style: remove unused variables

ciphers/hill_cipher.cpp

@@ -379,7 +379,6 @@ class HillCipher {
         int mat_determinant = det_encrypt < 0 ? det_encrypt % L : det_encrypt;
 
         matrix<double> tmp_inverse = get_inverse(encrypt_key);
-        double d2 = determinant_lu(decrypt_key);
 
         // find co-prime factor for inversion
         int det_inv = -1;

data_structures/rb_tree.cpp

@@ -33,7 +33,7 @@ class RBtree
 };
 void RBtree::insert()
 {
-	int z, i = 0;
+	int z;
 	cout << "\nEnter key of the node to be inserted: ";
 	cin >> z;
 	node *p, *q;

dynamic_programming/maximum_circular_subarray.cpp

@@ -67,7 +67,6 @@ static void test() {
     // Output: 22 
     // Explanation: Subarray 12, 8, -8, 9, -9, 10 gives the maximum sum, that is 22.
 
-    int n = 7; // size of the array
     std::vector<int> arr = {8, -8, 9, -9, 10, -11, 12}; 
     assert(dynamic_programming::maxCircularSum(arr) == 22); // this ensures that the algorithm works as expected
 

graph/hopcroft_karp.cpp

@@ -254,7 +254,7 @@ using graph::HKGraph;
  */
 void tests(){
      // Sample test case 1
-	     int v1a = 3, v1b = 5, e1 = 2;  // vertices of left side, right side and edges
+	     int v1a = 3, v1b = 5;  // vertices of left side, right side and edges
 	     HKGraph g1(v1a, v1b); // execute the algorithm 
 
 	     g1.addEdge(0,1);
@@ -266,7 +266,7 @@ void tests(){
 	     assert(res1 == expected_res1); // assert check to ensure that the algorithm executed correctly for test 1
 
      // Sample test case 2
-     	     int v2a = 4, v2b = 4, e2 = 6;  // vertices of left side, right side and edges
+     	     int v2a = 4, v2b = 4;  // vertices of left side, right side and edges
 	     HKGraph g2(v2a, v2b); // execute the algorithm 
 
              g2.addEdge(1,1);
@@ -282,7 +282,7 @@ void tests(){
 	     assert(res2 == expected_res2); // assert check to ensure that the algorithm executed correctly for test 2
 
       // Sample test case 3
-     	     int v3a = 6, v3b = 6, e3 = 4;  // vertices of left side, right side and edges
+     	     int v3a = 6, v3b = 6;  // vertices of left side, right side and edges
 	     HKGraph g3(v3a, v3b); // execute the algorithm 
 
              g3.addEdge(0,1);

hashing/double_hash_hash_table.cpp

@@ -248,7 +248,7 @@ using double_hashing::totalSize;
  * @returns 0 on success
  */
 int main() {
-    int cmd = 0, hash = 0, key = 0;
+    int cmd = 0, key = 0;
     std::cout << "Enter the initial size of Hash Table. = ";
     std::cin >> totalSize;
     table = std::vector<Entry>(totalSize);

hashing/linear_probing_hash_table.cpp

@@ -222,7 +222,7 @@ using linear_probing::totalSize;
  * @returns 0 on success
  */
 int main() {
-    int cmd = 0, hash = 0, key = 0;
+    int cmd = 0, key = 0;
     std::cout << "Enter the initial size of Hash Table. = ";
     std::cin >> totalSize;
     table = std::vector<Entry>(totalSize);

hashing/quadratic_probing_hash_table.cpp

@@ -244,7 +244,7 @@ using quadratic_probing::totalSize;
  * @returns None
  */
 int main() {
-    int cmd = 0, hash = 0, key = 0;
+    int cmd = 0, key = 0;
     std::cout << "Enter the initial size of Hash Table. = ";
     std::cin >> totalSize;
     table = std::vector<Entry>(totalSize);

machine_learning/kohonen_som_trace.cpp

@@ -103,7 +103,7 @@ namespace machine_learning {
 void update_weights(const std::valarray<double> &x,
                     std::vector<std::valarray<double>> *W,
                     std::valarray<double> *D, double alpha, int R) {
-    int j = 0, k = 0;
+    int j = 0;
     int num_out = W->size();  // number of SOM output nodes
     // int num_features = x.size();  // number of data features
 

machine_learning/ordinary_least_squares_regressor.cpp

@@ -367,8 +367,6 @@ std::vector<float> predict_OLS_regressor(std::vector<std::vector<T>> const &X,
 
 /** Self test checks */
 void ols_test() {
-    int F = 3, N = 5;
-
     /* test function = x^2 -5 */
     std::cout << "Test 1 (quadratic function)....";
     // create training data set with features = x, x^2, x^3

numerical_methods/successive_approximation.cpp

@@ -18,7 +18,7 @@ static float eqd(float y) { return 0.5 * (cos(y) + 2); }
 
 /** Main function */
 int main() {
-    float y, x1, x2, x3, sum, s, a, f1, f2, gd;
+    float y, x1, x2, sum;
     int i, n;
 
     for (i = 0; i < 10; i++) {

others/matrix_exponentiation.cpp

@@ -128,7 +128,7 @@ int main() {
     cout.tie(0);
     ll t;
     cin >> t;
-    ll i, j, x;
+    ll i, x;
     while (t--) {
         cin >> mat_size;
         for (i = 0; i < mat_size; i++) {

range_queries/mo.cpp

@@ -29,7 +29,7 @@ bool mycmp(query x, query y) {
 }
 
 int main() {
-    int n, t, i, j, k = 0;
+    int n, t, i;
     scanf("%d", &n);
     for (i = 0; i < n; i++) scanf("%d", &a[i]);
     bucket_size = ceil(sqrt(n));