Numerix

v0.6.0

Numerix v0.6.0 Numerix.Distance View Source

Distance functions between two vectors.

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Functions

euclidean(x, y)

The Euclidean distance between two vectors.

jaccard(vector1, vector2)

The Jaccard distance (1 - Jaccard index) between two vectors.

manhattan(x, y)

The Manhattan distance between two vectors.

minkowski(x, y, p \\ 3)

The Minkowski distance between two vectors.

mse(x, y)

Mean squared error, the average of the squares of the errors betwen two vectors, i.e. the difference between predicted and actual values.

pearson(vector1, vector2)

The Pearson's distance between two vectors.

rmse(vector1, vector2)

Root mean square error of two vectors, or simply the square root of mean squared error of the same set of values. It is a measure of the differences between predicted and actual values.

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euclidean(x, y)

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euclidean(Numerix.Common.vector(), Numerix.Common.vector()) ::
  Numerix.Common.maybe_float()

The Euclidean distance between two vectors.

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jaccard(vector1, vector2)

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jaccard(Numerix.Common.vector(), Numerix.Common.vector()) ::
  Numerix.Common.maybe_float()

The Jaccard distance (1 - Jaccard index) between two vectors.

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manhattan(x, y)

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manhattan(Numerix.Common.vector(), Numerix.Common.vector()) ::
  Numerix.Common.maybe_float()

The Manhattan distance between two vectors.

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minkowski(x, y, p \\ 3)

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minkowski(Numerix.Common.vector(), Numerix.Common.vector(), integer()) ::
  Numerix.Common.maybe_float()

The Minkowski distance between two vectors.

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mse(x, y)

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mse(Numerix.Common.vector(), Numerix.Common.vector()) ::
  Numerix.Common.maybe_float()

Mean squared error, the average of the squares of the errors betwen two vectors, i.e. the difference between predicted and actual values.

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pearson(vector1, vector2)

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pearson(Numerix.Common.vector(), Numerix.Common.vector()) ::
  Numerix.Common.maybe_float()

The Pearson's distance between two vectors.

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rmse(vector1, vector2)

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rmse(Numerix.Common.vector(), Numerix.Common.vector()) ::
  Numerix.Common.maybe_float()

Root mean square error of two vectors, or simply the square root of mean squared error of the same set of values. It is a measure of the differences between predicted and actual values.