Poprawienie dobierania jedzenia przez agenta i spowolnienie głodu

Animals/bat.py

@@ -16,7 +16,7 @@ class Bat(Animal):
     def getting_hungry(self, const):
         checktime = datetime.now()
         delta = checktime - self._starttime
-        minutes_passed = delta.total_seconds() / 30*5
+        minutes_passed = delta.total_seconds() / 35*5
         self._starttime = checktime
 
         if const.IS_NIGHT and self._feed < 10:

Animals/giraffe.py

@@ -17,7 +17,7 @@ class Giraffe(Animal):
     def getting_hungry(self, const):
         checktime = datetime.now()
         delta = checktime - self._starttime
-        minutes_passed = delta.total_seconds() / 30*5
+        minutes_passed = delta.total_seconds() / 35*5
         self._starttime = checktime
 
         if not const.IS_NIGHT and self._feed < 10:

Animals/owl.py

@@ -16,7 +16,7 @@ class Owl(Animal):
     def getting_hungry(self, const):
         checktime = datetime.now()
         delta = checktime - self._starttime
-        minutes_passed = delta.total_seconds() / 25*5
+        minutes_passed = delta.total_seconds() / 30*5
         self._starttime = checktime
 
         if const.IS_NIGHT and self._feed < 10:

Animals/parrot.py

@@ -17,7 +17,7 @@ class Parrot(Animal):
     def getting_hungry(self, const):
         checktime = datetime.now()
         delta = checktime - self._starttime
-        minutes_passed = delta.total_seconds() / 20*5
+        minutes_passed = delta.total_seconds() / 25*5
         self._starttime = checktime
 
         if not const.IS_NIGHT and self._feed < 10:

Animals/penguin.py

@@ -17,7 +17,7 @@ class Penguin(Animal):
     def getting_hungry(self, const):
         checktime = datetime.now()
         delta = checktime - self._starttime
-        minutes_passed = delta.total_seconds() / 15*5
+        minutes_passed = delta.total_seconds() / 20*5
         self._starttime = checktime
 
         if not const.IS_NIGHT and self._feed < 10:

agent.py

@@ -103,7 +103,7 @@ def take_food(self):
     house_x = 3
     house_y = 1
     if self.x == house_x and self.y == house_y:
-        if self._dryfood == 0 or self._wetfood == 0:
+        if self._dryfood < 1 or self._wetfood < 1:
             self._dryfood = 50
             self._wetfood = 50
             print("Agent took food and current food level is", self._dryfood, self._wetfood)

decision_tree.py

@@ -6,25 +6,26 @@ import matplotlib.pyplot as plt
 headers = ['adult','active_time','ill','season','guests','hunger','wet_food','dry_food']
 # Wczytanie danych
 data = pd.read_csv('dane.csv', header=0) 
-X = data[['adult','active_time','ill','season','guests','hunger','wet_food','dry_food']]
+X = data[headers]
 Y = data['decision']
 X = pd.get_dummies(data=X, columns=['season'])
-
-X_train, X_test, Y_train, Y_test = train_test_split(X, Y, train_size=0.8)
 clf = DecisionTreeClassifier(random_state=0, min_samples_leaf = 4, min_samples_split=2)
+clf = clf.fit(X,Y)
+#skuteczność  drzewa
+'''
+X_train, X_test, Y_train, Y_test = train_test_split(X, Y, train_size=0.8)
 clf = clf.fit(X_train, Y_train)
 Y_pred = clf.predict(X_test)
 accuracy = accuracy_score(Y_test, Y_pred)
 print("Dokładność:", accuracy)
-
+'''
 
 
 #zapisanie drzewa do pliku
 plt.figure(figsize=(50,30))  
-plot_tree(clf, filled=True, feature_names=X.columns, class_names=['nie karmi', 'karmi mokrą karmą', 'karmi suchą karmą'])  # filled=True koloruje węzły
-plt.savefig('tree.png')
+plot_tree(clf, filled=True, feature_names=X.columns, class_names=['nie karmi', 'karmi mokrą karmą', 'karmi suchą karmą']) 
 
-# Nowe dane
+# dane do decyzji
 def feed_decision(adult,active_time,ill,season,guests,hunger,dry_food,wet_food):
     X_new = pd.DataFrame({
         'adult': [adult],