Package ai.sklearn4j.preprocessing.data

Class MaximumAbsoluteScaler

  • public class MaximumAbsoluteScaler
extends TransformerMixin<NumpyArray<Double>,​NumpyArray<Double>>
    Scale each feature by its maximum absolute value. This estimator scales and translates each feature individually such that the maximal absolute value of each feature in the training set will be 1.0. It does not shift/center the data, and thus does not destroy any sparsity. This scaler can also be applied to sparse CSR or CSC matrices.

    • Constructor Detail

      • MaximumAbsoluteScaler

        public MaximumAbsoluteScaler()
        Instantiate a new object of MaximumAbsoluteScaler.

    • Method Detail

      • setScale

        public void setScale​(NumpyArray value)
        Sets the Per feature relative scaling of the data.
        Parameters:
        value - The new value for scale.

      • getScale

        public NumpyArray getScale()
        Gets the Per feature relative scaling of the data.

      • setMaxAbs

        public void setMaxAbs​(NumpyArray value)
        Sets the Per feature maximum absolute value.
        Parameters:
        value - The new value for maxAbs.

      • getMaxAbs

        public NumpyArray getMaxAbs()
        Gets the Per feature maximum absolute value.

      • setNFeaturesIn

        public void setNFeaturesIn​(long value)
        Sets the Number of features seen during `fit`.
        Parameters:
        value - The new value for nFeaturesIn.

      • getNFeaturesIn

        public long getNFeaturesIn()
        Gets the Number of features seen during `fit`.

      • setFeatureNamesIn

        public void setFeatureNamesIn​(String[] value)
        Sets the Names of features seen during `fit`. Defined only when `X` has feature names that are all strings.
        Parameters:
        value - The new value for featureNamesIn.

      • getFeatureNamesIn

        public String[] getFeatureNamesIn()
        Gets the Names of features seen during `fit`. Defined only when `X` has feature names that are all strings.

      • setNSamplesSeen

        public void setNSamplesSeen​(long value)
        Sets the The number of samples processed by the estimator. Will be reset on new calls to fit, but increments across `partial_fit` calls.
        Parameters:
        value - The new value for nSamplesSeen.

      • getNSamplesSeen

        public long getNSamplesSeen()
        Gets the The number of samples processed by the estimator. Will be reset on new calls to fit, but increments across `partial_fit` calls.