PerceptronFHMM-master/edu.cmu.lti.weizh.cda/src/main/java/edu/cmu/lti/weizh/utils/Mcsrch.java
package edu.cmu.lti.weizh.utils;

/* Mcsrch.java
 * Copyright (C) 1999, Robert Dodier (robert_dodier@yahoo.com)
 *
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of version 2.1 of the GNU Lesser General Public License
 * as published by the Free Software Foundation.
 *
 * 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 Lesser General Public License for more details.
 *
 * You should have received a copy of the GNU Lesser General Public License
 * along with this program; if not, write to the Free Software
 * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA, 02111-1307, USA,
 * or visit the GNU web site, www.gnu.org.
 */

/**
 * This class implements an algorithm for multi-dimensional line search. This
 * file is a translation of Fortran code written by Jorge Nocedal. See comments
 * in the file <tt>LBFGS.java</tt> for more information.
 */
public class Mcsrch {
	private static int infoc[] = new int[1], j = 0;
	private static double dg = 0, dgm = 0, dginit = 0, dgtest = 0,
			dgx[] = new double[1], dgxm[] = new double[1],
			dgy[] = new double[1], dgym[] = new double[1], finit = 0,
			ftest1 = 0, fm = 0, fx[] = new double[1], fxm[] = new double[1],
			fy[] = new double[1], fym[] = new double[1];
	static double stx[] = new double[1];
	static double sty[] = new double[1];
	static double stmin = 0, stmax = 0, width = 0, width1 = 0;
	static boolean brackt[] = new boolean[1];
	static boolean stage1 = false;
	private static final double ONE_HALF = 0.5, TWO_THIRDS = 0.66, FOUR = 4;

	static double sqr(double x) {
		return x * x;
	}

	static double max3(double x, double y, double z) {
		return x < y ? (y < z ? z : y) : (x < z ? z : x);
	}

	/**
	 * Minimize a function along a search direction. This code is a Java
	 * translation of the function <code>MCSRCH</code> from <code>lbfgs.f</code>
	 * , which in turn is a slight modification of the subroutine
	 * <code>CSRCH</code> of More' and Thuente. The changes are to allow reverse
	 * communication, and do not affect the performance of the routine. This
	 * function, in turn, calls <code>mcstep</code>.
	 * <p>
	 * 
	 * The Java translation was effected mostly mechanically, with some manual
	 * clean-up; in particular, array indices start at 0 instead of 1. Most of
	 * the comments from the Fortran code have been pasted in here as well.
	 * <p>
	 * 
	 * The purpose of <code>mcsrch</code> is to find a step which satisfies a
	 * sufficient decrease condition and a curvature condition.
	 * <p>
	 * 
	 * At each stage this function updates an interval of uncertainty with
	 * endpoints <code>stx</code> and <code>sty</code>. The interval of
	 * uncertainty is initially chosen so that it contains a minimizer of the
	 * modified function
	 * 
	 * <pre>
	 *      f(x+stp*s) - f(x) - ftol*stp*(gradf(x)'s).
	 * </pre>
	 * 
	 * 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
	 * <code>f(x+stp*s)</code>.
	 * <p>
	 * 
	 * The algorithm is designed to find a step which satisfies the sufficient
	 * decrease condition
	 * 
	 * <pre>
	 *       f(x+stp*s) &lt;= f(X) + ftol*stp*(gradf(x)'s),
	 * </pre>
	 * 
	 * and the curvature condition
	 * 
	 * <pre>
	 *       abs(gradf(x+stp*s)'s)) &lt;= gtol*abs(gradf(x)'s).
	 * </pre>
	 * 
	 * If <code>ftol</code> is less than <code>gtol</code> 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 <code>stp</code> only satisfies the
	 * sufficient decrease condition.
	 * <p>
	 * 
	 * @author Original Fortran version by Jorge J. More' and David J. Thuente
	 *         as part of the Minpack project, June 1983, Argonne National
	 *         Laboratory. Java translation by Robert Dodier, August 1997.
	 * 
	 * @param n
	 *            The number of variables.
	 * 
	 * @param x
	 *            On entry this contains the base point for the line search. On
	 *            exit it contains <code>x + stp*s</code>.
	 * 
	 * @param f
	 *            On entry this contains the value of the objective function at
	 *            <code>x</code>. On exit it contains the value of the objective
	 *            function at <code>x + stp*s</code>.
	 * 
	 * @param g
	 *            On entry this contains the gradient of the objective function
	 *            at <code>x</code>. On exit it contains the gradient at
	 *            <code>x + stp*s</code>.
	 * 
	 * @param s
	 *            The search direction.
	 * 
	 * @param stp
	 *            On entry this contains an initial estimate of a satifactory
	 *            step length. On exit <code>stp</code> contains the final
	 *            estimate.
	 * 
	 * @param ftol
	 *            Tolerance for the sufficient decrease condition.
	 * 
	 * @param xtol
	 *            Termination occurs when the relative width of the interval of
	 *            uncertainty is at most <code>xtol</code>.
	 * 
	 * @param maxfev
	 *            Termination occurs when the number of evaluations of the
	 *            objective function is at least <code>maxfev</code> by the end
	 *            of an iteration.
	 * 
	 * @param info
	 *            This is an output variable, which can have these values:
	 *            <ul>
	 *            <li><code>info = 0</code> Improper input parameters. <li>
	 *            <code>info = -1</code> A return is made to compute the
	 *            function and gradient. <li><code>info = 1</code> The
	 *            sufficient decrease condition and the directional derivative
	 *            condition hold. <li><code>info = 2</code> Relative width of
	 *            the interval of uncertainty is at most <code>xtol</code>. <li>
	 *            <code>info = 3</code> Number of function evaluations has
	 *            reached <code>maxfev</code>. <li><code>info = 4</code> The
	 *            step is at the lower bound <code>stpmin</code>. <li><code>info
	 *            = 5</code> The step is at the upper bound <code>stpmax</code>.
	 *            <li><code>info = 6</code> Rounding errors prevent further
	 *            progress. There may not be a step which satisfies the
	 *            sufficient decrease and curvature conditions. Tolerances may
	 *            be too small.
	 *            </ul>
	 * 
	 * @param nfev
	 *            On exit, this is set to the number of function evaluations.
	 * 
	 * @param wa
	 *            Temporary storage array, of length <code>n</code>.
	 */

	public static void mcsrch(final int n, double[] x, double f, double[] g,
			double[] s, int is0, double[] stp, double ftol, double xtol,
			int maxfev, int[] info, int[] nfev, double[] wa, int[] iprint) {
//		System.err.println("mc " + f + " " + stp[0]);

		if (info[0] != -1) {
			infoc[0] = 1;
			if (n <= 0 || stp[0] <= 0 || ftol < 0 || LBFGS.gtol < 0 || xtol < 0
					|| LBFGS.stpmin < 0 || LBFGS.stpmax < LBFGS.stpmin
					|| maxfev <= 0)
				return;

			// Compute the initial gradient in the search direction
			// and check that s is a descent direction.
			dginit = 0;
			for (j = 1; j <= n; j += 1) {
				dginit += g[j - 1] * s[is0 + j - 1];
				if (Double.isNaN(dginit))
					System.err
							.println("NaN " + g[j - 1] + " " + s[is0 + j - 1]);
			}
			if (dginit >= 0) {
				System.out
						.println("The search direction is not a descent direction.");
				return;
			}

			brackt[0] = false;
			stage1 = true;
			nfev[0] = 0;
			finit = f;
			dgtest = ftol * dginit;
			width = LBFGS.stpmax - LBFGS.stpmin;
			width1 = width / ONE_HALF;

			for (j = 1; j <= n; j += 1) {
				wa[j - 1] = x[j - 1];
			}

			// 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.
			stx[0] = 0;
			fx[0] = finit;
			dgx[0] = dginit;
			sty[0] = 0;
			fy[0] = finit;
			dgy[0] = dginit;

			if (iprint[0] > 0)
				System.err.println("new line search f=" + finit + " dg="
						+ dginit + " stp=" + stp[0]);
		}

		while (true) {
			if (info[0] != -1) {
				// Set the minimum and maximum steps to correspond
				// to the present interval of uncertainty.
				if (brackt[0]) {
					stmin = Math.min(stx[0], sty[0]);
					stmax = Math.max(stx[0], sty[0]);
				} else {
					stmin = stx[0];
					stmax = stp[0] + FOUR * (stp[0] - stx[0]);
				}

				// Force the step to be within the bounds stpmax and stpmin.
				setSTP(stp, Math.max(stp[0], LBFGS.stpmin));
				setSTP(stp, Math.min(stp[0], LBFGS.stpmax));

				// If an unusual termination is to occur then let
				// stp be the lowest point obtained so far.
				if ((brackt[0] && (stp[0] <= stmin || stp[0] >= stmax || stmax
						- stmin <= xtol * stmax))
						|| nfev[0] >= maxfev - 1 || infoc[0] == 0)
					setSTP(stp, stx[0]);

				// Evaluate the function and gradient at stp
				// and compute the directional derivative.
				// We return to main program to obtain F and G.
				for (j = 1; j <= n; j += 1) {
					x[j - 1] = wa[j - 1] + stp[0] * s[is0 + j - 1];
					if (Math.abs(x[j - 1]) > 40) {
						System.err.println("big wt " + j + " " + stp[0] + " "
								+ s[is0 + j - 1]);
					}
				}

				info[0] = -1;
				return;
			}

			info[0] = 0;
			nfev[0] = nfev[0] + 1;
			dg = 0;

			for (j = 1; j <= n; j += 1) {
				dg = dg + g[j - 1] * s[is0 + j - 1];
			}

			ftest1 = finit + stp[0] * dgtest;

			// Test for convergence.
			if ((brackt[0] && (stp[0] <= stmin || stp[0] >= stmax))
					|| infoc[0] == 0)
				// Rounding errors prevent further
				// progress. There may not be a step which satisfies the
				// sufficient decrease and curvature conditions. Tolerances
				// may
				// be too small.
				info[0] = 6;

			if (stp[0] == LBFGS.stpmax && f <= ftest1 && dg <= dgtest)
				// The step is at the upper bound <code>stpmax</code>.
				info[0] = 5;

			if (stp[0] == LBFGS.stpmin && (f > ftest1 || dg >= dgtest))
				// The step is at the lower bound <code>stpmin</code>.
				info[0] = 4;

			if (nfev[0] >= maxfev)
				// Number of function evaluations has reached
				// <code>maxfev</code>
				info[0] = 3;

			if (brackt[0] && stmax - stmin <= xtol * stmax)
				// Relative width of the interval of uncertainty is at most
				// <code>xtol</code>
				info[0] = 2;

			if (f <= ftest1 && Math.abs(dg) <= LBFGS.gtol * (-dginit))
				// The sufficient decrease condition and the directional
				// derivative condition hold.
				info[0] = 1;

			// Check for termination.
			if (info[0] != 0) {
				info[0] = 1;
				return;
			}

			// In the first stage we seek a step for which the modified
			// function has a nonpositive value and nonnegative derivative.
			if (stage1 && f <= ftest1
					&& dg >= Math.min(ftol, LBFGS.gtol) * dginit)
				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 (stage1 && f <= fx[0] && f > ftest1) {
				// Define the modified function and derivative values.
				fm = f - stp[0] * dgtest;
				fxm[0] = fx[0] - stx[0] * dgtest;
				fym[0] = fy[0] - sty[0] * dgtest;
				dgm = dg - dgtest;
				dgxm[0] = dgx[0] - dgtest;
				dgym[0] = dgy[0] - dgtest;

				// Call cstep to update the interval of uncertainty
				// and to compute the new step.
				mcstep(stx, fxm, dgxm, sty, fym, dgym, stp, fm, dgm, brackt,
						stmin, stmax, infoc, iprint);

				// Reset the function and gradient values for f.
				fx[0] = fxm[0] + stx[0] * dgtest;
				fy[0] = fym[0] + sty[0] * dgtest;
				dgx[0] = dgxm[0] + dgtest;
				dgy[0] = dgym[0] + dgtest;
			} else {
				// Call mcstep to update the interval of uncertainty
				// and to compute the new step.
				mcstep(stx, fx, dgx, sty, fy, dgy, stp, f, dg, brackt, stmin,
						stmax, infoc, iprint);
			}

			if (iprint[0] > 0)
				System.err.println(" msrch internal f=" + f + " dg=" + dg
						+ " stx=" + stx[0] + " sty=" + sty[0] + " brackt="
						+ brackt[0] + " stp=" + stp[0]);

			// Force a sufficient decrease in the size of the
			// interval of uncertainty.
			if (brackt[0]) {
				if (Math.abs(sty[0] - stx[0]) >= TWO_THIRDS * width1)
					setSTP(stp, (sty[0] + stx[0]) / 2.0);
				width1 = width;
				width = Math.abs(sty[0] - stx[0]);
			}
		}
	}

	/**
	 * The purpose of this function is to compute a safeguarded step for a
	 * linesearch and to update an interval of uncertainty for a minimizer of
	 * the function.
	 * <p>
	 * 
	 * The parameter <code>stx</code> contains the step with the least function
	 * value. The parameter <code>stp</code> contains the current step. It is
	 * assumed that the derivative at <code>stx</code> is negative in the
	 * direction of the step. If <code>brackt[0]</code> is <code>true</code>
	 * when <code>mcstep</code> returns then a minimizer has been bracketed in
	 * an interval of uncertainty with endpoints <code>stx</code> and
	 * <code>sty</code>.
	 * <p>
	 * 
	 * Variables that must be modified by <code>mcstep</code> are implemented as
	 * 1-element arrays.
	 * 
	 * @param stx1
	 *            Step at the best step obtained so far. This variable is
	 *            modified by <code>mcstep</code>.
	 * @param fx1
	 *            Function value at the best step obtained so far. This variable
	 *            is modified by <code>mcstep</code>.
	 * @param dx
	 *            Derivative at the best step obtained so far. The derivative
	 *            must be negative in the direction of the step, that is,
	 *            <code>dx</code> and <code>stp-stx</code> must have opposite
	 *            signs. This variable is modified by <code>mcstep</code>.
	 * 
	 * @param sty1
	 *            Step at the other endpoint of the interval of uncertainty.
	 *            This variable is modified by <code>mcstep</code>.
	 * @param fy1
	 *            Function value at the other endpoint of the interval of
	 *            uncertainty. This variable is modified by <code>mcstep</code>.
	 * @param dy
	 *            Derivative at the other endpoint of the interval of
	 *            uncertainty. This variable is modified by <code>mcstep</code>.
	 * 
	 * @param stp
	 *            Step at the current step. If <code>brackt</code> is set then
	 *            on input <code>stp</code> must be between <code>stx</code> and
	 *            <code>sty</code>. On output <code>stp</code> is set to the new
	 *            step.
	 * @param fp
	 *            Function value at the current step.
	 * @param dp
	 *            Derivative at the current step.
	 * 
	 * @param brackt1
	 *            Tells whether a minimizer has been bracketed. If the minimizer
	 *            has not been bracketed, then on input this variable must be
	 *            set <code>false</code>. If the minimizer has been bracketed,
	 *            then on output this variable is <code>true</code>.
	 * 
	 * @param stpmin
	 *            Lower bound for the step.
	 * @param stpmax
	 *            Upper bound for the step.
	 * 
	 * @param info
	 *            On return from <code>mcstep</code>, this is set as follows: If
	 *            <code>info</code> is 1, 2, 3, or 4, then the step has been
	 *            computed successfully. Otherwise <code>info</code> = 0, and
	 *            this indicates improper input parameters.
	 * 
	 * @author Jorge J. More, David J. Thuente: original Fortran version, as
	 *         part of Minpack project. Argonne Nat'l Laboratory, June 1983.
	 *         Robert Dodier: Java translation, August 1997.
	 */
	public static void mcstep(double[] stx1, double[] fx1, double[] dx,
			double[] sty1, double[] fy1, double[] dy, double[] stp, double fp,
			double dp, boolean[] brackt1, double stpmin, double stpmax,
			int[] info, int[] iprint) {
		boolean bound;
		double gamma, p, q, r, s, sgnd, stpc, stpf, stpq, theta;

		info[0] = 0;

		if ((brackt1[0] && (stp[0] <= Math.min(stx1[0], sty1[0]) || stp[0] >= Math
				.max(stx1[0], sty1[0])))
				|| dx[0] * (stp[0] - stx1[0]) >= 0.0 || stpmax < stpmin) {
			if (iprint[0] > 0)
				System.err.println("mcstep=0 " + brackt1[0] + " " + stp[0]
						+ " " + stx1[0] + " " + sty1[0] + " " + dx[0] + " "
						+ stpmax + " " + stpmin);
			return;
		}

		// Determine if the derivatives have opposite sign.

		sgnd = dp * (dx[0] / Math.abs(dx[0]));

		if (fp > fx1[0]) {
			// 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.

			info[0] = 1;
			bound = true;
			theta = 3 * (fx1[0] - fp) / (stp[0] - stx1[0]) + dx[0] + dp;
			s = max3(Math.abs(theta), Math.abs(dx[0]), Math.abs(dp));
			gamma = s * Math.sqrt(sqr(theta / s) - (dx[0] / s) * (dp / s));
			if (stp[0] < stx1[0])
				gamma = -gamma;
			p = (gamma - dx[0]) + theta;
			q = ((gamma - dx[0]) + gamma) + dp;
			r = p / q;
			stpc = stx1[0] + r * (stp[0] - stx1[0]);
			stpq = stx1[0]
					+ ((dx[0] / ((fx1[0] - fp) / (stp[0] - stx1[0]) + dx[0])) / 2)
					* (stp[0] - stx1[0]);
			if (Math.abs(stpc - stx1[0]) < Math.abs(stpq - stx1[0])) {
				stpf = stpc;
			} else {
				stpf = stpc + (stpq - stpc) / 2;
			}
			brackt1[0] = true;
		} else if (sgnd < 0.0) {
			// 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[0] = 2;
			bound = false;
			theta = 3 * (fx1[0] - fp) / (stp[0] - stx1[0]) + dx[0] + dp;
			s = max3(Math.abs(theta), Math.abs(dx[0]), Math.abs(dp));
			gamma = s * Math.sqrt(sqr(theta / s) - (dx[0] / s) * (dp / s));
			if (stp[0] > stx1[0])
				gamma = -gamma;
			p = (gamma - dp) + theta;
			q = ((gamma - dp) + gamma) + dx[0];
			r = p / q;
			stpc = stp[0] + r * (stx1[0] - stp[0]);
			stpq = stp[0] + (dp / (dp - dx[0])) * (stx1[0] - stp[0]);
			if (Math.abs(stpc - stp[0]) > Math.abs(stpq - stp[0])) {
				stpf = stpc;
			} else {
				stpf = stpq;
			}
			brackt1[0] = true;
		} else if (Math.abs(dp) < Math.abs(dx[0])) {
			// 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[0] = 3;
			bound = true;
			theta = 3 * (fx1[0] - fp) / (stp[0] - stx1[0]) + dx[0] + dp;
			s = max3(Math.abs(theta), Math.abs(dx[0]), Math.abs(dp));
			gamma = s
					* Math.sqrt(Math.max(0, sqr(theta / s) - (dx[0] / s)
							* (dp / s)));
			if (stp[0] > stx1[0])
				gamma = -gamma;
			p = (gamma - dp) + theta;
			q = (gamma + (dx[0] - dp)) + gamma;
			r = p / q;
			if (r < 0.0 && gamma != 0.0) {
				stpc = stp[0] + r * (stx1[0] - stp[0]);
			} else if (stp[0] > stx1[0]) {
				stpc = stpmax;
			} else {
				stpc = stpmin;
			}
			stpq = stp[0] + (dp / (dp - dx[0])) * (stx1[0] - stp[0]);
			if (brackt1[0]) {
				if (Math.abs(stp[0] - stpc) < Math.abs(stp[0] - stpq)) {
					stpf = stpc;
				} else {
					stpf = stpq;
				}
			} else {
				if (Math.abs(stp[0] - stpc) > Math.abs(stp[0] - 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[0] = 4;
			bound = false;
			if (brackt1[0]) {
				theta = 3 * (fp - fy1[0]) / (sty1[0] - stp[0]) + dy[0] + dp;
				s = max3(Math.abs(theta), Math.abs(dy[0]), Math.abs(dp));
				gamma = s * Math.sqrt(sqr(theta / s) - (dy[0] / s) * (dp / s));
				if (stp[0] > sty1[0])
					gamma = -gamma;
				p = (gamma - dp) + theta;
				q = ((gamma - dp) + gamma) + dy[0];
				r = p / q;
				stpc = stp[0] + r * (sty1[0] - stp[0]);
				stpf = stpc;
			} else if (stp[0] > stx1[0]) {
				stpf = stpmax;
			} else {
				stpf = stpmin;
			}
		}

		// Update the interval of uncertainty. This update does not
		// depend on the new step or the case analysis above.

		if (fp > fx1[0]) {
			sty1[0] = stp[0];
			fy1[0] = fp;
			dy[0] = dp;
		} else {
			if (sgnd < 0.0) {
				sty1[0] = stx1[0];
				fy1[0] = fx1[0];
				dy[0] = dx[0];
			}
			stx1[0] = stp[0];
			fx1[0] = fp;
			dx[0] = dp;
		}

		// Compute the new step and safeguard it.

		stpf = Math.min(stpmax, stpf);
		stpf = Math.max(stpmin, stpf);
		setSTP(stp, stpf);

		if (brackt1[0] && bound) {
			double possibleStep = stx1[0] + TWO_THIRDS * (sty1[0] - stx1[0]);
			if (sty1[0] > stx1[0]) {
				setSTP(stp, Math.min(possibleStep, stp[0]));
			} else {
				setSTP(stp, Math.max(possibleStep, stp[0]));
			}
		}
//		System.err.println("mcsetp stp => " + stp[0] + " info=" + info[0]);

		return;
	}

	static void setSTP(double[] stp, double value) {
		if (value < 0)// || value > 10)
			throw new IllegalArgumentException(value + "");
		if (value > 1000) {
			stp[0] = 1000;
			System.err.println("Reducing step from " + value + " to 1000");
		} else {
			stp[0] = value;
		}
		// curStp = stp[0];
	}
}