yunfengzhou-hub commented on a change in pull request #37:
URL: https://github.com/apache/flink-ml/pull/37#discussion_r770236389



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File path: 
flink-ml-core/src/main/java/org/apache/flink/ml/linalg/SparseVector.java
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@@ -0,0 +1,174 @@
+/*
+ * 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 "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.flink.ml.linalg;
+
+import org.apache.flink.api.common.typeinfo.TypeInfo;
+import org.apache.flink.ml.linalg.typeinfo.SparseVectorTypeInfoFactory;
+import org.apache.flink.util.Preconditions;
+
+import java.util.Arrays;
+import java.util.Objects;
+
+/** A sparse vector of double values. */
+@TypeInfo(SparseVectorTypeInfoFactory.class)

Review comment:
       Currently we have not encountered an algorithm that needs BLAS operation 
on SparseVectors. Maybe we can add such support when there is such need from 
algorithms.
   
   As for the second suggestion, I agree that in high dimension model's case we 
may need more than integer can express. For now I believe having integer 
implementation as the default for vector classes is enough.




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