tsp v4
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d7c2e446a3
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2908d62fb3
@ -12,7 +12,7 @@ def getPrediction(img_path, network_name):
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img = Image.open(img_path)
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img = Image.open(img_path)
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transform_tensor = transforms.ToTensor()(img).unsqueeze_(0)
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transform_tensor = transforms.ToTensor()(img).unsqueeze_(0)
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classes = ['glass', 'metal', 'paper', 'plastic']
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classes = ['glass', 'metal', 'paper', 'plastic']
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neural_net.load_state_dict(torch.load(PATH + network_name))
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neural_net.load_state_dict(torch.load(PATH + network_name, map_location='cpu'))
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neural_net.eval()
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neural_net.eval()
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outputs = neural_net(transform_tensor)
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outputs = neural_net(transform_tensor)
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@ -4,46 +4,46 @@
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| | | |--- class: 0
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| | | |--- class: 0
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| | |--- feature_0 > 1.50
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| | |--- feature_0 > 1.50
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| | | |--- feature_3 <= 3.50
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| | | |--- feature_3 <= 3.50
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| | | | |--- feature_2 <= 2.50
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| | | | |--- feature_4 <= 2.50
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| | | | | |--- class: 1
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| | | | | |--- class: 1
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| | | | |--- feature_2 > 2.50
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| | | | |--- feature_4 > 2.50
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| | | | | |--- feature_4 <= 2.50
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| | | | | |--- feature_2 <= 2.50
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| | | | | | |--- class: 1
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| | | | | | |--- class: 1
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| | | | | |--- feature_4 > 2.50
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| | | | | |--- feature_2 > 2.50
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| | | | | | |--- class: 0
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| | | | | | |--- class: 0
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| | | |--- feature_3 > 3.50
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| | | |--- feature_3 > 3.50
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| | | | |--- feature_3 <= 4.50
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| | | | |--- feature_3 <= 4.50
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| | | | | |--- feature_1 <= 2.50
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| | | | | |--- feature_1 <= 2.50
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| | | | | | |--- feature_0 <= 2.50
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| | | | | | |--- feature_0 <= 2.50
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| | | | | | | |--- feature_1 <= 1.50
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| | | | | | | |--- feature_1 <= 1.50
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| | | | | | | | |--- feature_4 <= 2.50
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| | | | | | | | |--- feature_2 <= 2.50
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| | | | | | | | | |--- class: 1
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| | | | | | | | | |--- class: 1
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| | | | | | | | |--- feature_4 > 2.50
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| | | | | | | | |--- feature_2 > 2.50
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| | | | | | | | | |--- feature_2 <= 2.00
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| | | | | | | | | |--- feature_4 <= 2.00
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| | | | | | | | | | |--- class: 1
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| | | | | | | | | | |--- class: 1
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| | | | | | | | | |--- feature_2 > 2.00
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| | | | | | | | | |--- feature_4 > 2.00
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| | | | | | | | | | |--- class: 0
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| | | | | | | | | | |--- class: 0
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| | | | | | | |--- feature_1 > 1.50
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| | | | | | | |--- feature_1 > 1.50
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| | | | | | | | |--- class: 0
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| | | | | | | | |--- class: 0
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| | | | | | |--- feature_0 > 2.50
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| | | | | | |--- feature_0 > 2.50
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| | | | | | | |--- feature_2 <= 2.50
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| | | | | | | |--- feature_4 <= 2.50
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| | | | | | | | |--- class: 1
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| | | | | | | | |--- class: 1
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| | | | | | | |--- feature_2 > 2.50
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| | | | | | | |--- feature_4 > 2.50
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| | | | | | | | |--- feature_4 <= 2.50
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| | | | | | | | |--- feature_2 <= 2.50
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| | | | | | | | | |--- class: 1
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| | | | | | | | | |--- class: 1
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| | | | | | | | |--- feature_4 > 2.50
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| | | | | | | | |--- feature_2 > 2.50
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| | | | | | | | | |--- class: 0
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| | | | | | | | | |--- class: 0
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| | | | | |--- feature_1 > 2.50
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| | | | | |--- feature_1 > 2.50
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| | | | | | |--- feature_1 <= 3.50
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| | | | | | |--- feature_1 <= 3.50
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| | | | | | | |--- feature_0 <= 3.50
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| | | | | | | |--- feature_0 <= 3.50
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| | | | | | | | |--- class: 0
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| | | | | | | | |--- class: 0
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| | | | | | | |--- feature_0 > 3.50
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| | | | | | | |--- feature_0 > 3.50
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| | | | | | | | |--- feature_4 <= 2.50
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| | | | | | | | |--- feature_2 <= 2.50
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| | | | | | | | | |--- class: 1
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| | | | | | | | | |--- class: 1
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| | | | | | | | |--- feature_4 > 2.50
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| | | | | | | | |--- feature_2 > 2.50
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| | | | | | | | | |--- feature_2 <= 2.00
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| | | | | | | | | |--- feature_4 <= 2.00
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| | | | | | | | | | |--- class: 1
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| | | | | | | | | | |--- class: 1
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| | | | | | | | | |--- feature_2 > 2.00
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| | | | | | | | | |--- feature_4 > 2.00
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| | | | | | | | | | |--- class: 0
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| | | | | | | | | | |--- class: 0
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| | | | | | |--- feature_1 > 3.50
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| | | | | | |--- feature_1 > 3.50
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| | | | | | | |--- class: 0
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| | | | | | | |--- class: 0
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@ -76,13 +76,13 @@
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| |--- feature_4 <= 1.50
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| |--- feature_4 <= 1.50
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| | |--- feature_1 <= 1.50
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| | |--- feature_1 <= 1.50
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| | | |--- feature_2 <= 4.50
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| | | |--- feature_2 <= 4.50
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| | | | |--- feature_3 <= 4.50
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| | | | |--- feature_0 <= 1.50
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| | | | | |--- feature_0 <= 1.50
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| | | | | | |--- class: 0
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| | | | | |--- feature_0 > 1.50
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| | | | | | |--- class: 1
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| | | | |--- feature_3 > 4.50
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| | | | | |--- class: 0
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| | | | | |--- class: 0
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| | | | |--- feature_0 > 1.50
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| | | | | |--- feature_3 <= 4.50
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| | | | | | |--- class: 1
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| | | | | |--- feature_3 > 4.50
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| | | | | | |--- class: 0
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| | | |--- feature_2 > 4.50
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| | | |--- feature_2 > 4.50
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| | | | |--- class: 0
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| | | | |--- class: 0
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| | |--- feature_1 > 1.50
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| | |--- feature_1 > 1.50
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BIN
last_map.nparr
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last_map.nparr
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32
main.py
32
main.py
@ -101,10 +101,10 @@ class Game():
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atrrs_container = i.get_attributes()
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atrrs_container = i.get_attributes()
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x, y = i.get_coords()
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x, y = i.get_coords()
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dec = decisionTree.decision(getTree(), *atrrs_container)
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dec = decisionTree.decision(getTree(), *atrrs_container)
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if dec[0] == 1:
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# if dec[0] == 1:
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self.positive_decision.append(i)
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self.positive_decision.append(i) # zmiana po to by losowało wszystkie smietniki a nie poprawne tylko, zeby ladniej bylo widac algorytm genetyczny
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else:
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# else:
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self.negative_decision.append(i)
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# self.negative_decision.append(i)
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print('positive actions')
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print('positive actions')
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print(len(self.positive_decision))
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print(len(self.positive_decision))
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@ -115,11 +115,25 @@ class Game():
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# print('----')
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# print('----')
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def decsion_tree_move(self):
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def decsion_tree_move(self):
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for i in self.positive_decision:
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for i in range(0,len(self.positive_decision)):
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# print(i.get_coords())
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# print(i.get_coords())
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print('action')
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print('action')
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trash_x, trash_y = i.get_coords()
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# trash_x, trash_y = i.get_coords()
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# for ii in self.tsp_list:
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temp_tsp = str(self.tsp_list[i])
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temp_tsp = temp_tsp.strip("()")
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temp_tsp = temp_tsp.split(",")
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trash_x = int(temp_tsp[0])
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trash_y = int(temp_tsp[1])
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print(trash_x, trash_y)
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action = a_star_controller.get_actions_for_target_coords(trash_x, trash_y, self)
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action = a_star_controller.get_actions_for_target_coords(trash_x, trash_y, self)
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print(action)
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print(action)
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self.t.startAiController(action)
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self.t.startAiController(action)
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@ -127,7 +141,7 @@ class Game():
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print('--rozpoczecie sortowania smietnika--')
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print('--rozpoczecie sortowania smietnika--')
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dir = "./resources/trash_dataset/test/all"
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dir = "./resources/trash_dataset/test/all"
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files = os.listdir(dir)
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files = os.listdir(dir)
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for i in range(0, 10):
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for j in range(0, 10):
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random = randint(0, 48)
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random = randint(0, 48)
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file = files[random]
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file = files[random]
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result = prediction.getPrediction(dir + '/' + file, 'trained_nn_20.pth')
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result = prediction.getPrediction(dir + '/' + file, 'trained_nn_20.pth')
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@ -155,8 +169,8 @@ class Game():
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# dist = a_star.get_cost
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# dist = a_star.get_cost
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tsp_list = TSP.geneticAlgorithmPlot(population=city_list, popSize=100, eliteSize=20, mutationRate=0.01, generations=200)
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self.tsp_list = TSP.geneticAlgorithmPlot(population=city_list, popSize=100, eliteSize=20, mutationRate=0.01, generations=200)
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print(tsp_list)
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print(self.tsp_list)
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def load_data(self):
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def load_data(self):
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game_folder = os.path.dirname(__file__)
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game_folder = os.path.dirname(__file__)
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@ -21,7 +21,7 @@ def generate_map():
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map[y][x] = 1
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map[y][x] = 1
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# generowanie smietnikow
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# generowanie smietnikow
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for i in range(0, 30):
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for i in range(0, 20):
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x = random.randint(0, MAP_WIDTH-1)
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x = random.randint(0, MAP_WIDTH-1)
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y = random.randint(0, MAP_HEIGHT-1)
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y = random.randint(0, MAP_HEIGHT-1)
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map[y][x] = 2
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map[y][x] = 2
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@ -84,7 +84,9 @@ def get_rotate_change(rotationA: Rotation, rotationB: Rotation) -> int:
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return int(rotationA) - int(rotationB)
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return int(rotationA) - int(rotationB)
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# get new rotation for target_node as neighbour of start_node
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# get new rotation for target_node as neighbour of start_node
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def get_needed_rotation(start_node: Node, target_node: Node) -> Rotation:
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def get_needed_rotation(start_node: Node or bool, target_node: Node) -> Rotation:
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if(start_node == False):
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return target_node.rotation
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if (start_node.x - target_node.x > 0):
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if (start_node.x - target_node.x > 0):
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return Rotation.LEFT
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return Rotation.LEFT
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if (start_node.x - target_node.x < 0):
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if (start_node.x - target_node.x < 0):
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