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Subject [GitHub] [flink] xuyang1706 commented on a change in pull request #9733: [FLINK-14154][ml] Add the class for multivariate Gaussian Distribution.
Date Thu, 24 Oct 2019 10:20:47 GMT
xuyang1706 commented on a change in pull request #9733: [FLINK-14154][ml] Add the class for
multivariate Gaussian Distribution.
URL: https://github.com/apache/flink/pull/9733#discussion_r338495227

 File path: flink-ml-parent/flink-ml-lib/src/test/java/org/apache/flink/ml/common/statistics/basicstatistic/MultivariateGaussianTest.java
 @@ -0,0 +1,57 @@
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+package org.apache.flink.ml.common.statistics.basicstatistic;
+import org.apache.flink.ml.common.linalg.DenseMatrix;
+import org.apache.flink.ml.common.linalg.DenseVector;
+import org.junit.Assert;
+import org.junit.Test;
+ * Test cases for MultivariateGaussian.
+ */
+public class MultivariateGaussianTest {
+	private static final double TOL = 1.0e-5;
 Review comment:
   The limited bi-section is used to compute the threshold in determing whether the singular
value of the covariance matrix is larger than zero. It is a way to improve numerical stability
when the covariance matrix is singular or nearly singular. The "TOL" in the unit test cases
is the tolerance between the computed pdf and theoretical pdf. We could only  achieve the
precision at around 1*e-5, because det(sigma) is computed with LAPACK's "dsyev", which uses
iterative algorithm to compute eigen values, thus have inherent errors.

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