Revert "Update bistability_finder.py"

This reverts commit 2dcfa85.
PNNL-Comp-Mass-Spec · Aug 25, 2021 · 2813e7b · 2813e7b
1 parent ec99d32
commit 2813e7b
Showing 1 changed file with 16 additions and 40 deletions.
diff --git a/crnt4sbml/bistability_finder.py b/crnt4sbml/bistability_finder.py
@@ -453,39 +453,6 @@ def __feasible_point_method(self, samples):
 
         return x_candidates
 
-    # @staticmethod
-    # def __confidence_level(obtained_minimums, change_in_rel_error, important_info):
-    #
-    #     a = 1
-    #     b = 5
-    #
-    #     unique_elements, counts_elements = numpy.unique(obtained_minimums, return_counts=True)
-    #     min_val_index = numpy.nanargmin(unique_elements)
-    #
-    #     f_til = unique_elements[min_val_index]
-    #
-    #     numpy_float64_smalles_positive_value = numpy.nextafter(numpy.float64(0), numpy.float64(1))
-    #
-    #     if f_til > numpy_float64_smalles_positive_value:
-    #
-    #         r = numpy.count_nonzero(
-    #             numpy.abs(f_til - obtained_minimums) / f_til < numpy.float64(change_in_rel_error))
-    #
-    #         n_til = obtained_minimums.shape[0]
-    #         a_bar = a + b - 1
-    #         b_bar = b - r - 1
-    #
-    #         prob = 1 - (math.factorial(n_til + a_bar) * math.factorial(2 * n_til + b_bar)) / (
-    #                 math.factorial(2 * n_til + a_bar) * math.factorial(n_til + b_bar))
-    #
-    #     else:
-    #
-    #         prob = 1.0
-    #
-    #     important_info += f"It was found that {unique_elements[min_val_index]} is the minimum objective function value with a confidence level of {prob}.\n"
-    #     return important_info
-
-
     @staticmethod
     def __confidence_level(obtained_minimums, change_in_rel_error, important_info):
 
@@ -497,14 +464,23 @@ def __confidence_level(obtained_minimums, change_in_rel_error, important_info):
 
         f_til = unique_elements[min_val_index]
 
-        r = numpy.count_nonzero(numpy.abs(f_til - obtained_minimums) / f_til < numpy.float64(change_in_rel_error))
+        numpy_float64_smalles_positive_value = numpy.nextafter(numpy.float64(0), numpy.float64(1))
+
+        if f_til > numpy_float64_smalles_positive_value:
+
+            r = numpy.count_nonzero(
+                numpy.abs(f_til - obtained_minimums) / f_til < numpy.float64(change_in_rel_error))
 
-        n_til = obtained_minimums.shape[0]
-        a_bar = a + b - 1
-        b_bar = b - r - 1
+            n_til = obtained_minimums.shape[0]
+            a_bar = a + b - 1
+            b_bar = b - r - 1
+
+            prob = 1 - (math.factorial(n_til + a_bar) * math.factorial(2 * n_til + b_bar)) / (
+                    math.factorial(2 * n_til + a_bar) * math.factorial(n_til + b_bar))
+
+        else:
 
-        prob = 1 - (math.factorial(n_til + a_bar) * math.factorial(2 * n_til + b_bar)) / (
-                math.factorial(2 * n_til + a_bar) * math.factorial(n_til + b_bar))
+            prob = 1.0
 
         important_info += f"It was found that {unique_elements[min_val_index]} is the minimum objective function value with a confidence level of {prob}.\n"
-        return important_info
+        return important_info