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SUBROUTINE NF01AY( NSMP, NZ, L, IPAR, LIPAR, WB, LWB, Z, LDZ,
$ Y, LDY, DWORK, LDWORK, INFO )
C
C SLICOT RELEASE 5.0.
C
C Copyright (c) 2002-2009 NICONET e.V.
C
C This program is free software: you can redistribute it and/or
C modify it under the terms of the GNU General Public License as
C published by the Free Software Foundation, either version 2 of
C the License, or (at your option) any later version.
C
C This program is distributed in the hope that it will be useful,
C but WITHOUT ANY WARRANTY; without even the implied warranty of
C MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
C GNU General Public License for more details.
C
C You should have received a copy of the GNU General Public License
C along with this program. If not, see
C <http://www.gnu.org/licenses/>.
C
C PURPOSE
C
C To calculate the output of a set of neural networks with the
C structure
C
C - tanh(w1'*z+b1) -
C / : \
C z --- : --- sum(ws(i)*...)+ b(n+1) --- y,
C \ : /
C - tanh(wn'*z+bn) -
C
C given the input z and the parameter vectors wi, ws, and b,
C where z, w1, ..., wn are vectors of length NZ, ws is a vector
C of length n, b(1), ..., b(n+1) are scalars, and n is called the
C number of neurons in the hidden layer, or just number of neurons.
C Such a network is used for each L output variables.
C
C ARGUMENTS
C
C Input/Output Parameters
C
C NSMP (input) INTEGER
C The number of training samples. NSMP >= 0.
C
C NZ (input) INTEGER
C The length of each input sample. NZ >= 0.
C
C L (input) INTEGER
C The length of each output sample. L >= 0.
C
C IPAR (input) INTEGER array, dimension (LIPAR)
C The integer parameters needed.
C IPAR(1) must contain the number of neurons, n, per output
C variable, denoted NN in the sequel. NN >= 0.
C
C LIPAR (input) INTEGER
C The length of the vector IPAR. LIPAR >= 1.
C
C WB (input) DOUBLE PRECISION array, dimension (LWB)
C The leading (NN*(NZ+2)+1)*L part of this array must
C contain the weights and biases of the network. This vector
C is partitioned into L vectors of length NN*(NZ+2)+1,
C WB = [ wb(1), ..., wb(L) ]. Each wb(k), k = 1, ..., L,
C corresponds to one output variable, and has the structure
C wb(k) = [ w1(1), ..., w1(NZ), ..., wn(1), ..., wn(NZ),
C ws(1), ..., ws(n), b(1), ..., b(n+1) ],
C where wi(j) are the weights of the hidden layer,
C ws(i) are the weights of the linear output layer, and
C b(i) are the biases, as in the scheme above.
C
C LWB (input) INTEGER
C The length of the array WB.
C LWB >= ( NN*(NZ + 2) + 1 )*L.
C
C Z (input) DOUBLE PRECISION array, dimension (LDZ, NZ)
C The leading NSMP-by-NZ part of this array must contain the
C set of input samples,
C Z = ( Z(1,1),...,Z(1,NZ); ...; Z(NSMP,1),...,Z(NSMP,NZ) ).
C
C LDZ INTEGER
C The leading dimension of the array Z. LDZ >= MAX(1,NSMP).
C
C Y (output) DOUBLE PRECISION array, dimension (LDY, L)
C The leading NSMP-by-L part of this array contains the set
C of output samples,
C Y = ( Y(1,1),...,Y(1,L); ...; Y(NSMP,1),...,Y(NSMP,L) ).
C
C LDY INTEGER
C The leading dimension of the array Y. LDY >= MAX(1,NSMP).
C
C Workspace
C
C DWORK DOUBLE PRECISION array, dimension (LDWORK)
C
C LDWORK INTEGER
C The length of the array DWORK. LDWORK >= 2*NN.
C For better performance, LDWORK should be larger.
C
C Error Indicator
C
C INFO INTEGER
C = 0: successful exit;
C < 0: if INFO = -i, the i-th argument had an illegal
C value.
C
C METHOD
C
C BLAS routines are used to compute the matrix-vector products.
C
C CONTRIBUTORS
C
C A. Riedel, R. Schneider, Chemnitz University of Technology,
C Oct. 2000, during a stay at University of Twente, NL.
C V. Sima, Research Institute for Informatics, Bucharest, Mar. 2001.
C
C REVISIONS
C
C -
C
C KEYWORDS
C
C Input output description, neural network, nonlinear system,
C simulation, system response.
C
C ******************************************************************
C
C .. Parameters ..
DOUBLE PRECISION ZERO, ONE, TWO
PARAMETER ( ZERO = 0.0D0, ONE = 1.0D0, TWO = 2.0D0 )
C .. Scalar Arguments ..
INTEGER INFO, L, LDWORK, LDY, LDZ, LIPAR, LWB, NSMP, NZ
C .. Array Arguments ..
DOUBLE PRECISION DWORK(*), WB(*), Y(LDY,*), Z(LDZ,*)
INTEGER IPAR(*)
C .. Local Scalars ..
LOGICAL LAST
INTEGER I, IB, J, K, LDWB, LJ, LK, M, MF, NN, NV, WS
DOUBLE PRECISION BIGNUM, DF, SMLNUM, TMP
C .. External Functions ..
DOUBLE PRECISION DDOT, DLAMCH
EXTERNAL DDOT, DLAMCH
C .. External Subroutines ..
EXTERNAL DCOPY, DGEMM, DGEMV, DLABAD, XERBLA
C .. Intrinsic Functions ..
INTRINSIC ABS, EXP, LOG, MAX, MIN, MOD
C ..
C .. Executable Statements ..
C
INFO = 0
NN = IPAR(1)
LDWB = NN*( NZ + 2 ) + 1
IF ( NSMP.LT.0 ) THEN
INFO = -1
ELSEIF ( NZ.LT.0 ) THEN
INFO = -2
ELSEIF ( L.LT.0 ) THEN
INFO = -3
ELSEIF ( NN.LT.0 ) THEN
INFO = -4
ELSEIF ( LIPAR.LT.1 ) THEN
INFO = -5
ELSEIF ( LWB.LT.LDWB*L ) THEN
INFO = -7
ELSEIF ( LDZ.LT.MAX( 1, NSMP ) ) THEN
INFO = -9
ELSEIF ( LDY.LT.MAX( 1, NSMP ) ) THEN
INFO = -11
ELSEIF ( LDWORK.LT.2*NN ) THEN
INFO = -13
ENDIF
C
C Return if there are illegal arguments.
C
IF( INFO.NE.0 ) THEN
CALL XERBLA( 'NF01AY', -INFO )
RETURN
ENDIF
C
C Quick return if possible.
C
IF ( MIN( NSMP, L ).EQ.0 )
$ RETURN
C
C Set parameters to avoid overflows and increase accuracy for
C extreme values.
C
SMLNUM = DLAMCH( 'Safe minimum' ) / DLAMCH( 'Precision' )
BIGNUM = ONE / SMLNUM
CALL DLABAD( SMLNUM, BIGNUM )
SMLNUM = LOG( SMLNUM )
BIGNUM = LOG( BIGNUM )
C
WS = NZ*NN + 1
IB = WS + NN - 1
LK = 0
IF ( MIN( NZ, NN ).EQ.0 ) THEN
NV = 2
ELSE
NV = ( LDWORK - NN )/NN
END IF
C
IF ( NV.GT.2 ) THEN
MF = ( NSMP/NV )*NV
LAST = MOD( NSMP, NV ).NE.0
C
C Some BLAS 3 calculations can be used.
C
DO 70 K = 0, L - 1
TMP = WB(IB+NN+1+LK)
C
DO 10 J = 1, NN
DWORK(J) = TWO*WB(IB+J+LK)
10 CONTINUE
C
DO 40 I = 1, MF, NV
C
C Compute -2*[w1 w2 ... wn]'*Z', where
C Z = [z(i)';...; z(i+NV-1)'].
C
CALL DGEMM( 'Transpose', 'Transpose', NN, NV, NZ, -TWO,
$ WB(1+LK), NZ, Z(I,1), LDZ, ZERO, DWORK(NN+1),
$ NN )
LJ = NN
C
DO 30 M = 1, NV
DO 20 J = 1, NN
C
C Compute tanh(wj'*z(i) + bj), j = 1:n.
C
LJ = LJ + 1
DF = DWORK(LJ) - DWORK(J)
IF ( ABS( DF ).GE.BIGNUM ) THEN
IF ( DF.GT.ZERO ) THEN
DWORK(LJ) = -ONE
ELSE
DWORK(LJ) = ONE
END IF
ELSE IF ( ABS( DF ).LE.SMLNUM ) THEN
DWORK(LJ) = ZERO
ELSE
DWORK(LJ) = TWO/( ONE + EXP( DF ) ) - ONE
END IF
20 CONTINUE
C
30 CONTINUE
C
Y(I, K+1) = TMP
CALL DCOPY( NV-1, Y(I, K+1), 0, Y(I+1, K+1), 1 )
CALL DGEMV( 'Transpose', NN, NV, ONE, DWORK(NN+1), NN,
$ WB(WS+LK), 1, ONE, Y(I, K+1), 1 )
40 CONTINUE
C
IF ( LAST ) THEN
C
C Process the last samples.
C
NV = NSMP - MF
I = MF + 1
C
C Compute -2*[w1 w2 ... wn]'*Z', where
C Z = [z(i)';...; z(NSMP)'].
C
CALL DGEMM( 'Transpose', 'Transpose', NN, NV, NZ, -TWO,
$ WB(1+LK), NZ, Z(I,1), LDZ, ZERO, DWORK(NN+1),
$ NN )
LJ = NN
C
DO 60 M = 1, NV
DO 50 J = 1, NN
C
C Compute tanh(wj'*z(i) + bj), j = 1:n.
C
LJ = LJ + 1
DF = DWORK(LJ) - DWORK(J)
IF ( ABS( DF ).GE.BIGNUM ) THEN
IF ( DF.GT.ZERO ) THEN
DWORK(LJ) = -ONE
ELSE
DWORK(LJ) = ONE
END IF
ELSE IF ( ABS( DF ).LE.SMLNUM ) THEN
DWORK(LJ) = ZERO
ELSE
DWORK(LJ) = TWO/( ONE + EXP( DF ) ) - ONE
END IF
50 CONTINUE
C
60 CONTINUE
C
Y(I, K+1) = TMP
IF ( NV.GT.1 )
$ CALL DCOPY( NV-1, Y(I, K+1), 0, Y(I+1, K+1), 1 )
CALL DGEMV( 'Transpose', NN, NV, ONE, DWORK(NN+1), NN,
$ WB(WS+LK), 1, ONE, Y(I, K+1), 1 )
END IF
C
LK = LK + LDWB
70 CONTINUE
C
ELSE
C
C BLAS 2 calculations only can be used.
C
DO 110 K = 0, L - 1
TMP = WB(IB+NN+1+LK)
C
DO 80 J = 1, NN
DWORK(J) = TWO*WB(IB+J+LK)
80 CONTINUE
C
DO 100 I = 1, NSMP
C
C Compute -2*[w1 w2 ... wn]'*z(i).
C
IF ( NZ.EQ.0 ) THEN
DWORK(NN+1) = ZERO
CALL DCOPY( NN, DWORK(NN+1), 0, DWORK(NN+1), 1 )
ELSE
CALL DGEMV( 'Transpose', NZ, NN, -TWO, WB(1+LK), NZ,
$ Z(I,1), LDZ, ZERO, DWORK(NN+1), 1 )
END IF
C
DO 90 J = NN + 1, 2*NN
C
C Compute tanh(wj'*z(i) + bj), j = 1:n.
C
DF = DWORK(J) - DWORK(J-NN)
IF ( ABS( DF ).GE.BIGNUM ) THEN
IF ( DF.GT.ZERO ) THEN
DWORK(J) = -ONE
ELSE
DWORK(J) = ONE
END IF
ELSE IF ( ABS( DF ).LE.SMLNUM ) THEN
DWORK(J) = ZERO
ELSE
DWORK(J) = TWO/( ONE + EXP( DF ) ) - ONE
END IF
90 CONTINUE
C
Y(I, K+1) = DDOT( NN, WB(WS+LK), 1, DWORK(NN+1), 1 ) +
$ TMP
100 CONTINUE
C
LK = LK + LDWB
110 CONTINUE
C
END IF
RETURN
C
C *** Last line of NF01AY ***
END
|
