Packages

  • package root

    Smile (Statistical Machine Intelligence and Learning Engine) is a fast and comprehensive machine learning, NLP, linear algebra, graph, interpolation, and visualization system in Java and Scala.

    Smile (Statistical Machine Intelligence and Learning Engine) is a fast and comprehensive machine learning, NLP, linear algebra, graph, interpolation, and visualization system in Java and Scala. With advanced data structures and algorithms, Smile delivers state-of-art performance.

    Smile covers every aspect of machine learning, including classification, regression, clustering, association rule mining, feature selection, manifold learning, multidimensional scaling, genetic algorithms, missing value imputation, efficient nearest neighbor search, etc.

    Definition Classes
    root
  • package smile
    Definition Classes
    root
  • package math

    Mathematical and statistical functions.

    Mathematical and statistical functions.

    Definition Classes
    smile
  • package distance

    Distance functions.

    Distance functions.

    Definition Classes
    math
  • Hamming
  • MeanMahanttan
  • PatternDifference
  • ShapeDifference
  • SizeDifference
  • Vari
p

smile.math

distance

package distance

Distance functions.

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Type Members

  1. class Hamming extends Distance[Array[Int]]

    Hamming distance between two bit strings.

    Hamming distance between two bit strings. Note that we treat each integer in the input array as a bit string. In contrast, HammingDistance treats each integer in the input array as a single value.

  2. class MeanMahanttan extends Distance[Array[Int]]

    Mean Mahantan distance.

    Mean Mahantan distance. Compared to regular Manhattan distance, it normalizes the distance by the sum of contingency table values.

    References

    • Seung-Seok Choi, et al. A Survey of Binary Similarity and Distance Measures. http://www.iiisci.org/journal/CV$/sci/pdfs/GS315JG.pdf
  3. class PatternDifference extends Distance[Array[Int]]

    Pattern difference distance.

    Pattern difference distance.

    References

    • Seung-Seok Choi, et al. A Survey of Binary Similarity and Distance Measures. http://www.iiisci.org/journal/CV$/sci/pdfs/GS315JG.pdf
  4. class ShapeDifference extends Distance[Array[Int]]

    Shape Difference distance.

    Shape Difference distance.

    References

    • Seung-Seok Choi, et al. A Survey of Binary Similarity and Distance Measures. http://www.iiisci.org/journal/CV$/sci/pdfs/GS315JG.pdf
  5. class SizeDifference extends Distance[Array[Int]]

    Size Difference distance.

    Size Difference distance.

    References

    • Seung-Seok Choi, et al. A Survey of Binary Similarity and Distance Measures. http://www.iiisci.org/journal/CV$/sci/pdfs/GS315JG.pdf
  6. class Vari extends Distance[Array[Int]]

    Vari distance class.

    Vari distance class.

    References

    • Seung-Seok Choi, et al. A Survey of Binary Similarity and Distance Measures. http://www.iiisci.org/journal/CV$/sci/pdfs/GS315JG.pdf

Value Members

  1. def contingency(x: Array[Int], y: Array[Int]): (Int, Int, Int, Int)

    Build the contingency matrix (a, b, c, d) where for each bite i, j of vector 1 and 2 :

    Build the contingency matrix (a, b, c, d) where for each bite i, j of vector 1 and 2 :

    • a is incremented if i = 1, j = 1
    • b is incremented if i = 1, j = 0
    • c is incremented if i = 0, j = 1
    • d is incremented if i = 0, j = 0

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