Adaboost classifier with quasi-randomized hidden layer

Parameters' description can be found at https://techtonique.github.io/nnetsauce/

AdaBoostClassifier(
  obj,
  n_estimators = 10L,
  learning_rate = 0.1,
  n_hidden_features = 1L,
  reg_lambda = 0,
  reg_alpha = 0.5,
  activation_name = "relu",
  a = 0.01,
  nodes_sim = "sobol",
  bias = TRUE,
  dropout = 0,
  direct_link = FALSE,
  n_clusters = 2L,
  cluster_encode = TRUE,
  type_clust = "kmeans",
  col_sample = 1,
  row_sample = 1,
  seed = 123L,
  verbose = 1,
  method = "SAMME",
  backend = c("cpu", "gpu", "tpu")
)

Examples

library(datasets)

X <- as.matrix(iris[, 1:4])
y <- as.integer(iris[, 5]) - 1L

n <- dim(X)[1]
p <- dim(X)[2]

set.seed(213)
train_index <- sample(x = 1:n, size = floor(0.8*n), replace = TRUE)
test_index <- -train_index

X_train <- as.matrix(iris[train_index, 1:4])
y_train <- as.integer(iris[train_index, 5]) - 1L
X_test <- as.matrix(iris[test_index, 1:4])
y_test <- as.integer(iris[test_index, 5]) - 1L

# ValueError: Sample weights must be 1D array or scalar
# obj <- sklearn$tree$DecisionTreeClassifier()
# obj2 <- AdaBoostClassifier(obj)
# obj2$fit(X_train, y_train)
# print(obj2$score(X_test, y_test))
# print(obj2$predict_proba(X_test))