Silenced type conversion warnings.

4 files changed, 19 insertions, 12 deletions

diff --git a/unsupported/Eigen/FFT b/unsupported/Eigen/FFT
index 8702120de..0cc10bafb 100644
--- a/unsupported/Eigen/FFT
+++ b/unsupported/Eigen/FFT
@@ -187,7 +187,7 @@ class FFT
     {
         m_impl.inv( dst,src,nfft );
         if ( HasFlag( Unscaled ) == false)
-          scale(dst,1./nfft,nfft);
+          scale(dst,_Scalar(1./nfft),nfft);
     }
 
     inline
@@ -237,8 +237,14 @@ class FFT
   private:
 
     template <typename _It,typename _Val>
-    inline
-    void scale(_It x,_Val s,int nx)
+    inline void scale(_It x,_Val s,int nx)
+    {
+      for (int k=0;k<nx;++k)
+        *x++ *= _Scalar(s);
+    }
+
+    template <typename _Val>
+    inline void scale(std::complex<_Val>* x,_Val s,int nx)
     {
       for (int k=0;k<nx;++k)
         *x++ *= s;
diff --git a/unsupported/Eigen/src/MatrixFunctions/MatrixFunctionAtomic.h b/unsupported/Eigen/src/MatrixFunctions/MatrixFunctionAtomic.h
index d7409371b..d82b7626f 100644
--- a/unsupported/Eigen/src/MatrixFunctions/MatrixFunctionAtomic.h
+++ b/unsupported/Eigen/src/MatrixFunctions/MatrixFunctionAtomic.h
@@ -85,7 +85,7 @@ MatrixType MatrixFunctionAtomic<MatrixType>::compute(const MatrixType& A)
 {
   // TODO: Use that A is upper triangular
   m_Arows = A.rows();
-  m_avgEival = A.trace() / Scalar(m_Arows);
+  m_avgEival = A.trace() / Scalar(RealScalar(m_Arows));
   m_Ashifted = A - m_avgEival * MatrixType::Identity(m_Arows, m_Arows);
   computeMu();
   MatrixType F = m_f(m_avgEival, 0) * MatrixType::Identity(m_Arows, m_Arows);
@@ -94,7 +94,7 @@ MatrixType MatrixFunctionAtomic<MatrixType>::compute(const MatrixType& A)
   for (int s = 1; s < 1.1 * m_Arows + 10; s++) { // upper limit is fairly arbitrary
     Fincr = m_f(m_avgEival, s) * P;
     F += Fincr;
-    P = (1/(s + 1.0)) * P * m_Ashifted;
+    P = Scalar(RealScalar(1.0/(s + 1))) * P * m_Ashifted;
     if (taylorConverged(s, F, Fincr, P)) {
       return F;
     }
@@ -127,9 +127,9 @@ bool MatrixFunctionAtomic<MatrixType>::taylorConverged(int s, const MatrixType&
     for (int r = 0; r < n; r++) {
       RealScalar mx = 0;
       for (int i = 0; i < n; i++)
-	mx = std::max(mx, std::abs(m_f(m_Ashifted(i, i) + m_avgEival, s+r)));
-       if (r != 0)
-	rfactorial *= r;
+        mx = std::max(mx, std::abs(m_f(m_Ashifted(i, i) + m_avgEival, s+r)));
+      if (r != 0)
+        rfactorial *= RealScalar(r);
       delta = std::max(delta, mx / rfactorial);
     }
     const RealScalar P_norm = P.cwiseAbs().rowwise().sum().maxCoeff();
diff --git a/unsupported/test/FFT.cpp b/unsupported/test/FFT.cpp
index 056be2ef3..9c2682724 100644
--- a/unsupported/test/FFT.cpp
+++ b/unsupported/test/FFT.cpp
@@ -46,10 +46,10 @@ complex<long double>  promote(long double x) { return complex<long double>( x);
         long double difpower=0;
         cerr <<"idx\ttruth\t\tvalue\t|dif|=\n";
         long double pi = acos((long double)-1);
-        for (int k0=0;k0<fftbuf.size();++k0) {
+        for (int k0=0;k0<static_cast<int>(fftbuf.size());++k0) {
             complex<long double> acc = 0;
             long double phinc = -2.*k0* pi / timebuf.size();
-            for (int k1=0;k1<timebuf.size();++k1) {
+            for (int k1=0;k1<static_cast<int>(timebuf.size());++k1) {
                 acc +=  promote( timebuf[k1] ) * exp( complex<long double>(0,k1*phinc) );
             }
             totalpower += norm(acc);
diff --git a/unsupported/test/matrix_function.cpp b/unsupported/test/matrix_function.cpp
index 3e25c6a6f..446fa7ec3 100644
--- a/unsupported/test/matrix_function.cpp
+++ b/unsupported/test/matrix_function.cpp
@@ -33,14 +33,15 @@ template<typename MatrixType>
 MatrixType createRandomMatrix(const int size)
 {
   typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
   MatrixType result;
   if (ei_random<int>(0,1) == 0) {
     result = MatrixType::Random(size, size);
   } else {
     MatrixType diag = MatrixType::Zero(size, size);
     for (int i = 0; i < size; ++i) {
-      diag(i, i) = static_cast<Scalar>(ei_random<int>(0,2))
-	+ ei_random<Scalar>() * static_cast<Scalar>(0.01);
+      diag(i, i) = Scalar(RealScalar(ei_random<int>(0,2)))
+	+ ei_random<Scalar>() * Scalar(RealScalar(0.01));
     }
     MatrixType A = MatrixType::Random(size, size);
     result = A.inverse() * diag * A;