************************************************************* MNLReport structure contains information about training process: * NGrad - number of gradient calculations * NHess - number of Hessian calculations *************************************************************************/ public struct mnlreport { public int ngrad; public int nhess; }; public const double xtol = 100*AP.Math.MachineEpsilon; public const double ftol = 0.0001; public const double gtol = 0.3; public const int maxfev = 20; public const double stpmin = 1.0E-2; public const double stpmax = 1.0E5; public const int logitvnum = 6; /************************************************************************* This subroutine trains logit model. INPUT PARAMETERS: XY - training set, array[0..NPoints-1,0..NVars] First NVars columns store values of independent variables, next column stores number of class (from 0 to NClasses-1) which dataset element belongs to. Fractional values are rounded to nearest integer. NPoints - training set size, NPoints>=1 NVars - number of independent variables, NVars>=1 NClasses - number of classes, NClasses>=2 OUTPUT PARAMETERS: Info - return code: * -2, if there is a point with class number outside of [0..NClasses-1]. * -1, if incorrect parameters was passed (NPoints=nclasses ) { info = -2; return; } } info = 1; // // Initialize data // rep.ngrad = 0; rep.nhess = 0; // // Allocate array // wdim = (nvars+1)*(nclasses-1); offs = 5; expoffs = offs+wdim; ssize = 5+(nvars+1)*(nclasses-1)+nclasses; lm.w = new double[ssize-1+1]; lm.w[0] = ssize; lm.w[1] = logitvnum; lm.w[2] = nvars; lm.w[3] = nclasses; lm.w[4] = offs; // // Degenerate case: all outputs are equal // allsame = true; for(i=1; i<=npoints-1; i++) { if( (int)Math.Round(xy[i,nvars])!=(int)Math.Round(xy[i-1,nvars]) ) { allsame = false; } } if( allsame ) { for(i=0; i<=(nvars+1)*(nclasses-1)-1; i++) { lm.w[offs+i] = 0; } v = -(2*Math.Log(AP.Math.MinRealNumber)); k = (int)Math.Round(xy[0,nvars]); if( k==nclasses-1 ) { for(i=0; i<=nclasses-2; i++) { lm.w[offs+i*(nvars+1)+nvars] = -v; } } else { for(i=0; i<=nclasses-2; i++) { if( i==k ) { lm.w[offs+i*(nvars+1)+nvars] = +v; } else { lm.w[offs+i*(nvars+1)+nvars] = 0; } } } return; } // // General case. // Prepare task and network. Allocate space. // mlpbase.mlpcreatec0(nvars, nclasses, ref network); mlpbase.mlpinitpreprocessor(ref network, ref xy, npoints); mlpbase.mlpproperties(ref network, ref nin, ref nout, ref wcount); for(i=0; i<=wcount-1; i++) { network.weights[i] = (2*AP.Math.RandomReal()-1)/nvars; } g = new double[wcount-1+1]; h = new double[wcount-1+1, wcount-1+1]; wbase = new double[wcount-1+1]; wdir = new double[wcount-1+1]; work = new double[wcount-1+1]; // // First stage: optimize in gradient direction. // for(k=0; k<=wcount/3+10; k++) { // // Calculate gradient in starting point // mlpbase.mlpgradnbatch(ref network, ref xy, npoints, ref e, ref g); v = 0.0; for(i_=0; i_<=wcount-1;i_++) { v += network.weights[i_]*network.weights[i_]; } e = e+0.5*decay*v; for(i_=0; i_<=wcount-1;i_++) { g[i_] = g[i_] + decay*network.weights[i_]; } rep.ngrad = rep.ngrad+1; // // Setup optimization scheme // for(i_=0; i_<=wcount-1;i_++) { wdir[i_] = -g[i_]; } v = 0.0; for(i_=0; i_<=wcount-1;i_++) { v += wdir[i_]*wdir[i_]; } wstep = Math.Sqrt(v); v = 1/Math.Sqrt(v); for(i_=0; i_<=wcount-1;i_++) { wdir[i_] = v*wdir[i_]; } mcstage = 0; mnlmcsrch(wcount, ref network.weights, ref e, ref g, ref wdir, ref wstep, ref mcinfo, ref mcnfev, ref work, ref mcstate, ref mcstage); while( mcstage!=0 ) { mlpbase.mlpgradnbatch(ref network, ref xy, npoints, ref e, ref g); v = 0.0; for(i_=0; i_<=wcount-1;i_++) { v += network.weights[i_]*network.weights[i_]; } e = e+0.5*decay*v; for(i_=0; i_<=wcount-1;i_++) { g[i_] = g[i_] + decay*network.weights[i_]; } rep.ngrad = rep.ngrad+1; mnlmcsrch(wcount, ref network.weights, ref e, ref g, ref wdir, ref wstep, ref mcinfo, ref mcnfev, ref work, ref mcstate, ref mcstage); } } // // Second stage: use Hessian when we are close to the minimum // while( true ) { // // Calculate and update E/G/H // mlpbase.mlphessiannbatch(ref network, ref xy, npoints, ref e, ref g, ref h); v = 0.0; for(i_=0; i_<=wcount-1;i_++) { v += network.weights[i_]*network.weights[i_]; } e = e+0.5*decay*v; for(i_=0; i_<=wcount-1;i_++) { g[i_] = g[i_] + decay*network.weights[i_]; } for(k=0; k<=wcount-1; k++) { h[k,k] = h[k,k]+decay; } rep.nhess = rep.nhess+1; // // Select step direction // NOTE: it is important to use lower-triangle Cholesky // factorization since it is much faster than higher-triangle version. // spd = trfac.spdmatrixcholesky(ref h, wcount, false); densesolver.spdmatrixcholeskysolve(ref h, wcount, false, ref g, ref solverinfo, ref solverrep, ref wdir); spd = solverinfo>0; if( spd ) { // // H is positive definite. // Step in Newton direction. // for(i_=0; i_<=wcount-1;i_++) { wdir[i_] = -1*wdir[i_]; } spd = true; } else { // // H is indefinite. // Step in gradient direction. // for(i_=0; i_<=wcount-1;i_++) { wdir[i_] = -g[i_]; } spd = false; } // // Optimize in WDir direction // v = 0.0; for(i_=0; i_<=wcount-1;i_++) { v += wdir[i_]*wdir[i_]; } wstep = Math.Sqrt(v); v = 1/Math.Sqrt(v); for(i_=0; i_<=wcount-1;i_++) { wdir[i_] = v*wdir[i_]; } mcstage = 0; mnlmcsrch(wcount, ref network.weights, ref e, ref g, ref wdir, ref wstep, ref mcinfo, ref mcnfev, ref work, ref mcstate, ref mcstage); while( mcstage!=0 ) { mlpbase.mlpgradnbatch(ref network, ref xy, npoints, ref e, ref g); v = 0.0; for(i_=0; i_<=wcount-1;i_++) { v += network.weights[i_]*network.weights[i_]; } e = e+0.5*decay*v; for(i_=0; i_<=wcount-1;i_++) { g[i_] = g[i_] + decay*network.weights[i_]; } rep.ngrad = rep.ngrad+1; mnlmcsrch(wcount, ref network.weights, ref e, ref g, ref wdir, ref wstep, ref mcinfo, ref mcnfev, ref work, ref mcstate, ref mcstage); } if( spd & (mcinfo==2 | mcinfo==4 | mcinfo==6) ) { break; } } // // Convert from NN format to MNL format // i1_ = (0) - (offs); for(i_=offs; i_<=offs+wcount-1;i_++) { lm.w[i_] = network.weights[i_+i1_]; } for(k=0; k<=nvars-1; k++) { for(i=0; i<=nclasses-2; i++) { s = network.columnsigmas[k]; if( (double)(s)==(double)(0) ) { s = 1; } j = offs+(nvars+1)*i; v = lm.w[j+k]; lm.w[j+k] = v/s; lm.w[j+nvars] = lm.w[j+nvars]+v*network.columnmeans[k]/s; } } for(k=0; k<=nclasses-2; k++) { lm.w[offs+(nvars+1)*k+nvars] = -lm.w[offs+(nvars+1)*k+nvars]; } } /************************************************************************* Procesing INPUT PARAMETERS: LM - logit model, passed by non-constant reference (some fields of structure are used as temporaries when calculating model output). X - input vector, array[0..NVars-1]. OUTPUT PARAMETERS: Y - result, array[0..NClasses-1] Vector of posterior probabilities for classification task. Subroutine does not allocate memory for this vector, it is responsibility of a caller to allocate it. Array must be at least [0..NClasses-1]. -- ALGLIB -- Copyright 10.09.2008 by Bochkanov Sergey *************************************************************************/ public static void mnlprocess(ref logitmodel lm, ref double[] x, ref double[] y) { int nvars = 0; int nclasses = 0; int offs = 0; int i = 0; int i1 = 0; double s = 0; System.Diagnostics.Debug.Assert((double)(lm.w[1])==(double)(logitvnum), "MNLProcess: unexpected model version"); nvars = (int)Math.Round(lm.w[2]); nclasses = (int)Math.Round(lm.w[3]); offs = (int)Math.Round(lm.w[4]); mnliexp(ref lm.w, ref x); s = 0; i1 = offs+(nvars+1)*(nclasses-1); for(i=i1; i<=i1+nclasses-1; i++) { s = s+lm.w[i]; } for(i=0; i<=nclasses-1; i++) { y[i] = lm.w[i1+i]/s; } } /************************************************************************* Unpacks coefficients of logit model. Logit model have form: P(class=i) = S(i) / (S(0) + S(1) + ... +S(M-1)) S(i) = Exp(A[i,0]*X[0] + ... + A[i,N-1]*X[N-1] + A[i,N]), when i=0 & (int)Math.Round(xy[i,nvars])(double)(0) ) { result = result-Math.Log(worky[(int)Math.Round(xy[i,nvars])]); } else { result = result-Math.Log(AP.Math.MinRealNumber); } } result = result/(npoints*Math.Log(2)); return result; } /************************************************************************* Relative classification error on the test set INPUT PARAMETERS: LM - logit model XY - test set NPoints - test set size RESULT: percent of incorrectly classified cases. -- ALGLIB -- Copyright 10.09.2008 by Bochkanov Sergey *************************************************************************/ public static double mnlrelclserror(ref logitmodel lm, ref double[,] xy, int npoints) { double result = 0; result = (double)(mnlclserror(ref lm, ref xy, npoints))/(double)(npoints); return result; } /************************************************************************* RMS error on the test set INPUT PARAMETERS: LM - logit model XY - test set NPoints - test set size RESULT: root mean square error (error when estimating posterior probabilities). -- ALGLIB -- Copyright 30.08.2008 by Bochkanov Sergey *************************************************************************/ public static double mnlrmserror(ref logitmodel lm, ref double[,] xy, int npoints) { double result = 0; double relcls = 0; double avgce = 0; double rms = 0; double avg = 0; double avgrel = 0; System.Diagnostics.Debug.Assert((int)Math.Round(lm.w[1])==logitvnum, "MNLRMSError: Incorrect MNL version!"); mnlallerrors(ref lm, ref xy, npoints, ref relcls, ref avgce, ref rms, ref avg, ref avgrel); result = rms; return result; } /************************************************************************* Average error on the test set INPUT PARAMETERS: LM - logit model XY - test set NPoints - test set size RESULT: average error (error when estimating posterior probabilities). -- ALGLIB -- Copyright 30.08.2008 by Bochkanov Sergey *************************************************************************/ public static double mnlavgerror(ref logitmodel lm, ref double[,] xy, int npoints) { double result = 0; double relcls = 0; double avgce = 0; double rms = 0; double avg = 0; double avgrel = 0; System.Diagnostics.Debug.Assert((int)Math.Round(lm.w[1])==logitvnum, "MNLRMSError: Incorrect MNL version!"); mnlallerrors(ref lm, ref xy, npoints, ref relcls, ref avgce, ref rms, ref avg, ref avgrel); result = avg; return result; } /************************************************************************* Average relative error on the test set INPUT PARAMETERS: LM - logit model XY - test set NPoints - test set size RESULT: average relative error (error when estimating posterior probabilities). -- ALGLIB -- Copyright 30.08.2008 by Bochkanov Sergey *************************************************************************/ public static double mnlavgrelerror(ref logitmodel lm, ref double[,] xy, int ssize) { double result = 0; double relcls = 0; double avgce = 0; double rms = 0; double avg = 0; double avgrel = 0; System.Diagnostics.Debug.Assert((int)Math.Round(lm.w[1])==logitvnum, "MNLRMSError: Incorrect MNL version!"); mnlallerrors(ref lm, ref xy, ssize, ref relcls, ref avgce, ref rms, ref avg, ref avgrel); result = avgrel; return result; } /************************************************************************* Classification error on test set = MNLRelClsError*NPoints -- ALGLIB -- Copyright 10.09.2008 by Bochkanov Sergey *************************************************************************/ public static int mnlclserror(ref logitmodel lm, ref double[,] xy, int npoints) { int result = 0; int nvars = 0; int nclasses = 0; int i = 0; int j = 0; double[] workx = new double[0]; double[] worky = new double[0]; int nmax = 0; int i_ = 0; System.Diagnostics.Debug.Assert((double)(lm.w[1])==(double)(logitvnum), "MNLClsError: unexpected model version"); nvars = (int)Math.Round(lm.w[2]); nclasses = (int)Math.Round(lm.w[3]); workx = new double[nvars-1+1]; worky = new double[nclasses-1+1]; result = 0; for(i=0; i<=npoints-1; i++) { // // Process // for(i_=0; i_<=nvars-1;i_++) { workx[i_] = xy[i,i_]; } mnlprocess(ref lm, ref workx, ref worky); // // Logit version of the answer // nmax = 0; for(j=0; j<=nclasses-1; j++) { if( (double)(worky[j])>(double)(worky[nmax]) ) { nmax = j; } } // // compare // if( nmax!=(int)Math.Round(xy[i,nvars]) ) { result = result+1; } } return result; } /************************************************************************* Internal subroutine. Places exponents of the anti-overflow shifted internal linear outputs into the service part of the W array. *************************************************************************/ private static void mnliexp(ref double[] w, ref double[] x) { int nvars = 0; int nclasses = 0; int offs = 0; int i = 0; int i1 = 0; double v = 0; double mx = 0; int i_ = 0; int i1_ = 0; System.Diagnostics.Debug.Assert((double)(w[1])==(double)(logitvnum), "LOGIT: unexpected model version"); nvars = (int)Math.Round(w[2]); nclasses = (int)Math.Round(w[3]); offs = (int)Math.Round(w[4]); i1 = offs+(nvars+1)*(nclasses-1); for(i=0; i<=nclasses-2; i++) { i1_ = (0)-(offs+i*(nvars+1)); v = 0.0; for(i_=offs+i*(nvars+1); i_<=offs+i*(nvars+1)+nvars-1;i_++) { v += w[i_]*x[i_+i1_]; } w[i1+i] = v+w[offs+i*(nvars+1)+nvars]; } w[i1+nclasses-1] = 0; mx = 0; for(i=i1; i<=i1+nclasses-1; i++) { mx = Math.Max(mx, w[i]); } for(i=i1; i<=i1+nclasses-1; i++) { w[i] = Math.Exp(w[i]-mx); } } /************************************************************************* Calculation of all types of errors -- ALGLIB -- Copyright 30.08.2008 by Bochkanov Sergey *************************************************************************/ private static void mnlallerrors(ref logitmodel lm, ref double[,] xy, int npoints, ref double relcls, ref double avgce, ref double rms, ref double avg, ref double avgrel) { int nvars = 0; int nclasses = 0; int i = 0; double[] buf = new double[0]; double[] workx = new double[0]; double[] y = new double[0]; double[] dy = new double[0]; int i_ = 0; System.Diagnostics.Debug.Assert((int)Math.Round(lm.w[1])==logitvnum, "MNL unit: Incorrect MNL version!"); nvars = (int)Math.Round(lm.w[2]); nclasses = (int)Math.Round(lm.w[3]); workx = new double[nvars-1+1]; y = new double[nclasses-1+1]; dy = new double[0+1]; bdss.dserrallocate(nclasses, ref buf); for(i=0; i<=npoints-1; i++) { for(i_=0; i_<=nvars-1;i_++) { workx[i_] = xy[i,i_]; } mnlprocess(ref lm, ref workx, ref y); dy[0] = xy[i,nvars]; bdss.dserraccumulate(ref buf, ref y, ref dy); } bdss.dserrfinish(ref buf); relcls = buf[0]; avgce = buf[1]; rms = buf[2]; avg = buf[3]; avgrel = buf[4]; } /************************************************************************* THE PURPOSE OF MCSRCH IS TO FIND A STEP WHICH SATISFIES A SUFFICIENT DECREASE CONDITION AND A CURVATURE CONDITION. AT EACH STAGE THE SUBROUTINE UPDATES AN INTERVAL OF UNCERTAINTY WITH ENDPOINTS STX AND STY. THE INTERVAL OF UNCERTAINTY IS INITIALLY CHOSEN SO THAT IT CONTAINS A MINIMIZER OF THE MODIFIED FUNCTION F(X+STP*S) - F(X) - FTOL*STP*(GRADF(X)'S). IF A STEP IS OBTAINED FOR WHICH THE MODIFIED FUNCTION HAS A NONPOSITIVE FUNCTION VALUE AND NONNEGATIVE DERIVATIVE, THEN THE INTERVAL OF UNCERTAINTY IS CHOSEN SO THAT IT CONTAINS A MINIMIZER OF F(X+STP*S). THE ALGORITHM IS DESIGNED TO FIND A STEP WHICH SATISFIES THE SUFFICIENT DECREASE CONDITION F(X+STP*S) .LE. F(X) + FTOL*STP*(GRADF(X)'S), AND THE CURVATURE CONDITION ABS(GRADF(X+STP*S)'S)) .LE. GTOL*ABS(GRADF(X)'S). IF FTOL IS LESS THAN GTOL AND IF, FOR EXAMPLE, THE FUNCTION IS BOUNDED BELOW, THEN THERE IS ALWAYS A STEP WHICH SATISFIES BOTH CONDITIONS. IF NO STEP CAN BE FOUND WHICH SATISFIES BOTH CONDITIONS, THEN THE ALGORITHM USUALLY STOPS WHEN ROUNDING ERRORS PREVENT FURTHER PROGRESS. IN THIS CASE STP ONLY SATISFIES THE SUFFICIENT DECREASE CONDITION. PARAMETERS DESCRIPRION N IS A POSITIVE INTEGER INPUT VARIABLE SET TO THE NUMBER OF VARIABLES. X IS AN ARRAY OF LENGTH N. ON INPUT IT MUST CONTAIN THE BASE POINT FOR THE LINE SEARCH. ON OUTPUT IT CONTAINS X+STP*S. F IS A VARIABLE. ON INPUT IT MUST CONTAIN THE VALUE OF F AT X. ON OUTPUT IT CONTAINS THE VALUE OF F AT X + STP*S. G IS AN ARRAY OF LENGTH N. ON INPUT IT MUST CONTAIN THE GRADIENT OF F AT X. ON OUTPUT IT CONTAINS THE GRADIENT OF F AT X + STP*S. S IS AN INPUT ARRAY OF LENGTH N WHICH SPECIFIES THE SEARCH DIRECTION. STP IS A NONNEGATIVE VARIABLE. ON INPUT STP CONTAINS AN INITIAL ESTIMATE OF A SATISFACTORY STEP. ON OUTPUT STP CONTAINS THE FINAL ESTIMATE. FTOL AND GTOL ARE NONNEGATIVE INPUT VARIABLES. TERMINATION OCCURS WHEN THE SUFFICIENT DECREASE CONDITION AND THE DIRECTIONAL DERIVATIVE CONDITION ARE SATISFIED. XTOL IS A NONNEGATIVE INPUT VARIABLE. TERMINATION OCCURS WHEN THE RELATIVE WIDTH OF THE INTERVAL OF UNCERTAINTY IS AT MOST XTOL. STPMIN AND STPMAX ARE NONNEGATIVE INPUT VARIABLES WHICH SPECIFY LOWER AND UPPER BOUNDS FOR THE STEP. MAXFEV IS A POSITIVE INTEGER INPUT VARIABLE. TERMINATION OCCURS WHEN THE NUMBER OF CALLS TO FCN IS AT LEAST MAXFEV BY THE END OF AN ITERATION. INFO IS AN INTEGER OUTPUT VARIABLE SET AS FOLLOWS: INFO = 0 IMPROPER INPUT PARAMETERS. INFO = 1 THE SUFFICIENT DECREASE CONDITION AND THE DIRECTIONAL DERIVATIVE CONDITION HOLD. INFO = 2 RELATIVE WIDTH OF THE INTERVAL OF UNCERTAINTY IS AT MOST XTOL. INFO = 3 NUMBER OF CALLS TO FCN HAS REACHED MAXFEV. INFO = 4 THE STEP IS AT THE LOWER BOUND STPMIN. INFO = 5 THE STEP IS AT THE UPPER BOUND STPMAX. INFO = 6 ROUNDING ERRORS PREVENT FURTHER PROGRESS. THERE MAY NOT BE A STEP WHICH SATISFIES THE SUFFICIENT DECREASE AND CURVATURE CONDITIONS. TOLERANCES MAY BE TOO SMALL. NFEV IS AN INTEGER OUTPUT VARIABLE SET TO THE NUMBER OF CALLS TO FCN. WA IS A WORK ARRAY OF LENGTH N. ARGONNE NATIONAL LABORATORY. MINPACK PROJECT. JUNE 1983 JORGE J. MORE', DAVID J. THUENTE *************************************************************************/ private static void mnlmcsrch(int n, ref double[] x, ref double f, ref double[] g, ref double[] s, ref double stp, ref int info, ref int nfev, ref double[] wa, ref logitmcstate state, ref int stage) { double v = 0; double p5 = 0; double p66 = 0; double zero = 0; int i_ = 0; // // init // p5 = 0.5; p66 = 0.66; state.xtrapf = 4.0; zero = 0; // // Main cycle // while( true ) { if( stage==0 ) { // // NEXT // stage = 2; continue; } if( stage==2 ) { state.infoc = 1; info = 0; // // CHECK THE INPUT PARAMETERS FOR ERRORS. // if( n<=0 | (double)(stp)<=(double)(0) | (double)(ftol)<(double)(0) | (double)(gtol)<(double)(zero) | (double)(xtol)<(double)(zero) | (double)(stpmin)<(double)(zero) | (double)(stpmax)<(double)(stpmin) | maxfev<=0 ) { stage = 0; return; } // // COMPUTE THE INITIAL GRADIENT IN THE SEARCH DIRECTION // AND CHECK THAT S IS A DESCENT DIRECTION. // v = 0.0; for(i_=0; i_<=n-1;i_++) { v += g[i_]*s[i_]; } state.dginit = v; if( (double)(state.dginit)>=(double)(0) ) { stage = 0; return; } // // INITIALIZE LOCAL VARIABLES. // state.brackt = false; state.stage1 = true; nfev = 0; state.finit = f; state.dgtest = ftol*state.dginit; state.width = stpmax-stpmin; state.width1 = state.width/p5; for(i_=0; i_<=n-1;i_++) { wa[i_] = x[i_]; } // // THE VARIABLES STX, FX, DGX CONTAIN THE VALUES OF THE STEP, // FUNCTION, AND DIRECTIONAL DERIVATIVE AT THE BEST STEP. // THE VARIABLES STY, FY, DGY CONTAIN THE VALUE OF THE STEP, // FUNCTION, AND DERIVATIVE AT THE OTHER ENDPOINT OF // THE INTERVAL OF UNCERTAINTY. // THE VARIABLES STP, F, DG CONTAIN THE VALUES OF THE STEP, // FUNCTION, AND DERIVATIVE AT THE CURRENT STEP. // state.stx = 0; state.fx = state.finit; state.dgx = state.dginit; state.sty = 0; state.fy = state.finit; state.dgy = state.dginit; // // NEXT // stage = 3; continue; } if( stage==3 ) { // // START OF ITERATION. // // SET THE MINIMUM AND MAXIMUM STEPS TO CORRESPOND // TO THE PRESENT INTERVAL OF UNCERTAINTY. // if( state.brackt ) { if( (double)(state.stx)<(double)(state.sty) ) { state.stmin = state.stx; state.stmax = state.sty; } else { state.stmin = state.sty; state.stmax = state.stx; } } else { state.stmin = state.stx; state.stmax = stp+state.xtrapf*(stp-state.stx); } // // FORCE THE STEP TO BE WITHIN THE BOUNDS STPMAX AND STPMIN. // if( (double)(stp)>(double)(stpmax) ) { stp = stpmax; } if( (double)(stp)<(double)(stpmin) ) { stp = stpmin; } // // IF AN UNUSUAL TERMINATION IS TO OCCUR THEN LET // STP BE THE LOWEST POINT OBTAINED SO FAR. // if( state.brackt & ((double)(stp)<=(double)(state.stmin) | (double)(stp)>=(double)(state.stmax)) | nfev>=maxfev-1 | state.infoc==0 | state.brackt & (double)(state.stmax-state.stmin)<=(double)(xtol*state.stmax) ) { stp = state.stx; } // // EVALUATE THE FUNCTION AND GRADIENT AT STP // AND COMPUTE THE DIRECTIONAL DERIVATIVE. // for(i_=0; i_<=n-1;i_++) { x[i_] = wa[i_]; } for(i_=0; i_<=n-1;i_++) { x[i_] = x[i_] + stp*s[i_]; } // // NEXT // stage = 4; return; } if( stage==4 ) { info = 0; nfev = nfev+1; v = 0.0; for(i_=0; i_<=n-1;i_++) { v += g[i_]*s[i_]; } state.dg = v; state.ftest1 = state.finit+stp*state.dgtest; // // TEST FOR CONVERGENCE. // if( state.brackt & ((double)(stp)<=(double)(state.stmin) | (double)(stp)>=(double)(state.stmax)) | state.infoc==0 ) { info = 6; } if( (double)(stp)==(double)(stpmax) & (double)(f)<=(double)(state.ftest1) & (double)(state.dg)<=(double)(state.dgtest) ) { info = 5; } if( (double)(stp)==(double)(stpmin) & ((double)(f)>(double)(state.ftest1) | (double)(state.dg)>=(double)(state.dgtest)) ) { info = 4; } if( nfev>=maxfev ) { info = 3; } if( state.brackt & (double)(state.stmax-state.stmin)<=(double)(xtol*state.stmax) ) { info = 2; } if( (double)(f)<=(double)(state.ftest1) & (double)(Math.Abs(state.dg))<=(double)(-(gtol*state.dginit)) ) { info = 1; } // // CHECK FOR TERMINATION. // if( info!=0 ) { stage = 0; return; } // // IN THE FIRST STAGE WE SEEK A STEP FOR WHICH THE MODIFIED // FUNCTION HAS A NONPOSITIVE VALUE AND NONNEGATIVE DERIVATIVE. // if( state.stage1 & (double)(f)<=(double)(state.ftest1) & (double)(state.dg)>=(double)(Math.Min(ftol, gtol)*state.dginit) ) { state.stage1 = false; } // // A MODIFIED FUNCTION IS USED TO PREDICT THE STEP ONLY IF // WE HAVE NOT OBTAINED A STEP FOR WHICH THE MODIFIED // FUNCTION HAS A NONPOSITIVE FUNCTION VALUE AND NONNEGATIVE // DERIVATIVE, AND IF A LOWER FUNCTION VALUE HAS BEEN // OBTAINED BUT THE DECREASE IS NOT SUFFICIENT. // if( state.stage1 & (double)(f)<=(double)(state.fx) & (double)(f)>(double)(state.ftest1) ) { // // DEFINE THE MODIFIED FUNCTION AND DERIVATIVE VALUES. // state.fm = f-stp*state.dgtest; state.fxm = state.fx-state.stx*state.dgtest; state.fym = state.fy-state.sty*state.dgtest; state.dgm = state.dg-state.dgtest; state.dgxm = state.dgx-state.dgtest; state.dgym = state.dgy-state.dgtest; // // CALL CSTEP TO UPDATE THE INTERVAL OF UNCERTAINTY // AND TO COMPUTE THE NEW STEP. // mnlmcstep(ref state.stx, ref state.fxm, ref state.dgxm, ref state.sty, ref state.fym, ref state.dgym, ref stp, state.fm, state.dgm, ref state.brackt, state.stmin, state.stmax, ref state.infoc); // // RESET THE FUNCTION AND GRADIENT VALUES FOR F. // state.fx = state.fxm+state.stx*state.dgtest; state.fy = state.fym+state.sty*state.dgtest; state.dgx = state.dgxm+state.dgtest; state.dgy = state.dgym+state.dgtest; } else { // // CALL MCSTEP TO UPDATE THE INTERVAL OF UNCERTAINTY // AND TO COMPUTE THE NEW STEP. // mnlmcstep(ref state.stx, ref state.fx, ref state.dgx, ref state.sty, ref state.fy, ref state.dgy, ref stp, f, state.dg, ref state.brackt, state.stmin, state.stmax, ref state.infoc); } // // FORCE A SUFFICIENT DECREASE IN THE SIZE OF THE // INTERVAL OF UNCERTAINTY. // if( state.brackt ) { if( (double)(Math.Abs(state.sty-state.stx))>=(double)(p66*state.width1) ) { stp = state.stx+p5*(state.sty-state.stx); } state.width1 = state.width; state.width = Math.Abs(state.sty-state.stx); } // // NEXT. // stage = 3; continue; } } } private static void mnlmcstep(ref double stx, ref double fx, ref double dx, ref double sty, ref double fy, ref double dy, ref double stp, double fp, double dp, ref bool brackt, double stmin, double stmax, ref int info) { bool bound = new bool(); double gamma = 0; double p = 0; double q = 0; double r = 0; double s = 0; double sgnd = 0; double stpc = 0; double stpf = 0; double stpq = 0; double theta = 0; info = 0; // // CHECK THE INPUT PARAMETERS FOR ERRORS. // if( brackt & ((double)(stp)<=(double)(Math.Min(stx, sty)) | (double)(stp)>=(double)(Math.Max(stx, sty))) | (double)(dx*(stp-stx))>=(double)(0) | (double)(stmax)<(double)(stmin) ) { return; } // // DETERMINE IF THE DERIVATIVES HAVE OPPOSITE SIGN. // sgnd = dp*(dx/Math.Abs(dx)); // // FIRST CASE. A HIGHER FUNCTION VALUE. // THE MINIMUM IS BRACKETED. IF THE CUBIC STEP IS CLOSER // TO STX THAN THE QUADRATIC STEP, THE CUBIC STEP IS TAKEN, // ELSE THE AVERAGE OF THE CUBIC AND QUADRATIC STEPS IS TAKEN. // if( (double)(fp)>(double)(fx) ) { info = 1; bound = true; theta = 3*(fx-fp)/(stp-stx)+dx+dp; s = Math.Max(Math.Abs(theta), Math.Max(Math.Abs(dx), Math.Abs(dp))); gamma = s*Math.Sqrt(AP.Math.Sqr(theta/s)-dx/s*(dp/s)); if( (double)(stp)<(double)(stx) ) { gamma = -gamma; } p = gamma-dx+theta; q = gamma-dx+gamma+dp; r = p/q; stpc = stx+r*(stp-stx); stpq = stx+dx/((fx-fp)/(stp-stx)+dx)/2*(stp-stx); if( (double)(Math.Abs(stpc-stx))<(double)(Math.Abs(stpq-stx)) ) { stpf = stpc; } else { stpf = stpc+(stpq-stpc)/2; } brackt = true; } else { if( (double)(sgnd)<(double)(0) ) { // // SECOND CASE. A LOWER FUNCTION VALUE AND DERIVATIVES OF // OPPOSITE SIGN. THE MINIMUM IS BRACKETED. IF THE CUBIC // STEP IS CLOSER TO STX THAN THE QUADRATIC (SECANT) STEP, // THE CUBIC STEP IS TAKEN, ELSE THE QUADRATIC STEP IS TAKEN. // info = 2; bound = false; theta = 3*(fx-fp)/(stp-stx)+dx+dp; s = Math.Max(Math.Abs(theta), Math.Max(Math.Abs(dx), Math.Abs(dp))); gamma = s*Math.Sqrt(AP.Math.Sqr(theta/s)-dx/s*(dp/s)); if( (double)(stp)>(double)(stx) ) { gamma = -gamma; } p = gamma-dp+theta; q = gamma-dp+gamma+dx; r = p/q; stpc = stp+r*(stx-stp); stpq = stp+dp/(dp-dx)*(stx-stp); if( (double)(Math.Abs(stpc-stp))>(double)(Math.Abs(stpq-stp)) ) { stpf = stpc; } else { stpf = stpq; } brackt = true; } else { if( (double)(Math.Abs(dp))<(double)(Math.Abs(dx)) ) { // // THIRD CASE. A LOWER FUNCTION VALUE, DERIVATIVES OF THE // SAME SIGN, AND THE MAGNITUDE OF THE DERIVATIVE DECREASES. // THE CUBIC STEP IS ONLY USED IF THE CUBIC TENDS TO INFINITY // IN THE DIRECTION OF THE STEP OR IF THE MINIMUM OF THE CUBIC // IS BEYOND STP. OTHERWISE THE CUBIC STEP IS DEFINED TO BE // EITHER STPMIN OR STPMAX. THE QUADRATIC (SECANT) STEP IS ALSO // COMPUTED AND IF THE MINIMUM IS BRACKETED THEN THE THE STEP // CLOSEST TO STX IS TAKEN, ELSE THE STEP FARTHEST AWAY IS TAKEN. // info = 3; bound = true; theta = 3*(fx-fp)/(stp-stx)+dx+dp; s = Math.Max(Math.Abs(theta), Math.Max(Math.Abs(dx), Math.Abs(dp))); // // THE CASE GAMMA = 0 ONLY ARISES IF THE CUBIC DOES NOT TEND // TO INFINITY IN THE DIRECTION OF THE STEP. // gamma = s*Math.Sqrt(Math.Max(0, AP.Math.Sqr(theta/s)-dx/s*(dp/s))); if( (double)(stp)>(double)(stx) ) { gamma = -gamma; } p = gamma-dp+theta; q = gamma+(dx-dp)+gamma; r = p/q; if( (double)(r)<(double)(0) & (double)(gamma)!=(double)(0) ) { stpc = stp+r*(stx-stp); } else { if( (double)(stp)>(double)(stx) ) { stpc = stmax; } else { stpc = stmin; } } stpq = stp+dp/(dp-dx)*(stx-stp); if( brackt ) { if( (double)(Math.Abs(stp-stpc))<(double)(Math.Abs(stp-stpq)) ) { stpf = stpc; } else { stpf = stpq; } } else { if( (double)(Math.Abs(stp-stpc))>(double)(Math.Abs(stp-stpq)) ) { stpf = stpc; } else { stpf = stpq; } } } else { // // FOURTH CASE. A LOWER FUNCTION VALUE, DERIVATIVES OF THE // SAME SIGN, AND THE MAGNITUDE OF THE DERIVATIVE DOES // NOT DECREASE. IF THE MINIMUM IS NOT BRACKETED, THE STEP // IS EITHER STPMIN OR STPMAX, ELSE THE CUBIC STEP IS TAKEN. // info = 4; bound = false; if( brackt ) { theta = 3*(fp-fy)/(sty-stp)+dy+dp; s = Math.Max(Math.Abs(theta), Math.Max(Math.Abs(dy), Math.Abs(dp))); gamma = s*Math.Sqrt(AP.Math.Sqr(theta/s)-dy/s*(dp/s)); if( (double)(stp)>(double)(sty) ) { gamma = -gamma; } p = gamma-dp+theta; q = gamma-dp+gamma+dy; r = p/q; stpc = stp+r*(sty-stp); stpf = stpc; } else { if( (double)(stp)>(double)(stx) ) { stpf = stmax; } else { stpf = stmin; } } } } } // // UPDATE THE INTERVAL OF UNCERTAINTY. THIS UPDATE DOES NOT // DEPEND ON THE NEW STEP OR THE CASE ANALYSIS ABOVE. // if( (double)(fp)>(double)(fx) ) { sty = stp; fy = fp; dy = dp; } else { if( (double)(sgnd)<(double)(0.0) ) { sty = stx; fy = fx; dy = dx; } stx = stp; fx = fp; dx = dp; } // // COMPUTE THE NEW STEP AND SAFEGUARD IT. // stpf = Math.Min(stmax, stpf); stpf = Math.Max(stmin, stpf); stp = stpf; if( brackt & bound ) { if( (double)(sty)>(double)(stx) ) { stp = Math.Min(stx+0.66*(sty-stx), stp); } else { stp = Math.Max(stx+0.66*(sty-stx), stp); } } } } }