Implemented _Adaptive interface for all adaptive filters; made edits …

…to javadoc comments

pom.xml

@@ -6,7 +6,7 @@
 
     <groupId>com.github.psambit9791</groupId>
     <artifactId>jdsp</artifactId>
-    <version>1.0.0-SNAPSHOT</version>
+    <version>1.0.0</version>
 
     <distributionManagement>
         <snapshotRepository>

src/main/java/com/github/psambit9791/jdsp/filter/_FIRFilter.java

@@ -21,7 +21,7 @@
  * @author  Sambit Paul
  * @version 1.2
  */
-abstract class _FIRFilter {
+public abstract class _FIRFilter {
 
     /**
      * FIR Filters follow the formula y_n = sum(b_i * x_(n-i)) for all coefficients i = 0 to M.

src/main/java/com/github/psambit9791/jdsp/filter/adaptive/AP.java

@@ -45,7 +45,7 @@ public class AP implements _Adaptive {
     private double[] output;
 
     /**
-     * This constructor initialises the prerequisites required for the LMS adaptive filter.
+     * This constructor initialises the prerequisites required for the AP adaptive filter.
      * @param order projection order to determine the memory
      * @param learningRate also known as step size. Determines how fast the adaptive filter changes its filter weights.
      *                     If it is too slow, the filter may have bad performance. If it is too high, the filter will

src/main/java/com/github/psambit9791/jdsp/filter/adaptive/GNGD.java

@@ -27,7 +27,7 @@
  * @author Sambit Paul
  * @version 1.0
  */
-public class GNGD {
+public class GNGD implements _Adaptive{
 
     private double[] weights;           // Weights of the filter
     private double[] error;
@@ -39,16 +39,6 @@ public class GNGD {
     private double last_e;
     private double[] last_x;
 
-    /**
-     * Dictates how the filter weights initialization should be done:
-     *      RANDOM: filter weights get an initial random value ranging from 0 to 1
-     *      ZEROS: filter weights get initial value 0
-     */
-    public enum WeightsFillMethod {
-        RANDOM,
-        ZEROS
-    }
-
     /**
      * This constructor initialises the prerequisites required for the GNGD adaptive filter.
      * @param learningRate learning rate

src/main/java/com/github/psambit9791/jdsp/filter/adaptive/LMS.java

@@ -15,23 +15,13 @@
  * @author Sibo Van Gool
  * @version 1.0
  */
-public class LMS {
+public class LMS implements _Adaptive {
     private final double learningRate;  // Learning rate (= step size)
     private final double leakageFactor; // Leakage factor
     private double[] weights;           // Weights of the filter
     private double[] error;             // Error of the filter
     private double[] output;            // Filtered output
 
-    /**
-     * Dictates how the filter weights initialization should be done:
-     *      RANDOM: filter weights get an initial random value ranging from 0 to 1
-     *      ZEROS: filter weights get initial value 0
-     */
-    public enum WeightsFillMethod {
-        RANDOM,
-        ZEROS
-    }
-
     /**
      * This constructor initialises the prerequisites required for the LMS adaptive filter.
      * @param learningRate also known as step size. Determines how fast the adaptive filter changes its filter weights.

src/main/java/com/github/psambit9791/jdsp/filter/adaptive/NLMS.java

@@ -19,23 +19,13 @@
  * @author Sibo Van Gool
  * @version 1.0
  */
-public class NLMS {
+public class NLMS implements _Adaptive {
     private final double learningRate;  // Learning rate (= step size)
     private final double leakageFactor; // Leakage factor
     private double[] weights;           // Weights of the filter
     private double[] error;             // Error of the filter
     private double[] output;            // Filtered output
 
-    /**
-     * Dictates how the filter weights initialization should be done:
-     *      RANDOM: filter weights get an initial random value ranging from 0 to 1
-     *      ZEROS: filter weights get initial value 0
-     */
-    public enum WeightsFillMethod {
-        RANDOM,
-        ZEROS
-    }
-
     /**
      * This constructor initialises the prerequisites required for the NLMS adaptive filter.
      * @param learningRate also known as step size. Determines how fast the adaptive filter changes its filter weights.

src/main/java/com/github/psambit9791/jdsp/filter/adaptive/NSSLMS.java

@@ -32,23 +32,13 @@
  * @author Sambit Paul
  * @version 1.0
  */
-public class NSSLMS {
+public class NSSLMS implements _Adaptive{
     private final double learningRate;  // Learning rate (= step size)
     private final double leakageFactor; // Leakage factor
     private double[] weights;           // Weights of the filter
     private double[] error;             // Error of the filter
     private double[] output;            // Filtered output
 
-    /**
-     * Dictates how the filter weights initialization should be done:
-     *      RANDOM: filter weights get an initial random value ranging from 0 to 1
-     *      ZEROS: filter weights get initial value 0
-     */
-    public enum WeightsFillMethod {
-        RANDOM,
-        ZEROS
-    }
-
     /**
      * Returns the sign of the input number.
      * @param err Value whose sign is to be determined

src/main/java/com/github/psambit9791/jdsp/filter/adaptive/RLS.java

@@ -25,23 +25,13 @@
  * @author Sambit Paul
  * @version 1.0
  */
-public class RLS {
+public class RLS implements _Adaptive{
     private double[] weights;           // Weights of the filter
     private double[] error;
     private double[] output;
     private double mu;                  // Forgetting factor
     private double[][] R;
 
-    /**
-     * Dictates how the filter weights initialization should be done:
-     *      RANDOM: filter weights get an initial random value ranging from 0 to 1
-     *      ZEROS: filter weights get initial value 0
-     */
-    public enum WeightsFillMethod {
-        RANDOM,
-        ZEROS
-    }
-
     /**
      * This constructor initialises the prerequisites required for the RLS adaptive filter.
      * @param mu forgetting factor. It is introduced to give exponentially less weight to older error samples.

src/main/java/com/github/psambit9791/jdsp/filter/adaptive/SSLMS.java

@@ -31,23 +31,13 @@
  * @author Sambit Paul
  * @version 1.0
  */
-public class SSLMS {
+public class SSLMS implements _Adaptive{
     private final double learningRate;  // Learning rate (= step size)
     private final double leakageFactor; // Leakage factor
     private double[] weights;           // Weights of the filter
     private double[] error;             // Error of the filter
     private double[] output;            // Filtered output
 
-    /**
-     * Dictates how the filter weights initialization should be done:
-     *      RANDOM: filter weights get an initial random value ranging from 0 to 1
-     *      ZEROS: filter weights get initial value 0
-     */
-    public enum WeightsFillMethod {
-        RANDOM,
-        ZEROS
-    }
-
     /**
      * Returns the sign of the input number.
      * @param err Value whose sign is to be determined

src/main/java/com/github/psambit9791/jdsp/filter/adaptive/_Adaptive.java

@@ -20,10 +20,10 @@
  * @author  Sambit Paul
  * @version 1.0
  */
-interface  _Adaptive {
+public interface _Adaptive {
 
     /**
-     * Dictates how the filter weights initialization should be done:
+     * Dictates how the filter weights initialization should be done.
      *      RANDOM: filter weights get an initial random value ranging from 0 to 1
      *      ZEROS: filter weights get initial value 0
      */

src/main/java/com/github/psambit9791/jdsp/signal/ComplexConvolution.java

@@ -20,7 +20,7 @@
 /**
  * <h1>Convolution for Complex Numbers</h1>
  * The ComplexConvolution class implements different modes of convolution for a list of complex numbers.
- * The window can be a list of Complex or double numbers.
+ * The window can be a list of Complex or real (double) numbers.
  * <p>
  *
  * @author  Sambit Paul