!************************************************************************* !* * !* Initialization of parameters for nonsmooth optimization based * !* clusterwise linear regression software (last modified 15.07.2016) * !* * !************************************************************************* !* !* Modules included: !* !* initclr ! Initialization of parameters for regerssion. !* initlmbm ! Initialization of LMBM -solver. !* MODULE initclr ! Initialization of parameters for clusterwise linear regression codes. USE r_precision, ONLY : prec ! Precision for reals. IMPLICIT NONE ! Names of input and output files: CHARACTER(LEN=80), SAVE :: & !infile = 'Carbonneau/acorn.txt', & !1 Name of dataset file. !infile = 'Carbonneau/brinks.txt', & !2 Name of dataset file. !infile = 'Carbonneau/carfuel.txt', & !3 Name of dataset file. !infile = 'Carbonneau/CEOsalaries.txt', & !4 Name of dataset file. !infile = 'Carbonneau/checkoff.txt', & !5 Name of dataset file. !infile = 'Carbonneau/cheesetaste.txt', & !6 Name of dataset file. !infile = 'Carbonneau/crimerate.txt', & !7 Name of dataset file. !infile = 'Carbonneau/diabetes.txt', & !8 Name of dataset file. !infile = 'Carbonneau/electricity.txt', & !9 Name of dataset file. !infile = 'Carbonneau/enrollment.txt', & !10 Name of dataset file. !infile = 'Carbonneau/extroversionCar.txt', & !11 Name of dataset file. !infile = 'Carbonneau/HousePrices.txt', & !12 Name of dataset file. !infile = 'Carbonneau/MercuryBass.txt', & !13 Name of dataset file. !infile = 'Carbonneau/Mortality.txt', & !14 Name of dataset file. !infile = 'Carbonneau/NuclearPlant.txt', & !15 Name of dataset file. !infile = 'Carbonneau/PolishingTime.txt', & !16 Name of dataset file. !infile = 'Carbonneau/PublicExpenditure.txt', & !17 Name of dataset file. !infile = 'Carbonneau/SmokingCancer.txt', & !18 Name of dataset file. !infile = 'Carbonneau/Temperatures.txt', & !19 Name of dataset file. !infile = 'Carbonneau/Votes.txt', & !20 Name of dataset file. ! !infile = 'DataSets/RealData/ConcreteCompressiveStrength.txt', & !1 iso 1030 & 8(9) Name of dataset file !infile = 'DataSets/RealData/airfoil_self_noise.txt', & !2 iso 1503 & 5(6) Name of dataset file !infile = 'DataSets/RealData/winequality-red.txt', & !3 iso 1599 & 11(12) Name of dataset file !infile = 'DataSets/RealData/winequality-white.txt', & !4 iso 4898 & 11(12) Name of dataset file !infile = 'DataSets/RealData/CombinedPowerPlant.txt', & !5 iso 9568 & 4(5) Name of dataset file !infile = 'DataSets/RealData/ProteinTertiary.txt', & !6 iso 45730 & 9(10) output=1 Name of dataset file !infile = 'OnlineNewsPopularity.txt', & !7 iso 39797 & 58(59) Name of dataset file !infile = 'DataSets/RealData/ticdata2000.txt', & !8 iso 9000 & 86 Name of dataset file infile = 'DataSets/RealData/BlogData_train.txt', & !9 iso 52397 & 281 Name of dataset file ! outfile1 = 'lmbm_hp_blog82.txt', & ! Result file with hyperplanes. outfile2 = 'lmbm_pred.txt', & ! Result file with predictions. outfile3 = 'lmbm_train.txt' ! Result file with training test. ! Input integer parameters. Give values here. INTEGER, PARAMETER :: & !nft = 128, & ! Number of features in data, from user. 1 nclust = 10, & ! Maximum number of clusters, from user. !nft = 8+1, & !1 iso Number of features in data, from user. !nrecord = 1030, & ! Number of records in data, from user. !nft = 5+1, & !2 iso Number of features in data, from user. !nrecord = 1503, & ! Number of records in data, from user. !nft = 11+1, & !3 iso Number of features in data, from user. !nrecord = 1599, & ! Number of records in data, from user. !nft = 11+1, & !4 iso Number of features in data, from user. !nrecord = 4898, & ! Number of records in data, from user. !nft = 4+1, & !5 iso Number of features in data, from user. !nrecord = 9568, & ! Number of records in data, from user. !nft = 9+1, & !6 iso Number of features in data, from user. !nrecord = 45730, & ! Number of records in data, from user. !nft = 58+1, & !7 iso Number of features in data, from user. !nrecord = 39644, & ! Number of records in data, from user. !nft = 86, & !8 iso Number of features in data, from user. !nrecord = 5822, & ! Number of records in data, from user. nft = 281, & !9 iso Number of features in data, from user. nrecord = 52397, & ! Number of records in data, from user. ! !nft = 4, & !1 Number of features in data, from user. acorn !nrecord = 39, & ! Number of records in data, from user. !nft = 5, & !2 Number of features in data, from user. !nrecord = 47, & ! Number of records in data, from user. !nft = 2, & !3 Number of features in data, from user. !nrecord = 82, & ! Number of records in data, from user. !nft = 2, & !4 Number of features in data, from user. !nrecord = 59, & ! Number of records in data, from user. !nft = 5, & !5 Number of features in data, from user. !nrecord = 56, & ! Number of records in data, from user. !nft = 4, & !6 Number of features in data, from user. !nrecord = 30, & ! Number of records in data, from user. !nft = 6, & !7 Number of features in data, from user. !nrecord = 47, & ! Number of records in data, from user. !nft = 3, & !8 Number of features in data, from user. !nrecord = 43, & ! Number of records in data, from user. !nft = 4, & !9 Number of features in data, from user. !nrecord = 50, & ! Number of records in data, from user. !nft = 5, & !10 Number of features in data, from user. !nrecord = 29, & ! Number of records in data, from user. !nft = 4, & !11 Number of features in data, from user. !nrecord = 40, & ! Number of records in data, from user. !nft = 2, & !12 Number of features in data, from user. !nrecord = 40, & ! Number of records in data, from user. !nft = 5, & !13 Number of features in data, from user. !nrecord = 53, & ! Number of records in data, from user. !nft = 4, & !14 Number of features in data, from user. !nrecord = 58, & ! Number of records in data, from user. !nft = 4, & !15 Number of features in data, from user. !nrecord = 32, & ! Number of records in data, from user. !nft = 3, & !16 Number of features in data, from user. !nrecord = 59, & ! Number of records in data, from user. !nft = 5, & !17 Number of features in data, from user. !nrecord = 48, & ! Number of records in data, from user. !nft = 2, & !18 Number of features in data, from user. !nrecord = 44, & ! Number of records in data, from user. !nft = 3, & !19 Number of features in data, from user. !nrecord = 56, & ! Number of records in data, from user. !nft = 2, & !20 Number of features in data, from user. !nrecord = 50, & ! Number of records in data, from user. !noutcom =1, & ! Number of the output column, from user. noutcom =nft, & ! Number of the output column, from user. itest = 1, & ! Choose: ! 1: if you do not use test set, ! 2: if you use one training set and one test set, ! 3: if you use cross-validation. ntrain = nrecord, & ! Number of points in training set. Choose: ! ntrain = nrecord, if itest = 1. nfoldmax = 1, & ! Number of cross validations. Choose: ! nfoldmax = 1, if itest = 1 or itest = 2. iscale = 1, & ! Choose: ! 1: if scaling is used, ! 2: if no scaling is used. ! maxdim = nclust*nft, & ! Maximum number of variables in optimization. maxinit = 10000*maxdim ! Maximum number of candidate points of data set, ! maxinit >> maxdim. Mistä tämä oikeasti nykyään riippuu? Pitää ehkä ottaa recordit mukaan ! Input real parameters. Give values here. ! If parameter value <= 0.0 the default value of the parameter will be used. ! REAL(KIND=prec), SAVE :: & ! REAL, SAVE :: & tlimit = 70000.0 ! Maximum CPU-time in seconds, from user. ! Other Real parameters. REAL(KIND=prec), SAVE :: & ! a(nrecord,nft), & ! Data matrix, from input file, give the name of the file above. a4(nrecord,nft), & ! Auxiliary data matrix. gamma1, & ! Parameter affecting the size of the set of starting point, gamma2, & ! Parameter affecting the size of the set of starting point, gamma3, & ! Parameter affecting the size of the set of starting point, gamma4, & ! Parameter affecting the size of the set of starting point, gam1, & ! Multiplier for gamma1 gam2, & ! Multiplier for gamma2 gam3, & ! Multiplier for gamma3 gam4, & ! Multiplier for gamma4 dminim(nrecord), & ! clin(nft), & dlin(nft), & toler ! Other integer parameters. INTEGER, SAVE :: & ! ! mf, & ! Number of used features: ! mf = nft, when no classes, nrecord1, & ! Number of instances used in computions. nrecord2, & ! Number of instances used in computions. nupdate, & ! Number of updates. naux2, & ! Number of first problem evaluations. naux, & ! Number of auxiliary function evaluations. nfit, & ! Number of function evaluations. nc, & ! Current number of clusters, loops from 1 to nclust ns, & ! Switch for auxiliary and real clustering problem. nlist(nrecord), & ! nlisttrain(nrecord), & ! nl, & ! m, & ! Number of variables in optimization: ! m = mf if ns = 1, ! m = mf*nc if ns = 2. ! nk(nclust,nrecord), & ! nel1, & ! nob1(nrecord), & nob(nclust,nrecord), & nel(nclust) ! ncand, & ! Number of canditate points. ! lcand(nrecord) ! CONTAINS SUBROUTINE init_clrpar() ! User supplied subroutine for further initialization of parameters (when needed). ! May be left empty. IMPLICIT NONE END SUBROUTINE init_clrpar ! korjattu regressio-ongelmille SUBROUTINE def_clrpar() ! Default values for parameters. USE param, ONLY : zero,one ! Parameters. IMPLICIT NONE gam1 = one gam2 = one gam3 = one gam4 = one IF (nrecord < 200) THEN gamma1 = 0.00_prec ! gamma1 in [0,1], smaller value -> more data points gamma2 = 20.0_prec ! gamma2 in [1, infty) -> smaller value less minimizers are chosen gamma3 = 20.0_prec ! gamma3 in [1, infty) -> smaller value less minimizers are chosen gamma4 = 0.10_prec !gamma1 = zero ! 4 gamma1 in [0,1], smaller value -> more data points !gamma2 = 20.0_prec ! gamma2 in [1, infty) -> smaller value less minimizers are chosen !gamma3 = 20.0_prec ! gamma3 in [1, infty) -> smaller value less minimizers are chosen !gamma4 = 0.10_prec !gamma1 = zero ! 3 gamma1 in [0,1], smaller value -> more data points !gamma2 = 20.0_prec ! gamma2 in [1, infty) -> smaller value less minimizers are chosen !gamma3 = 20.0_prec ! gamma3 in [1, infty) -> smaller value less minimizers are chosen !gamma4 = 0.10_prec ! gamma1 = 0.1 ! 5 gamma1 in [0,1], smaller value -> more data points ! gamma2 = 10.0_prec ! gamma2 in [1, infty) -> smaller value less minimizers are chosen ! gamma3 = 2.50_prec ! gamma3 in [1, infty) -> smaller value less minimizers are chosen ! gamma4 = 0.30_prec !gamma1 = zero ! 2 gamma1 in [0,1], smaller value -> more data points !gamma2 = 2.0_prec ! gamma2 in [1, infty) -> smaller value less minimizers are chosen !gamma3 = 2.0_prec ! gamma3 in [1, infty) -> smaller value less minimizers are chosen !gamma4 = 0.5_prec !gamma1 = zero ! 1 gamma1 in [0,1], smaller value -> more data points !gamma2 = 20.0_prec ! gamma2 in [1, infty) -> smaller value less minimizers are chosen !gamma3 = 20.0_prec ! gamma3 in [1, infty) -> smaller value less minimizers are chosen !gamma4 = 0.2_prec ELSE IF (nrecord < 1000) THEN ! gamma1 = 0.50_prec gamma2 = 2.5_prec gamma3 = 2.25_prec gamma4 = 0.50_prec ELSE IF (nrecord < 4000) THEN gamma1=0.6_prec ! 1 2 3 tehtävälle gamma2=2.50_prec gamma3=1.250_prec gamma4=1.0_prec ELSE IF (nrecord < 15000) THEN ! 4,5 gamma1=0.95_prec !tämä 4 ja 5 tehtävälle gamma2=2.0_prec gamma3=1.150_prec gamma4=1.0_prec ELSE IF (nrecord < 50000) THEN ! nämä 6 tehtävässä gamma1=0.9750_prec ! 64 gamma2=2.0_prec ! gamma3=1.050_prec ! gamma4=1.0_prec ! !gamma3=1.010_prec ! news2. Tulokset samoja kuin tuolla ylempänä, joten nekin parametrit käy ! vain tol2 modifiointi ei toleria kummassakaan yllä tosin 6 kai ei vaikuta !gamma1=0.980_prec ! nämä nykyisessä 6 tehtävässä output=last !gamma2=10.0_prec !gamma3=1.050_prec ! tämä 64 !gamma4=1.0_prec !gamma1=0.9750_prec ! Adilin parametrit !gamma2=1.05_prec !gamma3=1.0250_prec !gamma3=1.020_prec ! testaa vielä tämä paremmalla lopetuksella !gamma3=1.05_prec ! !gamma4=1.0_prec !gamma1=0.96_prec ! !gamma2=2.0_prec !gamma3=1.10_prec !gamma4=1.0_prec !gamma1=0.9950_prec ! nämä oli 2 !gamma2=1.025_prec !gamma3=1.010_prec !gamma4=1.0_prec ELSE ! nämä blog2 !gamma1=0.995_prec !gamma2=1.025_prec !gamma3=1.01_prec !gamma4=1.0_prec !gamma1=0.997_prec !3 !gamma2=1.02_prec !gamma3=1.01_prec !gamma4=1.0_prec !gamma1=0.999_prec !5 pistä tämä vielä viikonloppuna muutoin 3 paras !gamma2=1.005_prec !gamma3=1.01_prec !gamma3=1.005_prec !6 !gamma4=1.0_prec !gamma1=0.995_prec !7 pistä tämä vielä viikonloppuna muutoin 3 paras !gamma2=1.01_prec !gamma3=1.01_prec !gamma4=1.0_prec gamma1=0.99_prec !8 pistä tämä vielä viikonloppuna muutoin 3 paras gamma2=1.01_prec ! katso tänne parhaat muihin gamma3=1.01_prec gamma4=1.0_prec !gamma1=0.997_prec !9 pistä tämä vielä viikonloppuna muutoin 3 paras !gamma2=1.01_prec ! katso tänne parhaat muihin !gamma3=1.01_prec !gamma4=1.0_prec !gamma1=0.998_prec !10 pistä tämä vielä viikonloppuna muutoin 3 paras !gamma2=1.015_prec ! katso tänne parhaat muihin !gamma3=1.015_prec !gamma4=1.0_prec !gamma1=0.999_prec !11 pistä tämä vielä viikonloppuna muutoin 3 paras !gamma2=1.001_prec ! katso tänne parhaat muihin !gamma3=1.025_prec !gamma4=1.0_prec !gamma1=0.999_prec !12 pistä tämä vielä viikonloppuna muutoin 3 paras !gamma2=1.001_prec ! katso tänne parhaat muihin !gamma3=1.05_prec !gamma4=1.0_prec !gamma1=0.995_prec !13 pistä tämä vielä viikonloppuna muutoin 3 paras !gamma2=1.001_prec ! katso tänne parhaat muihin !gamma3=1.05_prec !gamma4=1.0_prec !DGM:llä huomattavasti vähemmän ns=3 ja ns=1 kutsuja katso mitä voit tehdä END IF RETURN END SUBROUTINE def_clrpar END MODULE initclr MODULE initlmbm ! Initialization of parameters for LMBM. USE r_precision, ONLY : prec ! Precision for reals. USE param, ONLY : zero, one ! Parameters. USE initclr, ONLY : maxdim ! Maximum dimension of x. IMPLICIT NONE ! Parameters INTEGER, PARAMETER :: & na = 2, & ! Maximum bundle dimension, na >= 2. mcu = 15, & ! Maximum number of stored corrections, mcu >=1. mcinit = 7, & ! Initial maximum number of stored corrections, mcu >= mcinit >= 3. ! If mcinit <= 0, the default value mcinit = 3 will be used. ! However, the value mcinit = 7 is recommented. inma = 3, & ! Selection of line search method: ! inma = 0, Armijo line search, ! inma = 1, nonmonotone Armijo line search. ! inma = 2, weak Wolfe line search. ! inma = 3, nonmonotone weak Wolfe line search. !mnma = 10, & ! Maximum number of function values used in nonmonotone line search. mnma = 10, & ! Maximum number of function values used in nonmonotone line search. maxnin = 20 ! Maximum number of interpolations, maxnin >= 0. !maxnin = 20 ! Maximum number of interpolations, maxnin >= 0. ! The value maxnin = 2-20 is recommented with inma=0, ! maxnin >= 20 with inma=1 and 3, and maxnin =200 with inma=2. ! For example: ! inma = 0, maxin = 20. ! inma = 1, mnma = 20, maxin = 30. ! inma = 2, maxnin = 200. ! inma = 3, mnma=10, maxnin = 20. ! Real parameters (if parameter value <= 0.0 the default value of the parameter will be used). REAL(KIND=prec), SAVE :: & tolb = zero, & ! Tolerance for the function value (default = -large). tolf = zero, & ! Tolerance for change of function values (default = 1.0E-8). tolf2 = -1.0_prec, & ! Second tolerance for change of function values. ! - If tolf2 < 0 the the parameter and the corresponding termination ! criterion will be ignored (recommended with inma=1,3). ! - If tolf2 = 0 the default value 1.0E+4 will be used. tolg = 1.0E-5_prec, & ! Tolerance for the termination criterion (default = 1.0E-5). tolg2 = 1.0E-3_prec, & ! Tolerance for the second termination criterion (default = 1.0E-3). eta = 1.0E-3_prec, & ! Distance measure parameter, eta > 0. ! - If eta < 0 the default value 0.0001 will be used. epsl = 0.22E+00, & ! Line search parameter, 0 < epsl < 0.25 (default = 0.24). ! use small value with inma=2 and larger value with inma=3 xmax = 1000.0_prec ! Maximum stepsize, 1 < XMAX (default = 1000). ! Integer parameters (if value <= 0 the default value of the parameter will be used). INTEGER, SAVE :: & n = maxdim, & ! Number of variables. mit = 5000, & ! Maximun number of iterations. mfe = 5000, & ! Maximun number of function evaluations. mtesf = 20, & ! Maximum number of iterations with changes of ! function values smaller than tolf (default = 20). iprint = 0, & ! Printout specification: ! -1 - No printout. ! 0 - Only the error messages. ! 1 - The final values of the objective function ! (default used if iprint < -1). ! 2 - The final values of the objective function and the ! most serious warning messages. ! 3 - The whole final solution. ! 4 - At each iteration values of the objective function. ! 5 - At each iteration the whole solution iscale = 0 ! Selection of the scaling with LMBM: ! 0 - Scaling at every iteration with STU/UTU (default). ! 1 - Scaling at every iteration with STS/STU. ! 2 - Interval scaling with STU/UTU. ! 3 - Interval scaling with STS/STU. ! 4 - Preliminary scaling with STU/UTU. ! 5 - Preliminary scaling with STS/STU. ! 6 - No scaling. REAL(KIND=prec), DIMENSION(maxdim), SAVE :: x ! Vector of variables CONTAINS SUBROUTINE defaults() ! Default values for parameters. USE param, ONLY: small, large, zero, one, half IMPLICIT NONE IF (iprint < -1) iprint = 1 ! Printout specification. IF (mit <= 0) mit = 5000 ! Maximum number of iterations. IF (mfe <= 0) mfe = n*mit ! Maximum number of function evaluations. IF (tolf <= zero) tolf = 1.0E-08_prec ! Tolerance for change of function values. IF (tolf2 == zero) tolf2 = 1.0E+04_prec ! Second tolerance for change of function values. IF (tolb == zero) tolb = -large + small ! Tolerance for the function value. IF (tolg <= zero) tolg = 1.0E-05_prec ! Tolerance for the termination criterion. IF (tolg2 <= zero) tolg = 1.0E-03_prec ! Tolerance for the second termination criterion. IF (xmax <= zero) xmax = 1000.0_prec ! Maximum stepsize. IF (eta < zero) eta = 1.0E-03_prec ! Distance measure parameter IF (epsl <= zero) epsl = 0.22_prec ! Line search parameter, IF (mtesf <= 0) mtesf = 20 ! Maximum number of iterations with changes ! of function values smaller than tolf. IF (iscale > 6 .OR. iscale < 0) iscale = 0 ! Selection of the scaling. END SUBROUTINE defaults SUBROUTINE init_lmbmpar() ! User supplied subroutine for further initialization of parameters ! (when needed) for LMBM. May be left empty. USE initclr, ONLY : ns,nc,nrecord IMPLICIT NONE ! For big data sets use larger values IF (nrecord < 500) THEN ! small data sets IF (ns == 3) THEN ! mit=5000 tolg2= 1.0E-12 tolg=1.0E-7 ELSE IF (ns == 1) THEN mit=5000 tolg2= 1.0E-12 tolg = 1.0E-5 ELSE mit=5000 tolg2 = 1.0E-8 tolg = 1.0E-5 END IF ELSE IF (nrecord < 4000) THEN ! medium data sets IF (ns == 3) THEN ! mit=5000 tolg2= 1.0E-12 tolg=1.0E-7 ELSE IF (ns == 1) THEN mit=5000 tolg2 = 1.0E-6 tolg = 1.0E-5 ! ELSE mit=5000 tolg2 = 1.0E-6 tolg = 1.0E-5 ! END IF ELSE IF (nrecord < 50000) THEN ! large datasets oikeasti täällä 50000 IF (ns == 3) THEN ! mit=5000 tolg2= 1.0E-12 !32 tolg=1.0E-7 ELSE IF (ns == 1) THEN mit=5000 tolg2 = 1.0E-5 !64 tolg = 1.0E-6 ! ELSE mit=5000 tolg2 = 1.0E+1 tolg = 1.0E-5 ! END IF ELSE ! very large datasets IF (ns == 3) THEN ! mit=5000 tolg2= 1.0E-8 !32 tolg=1.0E-7 ELSE IF (ns == 1) THEN mit=5000 tolg2 = 1.0E-3 !32 tolg = 1.0E-6 ! ELSE mit=5000 tolg2 = 1.0E+2 tolg = 1.0E-4 ! END IF END IF END SUBROUTINE init_lmbmpar END MODULE initlmbm