poprawienie karmienia

agent.py

@@ -3,7 +3,7 @@ import random
 from constants import Constants
 from state_space_search import is_border, is_obstacle
 from night import draw_night
-from decision_tree import add_data
+from decision_tree import feed_decision
 from constants import Constants
 
 class Agent:
@@ -76,15 +76,15 @@ def feed_animal(self, animals, goal,const):
                 else:
                     activity_time = False
                 guests = random.randint(1, 15)
-                feed_decision = add_data(animal.adult, activity_time, animal.ill, const.season, guests, animal._feed, self._dryfood, self._wetfood)
+                decision = feed_decision(animal.adult, activity_time, animal.ill, const.season, guests, animal._feed, self._dryfood, self._wetfood)
-                if feed_decision != 1:
+                if decision != [1]:
-                    if feed_decision == 2:
+                    if decision == [2]:
                         if animal.getting_hungry(const=Constants()) < self._wetfood :
                             self._wetfood -= animal._feed
                             animal._feed = 0
                         else:
                             animal._feed -= self._wetfood
-                            self._food = 0
+                            self._wetfood = 0
                         print(animal.name, "fed with wet food")
                     else:
                         if animal.getting_hungry(const=Constants()) < self._dryfood :

dane.csv

@@ -92,6 +92,7 @@ True,False,True,spring,14,5.12387,44.44585,8.35502,3
 True,True,False,autumn,10,4.90335,43.27857,27.22901,1
 True,False,False,winter,11,5.89082,28.91495,15.58095,1
 True,False,True,autumn,3,7.39589,21.53402,44.50694,3
+True,False,True,autumn,3,7.39589,21.53402,0.00000,2
 True,False,False,summer,12,5.68671,49.18777,22.53807,3
 False,False,False,winter,0,0.86765,41.28704,33.77284,1
 False,False,True,summer,10,5.97643,36.23669,48.32615,2
@@ -128,11 +129,13 @@ True,True,False,summer,11,4.65313,3.44791,3.96313,3
 True,False,True,summer,2,2.56716,10.85536,49.88738,1
 False,False,True,autumn,4,8.19265,5.43942,48.74041,3
 False,True,False,autumn,5,5.6574,9.75738,25.96888,3
+False,True,False,autumn,5,5.6574,9.75738,0.00000,2
 False,True,False,winter,14,4.87066,33.40134,18.98246,2
 True,True,True,winter,6,8.2623,37.47298,33.76759,2
 True,True,True,spring,10,2.20409,13.6178,5.80078,2
 True,False,True,autumn,7,9.06057,37.8724,23.62209,2
 False,False,True,autumn,3,6.69861,37.07336,16.87187,2
+False,False,True,autumn,3,6.69861,0.00000,16.87187,3
 True,False,True,autumn,13,4.96475,46.87852,3.1412,1
 True,False,True,autumn,9,2.65212,19.06994,37.33364,1
 True,True,False,summer,15,3.47148,35.84529,0.00000,1
@@ -284,6 +287,11 @@ True,False,True,autumn,7,3.96302,39.84093,47.0172,1
 True,False,False,summer,1,0.65085,20.20581,14.96995,1
 True,False,False,winter,2,9.24331,26.34543,30.5147,2
 True,False,False,summer,3,2.86309,15.56342,1.04324,1
+True,True,True,autumn,8,5.71809,24.41045,48.78273,2
+True,True,True,autumn,8,5.71809,24.41045,48.78273,2
+True,True,True,winter,8,5.71809,0.99999,48.78273,3
+True,True,True,winter,8,5.71809,1.98675,48.79993,3
+True,True,False,winter,8,7.71809,1.02345,38.78273,3
 True,False,False,summer,1,4.03947,33.84073,6.48891,1
 True,False,False,winter,4,2.46471,24.07929,17.77792,1
 False,False,False,winter,9,3.66415,23.68306,24.43865,1
@@ -298,4 +306,8 @@ False,False,False,autumn,1,2.5607,26.85209,47.10784,1
 True,True,True,winter,14,1.56638,18.02703,7.05011,1
 False,False,False,winter,13,3.86632,28.9884,20.1928,1
 True,False,False,summer,13,6.37654,34.3833,34.53892,3
-True,True,False,summer,5,8.10422,7.6617,23.41017,3
+True,False,False,summer,13,6.37654,34.3833,34.53892,3
+True,False,False,spring,11,8.35634,0.00000,34.53892,3
+True,False,True,summer,13,9.37654,34.3833,0.00000,2
+True,False,False,winter,13,7.77754,34.3833,0.00000,2
+True,True,True,summer,5,8.10422,7.6617,23.41017,3

decision_tree.py

@@ -25,7 +25,7 @@ plot_tree(clf, filled=True, feature_names=X.columns, class_names=['nie karmi', '
 plt.savefig('tree.png')
 
 # Nowe dane
-def add_data(adult,active_time,ill,season,guests,hunger,wet_food,dry_food):
+def feed_decision(adult,active_time,ill,season,guests,hunger,dry_food,wet_food):
     X_new = pd.DataFrame({
         'adult': [adult],
         'active_time': [active_time],
@@ -36,13 +36,13 @@ def add_data(adult,active_time,ill,season,guests,hunger,wet_food,dry_food):
         'wet_food': [wet_food],    
         'dry_food': [dry_food]      
 })
-    X_new_encoded = pd.get_dummies(X_new)
+    X_new = pd.get_dummies(X_new)
-    missing_columns = set(X.columns) - set(X_new.columns)
+    missing_columns = set(X.columns) - set(X_new)
     for col in missing_columns:
-        X_new_encoded[col] = 0
+        X_new[col] = False
-        X_new_encoded = X_new_encoded.reindex(columns=X.columns, fill_value=0)
+        X_new = X_new.reindex(columns=X.columns, fill_value=0)
     print("Atrybuty zwierzęcia:", adult,active_time,ill,season,guests,hunger,wet_food,dry_food)
-    return (clf.predict(X_new_encoded))
+    return (clf.predict(X_new))
 
 
 

main.py

@@ -3,7 +3,6 @@ import pygame
 import sys
 
 sys.path.append('./Animals')
-from decision_tree import add_data
 from animals import create_animals, draw_Animals
 from agent import Agent
 from enclosure import create_enclosures, draw_enclosures, draw_gates