Numerix v0.4.2 Numerix.Distance

Distance functions between two vectors.

Summary

Functions

The Euclidean distance between two vectors

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

The Manhattan distance between two vectors

The Minkowski distance between two vectors

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

The Pearson’s distance between two vectors

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

Functions

euclidean(vector1, vector2)
euclidean([number], [number]) :: Numerix.Common.maybe_float

The Euclidean distance between two vectors.

jaccard(vector1, vector2)
jaccard([number], [number]) :: Numerix.Common.maybe_float

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

manhattan(vector1, vector2)
manhattan([number], [number]) :: Numerix.Common.maybe_float

The Manhattan distance between two vectors.

minkowski(vector1, vector2, p \\ 3)
minkowski([number], [number], integer) :: Numerix.Common.maybe_float

The Minkowski distance between two vectors.

mse(vector1, vector2)
mse([number], [number]) :: 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.

pearson(vector1, vector2)
pearson([number], [number]) :: Numerix.Common.maybe_float

The Pearson’s distance between two vectors.

rmse(vector1, vector2)
rmse([number], [number]) :: 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.