Hi, folks.
I'm implementing an algorithm and must pass many arguments to the init function
in a module that does the configuration. Then, those parameters will be used in
many functions, including passing them to functions in other modules. I was
wondering what the best way of doing that in Nim is.
For instance, the code below is how it's commonly done in Python. Since it's a
class, the methods can access those attributes, meaning I won't have to pass
them as arguments. In Nim, should I create all those "self" attributes as
globals?
def __init__(
self,
loss,
*,
learning_rate,
max_iter,
max_leaf_nodes,
max_depth,
min_samples_leaf,
l2_regularization,
max_features,
max_bins,
categorical_features,
monotonic_cst,
interaction_cst,
warm_start,
early_stopping,
scoring,
validation_fraction,
n_iter_no_change,
tol,
verbose,
random_state,
):
self.loss = loss
self.learning_rate = learning_rate
self.max_iter = max_iter
self.max_leaf_nodes = max_leaf_nodes
self.max_depth = max_depth
self.min_samples_leaf = min_samples_leaf
self.l2_regularization = l2_regularization
self.max_features = max_features
self.max_bins = max_bins
self.monotonic_cst = monotonic_cst
self.interaction_cst = interaction_cst
self.categorical_features = categorical_features
self.warm_start = warm_start
self.early_stopping = early_stopping
self.scoring = scoring
self.validation_fraction = validation_fraction
self.n_iter_no_change = n_iter_no_change
self.tol = tol
self.verbose = verbose
self.random_state = random_state
Run
Thanks.
