JAVA实现FFT算法
JAVA实现FFT算法
关于快速傅里叶变换（FFT）和傅里叶变换的理论知识这里我就不提了，本文主要讲解FFT实现：
之前想找一个FFT代码，在网上找了很多都是有问题的，下面我完善了一个供大家学习交流；
首先粘贴FFT的代码如下：
/******************************************************************************
-
Compilation: javac FFT.java
-
Execution: java FFT n
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Dependencies: Complex.java
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Compute the FFT and inverse FFT of a length n complex sequence
-
using the radix 2 Cooley-Tukey algorithm.
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Bare bones implementation that runs in O(n log n) time. Our goal
-
is to optimize the clarity of the code, rather than performance.
-
Limitations
-
-
- assumes n is a power of 2
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- not the most memory efficient algorithm (because it uses
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an object type for representing complex numbers and because -
it re-allocates memory for the subarray, instead of doing -
in-place or reusing a single temporary array) -
For an in-place radix 2 Cooley-Tukey FFT, see
-
https://introcs.cs.princeton.edu/java/97data/InplaceFFT.java.html
******************************************************************************/
import edu.princeton.cs.algs4.StdDraw;
import edu.princeton.cs.algs4.StdOut;
public class FFT {
// compute the FFT of x[], assuming its length is a power of 2
public static Complex[] fft(Complex[] x) {
int n = x.length;
// base case
if (n == 1) return new Complex[] { x[0] };
// radix 2 Cooley-Tukey FFT
if (n % 2 != 0) {
throw new IllegalArgumentException("n is not a power of 2");
}
// fft of even terms
Complex[] even = new Complex[n/2];
for (int k = 0; k < n/2; k++) {
even[k] = x[2*k];
}
Complex[] q = fft(even);
// fft of odd terms
Complex[] odd = even; // reuse the array
for (int k = 0; k < n/2; k++) {
odd[k] = x[2*k + 1];
}
Complex[] r = fft(odd);
// combine
Complex[] y = new Complex[n];
for (int k = 0; k < n/2; k++) {
double kth = -2 * k * Math.PI / n;
Complex wk = new Complex(Math.cos(kth), Math.sin(kth));
y[k] = q[k].plus(wk.times(r[k]));
y[k + n/2] = q[k].minus(wk.times(r[k]));
}
return y;
}
// compute the inverse FFT of x[], assuming its length is a power of 2
public static Complex[] ifft(Complex[] x) {
int n = x.length;
Complex[] y = new Complex[n];
// take conjugate
for (int i = 0; i < n; i++) {
y[i] = x[i].conjugate();
}
// compute forward FFT
y = fft(y);
// take conjugate again
for (int i = 0; i < n; i++) {
y[i] = y[i].conjugate();
}
// divide by n
for (int i = 0; i < n; i++) {
y[i] = y[i].scale(1.0 / n);
}
return y;
}
// compute the circular convolution of x and y
public static Complex[] cconvolve(Complex[] x, Complex[] y) {
// should probably pad x and y with 0s so that they have same length
// and are powers of 2
if (x.length != y.length) {
throw new IllegalArgumentException("Dimensions don't agree");
}
int n = x.length;
// compute FFT of each sequence
Complex[] a = fft(x);
Complex[] b = fft(y);
// point-wise multiply
Complex[] c = new Complex[n];
for (int i = 0; i < n; i++) {
c[i] = a[i].times(b[i]);
}
// compute inverse FFT
return ifft(c);
}
// compute the linear convolution of x and y
public static Complex[] convolve(Complex[] x, Complex[] y) {
Complex ZERO = new Complex(0, 0);
Complex[] a = new Complex[2*x.length];
for (int i = 0; i < x.length; i++) a[i] = x[i];
for (int i = x.length; i < 2*x.length; i++) a[i] = ZERO;
Complex[] b = new Complex[2*y.length];
for (int i = 0; i < y.length; i++) b[i] = y[i];
for (int i = y.length; i < 2*y.length; i++) b[i] = ZERO;
return cconvolve(a, b);
}
// display an array of Complex numbers to standard output
public static void show(Complex[] x, String title) {
StdOut.println(title);
StdOut.println("-------------------");
for (int i = 0; i < x.length; i++) {
StdOut.println(x[i]);
}
StdOut.println();
}
/***************************************************************************
* Test client and sample execution
*
* % java FFT 4
* x
* -------------------
* -0.03480425839330703
* 0.07910192950176387
* 0.7233322451735928
* 0.1659819820667019
*
* y = fft(x)
* -------------------
* 0.9336118983487516
* -0.7581365035668999 + 0.08688005256493803i
* 0.44344407521182005
* -0.7581365035668999 - 0.08688005256493803i
*
* z = ifft(y)
* -------------------
* -0.03480425839330703
* 0.07910192950176387 + 2.6599344570851287E-18i
* 0.7233322451735928
* 0.1659819820667019 - 2.6599344570851287E-18i
*
* c = cconvolve(x, x)
* -------------------
* 0.5506798633981853
* 0.23461407150576394 - 4.033186818023279E-18i
* -0.016542951108772352
* 0.10288019294318276 + 4.033186818023279E-18i
*
* d = convolve(x, x)
* -------------------
* 0.001211336402308083 - 3.122502256758253E-17i
* -0.005506167987577068 - 5.058885073636224E-17i
* -0.044092969479563274 + 2.1934338938072244E-18i
* 0.10288019294318276 - 3.6147323062478115E-17i
* 0.5494685269958772 + 3.122502256758253E-17i
* 0.240120239493341 + 4.655566391833896E-17i
* 0.02755001837079092 - 2.1934338938072244E-18i
* 4.01805098805014E-17i
*
***************************************************************************/
public static void main(String[] args) {
//int n = Integer.parseInt(args[0]);
int n = 4;
Complex[] x = new Complex[n];
// original data
for (int i = 0; i < n; i++) {
x[i] = new Complex(i, 0);
x[i] = new Complex(-2*Math.random() + 1, 0);
}
show(x, "x");
// FFT of original data
Complex[] y = fft(x);
show(y, "y = fft(x)");
// take inverse FFT
Complex[] z = ifft(y);
show(z, "z = ifft(y)");
// circular convolution of x with itself
Complex[] c = cconvolve(x, x);
show(c, "c = cconvolve(x, x)");
// linear convolution of x with itself
Complex[] d = convolve(x, x);
show(d, "d = convolve(x, x)");
}
}
到这里如果光粘贴此部分代码是无法实现FFT的，因为JAVA本身没得Complex(复数）类，所以我们需要添加一个复数类；
粘贴复数类代码如下：
import java.util.Objects;
public class Complex {
private final double re; // the real part
private final double im; // the imaginary part
// create a new object with the given real and imaginary parts
public Complex(double real, double imag) {
re = real;
im = imag;
}
// return a string representation of the invoking Complex object
public String toString() {
if (im == 0) return re + "";
if (re == 0) return im + "i";
if (im < 0) return re + " - " + (-im) + "i";
return re + " + " + im + "i";
}
// return abs/modulus/magnitude
public double abs() {
return Math.hypot(re, im);
}
// return angle/phase/argument, normalized to be between -pi and pi
public double phase() {
return Math.atan2(im, re);
}
// return a new Complex object whose value is (this + b)
public Complex plus(Complex b) {
Complex a = this; // invoking object
double real = a.re + b.re;
double imag = a.im + b.im;
return new Complex(real, imag);
}
// return a new Complex object whose value is (this - b)
public Complex minus(Complex b) {
Complex a = this;
double real = a.re - b.re;
double imag = a.im - b.im;
return new Complex(real, imag);
}
// return a new Complex object whose value is (this * b)
public Complex times(Complex b) {
Complex a = this;
double real = a.re * b.re - a.im * b.im;
double imag = a.re * b.im + a.im * b.re;
return new Complex(real, imag);
}
// return a new object whose value is (this * alpha)
public Complex scale(double alpha) {
return new Complex(alpha * re, alpha * im);
}
// return a new Complex object whose value is the conjugate of this
public Complex conjugate() {
return new Complex(re, -im);
}
// return a new Complex object whose value is the reciprocal of this
public Complex reciprocal() {
double scale = re*re + im*im;
return new Complex(re / scale, -im / scale);
}
// return the real or imaginary part
public double re() { return re; }
public double im() { return im; }
// return a / b
public Complex divides(Complex b) {
Complex a = this;
return a.times(b.reciprocal());
}
// return a new Complex object whose value is the complex exponential of this
public Complex exp() {
return new Complex(Math.exp(re) * Math.cos(im), Math.exp(re) * Math.sin(im));
}
// return a new Complex object whose value is the complex sine of this
public Complex sin() {
return new Complex(Math.sin(re) * Math.cosh(im), Math.cos(re) * Math.sinh(im));
}
// return a new Complex object whose value is the complex cosine of this
public Complex cos() {
return new Complex(Math.cos(re) * Math.cosh(im), -Math.sin(re) * Math.sinh(im));
}
// return a new Complex object whose value is the complex tangent of this
public Complex tan() {
return sin().divides(cos());
}
// a static version of plus
public static Complex plus(Complex a, Complex b) {
double real = a.re + b.re;
double imag = a.im + b.im;
Complex sum = new Complex(real, imag);
return sum;
}
// See Section 3.3.
public boolean equals(Object x) {
if (x == null) return false;
if (this.getClass() != x.getClass()) return false;
Complex that = (Complex) x;
return (this.re == that.re) && (this.im == that.im);
}
// See Section 3.3.
public int hashCode() {
return Objects.hash(re, im);
}
// sample client for testing
public static void main(String[] args) {
Complex a = new Complex(5.0, 6.0);
Complex b = new Complex(-3.0, 4.0);
System.out.println("a = " + a);
System.out.println("b = " + b);
System.out.println("Re(a) = " + a.re());
System.out.println("Im(a) = " + a.im());
System.out.println("b + a = " + b.plus(a));
System.out.println("a - b = " + a.minus(b));
System.out.println("a * b = " + a.times(b));
System.out.println("b * a = " + b.times(a));
System.out.println("a / b = " + a.divides(b));
System.out.println("(a / b) * b = " + a.divides(b).times(b));
System.out.println("conj(a) = " + a.conjugate());
System.out.println("|a| = " + a.abs());
System.out.println("tan(a) = " + a.tan());
}
}
把这两个文件都复制下来运行的话还是会报错，由于上述用到了StdOut类，这是一个数据输出类大家不清楚的可以百度一下，这个需要下载一个jar包，下载地址我粘贴在下方：
链接：https://pan.baidu.com/s/1Vg_iIDiTKhTajQtJEy080g
提取码：2qon
把这个jar包导入文件就可以用了，整个demo代码已上传：https://download.csdn.net/download/systemlsy/10893892
由于最少需要一个积分下载没办法免费分享了。
好了整个代码就完了，大家有需要的自行修改。