new version, full rewrite

DESCRIPTION

@@ -6,7 +6,7 @@ Authors@R: c(person(given = "Michal",
              role = c("aut", "cre", "cph"),
              email = "michal.ovadek@gmail.com",
              comment = c(ORCID = "0000-0002-2552-2580")))
-Description: What the package does (one paragraph).
+Description: Factorize binary matrices into rank-k components.
 License: MIT + file LICENSE
 Encoding: UTF-8
 Language: en-GB

NEWS.md

@@ -1,3 +1,7 @@
+# nmfbin 0.2.0
+
+* Full rewrite, simplification, improved terminology
+
 # nmfbin 0.1.0
 
 * Initial experimental release, buggy and incomplete

R/binary_cross_entropy.R

@@ -0,0 +1,53 @@
+#' Sigmoid function
+#'
+#' @param z A numeric value or vector.
+#' @return The sigmoid of z.
+#' @noRd
+sigmoid <- function(z) {
+  1 / (1 + exp(-z))
+}
+
+#' Binary cross-entropy loss function
+#'
+#' @param p Predicted probabilities.
+#' @param y Actual labels (0 or 1).
+#' @return The binary cross-entropy loss.
+#' @noRd
+binary_crossentropy <- function(p, y) {
+  -sum(y * log(p) + (1 - y) * log(1 - p))
+}
+
+#' Gradient of binary cross-entropy with respect to weight w
+#'
+#' @param x Input features.
+#' @param y Actual labels (0 or 1).
+#' @param w Weight.
+#' @return The gradient of the loss with respect to w.
+#' @noRd
+gradient <- function(x, y, w) {
+  p <- sigmoid(w * x)
+  sum(x * (p - y))
+}
+
+#' Gradient Descent for minimizing binary cross-entropy
+#'
+#' @param x Input features.
+#' @param y Actual labels (0 or 1).
+#' @param starting_point Initial weight value.
+#' @param learning_rate Learning rate for gradient descent.
+#' @param n_iterations Number of iterations for the gradient descent.
+#' @return Estimated weight after gradient descent.
+#' @noRd
+gradient_descent <- function(x, y, starting_point, learning_rate, n_iterations) {
+  w <- starting_point
+  for (i in 1:n_iterations) {
+    grad <- gradient(x, y, w)
+    w <- w - learning_rate * grad
+
+    # Print progress
+    p <- sigmoid(w * x)
+    loss <- binary_crossentropy(p, y)
+    cat(sprintf("Iteration %d: w = %f, Loss = %f\n", i, w, loss))
+  }
+  return(w)
+}