walterddr commented on a change in pull request #9413: [FLINK-13676][ml] Add 
class of Vector to Columns mapper
URL: https://github.com/apache/flink/pull/9413#discussion_r366658844
 
 

 ##########
 File path: 
flink-ml-parent/flink-ml-lib/src/main/java/org/apache/flink/ml/common/dataproc/vector/VectorToColumnsMapper.java
 ##########
 @@ -0,0 +1,104 @@
+/*
+ * 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.common.dataproc.vector;
+
+import org.apache.flink.api.common.typeinfo.TypeInformation;
+import org.apache.flink.api.common.typeinfo.Types;
+import org.apache.flink.ml.api.misc.param.Params;
+import org.apache.flink.ml.common.linalg.DenseVector;
+import org.apache.flink.ml.common.linalg.SparseVector;
+import org.apache.flink.ml.common.linalg.Vector;
+import org.apache.flink.ml.common.mapper.Mapper;
+import org.apache.flink.ml.common.utils.OutputColsHelper;
+import org.apache.flink.ml.common.utils.TableUtil;
+import org.apache.flink.ml.params.dataproc.vector.VectorToColumnsParams;
+import org.apache.flink.table.api.TableSchema;
+import org.apache.flink.types.Row;
+
+/**
+ * This mapper maps vector to table columns.
+ */
+public class VectorToColumnsMapper extends Mapper {
+       private int colSize;
+       private int idx;
+       private OutputColsHelper outputColsHelper;
+
+       public VectorToColumnsMapper(TableSchema dataSchema, Params params) {
+               super(dataSchema, params);
+               String selectedColName = 
this.params.get(VectorToColumnsParams.SELECTED_COL);
+               idx = TableUtil.findColIndex(dataSchema.getFieldNames(), 
selectedColName);
+               if (idx < 0) {
+                       throw new IllegalArgumentException("Can not find 
column: " + selectedColName);
+               }
+               String[] outputColNames = 
this.params.get(VectorToColumnsParams.OUTPUT_COLS);
+               if (outputColNames == null) {
+                       throw new IllegalArgumentException("VectorToTable: 
outputColNames must set.");
+               }
+               this.colSize = outputColNames.length;
+               TypeInformation[] types = new TypeInformation[colSize];
+               for (int i = 0; i < colSize; ++i) {
+                       types[i] = Types.DOUBLE;
+               }
+               this.outputColsHelper = new OutputColsHelper(dataSchema, 
outputColNames, types,
+                       this.params.get(VectorToColumnsParams.RESERVED_COLS));
+       }
+
+       @Override
+       public Row map(Row row) {
+               Row result = new Row(colSize);
+               Object obj = row.getField(idx);
+               if (null == obj) {
+                       for (int i = 0; i < colSize; i++) {
+                               result.setField(i, null);
+                       }
+                       return outputColsHelper.getResultRow(row, result);
+               }
+
+               Vector vec = (Vector) obj;
+
+               if (vec instanceof SparseVector) {
+                       for (int i = 0; i < colSize; ++i) {
+                               result.setField(i, 0.0);
+                       }
+                       SparseVector sparseVector = (SparseVector) vec;
+                       int nnz = sparseVector.numberOfValues();
+                       int[] indices = sparseVector.getIndices();
+                       double[] values = sparseVector.getValues();
+                       for (int i = 0; i < nnz; ++i) {
+                               if (indices[i] < colSize) {
+                                       result.setField(indices[i], values[i]);
+                               } else {
+                                       break;
 
 Review comment:
   ```
   int i = 0;
   while (indices[i] < colSize) {
     result.setField(indices[i], values[i]);
     I++;
   }
   ```
   also, are we always assume users' sparse vector indices larger than colSize 
can be ignored?

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