pub fn update_logits(
	trees_for_round: &[TrainTree],
	binned_features: ArrayView2<TableValue>,
	mut predictions: ArrayViewMut2<f32>,
) {
	let features_rows = binned_features.axis_iter(Axis(0));
	let logits_rows = predictions.axis_iter_mut(Axis(1));
	for (features, mut logits) in zip!(features_rows, logits_rows) {
		for (logit, tree) in zip!(logits.iter_mut(), trees_for_round.iter()) {
			*logit += tree.predict(features.as_slice().unwrap());
		}
	}
}
/// This function is used by the common train function to update the logits after each round of trees is trained for multiclass classification.
This function is used by the common train function to update the logits after each round of trees is trained for multiclass classification.
This function is used by the common train function to update the logits after each round of trees is trained for multiclass classification .
pub fn update_logits(
	trees_for_round: &[TrainTree],
	binned_features: ArrayView2<TableValue>,
	mut predictions: ArrayViewMut2<f32>,
) {
	let features_rows = binned_features.axis_iter(Axis(0));
	let logits_rows = predictions.axis_iter_mut(Axis(1));
	for (features, mut logits) in zip!(features_rows, logits_rows) {
		for (logit, tree) in zip!(logits.iter_mut(), trees_for_round.iter()) {
			*logit += tree.predict(features.as_slice().unwrap());
		}
	}
}
pub fn update_logits ( trees_for_round : & [ TrainTree ] , binned_features : ArrayView2 < TableValue > , mut predictions : ArrayViewMut2 < f32 > , ) { let features_rows = binned_features . axis_iter ( Axis ( 0 ) ) ; let logits_rows = predictions . axis_iter_mut ( Axis ( 1 ) ) ; for ( features , mut logits ) in zip ! ( features_rows logits_rows ) { for ( logit , tree ) in zip ! ( logits iter_mut ( ) trees_for_round iter ( ) ) { * logit += tree . predict ( features . as_slice ( ) . unwrap ( ) ) ; } } }
This function is used by the common train function to update the logits after each round of trees is trained for multiclass classification.
This function is used by the common train function to update the logits after each round of trees is trained for multiclass classification .
trees_for_round
&[TrainTree]
binned_features
ArrayView2<TableValue>
predictions
ArrayViewMut2<f32>