Testing the new strategy

Still not good enough for me, thinking of just using vlx near walls 
considering the orientation given by the odometry to choose which vlx to 
use in order to extrapolate position

tools/localisation_machine_learning/data_obtention.py

@@ -220,6 +220,49 @@ def test_regression():
                 if abs(predicted_position-position[0])>max:
                     nbmax += 1
                     max = abs(predicted_position-position[0])
+                    print(position)
     print(difftot/nb)
     print(max)
     print(nbmax)
+
+def test_regression_rework():
+    regr = joblib.load('test.sav')
+    difftot = 0
+    nb = 0
+    max = 0
+    nbmax = -1
+
+    for k in range(y_sample):
+        for j in range(x_sample):
+            for i in range(len(full_turn)):
+                angle = False
+                tr_x = x + j*((1.5-x)/x_sample)
+                tr_y = y + k*((1.0-y)/y_sample)
+                if tr_x < 0.3 and tr_y < 0.3:
+                    angle = True
+                if check_for_sample(1, tr_x, tr_y) and not angle:
+                    rotation_field.setSFRotation(full_turn[i])
+                    translationtion_field.setSFVec3f([tr_x, 0.17, tr_y])
+                    robot.step(1)
+                    time.sleep(0.0005)
+                    values = get_vlx_values()
+                    predicted_position = regr.predict([values])
+                    position = [asterix.getPosition()[0], 2 - asterix.getPosition()[2]]
+                    difftot += abs(predicted_position-position[0])
+                    nb += 1
+                    if abs(predicted_position-position[0])>max:
+                        nbmax += 1
+                        max = abs(predicted_position-position[0])
+                        print(position)
+    print(difftot/nb)
+    print(max)
+    print(nbmax)
+
+
+def print_actual_vlx_states():
+    print([asterix.getPosition()[0], 2 - asterix.getPosition()[2]])
+    print(math.acos(asterix.getOrientation()[0]))
+    robot.step(1)
+    print(get_vlx_values())
+
+test_regression_rework()