chore: use iwyu on machine_learning/**.cpp

Author: realstealthninja

machine_learning/a_star_search.cpp

@@ -19,15 +19,21 @@
  * values having minimum F(state).
  * @author [Ashish Daulatabad](https://github.com/AshishYUO)
  */
-#include <algorithm>   /// for `std::reverse` function
-#include <array>       /// for `std::array`, representing `EightPuzzle` board
-#include <cassert>     /// for `assert`
-#include <functional>  /// for `std::function` STL
-#include <iostream>    /// for IO operations
-#include <map>         /// for `std::map` STL
-#include <memory>      /// for `std::shared_ptr`
-#include <set>         /// for `std::set` STL
-#include <vector>      /// for `std::vector` STL
+#include <stdint.h>  // for uint32_t, int8_t
+
+#include <algorithm>   // for max, min
+#include <array>       // for array, operator==
+#include <cassert>     // for assert
+#include <cstddef>     // for size_t
+#include <functional>  // for function
+#include <iostream>    // for operator<<, basic_ostream, char_traits, cout
+#include <map>         // for operator!=, map, operator==
+#include <memory>      // for shared_ptr, make_shared, operator!=
+#include <set>         // for set
+#include <tuple>       // for tie, tuple
+#include <utility>     // for pair, move, make_pair, swap
+#include <vector>      // for vector
+
 /**
  * @namespace machine_learning
  * @brief Machine learning algorithms

machine_learning/adaline_learning.cpp

@@ -26,15 +26,14 @@
  * computed using stochastic gradient descent method.
  */
 
-#include <array>
-#include <cassert>
-#include <climits>
-#include <cmath>
-#include <cstdlib>
-#include <ctime>
-#include <iostream>
-#include <numeric>
-#include <vector>
+#include <bits/std_abs.h>  // for abs
+#include <array>           // for array
+#include <cassert>         // for assert
+#include <cstdlib>         // for rand, exit, srand, strtof, EXIT_FAILURE
+#include <ctime>           // for time
+#include <iostream>        // for basic_ostream, operator<<, char_traits, cout
+#include <numeric>         // for inner_product
+#include <vector>          // for vector
 
 /** Maximum number of iterations to learn */
 constexpr int MAX_ITER = 500;  // INT_MAX

machine_learning/k_nearest_neighbors.cpp

@@ -9,13 +9,16 @@
  * the K-nearest neighbors.
  */
 
-#include <algorithm>      /// for std::transform and std::sort
-#include <cassert>        /// for assert
-#include <cmath>          /// for std::pow and std::sqrt
-#include <iostream>       /// for std::cout
-#include <numeric>        /// for std::accumulate
-#include <unordered_map>  /// for std::unordered_map
-#include <vector>         /// for std::vector
+#include <algorithm>      // for sort, transform
+#include <cassert>        // for assert
+#include <cmath>          // for sqrt, pow
+#include <cstddef>        // for size_t
+#include <iostream>       // for basic_ostream, operator<<, char_traits, cout
+#include <iterator>       // for back_inserter
+#include <numeric>        // for accumulate
+#include <unordered_map>  // for unordered_map, _Node_iterator
+#include <utility>        // for pair
+#include <vector>         // for vector
 
 /**
  * @namespace machine_learning
@@ -65,7 +68,7 @@ class Knn {
      * @param Y labels vector
      */
     explicit Knn(std::vector<std::vector<double>>& X, std::vector<int>& Y)
-        : X_(X), Y_(Y){};
+        : X_(X), Y_(Y) {};
 
     /**
      * Copy Constructor for class Knn.

machine_learning/neural_network.cpp

@@ -27,18 +27,20 @@
  * MSE as loss function. Bias is also not included.
  */
 
-#include <algorithm>
-#include <cassert>
-#include <chrono>
-#include <cmath>
-#include <fstream>
-#include <iostream>
-#include <sstream>
-#include <string>
-#include <valarray>
-#include <vector>
-
-#include "vector_ops.hpp"  // Custom header file for vector operations
+#include <bits/chrono.h>   // for duration, duration_cast, high_resolution_c...
+#include <algorithm>       // for max, min
+#include <cassert>         // for assert
+#include <cmath>           // for exp
+#include <fstream>         // for basic_ostream, operator<<, endl, basic_ist...
+#include <iostream>        // for cerr, cout
+#include <sstream>         // for basic_stringstream
+#include <string>          // for char_traits, basic_string, operator<<, ope...
+#include <valarray>        // for valarray
+#include <vector>          // for vector
+#include <cstdlib>         // for exit, size_t, EXIT_FAILURE
+#include <utility>         // for pair, make_pair
+
+#include "vector_ops.hpp"  // for argmax, apply_function, get_shape, multiply
 
 /** \namespace machine_learning
  * \brief Machine learning algorithms

machine_learning/ordinary_least_squares_regressor.cpp

@@ -9,11 +9,13 @@
  *
  * \author [Krishna Vedala](https://github.com/kvedala)
  */
-#include <cassert>
-#include <cmath>    // for std::abs
-#include <iomanip>  // for print formatting
-#include <iostream>
-#include <vector>
+#include <bits/std_abs.h>  // for abs
+
+#include <cassert>   // for assert
+#include <cstddef>   // for size_t
+#include <iomanip>   // for operator<<, setfill, setw, _Setfill, _Setw
+#include <iostream>  // for basic_ostream, operator<<, char_traits, cout
+#include <vector>    // for vector
 
 /**
  * operator to print a matrix