ml

Type Members

1. class DLClassifier[T] extends com.intel.analytics.bigdl.dlframes.DLClassifier[T]

   

   Deprecated.

   Deprecated. Please refer to package com.intel.analytics.bigdl.dlframes.

   DLClassifier is a specialized DLEstimator that simplifies the data format for classification tasks. It only supports label column of DoubleType. and the fitted DLClassifierModel will have the prediction column of DoubleType.

   Annotations

   @deprecated

   Deprecated

   (Since version 0.5.0)

2. class DLClassifierModel[T] extends com.intel.analytics.bigdl.dlframes.DLClassifierModel[T]

   

   Deprecated.

   Deprecated. Please refer to package com.intel.analytics.bigdl.dlframes.

   DLClassifierModel is a specialized DLModel for classification tasks. The prediction column will have the datatype of Double.

   Annotations

   @deprecated

   Deprecated

   (Since version 0.5.0)

3. class DLEstimator[T] extends com.intel.analytics.bigdl.dlframes.DLEstimator[T]

   

   Deprecated.

   Deprecated. Please refer to package com.intel.analytics.bigdl.dlframes.

   DLEstimator helps to train a BigDL Model with the Spark ML Estimator/Transfomer pattern, thus Spark users can conveniently fit BigDL into Spark ML pipeline.

   DLEstimator supports feature and label data in the format of Array[Double], Array[Float], org.apache.spark.mllib.linalg.{Vector, VectorUDT}, org.apache.spark.ml.linalg.{Vector, VectorUDT}, Double and Float.

   User should specify the feature data dimensions and label data dimensions via the constructor parameters featureSize and labelSize respectively. Internally the feature and label data are converted to BigDL tensors, to further train a BigDL model efficiently.

   For details usage, please refer to examples in package com.intel.analytics.bigdl.example.MLPipeline

   Annotations

   @deprecated

   Deprecated

   (Since version 0.5.0)

4. class DLModel[T] extends com.intel.analytics.bigdl.dlframes.DLModel[T]

   

   Deprecated.

   Deprecated. Please refer to package com.intel.analytics.bigdl.dlframes.

   DLModel helps embed a BigDL model into a Spark Transformer, thus Spark users can conveniently merge BigDL into Spark ML pipeline. DLModel supports feature data in the format of Array[Double], Array[Float], org.apache.spark.mllib.linalg.{Vector, VectorUDT}, org.apache.spark.ml.linalg.{Vector, VectorUDT}, Double and Float. Internally DLModel use features column as storage of the feature data, and create Tensors according to the constructor parameter featureSize.

   DLModel is compatible with both spark 1.5-plus and 2.0 by extending ML Transformer.

   Annotations

   @deprecated

   Deprecated

   (Since version 0.5.0)