--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/pyPRADA_1.2/tools/samtools-0.1.16/bcftools/kmin.c	Thu Feb 20 00:44:58 2014 -0500
@@ -0,0 +1,209 @@
+/* The MIT License
+
+   Copyright (c) 2008, 2010 by Attractive Chaos <attractor@live.co.uk>
+
+   Permission is hereby granted, free of charge, to any person obtaining
+   a copy of this software and associated documentation files (the
+   "Software"), to deal in the Software without restriction, including
+   without limitation the rights to use, copy, modify, merge, publish,
+   distribute, sublicense, and/or sell copies of the Software, and to
+   permit persons to whom the Software is furnished to do so, subject to
+   the following conditions:
+
+   The above copyright notice and this permission notice shall be
+   included in all copies or substantial portions of the Software.
+
+   THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+   EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+   MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+   NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS
+   BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN
+   ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+   CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+   SOFTWARE.
+*/
+
+/* Hooke-Jeeves algorithm for nonlinear minimization
+ 
+   Based on the pseudocodes by Bell and Pike (CACM 9(9):684-685), and
+   the revision by Tomlin and Smith (CACM 12(11):637-638). Both of the
+   papers are comments on Kaupe's Algorithm 178 "Direct Search" (ACM
+   6(6):313-314). The original algorithm was designed by Hooke and
+   Jeeves (ACM 8:212-229). This program is further revised according to
+   Johnson's implementation at Netlib (opt/hooke.c).
+ 
+   Hooke-Jeeves algorithm is very simple and it works quite well on a
+   few examples. However, it might fail to converge due to its heuristic
+   nature. A possible improvement, as is suggested by Johnson, may be to
+   choose a small r at the beginning to quickly approach to the minimum
+   and a large r at later step to hit the minimum.
+ */
+
+#include <stdlib.h>
+#include <string.h>
+#include <math.h>
+#include "kmin.h"
+
+static double __kmin_hj_aux(kmin_f func, int n, double *x1, void *data, double fx1, double *dx, int *n_calls)
+{
+	int k, j = *n_calls;
+	double ftmp;
+	for (k = 0; k != n; ++k) {
+		x1[k] += dx[k];
+		ftmp = func(n, x1, data); ++j;
+		if (ftmp < fx1) fx1 = ftmp;
+		else { /* search the opposite direction */
+			dx[k] = 0.0 - dx[k];
+			x1[k] += dx[k] + dx[k];
+			ftmp = func(n, x1, data); ++j;
+			if (ftmp < fx1) fx1 = ftmp;
+			else x1[k] -= dx[k]; /* back to the original x[k] */
+		}
+	}
+	*n_calls = j;
+	return fx1; /* here: fx1=f(n,x1) */
+}
+
+double kmin_hj(kmin_f func, int n, double *x, void *data, double r, double eps, int max_calls)
+{
+	double fx, fx1, *x1, *dx, radius;
+	int k, n_calls = 0;
+	x1 = (double*)calloc(n, sizeof(double));
+	dx = (double*)calloc(n, sizeof(double));
+	for (k = 0; k != n; ++k) { /* initial directions, based on MGJ */
+		dx[k] = fabs(x[k]) * r;
+		if (dx[k] == 0) dx[k] = r;
+	}
+	radius = r;
+	fx1 = fx = func(n, x, data); ++n_calls;
+	for (;;) {
+		memcpy(x1, x, n * sizeof(double)); /* x1 = x */
+		fx1 = __kmin_hj_aux(func, n, x1, data, fx, dx, &n_calls);
+		while (fx1 < fx) {
+			for (k = 0; k != n; ++k) {
+				double t = x[k];
+				dx[k] = x1[k] > x[k]? fabs(dx[k]) : 0.0 - fabs(dx[k]);
+				x[k] = x1[k];
+				x1[k] = x1[k] + x1[k] - t;
+			}
+			fx = fx1;
+			if (n_calls >= max_calls) break;
+			fx1 = func(n, x1, data); ++n_calls;
+			fx1 = __kmin_hj_aux(func, n, x1, data, fx1, dx, &n_calls);
+			if (fx1 >= fx) break;
+			for (k = 0; k != n; ++k)
+				if (fabs(x1[k] - x[k]) > .5 * fabs(dx[k])) break;
+			if (k == n) break;
+		}
+		if (radius >= eps) {
+			if (n_calls >= max_calls) break;
+			radius *= r;
+			for (k = 0; k != n; ++k) dx[k] *= r;
+		} else break; /* converge */
+	}
+	free(x1); free(dx);
+	return fx1;
+}
+
+// I copied this function somewhere several years ago with some of my modifications, but I forgot the source.
+double kmin_brent(kmin1_f func, double a, double b, void *data, double tol, double *xmin)
+{
+	double bound, u, r, q, fu, tmp, fa, fb, fc, c;
+	const double gold1 = 1.6180339887;
+	const double gold2 = 0.3819660113;
+	const double tiny = 1e-20;
+	const int max_iter = 100;
+
+	double e, d, w, v, mid, tol1, tol2, p, eold, fv, fw;
+	int iter;
+
+	fa = func(a, data); fb = func(b, data);
+	if (fb > fa) { // swap, such that f(a) > f(b)
+		tmp = a; a = b; b = tmp;
+		tmp = fa; fa = fb; fb = tmp;
+	}
+	c = b + gold1 * (b - a), fc = func(c, data); // golden section extrapolation
+	while (fb > fc) {
+		bound = b + 100.0 * (c - b); // the farthest point where we want to go
+		r = (b - a) * (fb - fc);
+		q = (b - c) * (fb - fa);
+		if (fabs(q - r) < tiny) { // avoid 0 denominator
+			tmp = q > r? tiny : 0.0 - tiny;
+		} else tmp = q - r;
+		u = b - ((b - c) * q - (b - a) * r) / (2.0 * tmp); // u is the parabolic extrapolation point
+		if ((b > u && u > c) || (b < u && u < c)) { // u lies between b and c
+			fu = func(u, data);
+			if (fu < fc) { // (b,u,c) bracket the minimum
+				a = b; b = u; fa = fb; fb = fu;
+				break;
+			} else if (fu > fb) { // (a,b,u) bracket the minimum
+				c = u; fc = fu;
+				break;
+			}
+			u = c + gold1 * (c - b); fu = func(u, data); // golden section extrapolation
+		} else if ((c > u && u > bound) || (c < u && u < bound)) { // u lies between c and bound
+			fu = func(u, data);
+			if (fu < fc) { // fb > fc > fu
+				b = c; c = u; u = c + gold1 * (c - b);
+				fb = fc; fc = fu; fu = func(u, data);
+			} else { // (b,c,u) bracket the minimum
+				a = b; b = c; c = u;
+				fa = fb; fb = fc; fc = fu;
+				break;
+			}
+		} else if ((u > bound && bound > c) || (u < bound && bound < c)) { // u goes beyond the bound
+			u = bound; fu = func(u, data);
+		} else { // u goes the other way around, use golden section extrapolation
+			u = c + gold1 * (c - b); fu = func(u, data);
+		}
+		a = b; b = c; c = u;
+		fa = fb; fb = fc; fc = fu;
+	}
+	if (a > c) u = a, a = c, c = u; // swap
+
+	// now, a<b<c, fa>fb and fb<fc, move on to Brent's algorithm
+	e = d = 0.0;
+	w = v = b; fv = fw = fb;
+	for (iter = 0; iter != max_iter; ++iter) {
+		mid = 0.5 * (a + c);
+		tol2 = 2.0 * (tol1 = tol * fabs(b) + tiny);
+		if (fabs(b - mid) <= (tol2 - 0.5 * (c - a))) {
+			*xmin = b; return fb; // found
+		}
+		if (fabs(e) > tol1) {
+			// related to parabolic interpolation
+			r = (b - w) * (fb - fv);
+			q = (b - v) * (fb - fw);
+			p = (b - v) * q - (b - w) * r;
+			q = 2.0 * (q - r);
+			if (q > 0.0) p = 0.0 - p;
+			else q = 0.0 - q;
+			eold = e; e = d;
+			if (fabs(p) >= fabs(0.5 * q * eold) || p <= q * (a - b) || p >= q * (c - b)) {
+				d = gold2 * (e = (b >= mid ? a - b : c - b));
+			} else {
+				d = p / q; u = b + d; // actual parabolic interpolation happens here
+				if (u - a < tol2 || c - u < tol2)
+					d = (mid > b)? tol1 : 0.0 - tol1;
+			}
+		} else d = gold2 * (e = (b >= mid ? a - b : c - b)); // golden section interpolation
+		u = fabs(d) >= tol1 ? b + d : b + (d > 0.0? tol1 : -tol1);
+		fu = func(u, data);
+		if (fu <= fb) { // u is the minimum point so far
+			if (u >= b) a = b;
+			else c = b;
+			v = w; w = b; b = u; fv = fw; fw = fb; fb = fu;
+		} else { // adjust (a,c) and (u,v,w)
+			if (u < b) a = u;
+			else c = u;
+			if (fu <= fw || w == b) {
+				v = w; w = u;
+				fv = fw; fw = fu;
+			} else if (fu <= fv || v == b || v == w) {
+				v = u; fv = fu;
+			}
+		}
+	}
+	*xmin = b;
+	return fb;
+}