Fixed Linux warnings

Author: hosseinmoein

docs/HTML/pca_by_eigen.html

@@ -98,19 +98,6 @@
       </td>
     </tr>
 
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     <tr bgcolor="Azure">
       <td bgcolor="blue"> <font color="white">
 <PRE><B>
@@ -129,7 +116,7 @@
         In linear algebra, SVD is a factorization of a real or complex matrix into a rotation, followed by a rescaling followed by another rotation. It generalizes the eigen-decomposition of a square normal matrix with an orthonormal eigenbasis to any ⁠mXn matrix.<BR>
         It returns the 3 metrices U, &Sigma;, and V inside a std::tuple.<BR><BR>
         <I>U</I> contains the left singular vectors of the original matrix, meaning its columns are orthonormal vectors that span the row space of the matrix.<BR>
-        <I>S</I> is a diagonal matrix that contains sqrt of eigenvalues of the original matrix, arranged in descending order.<BR>
+        <I>&Sigma;</I> is a diagonal matrix that contains sqrt of eigenvalues of the original matrix, arranged in descending order.<BR>
         <I>V</I> contains the right singular vectors of the original matrix, represented as its columns.<BR>
         Original matrix (A) = U * &Sigma; * V<sup>T</sup><BR>
       </td>

include/DataFrame/DataFrameMLVisitors.h

@@ -490,8 +490,7 @@ struct  AffinityPropVisitor  {
         const long  col_s = std::min(std::distance(idx_begin, idx_end),
                                      std::distance(column_begin, column_end));
 
-        const vec_t<double> simil =
-            std::move(get_similarity_(column_begin, col_s));
+        const vec_t<double> simil = get_similarity_(column_begin, col_s);
         vec_t<double>       avail;
         vec_t<double>       respon;
 

include/DataFrame/DataFrameStatsVisitors.h

@@ -3689,7 +3689,7 @@ struct  SymmTriangleMovingMeanVisitor  {
                 break;
 
         const auto  triangle =
-            std::move(gen_sym_triangle<value_type>(roll_period_, 1, true));
+            gen_sym_triangle<value_type>(roll_period_, 1, true);
         result_type result (col_s, std::numeric_limits<T>::quiet_NaN());
 
         for (size_type i { starting + roll_period_ };

include/DataFrame/Internals/DataFrame_misc.tcc

@@ -238,7 +238,7 @@ DataFrame<I, H>::print_binary_functor_<Ts ...>::operator() (const T &vec)  {
 
     char    col_name[64];
 
-    std::strncpy(col_name, name, sizeof(col_name));
+    std::strncpy(col_name, name, sizeof(col_name) - 1);
     os.write(col_name, sizeof(col_name));
 
     const long  local_start_row = std::min (long(vec.size()), start_row);

include/DataFrame/Internals/DataFrame_read.tcc

@@ -787,7 +787,8 @@ read_csv2_(std::FILE *stream,
     spec_vec.reserve(32);
     while (! std::feof(stream)) {
         line[0] = '\0';
-        std::fgets(line, sizeof(line) - 1, stream);
+        if (std::fgets(line, sizeof(line) - 1, stream) == nullptr)
+            continue;
 
         if (line[0] == '\0' || line[0] == '#') [[unlikely]]  continue;
 

include/DataFrame/Internals/DataFrame_set.tcc

@@ -74,7 +74,7 @@ void DataFrame<I, H>::remove_column (const char *name)  {
 
     // Free the memory space
     //
-    vec = std::move(ColumnVecType<T>{ });
+    vec = ColumnVecType<T>{ };
 
     // I do not erase the column from the data_ vector, because it will mess up
     // indices in the hash table column_tb_
@@ -213,8 +213,7 @@ DataFrame<I, H>::load_data (IndexVecType &&indices, Ts&& ... args)  {
                   "Only a StdDataFrame can call load_data()");
 
     size_type   cnt = load_index(std::forward<IndexVecType>(indices));
-    auto        args_tuple =
-        std::move(std::tuple<Ts ...>(std::forward<Ts>(args) ...));
+    auto        args_tuple = std::tuple<Ts ...>(std::forward<Ts>(args) ...);
     // const size_type tuple_size =
     //     std::tuple_size<decltype(args_tuple)>::value;
     auto        fc = [this, &cnt](auto &pa) mutable -> void {
@@ -837,8 +836,7 @@ DataFrame<I, H>::append_row (IndexType *idx_val, Ts&& ... args)  {
         indices_.push_back(*idx_val);
 
     size_type   cnt = 1;
-    auto        args_tuple =
-        std::move(std::tuple<Ts ...>(std::forward<Ts>(args) ...));
+    auto        args_tuple = std::tuple<Ts ...>(std::forward<Ts>(args) ...);
     auto        fc = [this, &cnt](auto &pa) mutable -> void {
                          cnt += this->append_row_(pa);
                      };

include/DataFrame/Internals/DataFrame_standalone.tcc

@@ -1067,9 +1067,9 @@ _write_binary_common_(STRM &strm, [[maybe_unused]] const V &vec,
     const auto  &citer = _typeinfo_name_.find(typeid(ValueType));
 
     if (citer != _typeinfo_name_.end()) [[likely]]
-        std::strncpy(buffer, citer->second, sizeof(buffer));
+        std::strncpy(buffer, citer->second, sizeof(buffer) - 1);
     else
-        std::strncpy(buffer, "N/A", sizeof(buffer));
+        std::strncpy(buffer, "N/A", sizeof(buffer) - 1);
     strm.write(buffer, sizeof(buffer));
 
     const uint64_t  vec_size = end_row - start_row;

include/DataFrame/Utils/Matrix.tcc

@@ -473,14 +473,14 @@ hessenberg_to_schur_(MA1 &e_vecs,
     size_type   iter { 0 };
     size_type   n { e_vecs.cols() - 1 };
     value_type  exshift { 0 };
-    value_type  p;
-    value_type  q;
-    value_type  oo;
-    value_type  s;
-    value_type  z;
-    value_type  w;
-    value_type  x;
-    value_type  y;
+    value_type  p { 0 };
+    value_type  q { 0 };
+    value_type  oo { 0 };
+    value_type  s { 0 };
+    value_type  z { 0 };
+    value_type  w { 0 };
+    value_type  x { 0 };
+    value_type  y { 0 };
 
     // Outer loop over eigenvalue index
     //