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# Flink ML (flink-ml_2.10)
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Apache Flink Machine Learning Library for Scala 2.10. This library provides distributed machine learning capabilities built on top of Apache Flink's stream and batch processing engine. Note: This version (1.3.3) contains a minimal implementation with stub functionality.
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## Package Information
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- **Package Name**: flink-ml_2.10
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- **Package Type**: maven
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- **Language**: Scala 2.10
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- **Installation**: 
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  ```xml
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  <dependency>
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      <groupId>org.apache.flink</groupId>
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      <artifactId>flink-ml_2.10</artifactId>
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      <version>1.3.3</version>
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  </dependency>
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  ```
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## Core Imports
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```scala
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import org.apache.flink.ml.MLPackage
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import org.apache.flink.ml.regression.MultipleLinearRegression
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```
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For Flink execution context:
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```scala
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import org.apache.flink.api.scala._
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```
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## Basic Usage
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```scala
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import org.apache.flink.api.scala._
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import org.apache.flink.ml.MLPackage
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import org.apache.flink.ml.regression.MultipleLinearRegression
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// Access package information
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val version = MLPackage.version
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val scalaVersion = MLPackage.scalaVersion
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// Create regression model (stub implementation)
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val regression = new MultipleLinearRegression()
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// Note: The fit method is a stub and returns dummy data
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// val model = regression.fit(trainingData)
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```
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## Capabilities
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### Package Information
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Access version and compatibility information for the ML library.
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```scala { .api }
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object MLPackage {
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  val version: String
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  val scalaVersion: String
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}
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```
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### Multiple Linear Regression
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Basic linear regression implementation for distributed machine learning. Note: This is a stub implementation in version 1.3.3.
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```scala { .api }
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class MultipleLinearRegression extends Serializable {
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  /**
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   * Fit the linear regression model
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   * 
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   * Parameters:
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   * - trainingData: DataSet[LabeledVector] - Training dataset with labeled vectors
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   * 
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   * Returns:
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   * DataSet[Array[Double]] - Model coefficients (stub implementation returns Array(0.0))
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   */
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  def fit(trainingData: DataSet[LabeledVector]): DataSet[Array[Double]]
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}
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```
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## Types
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The following types are used in the API:
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```scala { .api }
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// DataSet is from Flink's core API (imported via org.apache.flink.api.scala._)
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// Represents a distributed dataset in Flink
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type DataSet[T] // Flink distributed dataset
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// Note: The following types are imported in MultipleLinearRegression.scala 
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// but are NOT defined in this stub implementation:
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// - org.apache.flink.ml.common.LabeledVector
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// - org.apache.flink.ml.common.LinearAlgebra
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// 
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// These imports exist in the source code but reference non-existent classes,
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// indicating this is an incomplete stub implementation.
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```
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## Implementation Status
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**Important**: This version (1.3.3) appears to be a minimal stub implementation. The actual Flink ML library documentation describes comprehensive machine learning capabilities including:
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- ALS (Alternating Least Squares)
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- SVM using CoCoA
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- k-Nearest Neighbors Join
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- Cross Validation
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- MinMax/Standard Scalers
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- Polynomial Features
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- Stochastic Outlier Selection
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- Distance Metrics
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- Pipelines
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However, these features are not present in the actual source code for this version. Only the basic package information and a stub MultipleLinearRegression class are available.
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## Dependencies
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This library depends on:
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- `flink-scala_2.10`: Core Flink Scala API
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- `flink-streaming-scala_2.10`: Flink streaming Scala API
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- `scala-library`: Scala 2.10.6 standard library