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599run.txt
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ian@ian-Aspire-One-753:~/599dir$ ls
599 599.f 599.gz.gz 599run a.out fort.10
ian@ian-Aspire-One-753:~/599dir$ gfortran 599.f -o 599run
599.f:619:72:
619 | IF (KFLAG) 4,4,6
| 1
Warning: Fortran 2018 deleted feature: Arithmetic IF statement at (1)
599.f:785:72:
785 | 13 SAMPLE(I)=SUNIF(IR) MAN 1200
| 1
Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 13 at (1)
599.f:795:72:
795 | 15 SAMPLE(I)=SEXPO(IR) MAN 1300
| 1
Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 15 at (1)
599.f:805:72:
805 | 17 SAMPLE(I)=SNORM(IR) MAN 1400
| 1
Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 17 at (1)
599.f:815:72:
815 | 19 SAMPLE(I)=SGAMMA(IR,A) MAN 1500
| 1
Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 19 at (1)
599.f:825:72:
825 | 21 SAMPLE(I)=FLOAT(KPOISS(IR,MU)) MAN 1600
| 1
Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 21 at (1)
599.f:849:72:
849 | 24 S1=S1+SAMPLE(I) MAN 1840
| 1
Warning: Fortran 2018 deleted feature: DO termination statement which is not END DO or CONTINUE with label 24 at (1)
ian@ian-Aspire-One-753:~/599dir$ cat 599.f
C ALGORITHM 599, COLLECTED ALGORITHMS FROM ACM.
C ALGORITHM APPEARED IN ACM-TRANS. MATH. SOFTWARE, VOL.9, NO. 2,
C JUN., 1983, P. 255-257.
C**********************************************************************CSUN 10
C**********************************************************************CSUN 20
C**********************************************************************CSUN 30
C CSUN 40
C CSUN 50
C CSUN 60
C F O R T R A N SOFTWARE PACKAGE FOR RANDOM NUMBER GENERATION CSUN 70
C CSUN 80
C CSUN 90
C CSUN 100
C**********************************************************************CSUN 110
C**********************************************************************CSUN 120
C**********************************************************************CSUN 130
C SUN 140
C SUN 150
C SUN 160
C CONTENTS: SUN 170
C SUN 180
C 1) SUNIF - 0,1 -UNIFORM DISTRIBUTION SUN 190
C SUN 200
C 2) SEXPO - (STANDARD-) EXPONENTIAL DISTRIBUTION SUN 210
C SUN 220
C 3) SNORM - (STANDARD-) NORMAL DISTRIBUTION SUN 230
C SUN 240
C 4) SGAMMA - (STANDARD-) GAMMA DISTRIBUTION SUN 250
C SUN 260
C 5) KPOISS - POISSON DISTRIBUTION SUN 270
C SUN 280
C SUN 290
C THIS PACKAGE CONSTITUTES A FORTRAN-77 DOCUMENTATION OF A SET OF SUN 300
C ASSEMBLER FUNCTIONS FOR SAMPLING FROM THE ABOVE DISTRIBUTIONS. SUN 310
C ALL ROUTINES MAKE AMPLE USE OF BINARY REPRESENTATIONS OF NUMBERS, SUN 320
C THEY ARE AMONG THE MOST ACCURATE AND FAST SAMPLING FUNCTIONS SUN 330
C KNOWN. THE FORTRAN PROGRAMS BELOW YIELD THE SAME RANDOM NUMBER SUN 340
C SEQUENCES AS THE ONES FROM OUR ASSEMBLER PACKAGE, BUT THEY ARE SUN 350
C OF COURSE MUCH SLOWER (BY FACTORS 5-8 ON OUR SIEMENS 7760 SUN 360
C COMPUTER.) SUN 370
C THE SET OF ROUTINES WILL ALSO BE ACCEPTABLE TO FORTRAN IV SUN 380
C COMPILERS WHICH ALLOW DATA STATEMENTS FOR ARRAYS WITHOUT SUN 390
C IMPLICIT DO-LOOPS. SUN 400
C SUN 410
C SUN 420
C REMARKS: SUN 430
C SUN 440
C - NO CARE IS TAKEN TO ENSURE THAT THE PARAMETER VALUES LIE SUN 450
C IN THE ALLOWED RANGE (E.G. A/MU > 0.0 FOR SGAMMA/KPOISS). SUN 460
C SUN 470
C - THE PARAMETER 'IR' MUST BE SET TO SOME 4*K+1 > 0 BEFORE SUN 480
C THE FIRST CALL OF ANY OF THE GENERATORS. THEREAFTER IR SUN 490
C MUST NOT BE ALTERED UNTIL A NEW INITIALIZATION IS DESIRED. SUN 500
C SUN 510
C - THE PACKAGE PROVIDES RANDOM DEVIATES OF 6-7 DIGITS ACCURACY. SUN 520
C ON MORE ACCURATE COMPUTERS THE CONSTANTS IN SEXPO, SNORM, SUN 530
C SGAMMA AND KPOISS OUGHT TO BE ADJUSTED ACCORDING TO LOCAL SUN 540
C COMMENTS OR WITH THE AID OF THE TABLES IN THE LITERATURE SUN 550
C QUOTED AT THE BEGINNING OF EACH FUNCTION. SUN 560
C SUN 570
C SUN 580
C**********************************************************************CSUN 590
C**********************************************************************CSUN 600
C CSUN 610
C CSUN 620
C 0 , 1 - U N I F O R M DISTRIBUTION CSUN 630
C CSUN 640
C CSUN 650
C**********************************************************************CSUN 660
C**********************************************************************CSUN 670
C CSUN 680
C FOR DETAILS SEE: CSUN 690
C CSUN 700
C AHRENS, J.H., DIETER, U. AND GRUBE, A. CSUN 710
C PSEUDO-RANDOM NUMBERS: A NEW PROPOSAL CSUN 720
C FOR THE CHOICE OF MULTIPLICATORS CSUN 730
C COMPUTING, 6 (1970), 121 - 138 CSUN 740
C CSUN 750
C**********************************************************************CSUN 760
C SUN 770
REAL FUNCTION SUNIF(IR) SUN 780
DOUBLE PRECISION R,FACTOR,TWO28
SAVE R
C
C FACTOR - INTEGER OF THE FORM 8*K+5 AS CLOSE AS POSSIBLE
C TO 2**26 * (SQRT(5)-1)/2 (GOLDEN SECTION)
C TWO28 = 2**28 (I.E. 28 SIGNIFICANT BITS FOR DEVIATES)
C
DATA FACTOR /41475557.0D0/, TWO28 /268435456.0D0/
C
C RETURNS SAMPLE U FROM THE 0,1 -UNIFORM DISTRIBUTION
C BY A MULTIPLICATIVE CONGRUENTIAL GENERATOR OF THE FORM
C R := R * FACTOR (MOD 1) .
C IN THE FIRST CALL R IS INITIALIZED TO
C R := IR / 2**28 ,
C WHERE IR MUST BE OF THE FORM IR = 4*K+1.
C THEN R ASSUMES ALL VALUES 0 < (4*K+1)/2**28 < 1 DURING
C A FULL PERIOD 2**26 OF SUNIF.
C THE PARAMETER IR IS USED ONLY IN THE FIRST CALL FOR
C INITIALIZATION OF SUNIF. THEREAFTER (WHEN NEGATIVE)
C IR BECOMES A DUMMY VARIABLE.
C
IF (IR .GE. 0) GO TO 1
C
C STANDARD CASE: SAMPLING
C
R=DMOD(R*FACTOR,1.0D0)
SUNIF=SNGL(R)
RETURN
C
C FIRST CALL: INITIALIZATION
C
1 R=DBLE(FLOAT(IR))/TWO28
R=DMOD(R*FACTOR,1.0D0)
SUNIF=SNGL(R)
IR=-1
RETURN
END
C SEX 10
C**********************************************************************CSEX 20
C**********************************************************************CSEX 30
C CSEX 40
C CSEX 50
C (STANDARD-) E X P O N E N T I A L DISTRIBUTION CSEX 60
C CSEX 70
C CSEX 80
C**********************************************************************CSEX 90
C**********************************************************************CSEX 100
C CSEX 110
C FOR DETAILS SEE: CSEX 120
C CSEX 130
C AHRENS, J.H. AND DIETER, U. CSEX 140
C COMPUTER METHODS FOR SAMPLING FROM THE CSEX 150
C EXPONENTIAL AND NORMAL DISTRIBUTIONS. CSEX 160
C COMM. ACM, 15,10 (OCT. 1972), 873 - 882. CSEX 170
C CSEX 180
C ALL STATEMENT NUMBERS CORRESPOND TO THE STEPS OF ALGORITHM CSEX 190
C 'SA' IN THE ABOVE PAPER (SLIGHTLY MODIFIED IMPLEMENTATION) CSEX 200
C CSEX 210
C**********************************************************************CSEX 220
C SEX 230
REAL FUNCTION SEXPO(IR) SEX 240
DIMENSION Q(8)
EQUIVALENCE (Q(1),Q1)
C
C Q(N) = SUM(ALOG(2.0)**K/K]) K=1,..,N , THE HIGHEST N
C (HERE 8) IS DETERMINED BY Q(N)=1.0 WITHIN STANDARD PRECISION
C
DATA Q/.6931472,.9333737,.9888778,.9984959,
,.9998293,.9999833,.9999986,.9999999/
C
1 A=0.0
U=SUNIF(IR)
GO TO 2
3 A=A+Q1
2 U=U+U
IF (U.LE.1.0) GO TO 3
4 U=U-1.0
IF (U.GT.Q1) GO TO 6
5 SEXPO=A+U
RETURN
6 I=1
USTAR=SUNIF(IR)
UMIN=USTAR
7 USTAR=SUNIF(IR)
IF (USTAR.LT.UMIN) UMIN=USTAR
8 I=I+1
IF (U.GT.Q(I)) GO TO 7
9 SEXPO=A+UMIN*Q1
RETURN
END
C SNO 10
C**********************************************************************CSNO 20
C**********************************************************************CSNO 30
C CSNO 40
C CSNO 50
C (STANDARD-) N O R M A L DISTRIBUTION CSNO 60
C CSNO 70
C CSNO 80
C**********************************************************************CSNO 90
C**********************************************************************CSNO 100
C CSNO 110
C FOR DETAILS SEE: CSNO 120
C CSNO 130
C AHRENS, J.H. AND DIETER, U. CSNO 140
C EXTENSIONS OF FORSYTHE'S METHOD FOR RANDOM CSNO 150
C SAMPLING FROM THE NORMAL DISTRIBUTION. CSNO 160
C MATH. COMPUT., 27,124 (OCT. 1973), 927 - 937. CSNO 170
C CSNO 180
C ALL STATEMENT NUMBERS CORRESPOND TO THE STEPS OF ALGORITHM 'FL' CSNO 190
C (M=5) IN THE ABOVE PAPER (SLIGHTLY MODIFIED IMPLEMENTATION) CSNO 200
C CSNO 210
C**********************************************************************CSNO 220
C SNO 230
REAL FUNCTION SNORM(IR) SNO 240
DIMENSION A(32),D(31),T(31),H(31)
C
C THE DEFINITIONS OF THE CONSTANTS A(K), D(K), T(K) AND
C H(K) ARE ACCORDING TO THE ABOVEMENTIONED ARTICLE
C
DATA A/0.0,.3917609E-1,.7841241E-1,.1177699,.1573107,
,.1970991,.2372021,.2776904,.3186394,.3601299,.4022501,
,.4450965,.4887764,.5334097,.5791322,.6260990,.6744898,
,.7245144,.7764218,.8305109,.8871466,.9467818,1.009990,
,1.077516,1.150349,1.229859,1.318011,1.417797,1.534121,
,1.675940,1.862732,2.153875/
DATA D/5*0.0,.2636843,.2425085,.2255674,.2116342,.1999243,
,.1899108,.1812252,.1736014,.1668419,.1607967,.1553497,
,.1504094,.1459026,.1417700,.1379632,.1344418,.1311722,
,.1281260,.1252791,.1226109,.1201036,.1177417,.1155119,
,.1134023,.1114027,.1095039/
DATA T/.7673828E-3,.2306870E-2,.3860618E-2,.5438454E-2,
,.7050699E-2,.8708396E-2,.1042357E-1,.1220953E-1,.1408125E-1,
,.1605579E-1,.1815290E-1,.2039573E-1,.2281177E-1,.2543407E-1,
,.2830296E-1,.3146822E-1,.3499233E-1,.3895483E-1,.4345878E-1,
,.4864035E-1,.5468334E-1,.6184222E-1,.7047983E-1,.8113195E-1,
,.9462444E-1,.1123001,.1364980,.1716886,.2276241,.3304980,
,.5847031/
DATA H/.3920617E-1,.3932705E-1,.3950999E-1,.3975703E-1,
,.4007093E-1,.4045533E-1,.4091481E-1,.4145507E-1,.4208311E-1,
,.4280748E-1,.4363863E-1,.4458932E-1,.4567523E-1,.4691571E-1,
,.4833487E-1,.4996298E-1,.5183859E-1,.5401138E-1,.5654656E-1,
,.5953130E-1,.6308489E-1,.6737503E-1,.7264544E-1,.7926471E-1,
,.8781922E-1,.9930398E-1,.1155599,.1404344,.1836142,.2790016,
,.7010474/
C
1 U=SUNIF(IR)
S=0.0
IF (U.GE.0.5) S=1.0
U=U+U-S
2 U=32.0*U
I=INT(U)
IF (I.EQ.0) GO TO 9
C
C START CENTER
C
3 USTAR=U-FLOAT(I)
AA=A(I)
4 IF (USTAR.LE.T(I)) GO TO 5
W=(USTAR-T(I))*H(I)
C
C EXIT (BOTH CASES)
C
17 Y=AA+W
SNORM=Y
IF (S.EQ.1.0) SNORM=-Y
RETURN
C
C CENTER CONTINUED
C
5 U=SUNIF(IR)
W=U*(A(I+1)-AA)
TT=(0.5*W+AA)*W
GO TO 6
8 TT=U
USTAR=SUNIF(IR)
6 IF (USTAR.GT.TT) GO TO 17
7 U=SUNIF(IR)
IF (USTAR.GE.U) GO TO 8
USTAR=SUNIF(IR)
GO TO 4
C
C START TAIL
C
9 I=6
AA=A(32)
GO TO 10
11 AA=AA+D(I)
I=I+1
10 U=U+U
IF (U.LT.1.0) GO TO 11
12 U=U-1.0
13 W=U*D(I)
TT=(0.5*W+AA)*W
GO TO 14
16 TT=U
14 USTAR=SUNIF(IR)
IF (USTAR.GT.TT) GO TO 17
15 U=SUNIF(IR)
IF (USTAR.GE.U) GO TO 16
U=SUNIF(IR)
GO TO 13
END
C SGA 10
C**********************************************************************CSGA 20
C**********************************************************************CSGA 30
C CSGA 40
C CSGA 50
C (STANDARD-) G A M M A DISTRIBUTION CSGA 60
C CSGA 70
C CSGA 80
C**********************************************************************CSGA 90
C**********************************************************************CSGA 100
C CSGA 110
C PARAMETER A >= 1.0 ] CSGA 120
C CSGA 130
C**********************************************************************CSGA 140
C CSGA 150
C FOR DETAILS SEE: CSGA 160
C CSGA 170
C AHRENS, J.H. AND DIETER, U. CSGA 180
C GENERATING GAMMA VARIATES BY A CSGA 190
C MODIFIED REJECTION TECHNIQUE. CSGA 200
C COMM. ACM, 25,1 (JAN. 1982), 47 - 54. CSGA 210
C CSGA 220
C STEP NUMBERS CORRESPOND TO ALGORITHM 'GD' IN THE ABOVE PAPER CSGA 230
C (STRAIGHTFORWARD IMPLEMENTATION) CSGA 240
C CSGA 250
C**********************************************************************CSGA 260
C CSGA 270
C PARAMETER 0.0 < A < 1.0 ] CSGA 280
C CSGA 290
C**********************************************************************CSGA 300
C CSGA 310
C FOR DETAILS SEE: CSGA 320
C CSGA 330
C AHRENS, J.H. AND DIETER, U. CSGA 340
C COMPUTER METHODS FOR SAMPLING FROM GAMMA, CSGA 350
C BETA, POISSON AND BINOMIAL DISTRIBUTIONS. CSGA 360
C COMPUTING, 12 (1974), 223 - 246. CSGA 370
C CSGA 380
C (ADAPTED IMPLEMENTATION OF ALGORITHM 'GS' IN THE ABOVE PAPER) CSGA 390
C CSGA 400
C**********************************************************************CSGA 410
C SGA 420
REAL FUNCTION SGAMMA(IR,A) SGA 430
C
C INPUT: IR=CURRENT STATE OF BASIC RANDOM NUMBER GENERATOR
C A =PARAMETER (MEAN) OF THE STANDARD GAMMA DISTRIBUTION
C OUTPUT: SGAMMA = SAMPLE FROM THE GAMMA-(A)-DISTRIBUTION
C
C COEFFICIENTS Q(K) - FOR Q0 = SUM(Q(K)*A**(-K))
C COEFFICIENTS A(K) - FOR Q = Q0+(T*T/2)*SUM(A(K)*V**K)
C COEFFICIENTS E(K) - FOR EXP(Q)-1 = SUM(E(K)*Q**K)
C
DATA Q1,Q2,Q3,Q4,Q5,Q6,Q7 /.04166669,.02083148,
,.00801191,.00144121,-.00007388,.00024511,.00024240/
DATA A1,A2,A3,A4,A5,A6,A7 /.3333333,-.2500030,
,.2000062,-.1662921,.1423657,-.1367177,.1233795/
DATA E1,E2,E3,E4,E5 /1.,.4999897,.1668290,.0407753,.0102930/
C
C PREVIOUS A PRE-SET TO ZERO - AA IS A', AAA IS A"
C SQRT32 IS THE SQUAREROOT OF 32 = 5.656854249492380
C
DATA AA /0.0/, AAA /0.0/, SQRT32 /5.656854/
C
IF (A .EQ. AA) GO TO 1
IF (A .LT. 1.0) GO TO 12
C
C STEP 1: RECALCULATIONS OF S2,S,D IF A HAS CHANGED
C
AA=A
S2=A-0.5
S=SQRT(S2)
D=SQRT32-12.0*S
C
C STEP 2: T=STANDARD NORMAL DEVIATE,
C X=(S,1/2)-NORMAL DEVIATE.
C IMMEDIATE ACCEPTANCE (I)
C
1 T=SNORM(IR)
X=S+0.5*T
SGAMMA=X*X
IF (T .GE. 0.0) RETURN
C
C STEP 3: U= 0,1 -UNIFORM SAMPLE. SQUEEZE ACCEPTANCE (S)
C
U=SUNIF(IR)
IF (D*U .LE. T*T*T) RETURN
C
C STEP 4: RECALCULATIONS OF Q0,B,SI,C IF NECESSARY
C
IF (A .EQ. AAA) GO TO 4
AAA=A
R=1.0/A
Q0=((((((Q7*R+Q6)*R+Q5)*R+Q4)*R+Q3)*R+Q2)*R+Q1)*R
C
C APPROXIMATION DEPENDING ON SIZE OF PARAMETER A
C THE CONSTANTS IN THE EXPRESSIONS FOR B, SI AND
C C WERE ESTABLISHED BY NUMERICAL EXPERIMENTS
C
IF (A .LE. 3.686) GO TO 3
IF (A .LE. 13.022) GO TO 2
C
C CASE 3: A .GT. 13.022
C
B=1.77
SI=.75
C=.1515/S
GO TO 4
C
C CASE 2: 3.686 .LT. A .LE. 13.022
C
2 B=1.654+.0076*S2
SI=1.68/S+.275
C=.062/S+.024
GO TO 4
C
C CASE 1: A .LE. 3.686
C
3 B=.463+S-.178*S2
SI=1.235
C=.195/S-.079+.016*S
C
C STEP 5: NO QUOTIENT TEST IF X NOT POSITIVE
C
4 IF (X .LE. 0.0) GO TO 7
C
C STEP 6: CALCULATION OF V AND QUOTIENT Q
C
V=T/(S+S)
IF (ABS(V) .LE. 0.25) GO TO 5
Q=Q0-S*T+0.25*T*T+(S2+S2)*ALOG(1.0+V)
GO TO 6
5 Q=Q0+0.5*T*T*((((((A7*V+A6)*V+A5)*V+A4)*V+A3)*V+A2)*V+A1)*V
C
C STEP 7: QUOTIENT ACCEPTANCE (Q)
C
6 IF (ALOG(1.0-U) .LE. Q) RETURN
C
C STEP 8: E=STANDARD EXPONENTIAL DEVIATE
C U= 0,1 -UNIFORM DEVIATE
C T=(B,SI)-DOUBLE EXPONENTIAL (LAPLACE) SAMPLE
C
7 E=SEXPO(IR)
U=SUNIF(IR)
U=U+U-1.0
T=B+SIGN(SI*E,U)
C
C STEP 9: REJECTION IF T .LT. TAU(1) = -.71874483771719
C
IF (T .LT. (-.7187449)) GO TO 7
C
C STEP 10: CALCULATION OF V AND QUOTIENT Q
C
V=T/(S+S)
IF (ABS(V) .LE. 0.25) GO TO 8
Q=Q0-S*T+0.25*T*T+(S2+S2)*ALOG(1.0+V)
GO TO 9
8 Q=Q0+0.5*T*T*((((((A7*V+A6)*V+A5)*V+A4)*V+A3)*V+A2)*V+A1)*V
C
C STEP 11: HAT ACCEPTANCE (H) (IF Q NOT POSITIVE GO TO STEP 8)
C
9 IF (Q .LE. 0.0) GO TO 7
IF (Q .LE. 0.5) GO TO 10
W=EXP(Q)-1.0
GO TO 11
10 W=((((E5*Q+E4)*Q+E3)*Q+E2)*Q+E1)*Q
C
C IF T IS REJECTED, SAMPLE AGAIN AT STEP 8
C
11 IF (C*ABS(U) .GT. W*EXP(E-0.5*T*T)) GO TO 7
X=S+0.5*T
SGAMMA=X*X
RETURN
C
C ALTERNATE METHOD FOR PARAMETERS A BELOW 1 (.3678794=EXP(-1.))
C
12 AA=0.0
B=1.0+.3678794*A
13 P=B*SUNIF(IR)
IF (P .GE. 1.0) GO TO 14
SGAMMA=EXP(ALOG(P)/A)
IF (SEXPO(IR) .LT. SGAMMA) GO TO 13
RETURN
14 SGAMMA=-ALOG((B-P)/A)
IF (SEXPO(IR) .LT. (1.0-A)*ALOG(SGAMMA)) GO TO 13
RETURN
END
C KPO 10
C**********************************************************************CKPO 20
C**********************************************************************CKPO 30
C CKPO 40
C CKPO 50
C P O I S S O N DISTRIBUTION CKPO 60
C CKPO 70
C CKPO 80
C**********************************************************************CKPO 90
C**********************************************************************CKPO 100
C CKPO 110
C FOR DETAILS SEE: CKPO 120
C CKPO 130
C AHRENS, J.H. AND DIETER, U. CKPO 140
C COMPUTER GENERATION OF POISSON DEVIATES CKPO 150
C FROM MODIFIED NORMAL DISTRIBUTIONS. CKPO 160
C ACM TRANS. MATH. SOFTWARE, 8,2 (JUNE 1982), 163 - 179. CKPO 170
C CKPO 180
C (SLIGHTLY MODIFIED VERSION OF THE PROGRAM IN THE ABOVE ARTICLE) CKPO 190
C CKPO 200
C**********************************************************************CKPO 210
C KPO 220
INTEGER FUNCTION KPOISS(IR,MU) KPO 230
C
C INPUT: IR=CURRENT STATE OF BASIC RANDOM NUMBER GENERATOR
C MU=MEAN MU OF THE POISSON DISTRIBUTION
C OUTPUT: KPOISS=SAMPLE FROM THE POISSON-(MU)-DISTRIBUTION
C
REAL MU, MUPREV, MUOLD
C
C MUPREV=PREVIOUS MU, MUOLD=MU AT LAST EXECUTION OF STEP P OR B.
C TABLES: COEFFICIENTS A0-A7 FOR STEP F. FACTORIALS FACT
C COEFFICIENTS A(K) - FOR PX = FK*V*V*SUM(A(K)*V**K)-DEL
C
DIMENSION FACT(10), PP(35)
DATA MUPREV,MUOLD /0.,0./
DATA A0,A1,A2,A3,A4,A5,A6,A7 /-.5,.3333333,-.2500068,
,.2000118,-.1661269,.1421878,-.1384794,.1250060/
DATA FACT /1.,1.,2.,6.,24.,120.,720.,5040.,40320.,362880./
C
C SEPARATION OF CASES A AND B
C
IF (MU .EQ. MUPREV) GO TO 1
IF (MU .LT. 10.0) GO TO 12
C
C C A S E A. (RECALCULATION OF S,D,L IF MU HAS CHANGED)
C
MUPREV=MU
S=SQRT(MU)
D=6.0*MU*MU
C
C THE POISSON PROBABILITIES PK EXCEED THE DISCRETE NORMAL
C PROBABILITIES FK WHENEVER K >= M(MU). L=IFIX(MU-1.1484)
C IS AN UPPER BOUND TO M(MU) FOR ALL MU >= 10 .
C
L=IFIX(MU-1.1484)
C
C STEP N. NORMAL SAMPLE - SNORM(IR) FOR STANDARD NORMAL DEVIATE
C
1 G=MU+S*SNORM(IR)
IF (G .LT. 0.0) GO TO 2
KPOISS=IFIX(G)
C
C STEP I. IMMEDIATE ACCEPTANCE IF KPOISS IS LARGE ENOUGH
C
IF (KPOISS .GE. L) RETURN
C
C STEP S. SQUEEZE ACCEPTANCE - SUNIF(IR) FOR (0,1)-SAMPLE U
C
FK=FLOAT(KPOISS)
DIFMUK=MU-FK
U=SUNIF(IR)
IF (D*U .GE. DIFMUK*DIFMUK*DIFMUK) RETURN
C
C STEP P. PREPARATIONS FOR STEPS Q AND H.
C (RECALCULATIONS OF PARAMETERS IF NECESSARY)
C .3989423=(2*PI)**(-.5) .416667E-1=1./24. .1428571=1./7.
C THE QUANTITIES B1, B2, C3, C2, C1, C0 ARE FOR THE HERMITE
C APPROXIMATIONS TO THE DISCRETE NORMAL PROBABILITIES FK.
C C=.1069/MU GUARANTEES MAJORIZATION BY THE 'HAT'-FUNCTION.
C
2 IF (MU .EQ. MUOLD) GO TO 3
MUOLD=MU
OMEGA=.3989423/S
B1=.4166667E-1/MU
B2=.3*B1*B1
C3=.1428571*B1*B2
C2=B2-15.*C3
C1=B1-6.*B2+45.*C3
C0=1.-B1+3.*B2-15.*C3
C=.1069/MU
3 IF (G .LT. 0.0) GO TO 5
C
C 'SUBROUTINE' F IS CALLED (KFLAG=0 FOR CORRECT RETURN)
C
KFLAG=0
GO TO 7
C
C STEP Q. QUOTIENT ACCEPTANCE (RARE CASE)
C
4 IF (FY-U*FY .LE. PY*EXP(PX-FX)) RETURN
C
C STEP E. EXPONENTIAL SAMPLE - SEXPO(IR) FOR STANDARD EXPONENTIAL
C DEVIATE E AND SAMPLE T FROM THE LAPLACE 'HAT'
C (IF T <= -.6744 THEN PK < FK FOR ALL MU >= 10.)
C
5 E=SEXPO(IR)
U=SUNIF(IR)
U=U+U-1.0
T=1.8+SIGN(E,U)
IF (T .LE. (-.6744)) GO TO 5
KPOISS=IFIX(MU+S*T)
FK=FLOAT(KPOISS)
DIFMUK=MU-FK
C
C 'SUBROUTINE' F IS CALLED (KFLAG=1 FOR CORRECT RETURN)
C
KFLAG=1
GO TO 7
C
C STEP H. HAT ACCEPTANCE (E IS REPEATED ON REJECTION)
C
6 IF (C*ABS(U) .GT. PY*EXP(PX+E)-FY*EXP(FX+E)) GO TO 5
RETURN
C
C STEP F. 'SUBROUTINE' F. CALCULATION OF PX,PY,FX,FY.
C CASE KPOISS .LT. 10 USES FACTORIALS FROM TABLE FACT
C
7 IF (KPOISS .GE. 10) GO TO 8
PX=-MU
PY=MU**KPOISS/FACT(KPOISS+1)
GO TO 11
C
C CASE KPOISS .GE. 10 USES POLYNOMIAL APPROXIMATION
C A0-A7 FOR ACCURACY WHEN ADVISABLE
C .8333333E-1=1./12. .3989423=(2*PI)**(-.5)
C
8 DEL=.8333333E-1/FK
DEL=DEL-4.8*DEL*DEL*DEL
V=DIFMUK/FK
IF (ABS(V) .LE. 0.25) GO TO 9
PX=FK*ALOG(1.0+V)-DIFMUK-DEL
GO TO 10
9 PX=FK*V*V*(((((((A7*V+A6)*V+A5)*V+A4)*V+A3)*V+A2)*V+A1)*V+A0)-DEL
10 PY=.3989423/SQRT(FK)
11 X=(0.5-DIFMUK)/S
XX=X*X
FX=-0.5*XX
FY=OMEGA*(((C3*XX+C2)*XX+C1)*XX+C0)
IF (KFLAG) 4,4,6
C
C C A S E B. (START NEW TABLE AND CALCULATE P0 IF NECESSARY)
C
12 MUPREV=0.0
IF (MU .EQ. MUOLD) GO TO 13
MUOLD=MU
M=MAX0(1,IFIX(MU))
L=0
P=EXP(-MU)
Q=P
P0=P
C
C STEP U. UNIFORM SAMPLE FOR INVERSION METHOD
C
13 U=SUNIF(IR)
KPOISS=0
IF (U .LE. P0) RETURN
C
C STEP T. TABLE COMPARISON UNTIL THE END PP(L) OF THE
C PP-TABLE OF CUMULATIVE POISSON PROBABILITIES
C (0.458=PP(9) FOR MU=10)
C
IF (L .EQ. 0) GO TO 15
J=1
IF (U .GT. 0.458) J=MIN0(L,M)
DO 14 K=J,L
IF (U .LE. PP(K)) GO TO 18
14 CONTINUE
IF (L .EQ. 35) GO TO 13
C
C STEP C. CREATION OF NEW POISSON PROBABILITIES P
C AND THEIR CUMULATIVES Q=PP(K)
C
15 L=L+1
DO 16 K=L,35
P=P*MU/FLOAT(K)
Q=Q+P
PP(K)=Q
IF (U .LE. Q) GO TO 17
16 CONTINUE
L=35
GO TO 13
17 L=K
18 KPOISS=K
RETURN
END
C**********************************************************************CMAN 10
C**********************************************************************CMAN 20
C**********************************************************************CMAN 30
C CMAN 40
C CMAN 50
C CMAN 60
C AUTOMATIC TEST DRIVER CMAN 70
C FOR CMAN 80
C RANDOM NUMBER PACKAGE AHRENS/DIETER/KOHRT CMAN 90
C (THIS DRIVER MAY CAUSE MANY EXP() UNDERFLOWS.) MAN 100
C CMAN 110
C CMAN 120
C CMAN 130
C**********************************************************************CMAN 140
C**********************************************************************CMAN 150
C**********************************************************************CMAN 160
C PROGRAM TEST MAN 170
REAL MU MAN 180
DIMENSION VPAR4(22),VPAR5(24),SAMPLE(10000),II(5) MAN 190
C MAN 200
C VPAR4 - VECTOR OF PARAMETER VALUES FOR CASE 4 : SGAMMA MAN 210
C MAN 220
DATA VPAR4 /.0001,.25,.5,.75,.9999,1.,1.5,2., MAN 230
,3.,4.,5.,7.,10.,15.,20.,30.,50.,100.,1000., MAN 240
,10000.,100000.,1000000./ MAN 250
C MAN 260
C VPAR5 - VECTOR OF PARAMETER VALUES FOR CASE 5 : KPOISS MAN 270
C MAN 280
DATA VPAR5 /.0001,1.,2.,5.,9.99,10., MAN 290
,12.,15.,20.,25.,30.,40.,50.,75.,100.,150., MAN 300
,200.,500.,1000.,2000.,5000.,1.E4,1.E5,1.E6/ MAN 310
C MAN 320
C II - NUMBER OF RUNS FOR EACH DISTRIBUTION MAN 330
C MAN 340
DATA II /1,1,1,22,24/ MAN 350
C MAN 360
C FORMAT STATEMENTS MAN 370
C MAN 380
1 FORMAT(' ',/,' ',/,' LISTING OF TRIAL RUNS', MAN 390
,' FOR RANDOM NUMBER PACKAGE AHRENS/DIETER/KOHRT', MAN 400
C ,/,' ===============================', MAN 410
,'====================================') MAN 420
2 FORMAT(' FIRST 100 SAMPLES:',/, MAN 430
,' ..................',/,' ',/,(5E15.6)) MAN 440
3 FORMAT(' ',/,' ',/,' TEST DATA:', MAN 450
,' (]BASED ON 10000 SAMPLES])', MAN 460
,/,' ..........',/,20X,'MEAN',11X, MAN 470
,'STD.DEV.',7X,'SKEWNESS', MAN 480
,7X,'EXCESS',/,' ',/, MAN 490
,' TRUE VALUES:',4E15.6,/, MAN 500
,' SAMPLE DATA:',4E15.6,/,' ') MAN 510
4 FORMAT(' ',/,' 1.) 0,1 -UNIFORM DISTRIBUTION:', MAN 520
,/,' ********************************') MAN 530
5 FORMAT(' ',/,' 2.) (STANDARD-) EXPONENTIAL DISTRIBUTION:', MAN 540
,/,' ******************************************') MAN 550
6 FORMAT(' ',/,' 3.) (STANDARD-) NORMAL DISTRIBUTION:', MAN 560
,/,' ******************************************') MAN 570
7 FORMAT(' ',/,' 4.) (STANDARD-) GAMMA-(A) DISTRIBUTION:', MAN 580
,/,' ****************************************') MAN 590
8 FORMAT(' ',/,' 5.) POISSON-(MU) DISTRIBUTION:', MAN 600
,/,' *******************************',/,' ', MAN 610
,/,' (INTEGER SAMPLES ARE DISPLAYED AS REALS])', MAN 620
,/,' ',/,' ') MAN 630
9 FORMAT(43X,' GAMMA-(A): A =',E13.6, MAN 640
,/,43X,' ----------------------------') MAN 650
10 FORMAT(43X,'POISSON-(MU): MU =',E13.6, MAN 660
,/,43X,'--------------------------------') MAN 670
11 FORMAT(' ',/,' ') MAN 680
C MAN 690
C DEFINE OUTPUT UNIT NUMBER MAN 700
C MAN 710
NOUT=10 MAN 720
C MAN 730
C OUTPUT: MAIN HEADING MAN 740
C MAN 750
WRITE (NOUT,1) MAN 760
C MAN 770
C TRIAL RUNS FOR 5 DIFFERENT CASES: MAN 780
C MAN 790
C NDIS=1 : 0,1 -UNIFORM DISTRIBUTION MAN 800
C NDIS=2 : (STANDARD-) EXPONENTIAL DISTRIBUTION MAN 810
C NDIS=3 : (STANDARD-) NORMAL DISTRIBUTION MAN 820
C NDIS=4 : (STANDARD-) GAMMA-(A) DISTRIBUTION MAN 830
C NDIS=5 : POISSON-(MU) DISTRIBUTION MAN 840
C MAN 850
DO 27 NDIS=1,5 MAN 860
NRUN=II(NDIS) MAN 870
C MAN 880
C OUTPUT: CASE HEADING MAN 890
C MAN 900
WRITE (NOUT,11) MAN 910
IF (NDIS .EQ. 1) WRITE (NOUT,4) MAN 920
IF (NDIS .EQ. 2) WRITE (NOUT,5) MAN 930
IF (NDIS .EQ. 3) WRITE (NOUT,6) MAN 940
IF (NDIS .EQ. 4) WRITE (NOUT,7) MAN 950
IF (NDIS .EQ. 5) WRITE (NOUT,8) MAN 960
C MAN 970
C EACH CASE: ONE RUN FOR EVERY PARAMETER VALUE MAN 980
C MAN 990
DO 26 NPAR=1,NRUN MAN 1000
C MAN 1010
C CASE 4 AND 5: SET PARAMETER VALUES ACCORDING TO DATA VECTOR MAN 1020
C MAN 1030
IF (NDIS .EQ. 4) A=VPAR4(NPAR) MAN 1040
IF (NDIS .EQ. 5) MU=VPAR5(NPAR) MAN 1050
C MAN 1060
C SEED FOR UNIFORM RANDOM NUMBER GENERATOR IS INITIALIZED TO 4*0+1 MAN 1070
C MAN 1080
IR=1 MAN 1090
C MAN 1100
C EACH CASE SEPARATELY: SAMPLING AND TEST DATA MAN 1110
C T2 - STANDARD DEVIATION (=SQRT(VARIANCE)), MAN 1120
C T1 - MEAN, T3 - SKEWNESS, T4 - EXCESS. MAN 1130
C MAN 1140
GO TO (12,14,16,18,20) , NDIS MAN 1150
C MAN 1160
C CASE 1 : 0,1 -UNIFORM DISTRIBUTION MAN 1170
C MAN 1180
12 DO 13 I=1,10000 MAN 1190
13 SAMPLE(I)=SUNIF(IR) MAN 1200
T1=0.5 MAN 1210
T2=1.0/SQRT(12.0) MAN 1220
T3=0.0 MAN 1230
T4=-1.2 MAN 1240
GO TO 23 MAN 1250
C MAN 1260
C (STANDARD-) EXPONENTIAL DISTRIBUTION MAN 1270
C MAN 1280
14 DO 15 I=1,10000 MAN 1290
15 SAMPLE(I)=SEXPO(IR) MAN 1300
T1=1.0 MAN 1310
T2=1.0 MAN 1320
T3=2.0 MAN 1330
T4=6.0 MAN 1340
GO TO 23 MAN 1350
C MAN 1360
C (STANDARD-) NORMAL DISTRIBUTION MAN 1370
C MAN 1380
16 DO 17 I=1,10000 MAN 1390
17 SAMPLE(I)=SNORM(IR) MAN 1400
T1=0.0 MAN 1410
T2=1.0 MAN 1420
T3=0.0 MAN 1430
T4=0.0 MAN 1440
GO TO 23 MAN 1450
C MAN 1460
C (STANDARD-) GAMMA-(A) DISTRIBUTION MAN 1470
C MAN 1480
18 DO 19 I=1,10000 MAN 1490
19 SAMPLE(I)=SGAMMA(IR,A) MAN 1500
T1=A MAN 1510
T2=SQRT(A) MAN 1520
T3=2.0/T2 MAN 1530
T4=6.0/A MAN 1540
GO TO 22 MAN 1550
C MAN 1560
C POISSON-(MU) DISTRIBUTION MAN 1570
C MAN 1580
20 DO 21 I=1,10000 MAN 1590
21 SAMPLE(I)=FLOAT(KPOISS(IR,MU)) MAN 1600
T1=MU MAN 1610
T2=SQRT(MU) MAN 1620
T3=1.0/T2 MAN 1630
T4=1.0/MU MAN 1640
C MAN 1650
C CASE 4 AND 5: OUTPUT: PARAMETER VALUE MAN 1660
C MAN 1670
22 IF (NPAR .NE. 1) WRITE (NOUT,11) MAN 1680
IF (NDIS .EQ. 4) WRITE (NOUT, 9) A MAN 1690
IF (NDIS .EQ. 5) WRITE (NOUT,10) MU MAN 1700
C MAN 1710
23 CONTINUE MAN 1720
C MAN 1730
C OUTPUT : FIRST 100 RANDOM DEVIATES FOR EACH RUN MAN 1740
C (INTEGER SAMPLES ARE DISPLAYED AS REALS]) MAN 1750
C MAN 1760
IF (NDIS .LE. 3) WRITE (NOUT,11) MAN 1770
WRITE (NOUT,2) (SAMPLE(I),I=1,100) MAN 1780
C MAN 1790
C EVALUATION OF SAMPLE MEAN: E1 - (1/N)*SUM(SAMPLE(I)) MAN 1800
C MAN 1810
S1=0.0 MAN 1820
DO 24 I=1,10000 MAN 1830
24 S1=S1+SAMPLE(I) MAN 1840
E1=S1/10000.0 MAN 1850
C MAN 1860
C EVALUATION OF FURTHER SAMPLE ESTIMATES : MAN 1870
C MAN 1880
C SK - (1/N)*SUM((SAMPLE(I)-E1)**K) MAN 1890
C SAMPLE CENTRAL MOMENTS (K=2,3,4) MAN 1900
C WITH RESPECT TO SAMPLE MEAN MAN 1910
C MAN 1920
C E2 - SQRT(S2) MAN 1930
C SAMPLE STANDARD DEVIATION MAN 1940
C (=SQRT(SAMPLE VARIANCE)) MAN 1950
C MAN 1960
C E3 - S3/S2**(3/2) MAN 1970
C SAMPLE SKEWNESS MAN 1980
C MAN 1990
C E4 - S4/S2**2-3 MAN 2000
C SAMPLE EXCESS MAN 2010
C MAN 2020
S2=0.0 MAN 2030
S3=0.0 MAN 2040
S4=0.0 MAN 2050
C MAN 2060
DO 25 I=1,10000 MAN 2070
C MAN 2080
X1=SAMPLE(I)-E1 MAN 2090
X2=X1*X1 MAN 2100
X3=X2*X1 MAN 2110
X4=X2*X2 MAN 2120
C MAN 2130
S2=S2+X2 MAN 2140
S3=S3+X3 MAN 2150
S4=S4+X4 MAN 2160
C MAN 2170
25 CONTINUE MAN 2180
C MAN 2190
S2=S2/10000.0 MAN 2200
S3=S3/10000.0 MAN 2210
S4=S4/10000.0 MAN 2220
C MAN 2230
E2=SQRT(S2) MAN 2240
E3=S3/SQRT(S2*S2*S2) MAN 2250
E4=S4/(S2*S2)-3.0 MAN 2260
C MAN 2270
C END OF EVALUATION MAN 2280
C OUTPUT: CHARACTERISTIC DATA MAN 2290
C MAN 2300
WRITE (NOUT,3) T1,T2,T3,T4,E1,E2,E3,E4 MAN 2310
C MAN 2320
C END OF PARAMETER LOOP MAN 2330
C MAN 2340
26 CONTINUE MAN 2350
C MAN 2360
C END OF DISTRIBUTION LOOP MAN 2370
C MAN 2380
27 CONTINUE MAN 2390
C MAN 2400
C END OF PROGRAM MAN 2410
C MAN 2420
STOP MAN 2430
END MAN 2440
ian@ian-Aspire-One-753:~/599dir$ sloccount 599.f
Have a non-directory at the top, so creating directory top_dir
Adding /home/ian/599dir/599.f to top_dir
Categorizing files.
Finding a working MD5 command....
Found a working MD5 command.
Computing results.
SLOC Directory SLOC-by-Language (Sorted)
399 top_dir fortran=399
Totals grouped by language (dominant language first):
fortran: 399 (100.00%)
Total Physical Source Lines of Code (SLOC) = 399
Development Effort Estimate, Person-Years (Person-Months) = 0.08 (0.91)
(Basic COCOMO model, Person-Months = 2.4 * (KSLOC**1.05))
Schedule Estimate, Years (Months) = 0.20 (2.42)
(Basic COCOMO model, Months = 2.5 * (person-months**0.38))
Estimated Average Number of Developers (Effort/Schedule) = 0.38
Total Estimated Cost to Develop = $ 10,296
(average salary = $56,286/year, overhead = 2.40).
SLOCCount, Copyright (C) 2001-2004 David A. Wheeler
SLOCCount is Open Source Software/Free Software, licensed under the GNU GPL.
SLOCCount comes with ABSOLUTELY NO WARRANTY, and you are welcome to
redistribute it under certain conditions as specified by the GNU GPL license;
see the documentation for details.
Please credit this data as "generated using David A. Wheeler's 'SLOCCount'."
ian@ian-Aspire-One-753:~/599dir$ ls
599 599.f 599.gz.gz 599run a.out fort.10
ian@ian-Aspire-One-753:~/599dir$ cat fort.10
LISTING OF TRIAL RUNS FOR RANDOM NUMBER PACKAGE AHRENS/DIETER/KOHRT====================================
1.) 0,1 -UNIFORM DISTRIBUTION:
********************************
FIRST 100 SAMPLES:
..................
0.154508E+00 0.688765E+00 0.799863E+00 0.190601E+00 0.791996E+00
0.800496E+00 0.960159E+00 0.192117E+00 0.746994E+00 0.828039E+00
0.820741E+00 0.508265E+00 0.577189E+00 0.250930E-01 0.468059E+00