Class MaxScaler

java.lang.Object
deepnetts.data.norm.AbstractScaler
deepnetts.data.norm.MaxScaler
All Implemented Interfaces:
Serializable, javax.visrec.ml.data.preprocessing.Scaler<javax.visrec.ml.data.DataSet<MLDataItem>>
public final class MaxScaler extends AbstractScaler
Performs max normalization, rescales data to corresponding max value in each column. Scales all values to interval [0, 1], by dividing columns with their corresponding maximum value. Performs normalization on both inputs and outputs.
See Also:

  • Constructor Details

    • MaxScaler

      public MaxScaler(javax.visrec.ml.data.DataSet<MLDataItem> dataSet)
      Creates a new instance of max normalizer initialized to max values in given data set.
      Parameters:
      dataSet -

  • Method Details

    • apply

      public void apply(javax.visrec.ml.data.DataSet<MLDataItem> dataSet)
      Performs normalization on the given inputs.
      Parameters:
      dataSet - data set to normalize

    • scaleInput

      public void scaleInput(TensorBase input)
      Description copied from class: AbstractScaler
      Normalize input of deployed model
      Specified by:
      scaleInput in class AbstractScaler
      Parameters:
      input -

    • getMaxInputs

      public TensorBase getMaxInputs()

    • setMaxInputs

      public void setMaxInputs(TensorBase maxInputs)

    • getMaxOutputs

      public TensorBase getMaxOutputs()

    • setMaxOutputs

      public void setMaxOutputs(TensorBase maxOutputs)

    • deNormalizeOutputs

      public void deNormalizeOutputs(TensorBase outputs)
      De-normalize given output vector in-place. Multiplies given vector with vector used for normalization, and stores these values in same memory location as input vector.
      Parameters:
      outputs -

    • deNormalizeInputs

      public void deNormalizeInputs(TensorBase inputs)

    • normalizeInput

      public void normalizeInput(TensorBase input)