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# Distance functions

This module supports functions to calculate the distance between two vectors.

## chebychev

`distance.``chebyshev`(s1, s2)

  Computes the chebychev distance between two vector.

  Parameters:
    s1: numpy array
      The first vector.
    s2: numpy array
      The second vector.
  Returns: float
    Chebychev distance between s1 and s2.

## NormXCorr

`distance.``NormXCorr`(s1, s2)

  Computes the normalized cross correlation distance between two vector.

  Parameters:
    s1: numpy array
      The first vector.
    s2: numpy array
      The second vector.
  Returns: float
    NormXCorr distance between s1 and s2, dist is between [-1, 1]. A value of one indicate a perfect match.

## SAM

`distance.``SAM`(s1, s2)

  Computes the spectral angle mapper between two vectors (in radians).

  Parameters:
    s1: numpy array
      The first vector.
    s2: numpy array
      The second vector.
  Returns: float
    The angle between vectors s1 and s2 in radians.

## SID

`distance.``SID`(s1, s2)

  Computes the spectral information divergence between two vectors.

  Parameters:
    s1: numpy array
      The first vector.
    s2: numpy array
      The second vector.
  Returns: float
    Spectral information divergence between s1 and s2.
  Reference
    C.-I. Chang, “An Information-Theoretic Approach to SpectralVariability, Similarity, and Discrimination for Hyperspectral Image” IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 46, NO. 5, AUGUST 2000.

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