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[Flink-27826] Support training very high dimensional logistic regression #237

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[Flink-27826] Support training very high dimensional logistic regression #237

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ce947de
Support SparseVector as input for LogisticRegression
zhipeng93 May 8, 2023
9d58fc7
[hotfix] Fix TableUtils.getRowTypeInfo when the input contains Tuple
zhipeng93 May 9, 2023
a92671c
Expand LogisticRegressionModelData as many pieces
zhipeng93 May 12, 2023
74b4b7c
[FLINK-27826] Support training very high dimensional logisticRegression
zhipeng93 May 12, 2023
e70c3fe
Average the gradient from workers
zhipeng93 May 29, 2023
3966321
Support pull/push value as array
zhipeng93 May 29, 2023
b04cd84
resolve comments
zhipeng93 May 30, 2023
d5dd3a9
add allreduce stage impl
zhipeng93 Jun 5, 2023
5c1d7aa
Reorganize Vectors and add SparseLongDoubleVector
zhipeng93 Jun 6, 2023
7325477
support allreduce aggregator for double[]
zhipeng93 Jun 7, 2023
e938e18
support allreduce aggregator for double[]
zhipeng93 Jun 7, 2023
3f45880
FTRL should not be aware of numWorkers
zhipeng93 Jun 7, 2023
ea4e159
Reorganize message infra
zhipeng93 Jun 7, 2023
6b4df2a
Support output from worker operator
zhipeng93 Jun 8, 2023
dce8b9b
Add test for trainingUtils.java
zhipeng93 Jun 8, 2023
4e77b2c
Rename LogisticRegressionModelData as LogisticRegressionModelDataSegment
zhipeng93 Jun 8, 2023
d853828
add bench io stuff, should be deleted in the real PR
zhipeng93 Jun 8, 2023
b83939c
cp
zhipeng93 Jun 9, 2023
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2 changes: 1 addition & 1 deletion docs/content/docs/operators/classification/knn.md
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Expand Up @@ -67,7 +67,7 @@ Below are the parameters required by `KnnModel`.
```java
import org.apache.flink.ml.classification.knn.Knn;
import org.apache.flink.ml.classification.knn.KnnModel;
import org.apache.flink.ml.linalg.DenseVector;
import org.apache.flink.ml.linalg.DenseIntDoubleVector;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
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2 changes: 1 addition & 1 deletion docs/content/docs/operators/classification/linearsvc.md
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Expand Up @@ -77,7 +77,7 @@ Below are the parameters required by `LinearSVCModel`.
```java
import org.apache.flink.ml.classification.linearsvc.LinearSVC;
import org.apache.flink.ml.classification.linearsvc.LinearSVCModel;
import org.apache.flink.ml.linalg.DenseVector;
import org.apache.flink.ml.linalg.DenseIntDoubleVector;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
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8 changes: 4 additions & 4 deletions docs/content/docs/operators/classification/logisticregression.md
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Expand Up @@ -74,7 +74,7 @@ Below are the parameters required by `LogisticRegressionModel`.
```java
import org.apache.flink.ml.classification.logisticregression.LogisticRegression;
import org.apache.flink.ml.classification.logisticregression.LogisticRegressionModel;
import org.apache.flink.ml.linalg.DenseVector;
import org.apache.flink.ml.linalg.DenseIntDoubleVector;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
Expand Down Expand Up @@ -251,9 +251,9 @@ import org.apache.flink.api.java.typeutils.RowTypeInfo;
import org.apache.flink.ml.classification.logisticregression.OnlineLogisticRegression;
import org.apache.flink.ml.classification.logisticregression.OnlineLogisticRegressionModel;
import org.apache.flink.ml.examples.util.PeriodicSourceFunction;
import org.apache.flink.ml.linalg.DenseVector;
import org.apache.flink.ml.linalg.DenseIntDoubleVector;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.ml.linalg.typeinfo.DenseVectorTypeInfo;
import org.apache.flink.ml.linalg.typeinfo.DenseIntDoubleVectorTypeInfo;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.SourceFunction;
Expand Down Expand Up @@ -323,7 +323,7 @@ public class OnlineLogisticRegressionExample {

// Creates an online LogisticRegression object and initializes its parameters and initial
// model data.
Row initModelData = Row.of(Vectors.dense(0.41233679404769874, -0.18088118293232122), 0L);
Row initModelData = Row.of(Vectors.dense(0.41233679404769874, -0.18088118293232122), 0L, 2L, 0L);
Table initModelDataTable = tEnv.fromDataStream(env.fromElements(initModelData));
OnlineLogisticRegression olr =
new OnlineLogisticRegression()
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2 changes: 1 addition & 1 deletion docs/content/docs/operators/classification/naivebayes.md
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Expand Up @@ -66,7 +66,7 @@ Below are parameters required by `NaiveBayesModel`.
```java
import org.apache.flink.ml.classification.naivebayes.NaiveBayes;
import org.apache.flink.ml.classification.naivebayes.NaiveBayesModel;
import org.apache.flink.ml.linalg.DenseVector;
import org.apache.flink.ml.linalg.DenseIntDoubleVector;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
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2 changes: 1 addition & 1 deletion docs/content/docs/operators/clustering/agglomerativeclustering.md
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Expand Up @@ -69,7 +69,7 @@ format of the merging information is
import org.apache.flink.ml.clustering.agglomerativeclustering.AgglomerativeClustering;
import org.apache.flink.ml.clustering.agglomerativeclustering.AgglomerativeClusteringParams;
import org.apache.flink.ml.common.distance.EuclideanDistanceMeasure;
import org.apache.flink.ml.linalg.DenseVector;
import org.apache.flink.ml.linalg.DenseIntDoubleVector;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
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6 changes: 3 additions & 3 deletions docs/content/docs/operators/clustering/kmeans.md
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Expand Up @@ -67,7 +67,7 @@ Below are the parameters required by `KMeansModel`.
```java
import org.apache.flink.ml.clustering.kmeans.KMeans;
import org.apache.flink.ml.clustering.kmeans.KMeansModel;
import org.apache.flink.ml.linalg.DenseVector;
import org.apache.flink.ml.linalg.DenseIntDoubleVector;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
Expand Down Expand Up @@ -228,9 +228,9 @@ import org.apache.flink.ml.clustering.kmeans.KMeansModelData;
import org.apache.flink.ml.clustering.kmeans.OnlineKMeans;
import org.apache.flink.ml.clustering.kmeans.OnlineKMeansModel;
import org.apache.flink.ml.examples.util.PeriodicSourceFunction;
import org.apache.flink.ml.linalg.DenseVector;
import org.apache.flink.ml.linalg.DenseIntDoubleVector;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.ml.linalg.typeinfo.DenseVectorTypeInfo;
import org.apache.flink.ml.linalg.typeinfo.DenseIntDoubleVectorTypeInfo;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.source.SourceFunction;
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2 changes: 1 addition & 1 deletion docs/content/docs/operators/feature/countvectorizer.md
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Expand Up @@ -72,7 +72,7 @@ Below are the parameters required by `CountVectorizerModel`.
```java
import org.apache.flink.ml.feature.countvectorizer.CountVectorizer;
import org.apache.flink.ml.feature.countvectorizer.CountVectorizerModel;
import org.apache.flink.ml.linalg.SparseVector;
import org.apache.flink.ml.linalg.SparseIntDoubleVector;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
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2 changes: 1 addition & 1 deletion docs/content/docs/operators/feature/dct.md
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Expand Up @@ -60,7 +60,7 @@ that the transform matrix is unitary (aka scaled DCT-II).

```java
import org.apache.flink.ml.feature.dct.DCT;
import org.apache.flink.ml.linalg.Vector;
import org.apache.flink.ml.linalg.IntDoubleVector;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
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2 changes: 1 addition & 1 deletion docs/content/docs/operators/feature/elementwiseproduct.md
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Expand Up @@ -58,7 +58,7 @@ scaling vector, the transformer will throw an IllegalArgumentException.

```java
import org.apache.flink.ml.feature.elementwiseproduct.ElementwiseProduct;
import org.apache.flink.ml.linalg.Vector;
import org.apache.flink.ml.linalg.IntDoubleVector;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
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2 changes: 1 addition & 1 deletion docs/content/docs/operators/feature/featurehasher.md
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Expand Up @@ -69,7 +69,7 @@ For the hashing trick, see https://en.wikipedia.org/wiki/Feature_hashing for det

```java
import org.apache.flink.ml.feature.featurehasher.FeatureHasher;
import org.apache.flink.ml.linalg.Vector;
import org.apache.flink.ml.linalg.IntDoubleVector;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
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2 changes: 1 addition & 1 deletion docs/content/docs/operators/feature/hashingtf.md
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Expand Up @@ -63,7 +63,7 @@ the output values are accumulated by default.
```java

import org.apache.flink.ml.feature.hashingtf.HashingTF;
import org.apache.flink.ml.linalg.SparseVector;
import org.apache.flink.ml.linalg.SparseIntDoubleVector;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
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2 changes: 1 addition & 1 deletion docs/content/docs/operators/feature/idf.md
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Expand Up @@ -73,7 +73,7 @@ Below are the parameters required by `IDFModel`.
```java
import org.apache.flink.ml.feature.idf.IDF;
import org.apache.flink.ml.feature.idf.IDFModel;
import org.apache.flink.ml.linalg.DenseVector;
import org.apache.flink.ml.linalg.DenseIntDoubleVector;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
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2 changes: 1 addition & 1 deletion docs/content/docs/operators/feature/interaction.md
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Expand Up @@ -62,7 +62,7 @@ be Vector(3, 6, 4, 8).

```java
import org.apache.flink.ml.feature.interaction.Interaction;
import org.apache.flink.ml.linalg.Vector;
import org.apache.flink.ml.linalg.IntDoubleVector;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
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2 changes: 1 addition & 1 deletion docs/content/docs/operators/feature/kbinsdiscretizer.md
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Expand Up @@ -70,7 +70,7 @@ Below are the parameters required by `KBinsDiscretizerModel`.
import org.apache.flink.ml.feature.kbinsdiscretizer.KBinsDiscretizer;
import org.apache.flink.ml.feature.kbinsdiscretizer.KBinsDiscretizerModel;
import org.apache.flink.ml.feature.kbinsdiscretizer.KBinsDiscretizerParams;
import org.apache.flink.ml.linalg.DenseVector;
import org.apache.flink.ml.linalg.DenseIntDoubleVector;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
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2 changes: 1 addition & 1 deletion docs/content/docs/operators/feature/maxabsscaler.md
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Expand Up @@ -59,7 +59,7 @@ It does not shift/center the data and thus does not destroy any sparsity.
```java
import org.apache.flink.ml.feature.maxabsscaler.MaxAbsScaler;
import org.apache.flink.ml.feature.maxabsscaler.MaxAbsScalerModel;
import org.apache.flink.ml.linalg.DenseVector;
import org.apache.flink.ml.linalg.DenseIntDoubleVector;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
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6 changes: 3 additions & 3 deletions docs/content/docs/operators/feature/minhashlsh.md
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Expand Up @@ -75,9 +75,9 @@ import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.ml.feature.lsh.MinHashLSH;
import org.apache.flink.ml.feature.lsh.MinHashLSHModel;
import org.apache.flink.ml.linalg.DenseVector;
import org.apache.flink.ml.linalg.SparseVector;
import org.apache.flink.ml.linalg.Vector;
import org.apache.flink.ml.linalg.DenseIntDoubleVector;
import org.apache.flink.ml.linalg.SparseIntDoubleVector;
import org.apache.flink.ml.linalg.IntDoubleVector;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
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2 changes: 1 addition & 1 deletion docs/content/docs/operators/feature/minmaxscaler.md
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Expand Up @@ -59,7 +59,7 @@ MinMaxScaler is an algorithm that rescales feature values to a common range
```java
import org.apache.flink.ml.feature.minmaxscaler.MinMaxScaler;
import org.apache.flink.ml.feature.minmaxscaler.MinMaxScalerModel;
import org.apache.flink.ml.linalg.DenseVector;
import org.apache.flink.ml.linalg.DenseIntDoubleVector;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
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2 changes: 1 addition & 1 deletion docs/content/docs/operators/feature/normalizer.md
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Expand Up @@ -57,7 +57,7 @@ A Transformer that normalizes a vector to have unit norm using the given p-norm.

```java
import org.apache.flink.ml.feature.normalizer.Normalizer;
import org.apache.flink.ml.linalg.Vector;
import org.apache.flink.ml.linalg.IntDoubleVector;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
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2 changes: 1 addition & 1 deletion docs/content/docs/operators/feature/onehotencoder.md
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Expand Up @@ -64,7 +64,7 @@ vector column for each input column.
```java
import org.apache.flink.ml.feature.onehotencoder.OneHotEncoder;
import org.apache.flink.ml.feature.onehotencoder.OneHotEncoderModel;
import org.apache.flink.ml.linalg.SparseVector;
import org.apache.flink.ml.linalg.SparseIntDoubleVector;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
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10 changes: 5 additions & 5 deletions docs/content/docs/operators/feature/onlinestandardscaler.md
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Expand Up @@ -91,9 +91,9 @@ import org.apache.flink.api.common.time.Time;
import org.apache.flink.ml.common.window.EventTimeTumblingWindows;
import org.apache.flink.ml.feature.standardscaler.OnlineStandardScaler;
import org.apache.flink.ml.feature.standardscaler.OnlineStandardScalerModel;
import org.apache.flink.ml.linalg.DenseVector;
import org.apache.flink.ml.linalg.DenseIntDoubleVector;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.ml.linalg.typeinfo.DenseVectorTypeInfo;
import org.apache.flink.ml.linalg.typeinfo.DenseIntDoubleVectorTypeInfo;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.DataTypes;
Expand Down Expand Up @@ -202,13 +202,13 @@ t_env = StreamTableEnvironment.create(env)

# Generates input data.
dense_vector_serializer = get_gateway().jvm.org.apache.flink.table.types.logical.RawType(
get_gateway().jvm.org.apache.flink.ml.linalg.DenseVector(0).getClass(),
get_gateway().jvm.org.apache.flink.ml.linalg.typeinfo.DenseVectorSerializer()
get_gateway().jvm.org.apache.flink.ml.linalg.DenseIntDoubleVector(0).getClass(),
get_gateway().jvm.org.apache.flink.ml.linalg.typeinfo.DenseIntDoubleVectorSerializer()
).getSerializerString()

schema = Schema.new_builder()
.column("ts", "TIMESTAMP_LTZ(3)")
.column("input", "RAW('org.apache.flink.ml.linalg.DenseVector', '{serializer}')"
.column("input", "RAW('org.apache.flink.ml.linalg.DenseIntDoubleVector', '{serializer}')"
.format(serializer=dense_vector_serializer))
.watermark("ts", "ts - INTERVAL '1' SECOND")
.build()
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2 changes: 1 addition & 1 deletion docs/content/docs/operators/feature/polynomialexpansion.md
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Expand Up @@ -63,7 +63,7 @@ http://en.wikipedia.org/wiki/Polynomial_expansion.

```java
import org.apache.flink.ml.feature.polynomialexpansion.PolynomialExpansion;
import org.apache.flink.ml.linalg.Vector;
import org.apache.flink.ml.linalg.IntDoubleVector;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
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2 changes: 1 addition & 1 deletion docs/content/docs/operators/feature/robustscaler.md
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Expand Up @@ -87,7 +87,7 @@ Below are the parameters required by `RobustScalerModel`.
```java
import org.apache.flink.ml.feature.robustscaler.RobustScaler;
import org.apache.flink.ml.feature.robustscaler.RobustScalerModel;
import org.apache.flink.ml.linalg.DenseVector;
import org.apache.flink.ml.linalg.DenseIntDoubleVector;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
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2 changes: 1 addition & 1 deletion docs/content/docs/operators/feature/standardscaler.md
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Expand Up @@ -59,7 +59,7 @@ the mean and scaling each dimension to unit variance.
```java
import org.apache.flink.ml.feature.standardscaler.StandardScaler;
import org.apache.flink.ml.feature.standardscaler.StandardScalerModel;
import org.apache.flink.ml.linalg.DenseVector;
import org.apache.flink.ml.linalg.DenseIntDoubleVector;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
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2 changes: 1 addition & 1 deletion docs/content/docs/operators/feature/univariatefeatureselector.md
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Expand Up @@ -105,7 +105,7 @@ Below are the parameters required by `UnivariateFeatureSelectorModel`.
```java
import org.apache.flink.ml.feature.univariatefeatureselector.UnivariateFeatureSelector;
import org.apache.flink.ml.feature.univariatefeatureselector.UnivariateFeatureSelectorModel;
import org.apache.flink.ml.linalg.DenseVector;
import org.apache.flink.ml.linalg.DenseIntDoubleVector;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
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2 changes: 1 addition & 1 deletion docs/content/docs/operators/feature/variancethresholdselector.md
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Expand Up @@ -69,7 +69,7 @@ Below are the parameters required by `VarianceThresholdSelectorModel`.
```java
import org.apache.flink.ml.feature.variancethresholdselector.VarianceThresholdSelector;
import org.apache.flink.ml.feature.variancethresholdselector.VarianceThresholdSelectorModel;
import org.apache.flink.ml.linalg.DenseVector;
import org.apache.flink.ml.linalg.DenseIntDoubleVector;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
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2 changes: 1 addition & 1 deletion docs/content/docs/operators/feature/vectorassembler.md
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Expand Up @@ -69,7 +69,7 @@ the strategy specified by the {@link HasHandleInvalid} parameter as follows:

```java
import org.apache.flink.ml.feature.vectorassembler.VectorAssembler;
import org.apache.flink.ml.linalg.Vector;
import org.apache.flink.ml.linalg.IntDoubleVector;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
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2 changes: 1 addition & 1 deletion docs/content/docs/operators/feature/vectorslicer.md
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Expand Up @@ -61,7 +61,7 @@ it throws an IllegalArgumentException.

```java
import org.apache.flink.ml.feature.vectorslicer.VectorSlicer;
import org.apache.flink.ml.linalg.Vector;
import org.apache.flink.ml.linalg.IntDoubleVector;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
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4 changes: 2 additions & 2 deletions docs/content/docs/operators/functions.md
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Expand Up @@ -38,7 +38,7 @@ of double arrays.

{{< tab "Java">}}
```java
import org.apache.flink.ml.linalg.Vector;
import org.apache.flink.ml.linalg.IntDoubleVector;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.ml.linalg.typeinfo.VectorTypeInfo;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
Expand Down Expand Up @@ -145,7 +145,7 @@ DenseVector instances.

{{< tab "Java">}}
```java
import org.apache.flink.ml.linalg.Vector;
import org.apache.flink.ml.linalg.IntDoubleVector;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
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2 changes: 1 addition & 1 deletion docs/content/docs/operators/regression/linearregression.md
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Expand Up @@ -72,7 +72,7 @@ Below are the parameters required by `LinearRegressionModel`.
{{< tab "Java">}}

```java
import org.apache.flink.ml.linalg.DenseVector;
import org.apache.flink.ml.linalg.DenseIntDoubleVector;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.ml.regression.linearregression.LinearRegression;
import org.apache.flink.ml.regression.linearregression.LinearRegressionModel;
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2 changes: 1 addition & 1 deletion docs/content/docs/try-flink-ml/java/build-your-own-project.md
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Expand Up @@ -176,7 +176,7 @@ package myflinkml;

import org.apache.flink.ml.clustering.kmeans.KMeans;
import org.apache.flink.ml.clustering.kmeans.KMeansModel;
import org.apache.flink.ml.linalg.DenseVector;
import org.apache.flink.ml.linalg.DenseIntDoubleVector;
import org.apache.flink.ml.linalg.Vectors;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
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10 changes: 5 additions & 5 deletions ...java/org/apache/flink/ml/benchmark/datagenerator/clustering/KMeansModelDataGenerator.java
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Expand Up @@ -23,8 +23,8 @@
import org.apache.flink.ml.benchmark.datagenerator.common.DenseVectorArrayGenerator;
import org.apache.flink.ml.benchmark.datagenerator.param.HasArraySize;
import org.apache.flink.ml.benchmark.datagenerator.param.HasVectorDim;
import org.apache.flink.ml.linalg.DenseVector;
import org.apache.flink.ml.linalg.typeinfo.DenseVectorTypeInfo;
import org.apache.flink.ml.linalg.DenseIntDoubleVector;
import org.apache.flink.ml.linalg.typeinfo.DenseIntDoubleVectorTypeInfo;
import org.apache.flink.ml.param.Param;
import org.apache.flink.ml.util.ParamUtils;
import org.apache.flink.table.api.DataTypes;
Expand Down Expand Up @@ -77,8 +77,8 @@ public Table[] getData(StreamTableEnvironment tEnv) {
* information.
*/
public static class GenerateWeightsFunction extends ScalarFunction {
public DenseVector eval(DenseVector[] centroids) {
return new DenseVector(centroids.length);
public DenseIntDoubleVector eval(DenseIntDoubleVector[] centroids) {
return new DenseIntDoubleVector(centroids.length);
}

@Override
Expand All @@ -87,7 +87,7 @@ public TypeInference getTypeInference(DataTypeFactory typeFactory) {
.outputTypeStrategy(
callContext ->
Optional.of(
DataTypes.of(DenseVectorTypeInfo.INSTANCE)
DataTypes.of(DenseIntDoubleVectorTypeInfo.INSTANCE)
.toDataType(typeFactory)))
.build();
}
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