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Posted to commits@commons.apache.org by er...@apache.org on 2021/07/18 13:13:51 UTC
[commons-math] 01/02: Use MathJaX (Javadoc).
This is an automated email from the ASF dual-hosted git repository.
erans pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/commons-math.git
commit 962d6b6543865ce9dba217b0223441a244d48a1a
Author: Gilles Sadowski <gi...@gmail.com>
AuthorDate: Sun Jul 18 12:50:33 2021 +0200
Use MathJaX (Javadoc).
---
.../commons/math4/transform/FastFourierTransform.java | 17 ++++++-----------
1 file changed, 6 insertions(+), 11 deletions(-)
diff --git a/commons-math-transform/src/main/java/org/apache/commons/math4/transform/FastFourierTransform.java b/commons-math-transform/src/main/java/org/apache/commons/math4/transform/FastFourierTransform.java
index 1dbad58..182b1c1 100644
--- a/commons-math-transform/src/main/java/org/apache/commons/math4/transform/FastFourierTransform.java
+++ b/commons-math-transform/src/main/java/org/apache/commons/math4/transform/FastFourierTransform.java
@@ -402,12 +402,10 @@ public class FastFourierTransform implements ComplexTransform {
* to use the <em>standard</em> normalization convention. This normalization
* convention is defined as follows
* <ul>
- * <li>forward transform: y<sub>n</sub> = ∑<sub>k=0</sub><sup>N-1</sup>
- * x<sub>k</sub> exp(-2πi n k / N),</li>
- * <li>inverse transform: x<sub>k</sub> = N<sup>-1</sup>
- * ∑<sub>n=0</sub><sup>N-1</sup> y<sub>n</sub> exp(2πi n k / N),</li>
+ * <li>forward transform: \( y_n = \sum_{k = 0}^{N - 1} x_k e^{-2 \pi i n k / N} \),</li>
+ * <li>inverse transform: \( x_k = \frac{1}{N} \sum_{n = 0}^{N - 1} y_n e^{2 \pi i n k / N} \),</li>
* </ul>
- * where N is the size of the data sample.
+ * where \( N \) is the size of the data sample.
*/
STD,
@@ -416,13 +414,10 @@ public class FastFourierTransform implements ComplexTransform {
* to use the <em>unitary</em> normalization convention. This normalization
* convention is defined as follows
* <ul>
- * <li>forward transform: y<sub>n</sub> = (1 / √N)
- * ∑<sub>k=0</sub><sup>N-1</sup> x<sub>k</sub>
- * exp(-2πi n k / N),</li>
- * <li>inverse transform: x<sub>k</sub> = (1 / √N)
- * ∑<sub>n=0</sub><sup>N-1</sup> y<sub>n</sub> exp(2πi n k / N),</li>
+ * <li>forward transform: \( y_n = \frac{1}{\sqrt{N}} \sum_{k = 0}^{N - 1} x_k e^{-2 \pi i n k / N} \),</li>
+ * <li>inverse transform: \( x_k = \frac{1}{\sqrt{N}} \sum_{n = 0}^{N - 1} y_n e^{2 \pi i n k / N} \),</li>
* </ul>
- * which makes the transform unitary. N is the size of the data sample.
+ * where \( N \) is the size of the data sample.
*/
UNIT;
}