 ## Package de.lmu.ifi.dbs.elki.math.linearalgebra.pca.weightfunctions

Weight functions used in weighted PCA via `WeightedCovarianceMatrixBuilder`

See:
Description Interface Summary
WeightFunction WeightFunction interface that allows the use of various distance-based weight functions.

Class Summary
ConstantWeight Constant Weight function The result is always 1.0
ErfcStddevWeight Gaussian Error Function Weight function, scaled using stddev.
ErfcWeight Gaussian Error Function Weight function, scaled such that the result it 0.1 at distance == max erfc(1.1630871536766736 * distance / max) The value of 1.1630871536766736 is erfcinv(0.1), to achieve the intended scaling.
ExponentialStddevWeight Exponential Weight function, scaled such that the result it 0.1 at distance == max stddev * exp(-.5 * distance/stddev) This is similar to the Gaussian weight function, except distance/stddev is not squared.
ExponentialWeight Exponential Weight function, scaled such that the result it 0.1 at distance == max exp(-2.3025850929940455 * distance/max) This is similar to the Gaussian weight function, except distance/max is not squared
GaussStddevWeight Gaussian Weight function, scaled such using standard deviation factor * exp(-.5 * (distance/stddev)^2) with factor being 1 / sqrt(2 * PI)
GaussWeight Gaussian Weight function, scaled such that the result it 0.1 at distance == max exp(-2.3025850929940455 * (distance/max)^2)
InverseLinearWeight Inverse Linear Weight Function.
InverseProportionalStddevWeight Inverse proportional weight function, scaled using the standard deviation. 1 / (1 + distance/stddev)
InverseProportionalWeight Inverse proportional weight function, scaled using the maximum. 1 / (1 + distance/max)
LinearWeight Linear weight function, scaled using the maximum such that it goes from 1.0 to 0.1 1 - 0.9 * (distance/max)
Weight functions used in weighted PCA via `WeightedCovarianceMatrixBuilder`