Add example exercise

src/main/resources/templates/matlab/exercise/.gitattributes

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+* text=auto
+
+*.fig binary
+*.mat binary
+*.mdl binary diff merge=mlAutoMerge
+*.mdlp binary
+*.mex* binary
+*.mlapp binary
+*.mldatx binary
+*.mlproj binary
+*.mlx binary
+*.p binary
+*.sfx binary
+*.sldd binary
+*.slreqx binary merge=mlAutoMerge
+*.slmx binary merge=mlAutoMerge
+*.sltx binary
+*.slxc binary
+*.slx binary merge=mlAutoMerge
+*.slxp binary
+
+## Other common binary file types
+*.docx binary
+*.exe binary
+*.jpg binary
+*.pdf binary
+*.png binary
+*.xlsx binary

src/main/resources/templates/matlab/exercise/.gitignore

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+# Autosave files
+*.asv
+*.m~
+*.autosave
+*.slx.r*
+*.mdl.r*
+
+# Derived content-obscured files
+*.p
+
+# Compiled MEX files
+*.mex*
+
+# Packaged app and toolbox files
+*.mlappinstall
+*.mltbx
+
+# Deployable archives
+*.ctf
+
+# Generated helpsearch folders
+helpsearch*/
+
+# Code generation folders
+slprj/
+sccprj/
+codegen/
+
+# Cache files
+*.slxc
+
+# Cloud based storage dotfile
+.MATLABDriveTag
+
+# buildtool cache folder
+.buildtool/

src/main/resources/templates/matlab/exercise/averageGradeByStudent.m

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+function avg = averageGradeByStudent(grades)
+    % TODO: Task 2
+end

src/main/resources/templates/matlab/exercise/finalGrade.m

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+function g = finalGrade(grades,weights)
+    % TODO: Task 3
+end

src/main/resources/templates/matlab/exercise/medianGradeByAssignment.m

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+function avg = medianGradeByAssignment(grades)
+    % TODO: Task 1
+end

src/main/resources/templates/matlab/readme

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+# Grading Statistics
+
+In this exercise, you will analyze student performance based on a matrix of
+grades. Each row in the matrix represents a student, and each column represents
+an assignment.
+
+Your task is to implement three functions that compute statistical measures for
+this data. Each function has a specific purpose, described below:
+
+## Task 1: Median Grade by Assignment
+Compute the median grade for each assignment across all students. The result
+should be a row vector where each element corresponds to the median of a
+specific assignment (column).
+
+- **Function to Implement**: `medianGradeByAssignment()`
+- **Input**: A matrix `grades` where rows are students and columns are assignments.
+- **Output**: A row vector of medians, one for each assignment.
+
+[task][Median Grade by Assignment](testMedianGradeByAssignment)
+
+---
+
+## Task 2: Average Grade by Student
+Calculate the arithmetic mean grade for each student across all their assignments.
+The result should be a row vector where each element corresponds to a student's
+arithmetic mean grade.
+
+- **Function to Implement**: `averageGradeByStudent()`
+- **Input**: A matrix `grades` where rows are students and columns are assignments.
+- **Output**: A row vector of averages, one for each student.
+
+[task][Average Grade by Student](testAverageGradeByStudent)
+
+---
+
+## Task 3: Final Grade
+Determine the weighted final grade for each student. In addition to the
+`grades` matrix, you are provided with a row vector `weights` (summing to 1)
+that represents the weight of each assignment. Compute the weighted average for
+each student and round the result to 1 decimal place. The result should be a
+row vector where each element corresponds to a student’s final grade.
+
+- **Function to Implement**: `finalGrade()`
+- **Input**:
+    1. A matrix `grades` where rows are students and columns are assignments.
+    2. A row vector `weights` with the same number of elements as columns in the `grades` matrix.
+- **Output**: A row vector of weighted final grades, rounded to 1 decimal place.
+
+[task][Final Grade](testFinalGrade)

src/main/resources/templates/matlab/solution/.gitattributes

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+* text=auto
+
+*.fig binary
+*.mat binary
+*.mdl binary diff merge=mlAutoMerge
+*.mdlp binary
+*.mex* binary
+*.mlapp binary
+*.mldatx binary
+*.mlproj binary
+*.mlx binary
+*.p binary
+*.sfx binary
+*.sldd binary
+*.slreqx binary merge=mlAutoMerge
+*.slmx binary merge=mlAutoMerge
+*.sltx binary
+*.slxc binary
+*.slx binary merge=mlAutoMerge
+*.slxp binary
+
+## Other common binary file types
+*.docx binary
+*.exe binary
+*.jpg binary
+*.pdf binary
+*.png binary
+*.xlsx binary

src/main/resources/templates/matlab/solution/.gitignore

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+# Autosave files
+*.asv
+*.m~
+*.autosave
+*.slx.r*
+*.mdl.r*
+
+# Derived content-obscured files
+*.p
+
+# Compiled MEX files
+*.mex*
+
+# Packaged app and toolbox files
+*.mlappinstall
+*.mltbx
+
+# Deployable archives
+*.ctf
+
+# Generated helpsearch folders
+helpsearch*/
+
+# Code generation folders
+slprj/
+sccprj/
+codegen/
+
+# Cache files
+*.slxc
+
+# Cloud based storage dotfile
+.MATLABDriveTag
+
+# buildtool cache folder
+.buildtool/

src/main/resources/templates/matlab/solution/averageGradeByStudent.m

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+function avg = averageGradeByStudent(grades)
+    avg = mean(grades,2).';
+end

src/main/resources/templates/matlab/solution/finalGrade.m

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+function g = finalGrade(grades,weights)
+    g = round((grades * weights.').',1);
+end

src/main/resources/templates/matlab/solution/medianGradeByAssignment.m

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+function avg = medianGradeByAssignment(grades)
+    avg = median(grades);
+end

src/main/resources/templates/matlab/test/tests/GradeTest.m

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+classdef GradeTest < matlab.unittest.TestCase
+    properties
+        grades
+    end
+
+    methods (TestClassSetup)
+        % Shared setup for the entire test class
+    end
+
+    methods (TestMethodSetup)
+        % Setup for each test
+
+        function setupGrades(testCase)
+            testCase.grades = [1.3 3.3 4.0 4.7
+                               2.7 1.7 4.0 1.7
+                               1.7 3.7 3.0 4.3
+                               4.3 2.3 1.7 3.3
+                               2.3 3.7 2.0 5.0];
+        end
+    end
+
+    methods (Test)
+        % Test methods
+
+        function testMedianGradeByAssignment(testCase)
+            actual = medianGradeByAssignment(testCase.grades);
+            expected = [2.3 3.3 3.0 4.3];
+            testCase.assertEqual(actual,expected,"median is incorrect",AbsTol=0.0001);
+        end
+
+        function testAverageGradeByStudent(testCase)
+            actual = averageGradeByStudent(testCase.grades);
+            expected = [3.325 2.525 3.175 2.9 3.25];
+            testCase.assertEqual(actual,expected,"average is incorrect",AbsTol=0.0001);
+        end
+
+        function testFinalGrade(testCase)
+            weights = [0.1 0.1 0.5 0.3];
+            actual = finalGrade(testCase.grades,weights);
+            expected = [3.9 3.0 3.3 2.5 3.1];
+            testCase.assertEqual(actual,expected,"final grades are incorrect",AbsTol=0.0001);
+        end
+    end
+
+end