example_data: Example data
In svilsen/BRVFL: Random Weight Neural Networks

example_data

R Documentation

Example data

Description

A data-set of 2000 observations were sampled independently according to the function:

y_n = \dfrac{1}{1 + \exp(-x_n^T\beta + \varepsilon_n)},

where x_n^T is a vector containing an intercept and five input features, \beta is a vector containing the parameters, (-1,\;2,\;1,\;2,\;0.5,\;3)^T, and \varepsilon_n is normally distributed noise with mean 0 and variance 0.1. Furthermore, the five features were generated as x_1 \sim \mathcal Unif(-5, 5), x_2 \sim \mathcal Unif(0, 2), x_3 \sim \mathcal N(2, 4), x_4 \sim \mathcal Gamma(2, 4), and x_5 \sim \mathcal Beta(10, 4), respectively.