predict.bernstein: Evaluate Bernstein approximations on data.

In brubinstein/diffpriv: Easy Differential Privacy

Evaluate Bernstein approximations on data.

Description

Evaluates a given S3 object of type bernstein on given data D.

Usage

## S3 method for class 'bernstein'
predict(object, D, ...)

Arguments

object	an S3 object of type bernstein.
D	either a numeric vector or matrix, all values in [0,1]. If numeric then length should be object$dims unless the latter is 1 in which case the length can be arbitrary. If a matrix then the number of columns should match object$dims.
...	additional arguments.

Value

a numeric vector of scalar real evaluations.

References

Francesco Aldà and Benjamin I. P. Rubinstein. "The Bernstein Mechanism: Function Release under Differential Privacy", in Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'2017), pp. 1705-1711, Feb 2017.

Examples

f <- function(x) x * sin(x*10)
b <- bernstein(f, dims = 1)
xs <- seq(from=0, to=1, length=50)
mean((f(xs) - predict(b,xs))^2)

brubinstein/diffpriv documentation built on July 7, 2022, 4:23 a.m.