ENH: Added a default compilation type (static) instead of an obligatory...

ENH: Added a default compilation type (static) instead of an obligatory DEPENDENCIES_LINK_TYPE parameter.

CMakeLists.txt

@@ -31,10 +31,9 @@ endif (NOT CMAKE_BUILD_TYPE)
 # Dependencies link type #
 #------------------------#
 if(NOT DEPENDENCIES_LINK_TYPE AND NOT ENV{DEPENDENCIES_LINK_TYPE})
-    message(FATAL_ERROR "Please, set the variable DEPENDENCIES_LINK_TYPE to either 'static' or 'shared'!")
-endif()
-
-if(ENV{DEPENDENCIES_LINK_TYPE})
+    set(DEPENDENCIES_LINK_TYPE "static")
+    message("DEPENDENCIES_LINK_TYPE is not specified - lib4neuro is going to be linked statically.")
+elseif(ENV{DEPENDENCIES_LINK_TYPE})
     set(DEPENDENCIES_LINK_TYPE ENV{DEPENDENCIES_LINK_TYPE})
 endif()
 
@@ -84,10 +83,6 @@ if(NOT Boost_FOUND)
     message(FATAL_ERROR "Boost was NOT found! Specify variables BOOST_INCLUDEDIR and BOOST_LIBRARYDIR!")
 endif()
 
-message("Boost_INCLUDE_DIRS: ${Boost_INCLUDE_DIRS}")
-message("Boost_LIBRARY_DIRS: ${Boost_LIBRARY_DIRS}")
-message("Boost_LIBRARIES: ${Boost_LIBRARIES}")
-
 find_package(exprtk)
 message("EXPRTK_INCLUDE_DIRS: ${EXPRTK_INCLUDE_DIRS}")
 

boost @ f6613334

-Subproject commit 6a58c03865b11c2a21229c6e71b8cebf15a9c8fb
+Subproject commit f66133343e03fad8d35e234c2f10bd791685466a

turtle @ fb8d7aba

-Subproject commit 4a853f9952708b5f1c57055a6c1944e2c4ad6544
+Subproject commit fb8d7abab4c3de2ad2c1df0b80fdd7687628c8d6

src/Exception/Exceptions.cpp

-//
-// Created by martin on 9/19/18.
-//
-
-#include "Exceptions.h"
-
-namespace lib4neuro {
-
-    InvalidDimension::InvalidDimension() : std::runtime_error("Invalid dimension specified!") {};
-    InvalidDimension::InvalidDimension(std::string msg) : std::runtime_error(msg.c_str()) {};
-
-    NotImplementedException::NotImplementedException() : std::runtime_error(
-            "This functions is not implemented yet!") {};
-    NotImplementedException::NotImplementedException(std::string msg) : std::runtime_error(msg.c_str()) {};
-
-    FileNotFoundException::FileNotFoundException() : std::runtime_error("The file was not found!") {};
-    FileNotFoundException::FileNotFoundException(std::string msg) : std::runtime_error(msg.c_str()) {};
-
-}
\ No newline at end of file

src/LearningMethods/GradientDescent.h

@@ -57,14 +57,22 @@ namespace lib4neuro {
         std::vector<double> *optimal_parameters;
 
         /**
+         * Adaptive calculation of the step-size based on several historical characteristics.
+         * ----------------------------------------------------------------------------------
+         * If the current error @fi is larger than the error in the previous step @fim, the rate of step-size change decreases (the algorithm is going in the direction too quickly)
+         * Otherwise the rate of step-size change increases (the algorithm is on the right path, we can attempts to push through more rapidly)
+         * ----------------------------------------------------------------------------------
+         * The step size is then calculated via: @c^(1-2@beta) * (@grad_norm_prev/@grad_norm)^(1/@c)
+         * If the previous gradient norm is lower then the current gradient norm, then the step-size decreases (as we probably took a too large of a step)
+         * Otherwise it increases (as we are likely on the right track, we can try to speed-up the convergence)
          *
-         * @param gamma
-         * @param beta
-         * @param c
-         * @param grad_norm_prev
-         * @param grad_norm
-         * @param fi
-         * @param fim
+         * @param gamma[in, out] a step size used in the last iteration
+         * @param beta[in] a number in the interval [0, 1]. it represents a measure of direction change between two last steps, 0: no change, 1:opposite directions
+         * @param c[in, out] greater than zero. it is a measure of a non-linear step-size change. the higher @c is, the more rapidly the step-size increases/decreases
+         * @param grad_norm_prev[in] gradient norm of the error in the previous iteration
+         * @param grad_norm[in] gradient norm of the error in the current iteration
+         * @param fi[in] value of the error
+         * @param fim[in] value of the error in the previous iteration
          */
         virtual void
         eval_step_size_mk(double &gamma,