core(test): refactor Java Eigenvalues/PCA tests

alalek · alalek · commit 53b5afbba92c · 2017-12-12T15:07:31.000+03:00
diff --git a/modules/core/misc/java/test/CoreTest.java b/modules/core/misc/java/test/CoreTest.java
@@ -394,26 +394,36 @@ public void testDivideMatMatMatDoubleInt() {
     }
 
     public void testEigen() {
-        Mat src = new Mat(3, 3, CvType.CV_32FC1, new Scalar(2.0));
+        Mat src = new Mat(3, 3, CvType.CV_32FC1) {
+            {
+                put(0, 0, 2, 0, 0);
+                put(1, 0, 0, 6, 0);
+                put(2, 0, 0, 0, 4);
+            }
+        };
         Mat eigenVals = new Mat();
         Mat eigenVecs = new Mat();
 
         Core.eigen(src, eigenVals, eigenVecs);
 
         Mat expectedEigenVals = new Mat(3, 1, CvType.CV_32FC1) {
             {
-                put(0, 0, 6, 0, 0);
-            }
-        };
-        Mat expectedEigenVecs = new Mat(3, 3, CvType.CV_32FC1) {
-            {
-                put(0, 0, 0.57735026, 0.57735026, 0.57735032);
-                put(1, 0, 0.70710677, -0.70710677, 0);
-                put(2, 0, -0.40824831, -0.40824831, 0.81649661);
+                put(0, 0, 6, 4, 2);
             }
         };
         assertMatEqual(eigenVals, expectedEigenVals, EPS);
-        assertMatEqual(eigenVecs, expectedEigenVecs, EPS);
+
+        // check by definition
+        double eps = 1e-3;
+        for(int i = 0; i < 3; i++)
+        {
+            Mat vec = eigenVecs.row(i).t();
+            Mat lhs = new Mat(3, 1, CvType.CV_32FC1);
+            Core.gemm(src, vec, 1.0, new Mat(), 1.0, lhs);
+            Mat rhs = new Mat(3, 1, CvType.CV_32FC1);
+            Core.gemm(vec, eigenVals.row(i), 1.0, new Mat(), 1.0, rhs);
+            assertMatEqual(lhs, rhs, eps);
+        }
     }
 
     public void testExp() {
@@ -1326,7 +1336,8 @@ public void testPCAComputeMatMatMat() {
         Mat vectors = new Mat();
 
         Core.PCACompute(data, mean, vectors);
-
+        //System.out.println(mean.dump());
+        //System.out.println(vectors.dump());
         Mat mean_truth = new Mat(1, 4, CvType.CV_32F) {
             {
                 put(0, 0, 2, 4, 4, 8);
@@ -1338,7 +1349,21 @@ public void testPCAComputeMatMatMat() {
             }
         };
         assertMatEqual(mean_truth, mean, EPS);
-        assertMatEqual(vectors_truth, vectors, EPS);
+
+        // eigenvectors are normalized (length = 1),
+        // but direction is unknown (v and -v are both eigen vectors)
+        // so this direct check doesn't work:
+        // assertMatEqual(vectors_truth, vectors, EPS);
+        for(int i = 0; i < 3; i++)
+        {
+            Mat vec0 = vectors_truth.row(i);
+            Mat vec1 = vectors.row(i);
+            Mat vec1_ = new Mat();
+            Core.subtract(new Mat(1, 4, CvType.CV_32F, new Scalar(0)), vec1, vec1_);
+            double scale1 = Core.norm(vec0, vec1);
+            double scale2 = Core.norm(vec0, vec1_);
+            assertTrue(Math.min(scale1, scale2) < EPS);
+        }
     }
 
     public void testPCAComputeMatMatMatInt() {
@@ -1365,7 +1390,20 @@ public void testPCAComputeMatMatMatInt() {
             }
         };
         assertMatEqual(mean_truth, mean, EPS);
-        assertMatEqual(vectors_truth, vectors, EPS);
+        // eigenvectors are normalized (length = 1),
+        // but direction is unknown (v and -v are both eigen vectors)
+        // so this direct check doesn't work:
+        // assertMatEqual(vectors_truth, vectors, EPS);
+        for(int i = 0; i < 1; i++)
+        {
+            Mat vec0 = vectors_truth.row(i);
+            Mat vec1 = vectors.row(i);
+            Mat vec1_ = new Mat();
+            Core.subtract(new Mat(1, 4, CvType.CV_32F, new Scalar(0)), vec1, vec1_);
+            double scale1 = Core.norm(vec0, vec1);
+            double scale2 = Core.norm(vec0, vec1_);
+            assertTrue(Math.min(scale1, scale2) < EPS);
+        }
     }
 
     public void testPCAProject() {
