Han*_*örr 19 java math optimization trigonometry
由于java.lang.Math中的三角函数非常慢:是否有一个快速和良好近似的库?似乎可以在不损失太多精度的情况下快速进行几次计算.(在我的机器上,乘法需要1.5ns,而java.lang.Math.sin需要46ns到116ns).遗憾的是,还没有办法使用硬件功能.
更新:功能应该足够准确,比如GPS计算.这意味着您需要至少7个十进制数字的精度,这排除了简单的查找表.它应该比基本x86系统上的java.lang.Math.sin快得多.否则就没有意义了.
对于pi/4以上的值,除硬件功能外,Java 还会进行一些昂贵的计算.这样做是有充分理由的,但有时你更关心速度而不是最后一位精度.
Dar*_*con 15
哈特的计算机近似.将Chebyshev经济的近似公式列表为不同精度的一系列函数.
编辑:将我的副本从架子上拿下来,结果证明这是一本听起来非常相似的书.这是使用其表格的sin函数.(在C中测试,因为这对我来说更方便.)我不知道这是否会比Java内置更快,但至少可以保证它不那么准确.:)您可能需要先缩小参数范围; 看约翰库克的建议.这本书还有arcsin和arctan.
#include <math.h>
#include <stdio.h>
// Return an approx to sin(pi/2 * x) where -1 <= x <= 1.
// In that range it has a max absolute error of 5e-9
// according to Hastings, Approximations For Digital Computers.
static double xsin (double x) {
double x2 = x * x;
return ((((.00015148419 * x2
- .00467376557) * x2
+ .07968967928) * x2
- .64596371106) * x2
+ 1.57079631847) * x;
}
int main () {
double pi = 4 * atan (1);
printf ("%.10f\n", xsin (0.77));
printf ("%.10f\n", sin (0.77 * (pi/2)));
return 0;
}
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fin*_*nnw 13
这是一组用于快速逼近trig函数的低级技巧.在C中有一些示例代码,我觉得很难遵循,但这些技术在Java中很容易实现.
这是我在Java中的invsqrt和atan2的等效实现.
我可以为其他trig函数做类似的事情,但我没有发现它是必要的,因为分析显示只有sqrt和atan/atan2是主要的瓶颈.
public class FastTrig
{
/** Fast approximation of 1.0 / sqrt(x).
* See <a href="http://www.beyond3d.com/content/articles/8/">http://www.beyond3d.com/content/articles/8/</a>
* @param x Positive value to estimate inverse of square root of
* @return Approximately 1.0 / sqrt(x)
**/
public static double
invSqrt(double x)
{
double xhalf = 0.5 * x;
long i = Double.doubleToRawLongBits(x);
i = 0x5FE6EB50C7B537AAL - (i>>1);
x = Double.longBitsToDouble(i);
x = x * (1.5 - xhalf*x*x);
return x;
}
/** Approximation of arctangent.
* Slightly faster and substantially less accurate than
* {@link Math#atan2(double, double)}.
**/
public static double fast_atan2(double y, double x)
{
double d2 = x*x + y*y;
// Bail out if d2 is NaN, zero or subnormal
if (Double.isNaN(d2) ||
(Double.doubleToRawLongBits(d2) < 0x10000000000000L))
{
return Double.NaN;
}
// Normalise such that 0.0 <= y <= x
boolean negY = y < 0.0;
if (negY) {y = -y;}
boolean negX = x < 0.0;
if (negX) {x = -x;}
boolean steep = y > x;
if (steep)
{
double t = x;
x = y;
y = t;
}
// Scale to unit circle (0.0 <= y <= x <= 1.0)
double rinv = invSqrt(d2); // rinv ? 1.0 / hypot(x, y)
x *= rinv; // x ? cos ?
y *= rinv; // y ? sin ?, hence ? ? asin y
// Hack: we want: ind = floor(y * 256)
// We deliberately force truncation by adding floating-point numbers whose
// exponents differ greatly. The FPU will right-shift y to match exponents,
// dropping all but the first 9 significant bits, which become the 9 LSBs
// of the resulting mantissa.
// Inspired by a similar piece of C code at
// http://www.shellandslate.com/computermath101.html
double yp = FRAC_BIAS + y;
int ind = (int) Double.doubleToRawLongBits(yp);
// Find ? (a first approximation of ?) from the LUT
double ? = ASIN_TAB[ind];
double c? = COS_TAB[ind]; // cos(?)
// sin(?) == ind / 256.0
// Note that s? is truncated, hence not identical to y.
double s? = yp - FRAC_BIAS;
double sd = y * c? - x * s?; // sin(?-?) ? sin? cos? - cos? sin?
// asin(sd) ? sd + ?sd³ (from first 2 terms of Maclaurin series)
double d = (6.0 + sd * sd) * sd * ONE_SIXTH;
double ? = ? + d;
// Translate back to correct octant
if (steep) { ? = Math.PI * 0.5 - ?; }
if (negX) { ? = Math.PI - ?; }
if (negY) { ? = -?; }
return ?;
}
private static final double ONE_SIXTH = 1.0 / 6.0;
private static final int FRAC_EXP = 8; // LUT precision == 2 ** -8 == 1/256
private static final int LUT_SIZE = (1 << FRAC_EXP) + 1;
private static final double FRAC_BIAS =
Double.longBitsToDouble((0x433L - FRAC_EXP) << 52);
private static final double[] ASIN_TAB = new double[LUT_SIZE];
private static final double[] COS_TAB = new double[LUT_SIZE];
static
{
/* Populate trig tables */
for (int ind = 0; ind < LUT_SIZE; ++ ind)
{
double v = ind / (double) (1 << FRAC_EXP);
double asinv = Math.asin(v);
COS_TAB[ind] = Math.cos(asinv);
ASIN_TAB[ind] = asinv;
}
}
}
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