generate doc

docgen/src/main.rs

@@ -42,6 +42,14 @@ fn main() {
     let trait_name: &str = "TreeRegressorTrait";
     doc_trait(trait_path, doc_path, label);
     doc_functions(trait_path, doc_path, trait_name, label);
+
+    // XGBOOST REGRESSOR DOC
+    let trait_path = "src/operators/ml/xgboost_regressor/core.cairo";
+    let doc_path = "docs/framework/operators/machine-learning/xgboost-regressor";
+    let label = "xgboost";
+    let trait_name: &str = "XGBoostRegressorTrait";
+    doc_trait(trait_path, doc_path, label);
+    doc_functions(trait_path, doc_path, trait_name, label);
 }
 
 fn doc_trait(trait_path: &str, doc_path: &str, label: &str) {

docs/SUMMARY.md

@@ -99,6 +99,8 @@
     * [Tree Regressor](framework/operators/machine-learning/tree-regressor/README.md)
       * [tree.fit](framework/operators/machine-learning/tree-regressor/tree.fit.md)
       * [tree.predict](framework/operators/machine-learning/tree-regressor/tree.predict.md)
+    * [XGBoost Regressor](framework/operators/machine-learning/xgboost-regressor/README.md)
+      * [xgboost.predict](framework/operators/machine-learning/xgboost-regressor/xgboost.predict.md)
 
 ## 🏛 Hub
 

docs/framework/operators/machine-learning/xgboost-regressor/README.md

@@ -0,0 +1,22 @@
+# Tree Regressor
+
+`XGBoostRegressorTrait` provides a trait definition for xgboost regression. This trait offers functionalities to predict target values based on input features.
+
+```rust
+use orion::operators::ml::XGBoostRegressorTrait;
+```
+
+### Data types
+
+Orion supports currently only fixed point data types for `XGBoostRegressorTrait`.
+
+| Data type            | dtype                                                         |
+| -------------------- | ------------------------------------------------------------- |
+| Fixed point (signed) | `TreeRegressorTrait<FP8x23 \| FP16x16 \| FP64x64 \| FP32x32>` |
+
+***
+
+| function | description |
+| --- | --- |
+| [`xgboost.predict`](xgboost.predict.md) | Predicts the target value for a set of features using the provided ensemble of decision trees. |
+

docs/framework/operators/machine-learning/xgboost-regressor/xgboost.predict.md

@@ -0,0 +1,44 @@
+# XGBoostRegressorTrait::predict
+
+```rust 
+   fn predict(ref self: Span<TreeNode<T>>, ref features: Span<T>, ref weights: Span<T>) -> T;
+```
+
+Predicts the target value for a set of features using the provided ensemble of decision trees
+and combining their results using given weights.
+
+## Args
+
+* `self`: A reference to a span representing a ensemble of decision trees.
+* `features`:  A reference to a span representing the features for which the prediction is to be made.
+* `weights`:  A reference to a span representing the weights applied to the predictions from each tree.
+
+## Returns
+
+The predicted target value.
+
+## Type Constraints
+
+Constrain input and output types to fixed point.
+
+## Examples
+
+```rust
+use orion::operators::ml::{FP16x16XGBoostRegressor, TreeRegressorTrait, TreeRegressor};
+use orion::numbers::{FP16x16, FixedTrait};
+
+fn xgboost_regressor_example(trees: Span<TreeRegressor<FP16x16>>) {
+
+    let mut features =  array![
+        FixedTrait::new_unscaled(1, false), 
+        FixedTrait::new_unscaled(2, false),
+    ].span();
+
+    let mut weights = array![
+        FixedTrait::new_unscaled(0.5, false),
+        FixedTrait::new_unscaled(0.5, false)
+    ].span();
+
+    FP16x16XGBoostRegressor::predict(ref trees, ref features, ref weights);
+}
+```