bgd: Gradient descent algorithms
In tbonza/supml: "Algorithms for Supervised Machine Learning"

Description Usage Arguments Value Examples

Meant for use with loss functions in supml Batch Gradient Descent

1	bgd(alpha, X, y, grad, N, precision = 1e-04, ...)

`alpha`	learning rate constant
`X`	matrix of predictors including 1 for beta 0
`y`	vector of dependent variable
`grad`	gradient function
`N`	maximum number of iterations
`precision`	constant used for approximation condition

list object with theta, convergence status, and number of iterations

x <- runif(1000, -5, 5)
y <- x + rnorm(1000) + 3
X <- cbind(1, matrix(x))
h <- bgd(0.1, X, y, least_squares_gradient, 1000)
h$theta
h$converged == TRUE
h$iterations