Linear regressor with a quasi-randomized layer

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

BaseRegressor(
  n_hidden_features = 5L,
  activation_name = "relu",
  a = 0.01,
  nodes_sim = "sobol",
  bias = TRUE,
  dropout = 0,
  direct_link = TRUE,
  n_clusters = 2L,
  cluster_encode = TRUE,
  type_clust = "kmeans",
  col_sample = 1,
  row_sample = 1,
  seed = 123L,
  backend = c("cpu", "gpu", "tpu")
)

Examples

set.seed(123)
n <- 50 ; p <- 3
X <- matrix(rnorm(n * p), n, p) # no intercept!
y <- rnorm(n)

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(X[train_index, ])
y_train <- y[train_index]
X_test <- as.matrix(X[test_index, ])
y_test <- y[test_index]

obj <- BaseRegressor(n_hidden_features=10L, dropout=0.9)
print(obj$fit(X_train, y_train))
#> Error in py_call_impl(callable, call_args$unnamed, call_args$named): AttributeError: 'list' object has no attribute 'dtype'
#> Run `reticulate::py_last_error()` for details.
print(obj$score(X_test, y_test))
#> Error in py_call_impl(callable, call_args$unnamed, call_args$named): AttributeError: 'NoneType' object has no attribute 'ndim'
#> Run `reticulate::py_last_error()` for details.