try to fix test error

.github/CONTRIBUTING.md

@@ -70,3 +70,15 @@ $ rust-code-analysis-cli -p src/algorithm/neighbour/fastpair.rs --ls 22 --le 213
 * **PRs on develop**: any change should be PRed first in `development`
 
 * **testing**:  everything should work and be tested as defined in the workflow. If any is failing for non-related reasons, annotate the test failure in the PR comment.
+
+
+## Suggestions for debugging
+1. Install `lldb` for your platform
+2. Run `rust-lldb target/debug/libsmartcore.rlib` in your command-line
+3. In lldb, set up some breakpoints using `b func_name` or `b src/path/to/file.rs:linenumber`
+4. In lldb, run a single test with `r the_name_of_your_test`
+
+Display variables in scope: `frame variable <name>` 
+
+Execute expression: `p <expr>`
+

src/ensemble/random_forest_classifier.rs

@@ -856,8 +856,10 @@ mod tests {
         // Test shape
         assert_eq!(probas.shape(), (10, 2));
 
+        let (pro_n_rows, _) = probas.shape();
+
         // Test probability sum
-        for i in 0..10 {
+        for i in 0..pro_n_rows {
             let row_sum: f64 = probas.get_row(i).sum();
             assert!(
                 (row_sum - 1.0).abs() < 1e-6,
@@ -866,7 +868,7 @@ mod tests {
         }
 
         // Test class prediction
-        let predictions: Vec<u32> = (0..10)
+        let predictions: Vec<u32> = (0..pro_n_rows)
             .map(|i| {
                 if probas.get((i, 0)) > probas.get((i, 1)) {
                     0
@@ -880,7 +882,7 @@ mod tests {
 
         // Test probability values
         // These values are approximate and based on typical random forest behavior
-        for i in 0..5 {
+        for i in 0..(pro_n_rows / 2) {
             assert!(
                 *probas.get((i, 0)) > f64::from_f32(0.6).unwrap(),
                 "Class 0 samples should have high probability for class 0"
@@ -891,7 +893,7 @@ mod tests {
             );
         }
 
-        for i in 5..10 {
+        for i in (pro_n_rows / 2)..pro_n_rows {
             assert!(
                 *probas.get((i, 1)) > f64::from_f32(0.6).unwrap(),
                 "Class 1 samples should have high probability for class 1"