bryangoodrich

loadInput <- function() {
    structure(c(0.051267, -0.092742, -0.21371, -0.375, -0.51325, 
    -0.52477, -0.39804, -0.30588, 0.016705, 0.13191, 0.38537, 0.52938, 
    0.63882, 0.73675, 0.54666, 0.322, 0.16647, -0.046659, -0.17339, 
    -0.47869, -0.60541, -0.62846, -0.59389, -0.42108, -0.11578, 0.20104, 
    0.46601, 0.67339, -0.13882, -0.29435, -0.26555, -0.16187, -0.17339, 
    -0.28283, -0.36348, -0.30012, -0.23675, -0.06394, 0.062788, 0.22984, 
    0.2932, 0.48329, 0.64459, 0.46025, 0.6273, 0.57546, 0.72523, 
    0.22408, 0.44297, 0.322, 0.13767, -0.0063364, -0.092742, -0.20795, 
    -0.20795, -0.43836, -0.21947, -0.13882, 0.18376, 0.22408, 0.29896, 

bryangoodrich

gradientDescent <- function(X, y, initial_theta, method = "BFGS", ...) {
  m <- nrow(y)
  
  sigmoid      <- function(x) 1 / (1 + exp(-x))
  gradFunction <- function(theta) (1/m) * (t(X) %*% (sigmoid(X %*% theta)-y))
  costFunction <- function(theta) 
    (1/m) * (t(-y) %*% log(sigmoid(X %*% theta)) - t(1-y) %*% log(1 - sigmoid(X %*% theta)))  
  
  optim(initial_theta, costFunction, gradFunction, method = method, ...)  
}