Features/kshape (#26)

* Adding khiva and updating copyright year to 2019

* Adding Clustering module.

* Change Test clustering

Author: avilchess

src/main/java/io/shapelets/khiva/Clustering.java

@@ -28,6 +28,7 @@ public class Clustering extends Library{
      * @param k                 The number of centroids.
      * @param tolerance         The maximum error tolerance.
      * @param maxIterations     The maximum number of iterations.
+     *                          
      * @return An Array of arrays with the resulting centroids and labels.
      */
     public static Array[] kMeans(Array tss, int k, float tolerance, int maxIterations) {
@@ -47,6 +48,7 @@ public static Array[] kMeans(Array tss, int k, float tolerance, int maxIteration
      * @param k                 The number of centroids.
      * @param tolerance         The maximum error tolerance.
      * @param maxIterations     The maximum number of iterations.
+     *
      * @return An Array of arrays with the resulting centroids and labels.
      */
     public static Array[] kShape(Array tss, int k, float tolerance, int maxIterations) {

src/test/java/io/shapelets/khiva/ClusteringTest.java

@@ -26,23 +26,25 @@ public void testKMeans() throws Exception {
                 8.0f, 5.0f,  3.0f, 1.0f, 15.0f, 10.0f, 5.0f, 0.0f, 7.0f, -7.0f,  1.0f, -1.0f};
         long[] dims = {4, 6, 1, 1};
 
-        float[] expected = {0.0f, 0.1667f, 0.3333f, 0.5f,
-                            1.5f, -1.5f, 0.8333f, -0.8333f,
-                            4.8333f, 3.6667f, 2.6667f, 1.6667f};
-
         float tolerance = 1e-10f;
         int maxIterations = 100;
         int k = 3;
+
         try (
                 Array a = new Array(tss, dims)
         ){
             Array[] result = Clustering.kMeans(a, k, tolerance, maxIterations);
+            float[] expected = {0.0f, 0.1667f, 0.3333f, 0.5f, 1.5f, -1.5f, 0.8333f, -0.8333f, 4.8333f, 3.6667f,
+                    2.6667f, 1.6667f};
             float[] centroids = result[0].getData();
 
             for (int i = 0; i < 4; i++){
                 Assert.assertEquals(expected[i] + expected[i + 4] + expected[i + 8],
                         centroids[i] + centroids[i + 4] + centroids[i + 8], 1e-4f);
             }
+
+            result[0].close();
+            result[1].close();
         }
     }
 
@@ -53,24 +55,25 @@ public void testKShape() throws Exception {
                 -6.0f, -1.0f, 2.0f,  9.0f,  -5.0f,  -5.0f, -6.0f, 7.0f, 9.0f,  9.0f, 0.0f};
         long[] dims = {7, 5, 1, 1};
 
-        float[] expected_c = {-0.5234f, 0.1560f, -0.3627f, -1.2764f, -0.7781f,  0.9135f,  1.8711f,
-                -0.7825f, 1.5990f,  0.1701f,  0.4082f,  0.8845f, -1.4969f, -0.7825f,
-                -0.6278f, 1.3812f, -2.0090f,  0.5022f,  0.6278f, -0.0000f,  0.1256f};
-
-
         float tolerance = 1e-10f;
         int maxIterations = 100;
         int k = 3;
+
         try (
                 Array a = new Array(tss, dims)
         ){
             Array[] result = Clustering.kShape(a, k, tolerance, maxIterations);
+            float[] expected_c = {-0.5234f, 0.1560f, -0.3627f, -1.2764f, -0.7781f,  0.9135f,  1.8711f,
+                    -0.7825f, 1.5990f,  0.1701f,  0.4082f,  0.8845f, -1.4969f, -0.7825f,
+                    -0.6278f, 1.3812f, -2.0090f,  0.5022f,  0.6278f, -0.0000f,  0.1256f};
             float[] centroids = result[0].getData();
 
             for (int i = 0; i < centroids.length; i++){
                 Assert.assertEquals(expected_c[i], centroids[i],1e-4f);
             }
-        }
 
+            result[0].close();
+            result[1].close();
+        }
     }
 }