From 9ed18c7064226e1edc06f5a51fd569083971d448 Mon Sep 17 00:00:00 2001
From: Hans-Christoph Steiner <eighthave@users.sourceforge.net>
Date: Thu, 20 Oct 2011 04:37:06 +0000
Subject: ported chaos to the Library Template, now with libchaos support

svn path=/trunk/externals/bbogart/chaos/; revision=15625
---
 libchaos.c | 90 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 90 insertions(+)
 create mode 100644 libchaos.c

(limited to 'libchaos.c')

diff --git a/libchaos.c b/libchaos.c
new file mode 100644
index 0000000..f4f6638
--- /dev/null
+++ b/libchaos.c
@@ -0,0 +1,90 @@
+#include <stdio.h>
+#include <math.h>
+#include "chaos.h"
+
+#define LY_ITERATIONS 50000
+
+//#define LY_ABERATION 1e-6
+#define LY_ABERATION 10e-15
+
+/**
+ * this expects the fractal to already be stable (ie iterate it a few hundred/thousand times).
+ 
+ Steps as described by Julian Sprott
+ 1. Start with any initial condition in the basin of attraction
+ 2. Iterate until the orbit is on the attractor
+ 3. Select (almost any) nearby point (separated by d0)
+ 4. Advancce both orbits one iteration and calculate the new separation d1
+ 5. Evaluate log(d1/d0) in any convienient base
+ 6. Readjust one orbit so its separation is d0 in the same direction as d1
+ 7. Repeat steps 4-6 many times and calculate the average of step 5
+ */
+double lyapunov_eval(void *fractal, t_gotfn calc, int var_count, double *vars, double *test) {
+	int i, j;
+	double exponent = 0.0, sum = 0.0, d2, df, rs;
+	double diff[MAX_VARS];
+	
+	for(i = 0; i < LY_ITERATIONS; i++) {
+		// 4. iterate each set
+		calc(fractal, vars);
+		calc(fractal, test);
+		// 5. Evaluate
+		d2 = 0.0;
+		for(j = 0; j < var_count; j++) {
+			diff[j] = test[j] - vars[j];
+			//fprintf(stderr, "vars[%d] = %0.30f\n test[%d] = %0.30f\n  diff[%d] = %0.30f\n",
+			//	j, vars[j], j, test[j], j, diff[j]);
+			d2 += diff[j] * diff[j];
+		}
+		df = 1000000000000.0 * d2;
+		rs = 1.0 / sqrt(df);
+		sum += log(df);
+		exponent = 0.721347 * sum / i;
+		//fprintf(stderr, "%d\n  d2 = %0.30f\n  df = %0.30f\n  rs = %0.30f\n  sum = %0.30f\nexponent = %0.30f\n\n",
+		//	i, d2, df, rs, sum, exponent);
+		// 6. adjust the orbit under test
+		for(j = 0; j < var_count; j++) {
+			test[j] = vars[j] + (rs * (test[j] - vars[j]));
+		}
+	}
+	return exponent;
+}
+
+double lyapunov(void *fractal, t_gotfn calc, int var_count, double *vars) {
+	int i;
+	double test[MAX_VARS];
+	
+	// 1. + 2. 'vars' is expected to be ready to start computing
+	// 3. create a test set of variables
+	test[0] = vars[0] + LY_ABERATION;
+	for(i = 1; i < var_count; i++) { test[i] = vars[i]; }
+	
+	return lyapunov_eval(fractal, calc, var_count, vars, test);
+}
+
+double *lyapunov_full(void *fractal, t_gotfn calc, int var_count, double *vars, double *results) {
+	int i, j;
+	double initial[MAX_VARS];
+	double test[MAX_VARS];
+	
+	// save the starting values
+	for(i = 0; i < var_count; i++) {
+		initial[i] = vars[i];
+	}
+	for(i = 0; i < var_count; i++) {
+		// aberate each variable in turn
+		for(j = 0; j < var_count; j++) {
+			if (j == i) {
+				test[j] = vars[j] + LY_ABERATION;
+			} else {
+				test[j] = vars[j];
+			}
+		}
+		results[i] = lyapunov_eval(fractal, calc, var_count, vars, test);
+		// set the vars back the initial values
+		for(j = 0; j < var_count; j++) {
+			vars[j] = initial[j];
+		}
+	}
+	return results;
+}
-- 
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