trait AbstractDataSet[D, DataSequence] extends AnyRef
A set of data which is used in the model optimization process. The dataset can be access in a random data sample sequence. In the training process, the data sequence is a looped endless sequence. While in the validation process, the data sequence is a limited length sequence. User can use the data() method to get the data sequence.
The sequence of the data is not fixed. It can be changed by the shuffle() method.
User can create a dataset from a RDD, an array and a folder, etc. The DataSet object provides many factory methods.
- D
Data type
- DataSequence
Represent a sequence of data
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abstract
def
data(train: Boolean): DataSequence
Get a sequence of data
Get a sequence of data
- train
if the data is used in train. If yes, the data sequence is a looped endless sequence, or it has a limited length.
- returns
data sequence
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abstract
def
shuffle(): Unit
Change the order of the data sequence from the data set
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abstract
def
size(): Long
Total size of the data set
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abstract
def
transform[C](transformer: Transformer[D, C])(implicit arg0: ClassTag[C]): DataSet[C]
Helper function to transform the data type in the data set.
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!=(arg0: Any): Boolean
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def
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def
->[C](transformer: Transformer[D, C])(implicit arg0: ClassTag[C]): DataSet[C]
Helper function to transform the data type in the data set.
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def
toDistributed(): DistributedDataSet[D]
Convert current DataSet to a distributed DataSet, in which we use a RDD to represent the data sequence.
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def
toLocal(): LocalDataSet[D]
Convert current DataSet to a local DataSet, in which we use an iterator to represent the data sequence.
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toString(): String
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