lbfgs.java

用java实现的关联规则算法Apriori算法

                {
                    if ( iter > m ) bound = m;
                    ys = ddot ( n , w , iypt + npt , 1 , w , ispt + npt , 1 );
                    if ( ! diagco )
                    {
                        yy = ddot ( n , w , iypt + npt , 1 , w , iypt + npt , 1 );

                        for ( i = 1 ; i <= n ; i += 1 )
                        {
                            diag [ i -1] = ys / yy;
                        }
                    }
                    else
                    {
                        iflag[0]=2;
                        return;
                    }
                }
            }

            if ( execute_entire_while_loop || iflag[0] == 2 )
            {
                if ( iter != 1 )
                {
                    if ( diagco )
                    {
                        for ( i = 1 ; i <= n ; i += 1 )
                        {
                            if ( diag [ i -1] <= 0 )
                            {
                                iflag[0]=-2;
                                throw new ExceptionWithIflag( iflag[0], "The "+i+"-th diagonal element of the inverse hessian approximation is not positive." );
                            }
                        }
                    }
                    cp= point;
                    if ( point == 0 ) cp = m;
                    w [ n + cp -1] = 1 / ys;

                    for ( i = 1 ; i <= n ; i += 1 )
                    {
                        w [ i -1] = - g [ i -1];
                    }

                    cp= point;

                    for ( i = 1 ; i <= bound ; i += 1 )
                    {
                        cp=cp-1;
                        if ( cp == - 1 ) cp = m - 1;
                        sq = ddot ( n , w , ispt + cp * n , 1 , w , 0 , 1 );
                        inmc=n+m+cp+1;
                        iycn=iypt+cp*n;
                        w [ inmc -1] = w [ n + cp + 1 -1] * sq;
                        daxpy ( n , - w [ inmc -1] , w , iycn , 1 , w , 0 , 1 );
                    }

                    for ( i = 1 ; i <= n ; i += 1 )
                    {
                        w [ i -1] = diag [ i -1] * w [ i -1];
                    }

                    for ( i = 1 ; i <= bound ; i += 1 )
                    {
                        yr = ddot ( n , w , iypt + cp * n , 1 , w , 0 , 1 );
                        beta = w [ n + cp + 1 -1] * yr;
                        inmc=n+m+cp+1;
                        beta = w [ inmc -1] - beta;
                        iscn=ispt+cp*n;
                        daxpy ( n , beta , w , iscn , 1 , w , 0 , 1 );
                        cp=cp+1;
                        if ( cp == m ) cp = 0;
                    }

                    for ( i = 1 ; i <= n ; i += 1 )
                    {
                        w [ ispt + point * n + i -1] = w [ i -1];
                    }
                }

                nfev[0]=0;
                stp[0]=1;
                if ( iter == 1 ) stp[0] = stp1;

                for ( i = 1 ; i <= n ; i += 1 )
                {
                    w [ i -1] = g [ i -1];
                }
            }

            Mcsrch.mcsrch ( n , x , f , g , w , ispt + point * n , stp , ftol , xtol , maxfev , info , nfev , diag );

            if ( info[0] == - 1 )
            {
                iflag[0]=1;
                return;
            }

            if ( info[0] != 1 )
            {
                iflag[0]=-1;
                throw new ExceptionWithIflag( iflag[0], "Line search failed. See documentation of routine mcsrch. Error return of line search: info = "+info[0]+" Possible causes: function or gradient are incorrect, or incorrect tolerances." );
            }

            nfun= nfun + nfev[0];
            npt=point*n;

            for ( i = 1 ; i <= n ; i += 1 )
            {
                w [ ispt + npt + i -1] = stp[0] * w [ ispt + npt + i -1];
                w [ iypt + npt + i -1] = g [ i -1] - w [ i -1];
            }

            point=point+1;
            if ( point == m ) point = 0;

            gnorm = Math.sqrt ( ddot ( n , g , 0 , 1 , g , 0 , 1 ) );
            xnorm = Math.sqrt ( ddot ( n , x , 0 , 1 , x , 0 , 1 ) );
            xnorm = Math.max ( 1.0 , xnorm );

            if ( gnorm / xnorm <= eps ) finish = true;

            if ( iprint [ 1 -1] >= 0 ) lb1 ( iprint , iter , nfun , gnorm , n , m , x , f , g , stp , finish );

            // Cache the current solution vector. Due to the spaghetti-like
            // nature of this code, it's not possible to quit here and return;
            // we need to go back to the top of the loop, and eventually call
            // mcsrch one more time -- but that will modify the solution vector.
            // So we need to keep a copy of the solution vector as it was at
            // the completion (info[0]==1) of the most recent line search.

            System.arraycopy( x, 0, solution_cache, 0, n );

            if ( finish )
            {
                iflag[0]=0;
                    return;
            }

            execute_entire_while_loop = true;		// from now on, execute whole loop
        }
    }

    /** Print debugging and status messages for <code>lbfgs</code>.
      * Depending on the parameter <code>iprint</code>, this can include
      * number of function evaluations, current function value, etc.
      * The messages are output to <code>System.err</code>.
      *
      * @param iprint Specifies output generated by <code>lbfgs</code>.<p>
      *		<code>iprint[0]</code> specifies the frequency of the output:
      *		<ul>
      *		<li> <code>iprint[0] &lt; 0</code>: no output is generated,
      *		<li> <code>iprint[0] = 0</code>: output only at first and last iteration,
      *		<li> <code>iprint[0] &gt; 0</code>: output every <code>iprint[0]</code> iterations.
      *		</ul><p>
      *
      *		<code>iprint[1]</code> specifies the type of output generated:
      *		<ul>
      *		<li> <code>iprint[1] = 0</code>: iteration count, number of function
      *			evaluations, function value, norm of the gradient, and steplength,
      *		<li> <code>iprint[1] = 1</code>: same as <code>iprint[1]=0</code>, plus vector of
      *			variables and  gradient vector at the initial point,
      *		<li> <code>iprint[1] = 2</code>: same as <code>iprint[1]=1</code>, plus vector of
      *			variables,
      *		<li> <code>iprint[1] = 3</code>: same as <code>iprint[1]=2</code>, plus gradient vector.
      *		</ul>
      * @param iter Number of iterations so far.
      * @param nfun Number of function evaluations so far.
      * @param gnorm Norm of gradient at current solution <code>x</code>.
      * @param n Number of free parameters.
      * @param m Number of corrections kept.
      * @param x Current solution.
      * @param f Function value at current solution.
      * @param g Gradient at current solution <code>x</code>.
      * @param stp Current stepsize.
      * @param finish Whether this method should print the ``we're done'' message.
      */
    public static void lb1 ( int[] iprint , int iter , int nfun , double gnorm , int n , int m , double[] x , double f , double[] g , double[] stp , boolean finish )
    {
        int i;

        if ( iter == 0 )
        {
            System.err.println( "*************************************************" );
            System.err.println( "  n = " + n + "   number of corrections = " + m + "\n       initial values" );
            System.err.println( " f =  " + f + "   gnorm =  " + gnorm );
            if ( iprint [ 2 -1] >= 1 )
            {
                System.err.print( " vector x =" );
                for ( i = 1; i <= n; i++ )
                    System.err.print( "  "+x[i-1] );
                System.err.println( "" );

                System.err.print( " gradient vector g =" );
                for ( i = 1; i <= n; i++ )
                    System.err.print( "  "+g[i-1] );
                System.err.println( "" );
            }
            System.err.println( "*************************************************" );
            System.err.println( "\ti\tnfn\tfunc\tgnorm\tsteplength" );
        }
        else
        {
            if ( ( iprint [ 1 -1] == 0 ) && ( iter != 1 && ! finish ) ) return;
            if ( iprint [ 1 -1] != 0 )
            {
                if ( (iter - 1) % iprint [ 1 -1] == 0 || finish )
                {
                    if ( iprint [ 2 -1] > 1 && iter > 1 )
                        System.err.println( "\ti\tnfn\tfunc\tgnorm\tsteplength" );
                    System.err.println( "\t"+iter+"\t"+nfun+"\t"+f+"\t"+gnorm+"\t"+stp[0] );
                }
                else
                {
                    return;
                }
            }
            else
            {
                if ( iprint [ 2 -1] > 1 && finish )
                        System.err.println( "\ti\tnfn\tfunc\tgnorm\tsteplength" );
                System.err.println( "\t"+iter+"\t"+nfun+"\t"+f+"\t"+gnorm+"\t"+stp[0] );
            }
            if ( iprint [ 2 -1] == 2 || iprint [ 2 -1] == 3 )
            {
                if ( finish )
                {
                    System.err.print( " final point x =" );
                }
                else
                {
                    System.err.print( " vector x =  " );
                }
                for ( i = 1; i <= n; i++ )
                    System.err.print( "  "+x[i-1] );
                System.err.println( "" );
                if ( iprint [ 2 -1] == 3 )
                {
                    System.err.print( " gradient vector g =" );
                    for ( i = 1; i <= n; i++ )
                        System.err.print( "  "+g[i-1] );
                    System.err.println( "" );
                }
            }
            if ( finish )
                System.err.println( " The minimization terminated without detecting errors. iflag = 0" );
        }
        return;
    }

    /** Compute the sum of a vector times a scalara plus another vector.
      * Adapted from the subroutine <code>daxpy</code> in <code>lbfgs.f</code>.
      * There could well be faster ways to carry out this operation; this
      * code is a straight translation from the Fortran.
      */
    public static void daxpy ( int n , double da , double[] dx , int ix0, int incx , double[] dy , int iy0, int incy )
    {
        int i, ix, iy, m, mp1;

        if ( n <= 0 ) return;

        if ( da == 0 ) return;

        if  ( ! ( incx == 1 && incy == 1 ) )
        {
            ix = 1;
            iy = 1;

            if ( incx < 0 ) ix = ( - n + 1 ) * incx + 1;
            if ( incy < 0 ) iy = ( - n + 1 ) * incy + 1;

            for ( i = 1 ; i <= n ; i += 1 )
            {
                dy [ iy0+iy -1] = dy [ iy0+iy -1] + da * dx [ ix0+ix -1];
                ix = ix + incx;
                iy = iy + incy;
            }

            return;
        }

        m = n % 4;
        if ( m != 0 )
        {
            for ( i = 1 ; i <= m ; i += 1 )
            {
                dy [ iy0+i -1] = dy [ iy0+i -1] + da * dx [ ix0+i -1];
            }

            if ( n < 4 ) return;
        }

        mp1 = m + 1;
        for ( i = mp1 ; i <= n ; i += 4 )
        {
            dy [ iy0+i -1] = dy [ iy0+i -1] + da * dx [ ix0+i -1];
            dy [ iy0+i + 1 -1] = dy [ iy0+i + 1 -1] + da * dx [ ix0+i + 1 -1];
            dy [ iy0+i + 2 -1] = dy [ iy0+i + 2 -1] + da * dx [ ix0+i + 2 -1];
            dy [ iy0+i + 3 -1] = dy [ iy0+i + 3 -1] + da * dx [ ix0+i + 3 -1];
        }
        return;
    }

    /** Compute the dot product of two vectors.
      * Adapted from the subroutine <code>ddot</code> in <code>lbfgs.f</code>.
      * There could well be faster ways to carry out this operation; this
      * code is a straight translation from the Fortran.
      */
    public static double ddot ( int n, double[] dx, int ix0, int incx, double[] dy, int iy0, int incy )
    {
        double dtemp;
        int i, ix, iy, m, mp1;

        dtemp = 0;

        if ( n <= 0 ) return 0;

        if ( !( incx == 1 && incy == 1 ) )
        {
            ix = 1;
            iy = 1;
            if ( incx < 0 ) ix = ( - n + 1 ) * incx + 1;
            if ( incy < 0 ) iy = ( - n + 1 ) * incy + 1;
            for ( i = 1 ; i <= n ; i += 1 )
            {
                dtemp = dtemp + dx [ ix0+ix -1] * dy [ iy0+iy -1];
                ix = ix + incx;
                iy = iy + incy;
            }
            return dtemp;
        }

        m = n % 5;
        if ( m != 0 )
        {
            for ( i = 1 ; i <= m ; i += 1 )
            {
                dtemp = dtemp + dx [ ix0+i -1] * dy [ iy0+i -1];
            }
            if ( n < 5 ) return dtemp;
        }

        mp1 = m + 1;
        for ( i = mp1 ; i <= n ; i += 5 )
        {
            dtemp = dtemp + dx [ ix0+i -1] * dy [ iy0+i -1] + dx [ ix0+i + 1 -1] * dy [ iy0+i + 1 -1] + dx [ ix0+i + 2 -1] * dy [ iy0+i + 2 -1] + dx [ ix0+i + 3 -1] * dy [ iy0+i + 3 -1] + dx [ ix0+i + 4 -1] * dy [ iy0+i + 4 -1];
        }

        return dtemp;
    }
}