Merge pull request OpenMS#4251 from biosustain/emg_separate_fit

[STYLE] Improve EmgGradientDescent's methods names

Author: timosachsenberg

src/openms/include/OpenMS/MATH/MISC/EmgGradientDescent.h

@@ -116,8 +116,53 @@ namespace OpenMS
       const double right_pos = 0.0
     ) const;
 
+    /**
+      @brief The implementation of the gradient descent algorithm for the EMG peak model
+
+      @param[in] xs Positions
+      @param[in] ys Intensities
+      @param[out] best_h `h` (amplitude) parameter
+      @param[out] best_mu `mu` (mean) parameter
+      @param[out] best_sigma `sigma` (standard deviation) parameter
+      @param[out] best_tau `tau` (exponent relaxation time) parameter
+
+      @return The number of iterations necessary to reach the best values for the parameters
+    */
+    UInt estimateEmgParameters(
+      const std::vector<double>& xs,
+      const std::vector<double>& ys,
+      double& best_h,
+      double& best_mu,
+      double& best_sigma,
+      double& best_tau
+    ) const;
+
+    /**
+      @brief Compute the EMG function on a set of points
+
+      If class parameter `compute_additional_points` is `"true"`, the algorithm
+      will detect which side of the peak is cutoff and add points to it.
+
+      @param[in] xs Positions
+      @param[in] h Amplitude
+      @param[in] mu Mean
+      @param[in] sigma Standard deviation
+      @param[in] tau Exponent relaxation time
+      @param[out] out_xs The output positions
+      @param[out] out_ys The output intensities
+    */
+    void applyEstimatedParameters(
+      const std::vector<double>& xs,
+      const double h,
+      const double mu,
+      const double sigma,
+      const double tau,
+      std::vector<double>& out_xs,
+      std::vector<double>& out_ys
+    ) const;
+
 protected:
-    void updateMembers_();
+    void updateMembers_() override;
 
     /**
       @brief Given a peak, extract a training set to be used with the gradient descent algorithm
@@ -181,27 +226,6 @@ namespace OpenMS
     ) const;
 
 private:
-    /**
-      @brief The implementation of the gradient descent algorithm for the EMG peak model
-
-      @param[in] xs Positions
-      @param[in] ys Intensities
-      @param[out] best_h `h` (amplitude) parameter
-      @param[out] best_mu `mu` (mean) parameter
-      @param[out] best_sigma `sigma` (standard deviation) parameter
-      @param[out] best_tau `tau` (exponent relaxation time) parameter
-
-      @return The number of iterations necessary to reach the best values for the parameters
-    */
-    UInt emg_gradient_descent(
-      const std::vector<double>& xs,
-      const std::vector<double>& ys,
-      double& best_h,
-      double& best_mu,
-      double& best_sigma,
-      double& best_tau
-    ) const;
-
     /**
       @brief Apply the iRprop+ algorithm for gradient descent
 
@@ -376,30 +400,6 @@ namespace OpenMS
       const double tau
     ) const;
 
-    /**
-      @brief Compute the EMG function on a set of points
-
-      If class parameter `compute_additional_points` is `"true"`, the algorithm
-      will detect which side of the peak is cutoff and add points to it.
-
-      @param[in] xs Positions
-      @param[in] h Amplitude
-      @param[in] mu Mean
-      @param[in] sigma Standard deviation
-      @param[in] tau Exponent relaxation time
-      @param[out] out_xs The output positions
-      @param[out] out_ys The output intensities
-    */
-    void emg_vector(
-      const std::vector<double>& xs,
-      const double h,
-      const double mu,
-      const double sigma,
-      const double tau,
-      std::vector<double>& out_xs,
-      std::vector<double>& out_ys
-    ) const;
-
     /**
       @brief Compute the EMG function on a single point
 
@@ -506,7 +506,7 @@ namespace OpenMS
       return emg_gd_.compute_z(x, mu, sigma, tau);
     }
 
-    void emg_vector(
+    void applyEstimatedParameters(
       const std::vector<double>& xs,
       const double h,
       const double mu,
@@ -516,7 +516,7 @@ namespace OpenMS
       std::vector<double>& out_ys
     ) const
     {
-      emg_gd_.emg_vector(xs, h, mu, sigma, tau, out_xs, out_ys);
+      emg_gd_.applyEstimatedParameters(xs, h, mu, sigma, tau, out_xs, out_ys);
     }
 
     double emg_point(

src/openms/source/MATH/MISC/EmgGradientDescent.cpp

@@ -285,7 +285,7 @@ namespace OpenMS
     return result;
   }
 
-  void EmgGradientDescent::emg_vector(
+  void EmgGradientDescent::applyEstimatedParameters(
     const std::vector<double>& xs,
     const double h,
     const double mu,
@@ -564,7 +564,7 @@ namespace OpenMS
     return (max_pos - min_pos) * 0.35;
   }
 
-  UInt EmgGradientDescent::emg_gradient_descent(
+  UInt EmgGradientDescent::estimateEmgParameters(
     const std::vector<double>& xs,
     const std::vector<double>& ys,
     double& best_h,
@@ -761,12 +761,12 @@ namespace OpenMS
 
     // EMG parameter estimation with gradient descent
     double h, mu, sigma, tau;
-    emg_gradient_descent(xs, ys, h, mu, sigma, tau);
+    estimateEmgParameters(xs, ys, h, mu, sigma, tau);
 
     // Estimate the intensities for each point
     std::vector<double> out_xs;
     std::vector<double> out_ys;
-    emg_vector(xs, h, mu, sigma, tau, out_xs, out_ys);
+    applyEstimatedParameters(xs, h, mu, sigma, tau, out_xs, out_ys);
 
     // Prepare the output peak
     output_peak = input_peak;

src/tests/class_tests/openms/source/EmgGradientDescent_test.cpp

@@ -594,7 +594,7 @@ START_SECTION(double emg_point(
 }
 END_SECTION
 
-START_SECTION(void emg_vector(
+START_SECTION(void applyEstimatedParameters(
   const std::vector<double>& xs,
   const double h,
   const double mu,
@@ -616,14 +616,14 @@ START_SECTION(void emg_vector(
 
   params.setValue("compute_additional_points", "false");
   emg_f.emg_gd_.setParameters(params);
-  emg_f.emg_vector(saturated_cutoff_pos_min, h, mu, sigma, tau, out_xs, out_ys);
+  emg_f.applyEstimatedParameters(saturated_cutoff_pos_min, h, mu, sigma, tau, out_xs, out_ys);
   TEST_EQUAL(out_xs.size(), saturated_cutoff_pos_min.size())
   TEST_REAL_SIMILAR(out_xs.front(), 14.3310337)
   TEST_REAL_SIMILAR(out_ys.front(), 2144281.1472228)
 
   params.setValue("compute_additional_points", "true");
   emg_f.emg_gd_.setParameters(params);
-  emg_f.emg_vector(saturated_cutoff_pos_min, h, mu, sigma, tau, out_xs, out_ys);
+  emg_f.applyEstimatedParameters(saturated_cutoff_pos_min, h, mu, sigma, tau, out_xs, out_ys);
   TEST_EQUAL(out_xs.size(), 71) // more points than before
   TEST_REAL_SIMILAR(out_xs.front(), 14.2717555076923) // peak was cutoff on the left side
   TEST_REAL_SIMILAR(out_ys.front(), 108845.941990663)