Add test

src/ensemble/random_forest_classifier.rs

@@ -317,7 +317,8 @@ impl<T: RealNumber> RandomForestClassifier<T> {
         which_max(&result)
     }
 
-    /// Predict the per-class probabilties for each observation. The probability is calculated as the fraction of trees that predicted a given class
+    /// Predict the per-class probabilties for each observation.
+    /// The probability is calculated as the fraction of trees that predicted a given class
     pub fn predict_probs<M: Matrix<T>>(&self, x: &M) -> Result<DenseMatrix<f64>, Failed> {
         let mut result = DenseMatrix::<f64>::zeros(x.shape().0, self.classes.len());
 
@@ -326,8 +327,8 @@ impl<T: RealNumber> RandomForestClassifier<T> {
         for i in 0..n {
             let row_probs = self.predict_probs_for_row(x, i);
 
-            for j in 0..row_probs.len() {
+            for (j, item) in row_probs.iter().enumerate() {
-                result.set(i, j, row_probs[j]);
+                result.set(i, j, *item);
             }
         }
 
@@ -559,9 +560,25 @@ mod tests_prob {
         )
         .unwrap();
 
-        let results = classifier.predict_probs(&x).unwrap();
+        println!("{:?}", classifier.classes);
 
+        let results = classifier.predict_probs(&x).unwrap();
+        println!("{:?}", x.shape());
         println!("{:?}", results);
+        println!("{:?}", results.shape());
+
+        assert_eq!(
+            results,
+            DenseMatrix::<f64>::from_array(
+                20,
+                2,
+                &[
+                    1.0, 0.78, 0.95, 0.82, 1.0, 0.92, 0.99, 0.96, 0.36, 0.33, 0.02, 0.02, 0.0, 0.0,
+                    0.0, 0.0, 0.03, 0.05, 0.0, 0.02, 0.0, 0.22, 0.05, 0.18, 0.0, 0.08, 0.01, 0.04,
+                    0.64, 0.67, 0.98, 0.98, 1.0, 1.0, 1.0, 1.0, 0.97, 0.95, 1.0, 0.98
+                ]
+            )
+        );
         assert!(false);
     }
 }