[fixes psambit9791#26] Rename LMS & NLMS filter classes

SiboVG · Nov 16, 2021 · f108d18 · f108d18
1 parent 645eaeb
commit f108d18
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Showing 4 changed files with 18 additions and 18 deletions.
diff --git a/...t9791/jdsp/filter/adaptive/LMSFilter.java → ...psambit9791/jdsp/filter/adaptive/LMS.java b/...t9791/jdsp/filter/adaptive/LMSFilter.java → ...psambit9791/jdsp/filter/adaptive/LMS.java
@@ -11,7 +11,7 @@
  * @author SiboVanGool
  * @version 1.0
  */
-public class LMSFilter {
+public class LMS {
     private final double learningRate;  // Learning rate (= step size)
     private double[] weights;           // Weights of the filter
     private double[] error;             // Error of the filter
@@ -37,7 +37,7 @@ public enum WeightsFillMethod {
      *                              With 'k' being a sample index, and n ranging from 0 to weights.length
      * @param weights initialized weights (size = number of taps of the filter)
      */
-    public LMSFilter(double learningRate, double[] weights) {
+    public LMS(double learningRate, double[] weights) {
         if (weights == null || weights.length == 0) {
             throw new IllegalArgumentException("Weights must be non-null and with a length greater than 0");
         }
@@ -56,7 +56,7 @@ public LMSFilter(double learningRate, double[] weights) {
      * @param length length (number of taps) of the filter
      * @param fillMethod determines how the weights should be initialized
      */
-    public LMSFilter(double learningRate, int length, WeightsFillMethod fillMethod) {
+    public LMS(double learningRate, int length, WeightsFillMethod fillMethod) {
         this.weights = new double[length];
         switch (fillMethod) {
             // Create random weights between 0 and 1

diff --git a/...9791/jdsp/filter/adaptive/NLMSFilter.java → ...sambit9791/jdsp/filter/adaptive/NLMS.java b/...9791/jdsp/filter/adaptive/NLMSFilter.java → ...sambit9791/jdsp/filter/adaptive/NLMS.java
@@ -14,7 +14,7 @@
  * @author SiboVanGool
  * @version 1.0
  */
-public class NLMSFilter {
+public class NLMS {
     private final double learningRate;  // Learning rate (= step size)
     private double[] weights;           // Weights of the filter
     private double[] error;             // Error of the filter
@@ -39,7 +39,7 @@ public enum WeightsFillMethod {
      *                          0 ≤ learningRate ≤ 2
      * @param weights initialized weights (size = number of taps of the filter)
      */
-    public NLMSFilter(double learningRate, double[] weights) {
+    public NLMS(double learningRate, double[] weights) {
         if (weights == null || weights.length == 0) {
             throw new IllegalArgumentException("Weights must be non-null and with a length greater than 0");
         }
@@ -60,7 +60,7 @@ public NLMSFilter(double learningRate, double[] weights) {
      * @param length length (number of taps) of the filter
      * @param fillMethod determines how the weights should be initialized
      */
-    public NLMSFilter(double learningRate, int length, WeightsFillMethod fillMethod) {
+    public NLMS(double learningRate, int length, WeightsFillMethod fillMethod) {
         if (learningRate < 0 || learningRate > 2) {
             System.err.println("Keep the learning rate between 0 and 2 to avoid diverging results");
         }