Numerix v0.4.0 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
Specs
euclidean([number], [number]) :: Numerix.Common.maybe_float
The Euclidean distance between two vectors.
Specs
jaccard([number], [number]) :: Numerix.Common.maybe_float
The Jaccard distance (1 - Jaccard index) between two vectors.
Specs
manhattan([number], [number]) :: Numerix.Common.maybe_float
The Manhattan distance between two vectors.
Specs
minkowski([number], [number], integer) :: Numerix.Common.maybe_float
The Minkowski distance between two vectors.
Specs
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.
Specs
pearson([number], [number]) :: Numerix.Common.maybe_float
The Pearson’s distance between two vectors.
Specs
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.