Merge branch 'fix_in_div_and_log_operations'

Author: Ezetowers

.gitignore

@@ -9,3 +9,12 @@ tests/.coverage
 # Ctags folders
 .tags
 .tags_sorted_by_file
+
+# Ignore files generated by precision_bug scripts
+bugs/precision_bug/*.txt
+
+# Ignore files generated by MATLAB test file generators
+scripts/*.txt
+
+# Ignore SQLite DBs
+*.db

MLC/Application.py

@@ -98,7 +98,8 @@ def go(self, to_generation, fig, from_generation=None):
         self.evaluate_population(self._simulation.get_last_generation(),
                                  self._simulation.number_of_generations())
 
-        self.show_best(self._simulation.get_last_generation())
+        if fig:
+            self.show_best(self._simulation.get_last_generation())
         MLCTable.get_instance().commit_changes()
 
         for i in range(from_generation + 1, self._simulation.number_of_generations() + 1):

MLC/Common/Lisp_Tree_Expr/Operation_Nodes.py

@@ -1,4 +1,3 @@
-import math
 import importlib
 
 import MLC.Log.log as lg
@@ -98,6 +97,7 @@ def op_compute(self, arg_list):
 
 class Division_Node(Internal_Node):
     PROTECTION = 0.001
+    SIMPLIFY_PROTECTION = 0.01
 
     def __init__(self):
         Internal_Node.__init__(self, "/", 1)
@@ -107,12 +107,11 @@ def formal(self):
 
     def _process_division(self, dividend, divisor):
         if type(divisor) == np.ndarray:
-            # Check if at least one element is below the protection value
-            if [x for x in divisor if abs(x) < Division_Node.PROTECTION] != []:
-                return np.sign(divisor) * dividend / np.repeat(Division_Node.PROTECTION, len(divisor))
+            new_divisor = [Division_Node.PROTECTION if np.abs(x) < Division_Node.PROTECTION else np.abs(x) for x in divisor]
+            return np.sign(divisor) * dividend / np.asarray(new_divisor)
         else:
             if abs(divisor) < Division_Node.PROTECTION:
-                return dividend / Division_Node.PROTECTION
+                return np.sign(divisor) * dividend / Division_Node.PROTECTION
 
         return dividend / divisor
 
@@ -127,7 +126,7 @@ def op_simplify(self):
 
         if not self._nodes[0].is_sensor() and not self._nodes[1].is_sensor():
             # FIXME: Harcoded number. Change it
-            if abs(float(self._nodes[1].to_string())) < 0.01:
+            if abs(float(self._nodes[1].to_string())) < Division_Node.SIMPLIFY_PROTECTION:
                 return Leaf_Node(process_float(0))
             else:
                 arg = float(self._nodes[0].to_string()) / float(self._nodes[1].to_string())
@@ -149,7 +148,7 @@ def formal(self):
 
     def op_simplify(self):
         if not self._nodes[0].is_sensor():
-            arg = math.sin(float(self._nodes[0].to_string()))
+            arg = np.sin(float(self._nodes[0].to_string()))
             return Leaf_Node(process_float(arg))
         else:
             return self
@@ -168,7 +167,7 @@ def formal(self):
 
     def op_simplify(self):
         if not self._nodes[0].is_sensor():
-            arg = math.cos(float(self._nodes[0].to_string()))
+            arg = np.cos(float(self._nodes[0].to_string()))
             return Leaf_Node(process_float(arg))
         else:
             return self
@@ -179,6 +178,7 @@ def op_compute(self, arg_list):
 
 class Logarithm_Node(Internal_Node):
     PROTECTION = 0.00001
+    SIMPLIFY_PROTECTION = 0.01
 
     def __init__(self):
         Internal_Node.__init__(self, "log", 5)
@@ -188,9 +188,7 @@ def formal(self):
 
     def _process_arg(self, arg):
         if type(arg) == np.ndarray:
-            # Check if at least one element is below the protection value
-            if [x for x in arg if abs(x) < Logarithm_Node.PROTECTION] != []:
-                return np.repeat(Logarithm_Node.PROTECTION, len(arg))
+            return [Logarithm_Node.PROTECTION if np.abs(x) < Logarithm_Node.PROTECTION else np.abs(x) for x in arg]
         else:
             if abs(arg) < Logarithm_Node.PROTECTION:
                 return Logarithm_Node.PROTECTION
@@ -199,10 +197,10 @@ def _process_arg(self, arg):
 
     def op_simplify(self):
         if not self._nodes[0].is_sensor():
-            if float(self._nodes[0].to_string()) < 0.01:
-                arg = math.log(0.01)
+            if float(self._nodes[0].to_string()) < Logarithm_Node.SIMPLIFY_PROTECTION:
+                arg = np.log(Logarithm_Node.SIMPLIFY_PROTECTION)
             else:
-                arg = math.log(float(self._nodes[0].to_string()))
+                arg = np.log(float(self._nodes[0].to_string()))
 
             return Leaf_Node(process_float(arg))
         else:
@@ -224,9 +222,9 @@ def op_simplify(self):
         if not self._nodes[0].is_sensor():
             lg.logger_.debug("[EXP NODE] Value: " + self._nodes[0].to_string())
             try:
-                arg = math.exp(float(self._nodes[0].to_string()))
+                arg = np.exp(float(self._nodes[0].to_string()))
             except OverflowError:
-                # FIXME: See what to do with this expression, because there are problems with
+                # FIXME: See what to do with this expression, because there are problems when
                 # an infinite value is the argumento of a sinusoidal function
                 return Leaf_Node(process_float(float("inf")))
 
@@ -248,7 +246,7 @@ def formal(self):
 
     def op_simplify(self):
         if not self._nodes[0].is_sensor():
-            arg = math.tanh(float(self._nodes[0].to_string()))
+            arg = np.tanh(float(self._nodes[0].to_string()))
             return Leaf_Node(process_float(arg))
         else:
             return self

MLC/Scripts/Evaluation/toy_problem.py

@@ -8,11 +8,7 @@
 
 
 def individual_data(indiv):
-    x = np.arange(-10, 10 + 0.1, 0.1)
-    # FIXME: Numpy precision it's driving me up the wall. It doesn't put a zero
-    # in the middle element of the array (put a extremely small number instead).
-    # That generates problems with the division operation
-    x[len(x) / 2] = 0
+    x = np.linspace(-10.0, 10.0, num=201)
     y = np.tanh(x**3 - x**2 - 1)
 
     eng = MatlabEngine.engine()

MLC/Scripts/Evaluation/toy_problem_python_ev.py

@@ -7,11 +7,7 @@
 
 
 def individual_data(indiv):
-    x = np.arange(-10, 10 + 0.1, 0.1)
-    # FIXME: Numpy precision it's driving me up the wall. It doesn't put a zero
-    # in the middle element of the array (put a extremely small number instead).
-    # That generates problems with the division operation
-    x[len(x) / 2] = 0
+    x = np.linspace(-10.0, 10.0, num=201)
     y = np.tanh(x**3 - x**2 - 1)
 
     config = Config.get_instance()

MLC/individual/Individual.py

@@ -461,7 +461,6 @@ def __change_const_tree(self, tree_expression, leaf_value_generator):
 
     def __str__(self):
         return "value: %s\n" % self.get_value() + \
-               "type: %s\n" % self.get_type() + \
                "cost_history: %s\n" % self.get_cost_history() + \
                "evaluation_time: %s\n" % self.get_evaluation_time() + \
                "appearences: %s\n" % self.get_appearences() + \

bugs/precision_bug/README

@@ -0,0 +1,6 @@
+Run MATLAB scripts with the following command:
+
+/path/to/MATLAB/dir/bin/matlab -nodisplay -nosplash -nodesktop -r script
+
+Note: Take a look to the lack of extension in the script. The script MUST be a .m file,
+yet the argument of the command receives it without extension

bugs/precision_bug/expr_test.py

@@ -0,0 +1,47 @@
+import sys
+sys.path.append("../..")
+import binascii
+import numpy as np
+import struct
+import MLC.Log.log as lg
+
+from MLC.Common.Lisp_Tree_Expr.Lisp_Tree_Expr import Lisp_Tree_Expr
+from MLC.Log.log import set_logger
+from MLC.mlc_parameters.mlc_parameters import Config
+
+
+def initialize_config():
+    config = Config.get_instance()
+    config.read('../../conf/configuration.ini')
+    return config
+
+# Set printable resolution (don't alter numpy interval resolution)
+np.set_printoptions(precision=9)
+# Show full arrays, no matter what size do they have
+np.set_printoptions(threshold=np.inf)
+# Don't show scientific notation
+np.set_printoptions(suppress=True)
+
+initialize_config()
+set_logger('console')
+
+# Full expression
+expr6 = "(root (cos (exp (- -6.3726 (* -7.1746 S0)))))"
+expr61 = "(root (exp (- -6.3726 (* -7.1746 S0))))"
+expr612 = "(root (- -6.3726 (* -7.1746 S0)))"
+
+tree = Lisp_Tree_Expr(expr6)
+x = np.linspace(-10.0, 10.0, num=201)
+mlc_y = tree.calculate_expression([x])
+
+# Calculate the Mean squared error
+y = np.tanh(x**3 - x**2 - 1)
+evaluation = float(np.sum((mlc_y - y)**2))
+
+# print mlc_y
+with open("./costs_python.txt", "w") as f:
+    for elem in mlc_y:
+        f.write("%50.50f\n" % elem)
+
+print evaluation
+print np.sum(mlc_y)

bugs/precision_bug/matlab_expr_test.m

@@ -0,0 +1,19 @@
+close all;
+clear all;
+
+%--------------------- %
+
+x = -10:0.1:10;
+m = 'cos(exp(((-6.3726) - ((-7.1746) .* S0))))';
+% m = '((-6.3726) - ((-7.1746) .* S0))';
+y = tanh(x.^3-x.^2-1);
+m = strrep(m, 'S0', 'x'); y2=eval(m); J=sum((y2-y).^2); fprintf('%.10f\n', J);
+
+filename = './costs_matlab.txt';
+file = fopen(filename, 'w');
+
+for i=1:length(x)
+    fprintf(file, '%50.50f\n', y2(i));
+end
+
+fclose(file);

main.py

@@ -22,7 +22,7 @@ def main():
 
     eng = MatlabEngine.engine()
     config = initialize_config()
-    # MatlabEngine.load_random_values("./tests/integration_tests/matlab_randoms.txt")
+    MatlabEngine.load_random_values("./tests/integration_tests/matlab_randoms.txt")
 
     # Create the MLC2 object and store it in the workspace. With this
     # feature we will be able to call every function of the MATLAB code