diff --git a/machine_learning/adaline_learning.cpp b/machine_learning/adaline_learning.cpp
index 562958d64..a484e27aa 100644
--- a/machine_learning/adaline_learning.cpp
+++ b/machine_learning/adaline_learning.cpp
@@ -255,7 +255,57 @@ void test2(double eta = 0.01) {
 
         std::cout << "Predict for x=(" << x0 << "," << x1 << "): " << predict;
 
-        int expected_val = (x0 + x1) > -1 ? 1 : -1;
+        int expected_val = (x0 + 3. * x1) > -1 ? 1 : -1;
+        assert(predict == expected_val);
+        std::cout << " ...passed" << std::endl;
+    }
+}
+
+/**
+ * test function to predict points in a 3D coordinate system lying within the
+ * sphere of radius 1 and centre at origin as +1 and others as -1. Note that
+ * each point is defined by 3 values but we use 6 features. The function will
+ * create random sample points for training and test purposes.
+ * \param[in] eta learning rate (optional, default=0.01)
+ */
+void test3(double eta = 0.01) {
+    adaline ada(6, eta);  // 2 features
+
+    const int N = 100;  // number of sample points
+
+    std::vector<double> X[N];
+    int Y[N];  // corresponding y-values
+
+    // generate sample points in the interval
+    // [-range2/100 , (range2-1)/100]
+    int range = 200;          // sample points full-range
+    int range2 = range >> 1;  // sample points half-range
+    for (int i = 0; i < N; i++) {
+        double x0 = ((std::rand() % range) - range2) / 100.f;
+        double x1 = ((std::rand() % range) - range2) / 100.f;
+        double x2 = ((std::rand() % range) - range2) / 100.f;
+        X[i] = {x0, x1, x2, x0 * x0, x1 * x1, x2 * x2};
+        Y[i] = ((x0 * x0) + (x1 * x1) + (x2 * x2)) <= 1.f ? 1 : -1;
+    }
+
+    std::cout << "------- Test 3 -------" << std::endl;
+    std::cout << "Model before fit: " << ada << std::endl;
+
+    ada.fit(X, Y);
+    std::cout << "Model after fit: " << ada << std::endl;
+
+    int N_test_cases = 5;
+    for (int i = 0; i < N_test_cases; i++) {
+        double x0 = ((std::rand() % range) - range2) / 100.f;
+        double x1 = ((std::rand() % range) - range2) / 100.f;
+        double x2 = ((std::rand() % range) - range2) / 100.f;
+
+        int predict = ada.predict({x0, x1, x2, x0 * x0, x1 * x1, x2 * x2});
+
+        std::cout << "Predict for x=(" << x0 << "," << x1 << "," << x2
+                  << "): " << predict;
+
+        int expected_val = ((x0 * x0) + (x1 * x1) + (x2 * x2)) <= 1.f ? 1 : -1;
         assert(predict == expected_val);
         std::cout << " ...passed" << std::endl;
     }
@@ -265,7 +315,7 @@ void test2(double eta = 0.01) {
 int main(int argc, char **argv) {
     std::srand(std::time(nullptr));  // initialize random number generator
 
-    double eta = 0.2;  // default value of eta
+    double eta = 0.1;  // default value of eta
     if (argc == 2)     // read eta value from commandline argument if present
         eta = strtof(argv[1], nullptr);
 
@@ -276,5 +326,10 @@ int main(int argc, char **argv) {
 
     test2(eta);
 
+    std::cout << "Press ENTER to continue..." << std::endl;
+    std::cin.get();
+
+    test3(eta);
+
     return 0;
 }