add github workflow for test-pypi cross-OS wheel builds

Author: AlePalu

include/fdaPDE-cpp/fdaPDE/core/fdaPDE/src/geoframe/data_layer.h

@@ -1117,7 +1117,7 @@ class scalar_data_layer {
             if (coltype == dtype::flt32) { print.template operator()<float       >(out[i], "flt32", desc); }
             if (coltype == dtype::int64) { print.template operator()<std::int64_t>(out[i], "int64", desc); }
             if (coltype == dtype::int32) { print.template operator()<std::int32_t>(out[i], "int32", desc); }
-            if (coltype == dtype::bin)   { print.template operator()<bool        >(out[i], "bin"  , desc); }
+            // if (coltype == dtype::bin)   { print.template operator()<bool        >(out[i], "bin"  , desc); }
             if (coltype == dtype::str)   { print.template operator()<std::string >(out[i], "str"  , desc); }
         }
         // pretty format (compute maximum number of chars to print)

include/fdaPDE-cpp/fdaPDE/src/distributions.h

@@ -189,13 +189,12 @@ struct bernoulli_distribution : public internals::distribution_base<std::bernoul
         for (int i = 0, n = x.rows(); i < n; ++i) { dev_ += deviance(x(i, 0), y(i, 0)); }
         return dev_;
     }
+    template <typename Derived> matrix_t transform(const Eigen::MatrixBase<Derived>& data) const {
+        return 0.5 * (data.array() + 0.5);
+    }
 #endif
-    template <typename T> auto transform(const T& data) const {
-        if (internals::is_eigen_dense_xpr_v<T>) {
-            return 0.5 * (data.array() + 0.5);
-        } else {
-            return Base::apply_(data, [](auto v) { return 0.5 * (v + 0.5); });
-        }
+    template <typename T> std::vector<T> transform(const std::vector<T>& data) const {
+        return Base::apply_(data, [](auto v) { return 0.5 * (v + 0.5); });
     }
     void set_param(param_type p) { p_ = p; }
 };
@@ -284,13 +283,12 @@ struct poisson_distribution : public internals::distribution_base<std::poisson_d
         return ((y.array() > 0).select(y.array() * ((y.array() / x.array()).log() - 1) + x.array(), x.array()))
           .sum();
     }
+    template <typename Derived> matrix_t transform(const Eigen::MatrixBase<Derived>& data) const {
+        return matrix_t((data.array() <= 0).select(1.0, data));
+    }
 #endif
-    template <typename T> auto transform(const T& data) const {
-        if (internals::is_eigen_dense_xpr_v<T>) {
-            return matrix_t((data.array() <= 0).select(1.0, data));
-        } else {
-            return Base::apply_(data, [](auto v) { return v <= 0 ? 1.0 : v; });
-        }
+    template <typename T> std::vector<T> transform(const std::vector<T>& data) const {
+        return Base::apply_(data, [](auto v) { return v <= 0 ? 1.0 : v; });
     }
     void set_param(param_type l) { l_ = l; }
 };