Package ai.sklearn4j.preprocessing.data

Class Normalizer

  • public class Normalizer
extends TransformerMixin<NumpyArray<Double>,​NumpyArray<Double>>
    Normalize samples individually to unit norm. Each sample (i.e. each row of the data matrix) with at least one non zero component is rescaled independently of other samples so that its norm (l1, l2 or inf) equals one. This transformer is able to work both with dense numpy arrays and scipy.sparse matrix (use CSR format if you want to avoid the burden of a copy / conversion). Scaling inputs to unit norms is a common operation for text classification or clustering for instance. For instance the dot product of two l2-normalized TF-IDF vectors is the cosine similarity of the vectors and is the base similarity metric for the Vector Space Model commonly used by the Information Retrieval community.

    • Constructor Detail

      • Normalizer

        public Normalizer()
        Instantiate a new object of Normalizer.

    • Method Detail

      • 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.

      • getNorm

        public String getNorm()
        Gets the type of norm that the object performs. The value is either l1, l2, or max.
        Returns:
        The norm applied by the Normalizer.

      • setNorm

        public void setNorm​(String norm)
        Sets the type of norm that the object performs. The value is either l1, l2, or max.
        Parameters:
        norm - The type of norm, either l1, l2, or max.