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newClasses
Author | SHA1 | Date | |
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47465989b2 |
@ -1,4 +1,4 @@
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (pythonProject)" project-jdk-type="Python SDK" />
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<component name="ProjectRootManager" version="2" languageLevel="JDK_19" project-jdk-name="Python 3.9" project-jdk-type="Python SDK" />
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</project>
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@ -1,7 +1,8 @@
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{
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"ExpandedNodes": [
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""
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"",
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"\\decisionTree"
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],
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"SelectedNode": "\\C:\\Users\\zmysz\\Desktop\\nowy-inteligentny-traktor",
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"SelectedNode": "\\decisionTree\\treemaker.py",
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"PreviewInSolutionExplorer": false
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}
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BIN
.vs/slnx.sqlite
@ -1,82 +0,0 @@
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from PIL import Image
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import random
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plants = [[], [], []]
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plants[0].append(Image.open("w1.png"))
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plants[0].append(Image.open("w2.png"))
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plants[0].append(Image.open("w3.png"))
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plants[1].append(Image.open("c1.png"))
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plants[1].append(Image.open("c2.png"))
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plants[1].append(Image.open("c3.png"))
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plants[2].append(Image.open("ca1.png"))
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plants[2].append(Image.open("ca2.png"))
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plants[2].append(Image.open("ca3.png"))
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b = [Image.open("b1.png").convert('RGBA'), Image.open("b2.png").convert('RGBA'), Image.open("b3.png").convert('RGBA')]
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def generate(water, fertilizer, plantf):
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if water == 1:
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new_im = Image.new('RGB', (100, 100),
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(160 + random.randint(-10, 10), 80 + random.randint(-10, 10), 40 + random.randint(-10, 10)))
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tmp = plants[plantf][random.randint(0, 2)].resize(
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(25 + random.randint(-10, 25), 25 + random.randint(-10, 25))).rotate(random.randint(0, 359))
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new_im.paste(tmp, (random.randint(0, 50), random.randint(0, 50)), tmp)
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if fertilizer:
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tmp = b[random.randint(0, 2)].resize(
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(20 + random.randint(0, 25), 20 + random.randint(0, 25))).rotate(random.randint(0, 359))
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new_im.paste(tmp, (random.randint(25, 75), random.randint(25, 75)), tmp)
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else:
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if fertilizer:
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new_im = Image.new('RGB', (100, 100),
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(
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50 + random.randint(-10, 10), 25 + random.randint(-10, 10),
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0 + random.randint(-10, 10)))
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tmp = plants[plantf][random.randint(0, 2)].resize(
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(25 + random.randint(-10, 25), 25 + random.randint(-10, 25))).rotate(random.randint(0, 359))
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new_im.paste(tmp, (random.randint(0, 50), random.randint(0, 50)), tmp)
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tmp = b[random.randint(0, 2)].resize(
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(20 + random.randint(0, 25), 20 + random.randint(0, 25))).rotate(random.randint(0, 359))
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new_im.paste(tmp, (random.randint(25, 75), random.randint(25, 75)), tmp)
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else:
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if random.randint(0, 1) == 1:
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new_im = Image.new('RGB', (100, 100),
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(50 + random.randint(-10, 10), 25 + random.randint(-10, 10),
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0 + random.randint(-10, 10)))
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else:
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new_im = Image.new('RGB', (100, 100),
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(160 + random.randint(-10, 10), 80 + random.randint(-10, 10),
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40 + random.randint(-10, 10)))
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if random.randint(0, 1) == 1: # big
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tmp = plants[plantf][random.randint(0, 2)].resize(
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(75 + random.randint(-10, 25), 75 + random.randint(-10, 25))).rotate(random.randint(0, 359))
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new_im.paste(tmp, (random.randint(0, 15), random.randint(0, 15)), tmp)
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else:
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tmp = plants[plantf][random.randint(0, 2)].resize(
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(random.randint(10, 80), random.randint(10, 80))).rotate(random.randint(0, 359))
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datas = tmp.getdata()
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new_image_data = []
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for item in datas:
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# change all white (also shades of whites) pixels to yellow
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if item[0] in list(range(190, 256)):
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new_image_data.append(
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(random.randint(0, 10), 255 + random.randint(-150, 0), random.randint(0, 10)))
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else:
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new_image_data.append(item)
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# update image data
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tmp.putdata(new_image_data)
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new_im.paste(tmp, (random.randint(0, 30), random.randint(0, 30)), tmp)
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return new_im
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for x in range(0, 1000):
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generate(0, 0, random.randint(0, 2)).save('datasets/00/' + str(x) + '.png')
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for x in range(0, 1000):
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generate(1, 0, random.randint(0, 2)).save('datasets/10/' + str(x) + '.png')
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for x in range(0, 1000):
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generate(0, 1, random.randint(0, 2)).save('datasets/01/' + str(x) + '.png')
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for x in range(0, 1000):
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generate(1, 1, random.randint(0, 2)).save('datasets/11/' + str(x) + '.png')
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BIN
NN/ca1.png
Before Width: | Height: | Size: 448 KiB |
BIN
NN/ca2.png
Before Width: | Height: | Size: 397 KiB |
BIN
NN/ca3.png
Before Width: | Height: | Size: 821 KiB |
@ -1,48 +0,0 @@
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import pathlib
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import random
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import torch
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from torch import nn
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from torch.utils.data import DataLoader
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from torchvision import datasets, transforms
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from torchvision.transforms import Lambda
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device = torch.device('cpu')
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def train(model, dataset, n_iter=100, batch_size=2560000):
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optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
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criterion = nn.NLLLoss()
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dl = DataLoader(dataset, batch_size=batch_size)
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model.train()
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for epoch in range(n_iter):
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for images, targets in dl:
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optimizer.zero_grad()
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out = model(images.to(device))
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loss = criterion(out, targets.to(device))
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loss.backward()
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optimizer.step()
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if epoch % 10 == 0:
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print('epoch: %3d loss: %.4f' % (epoch, loss))
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image_path_list = list(pathlib.Path('./').glob("*/*/*.png"))
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random_image_path = random.choice(image_path_list)
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data_transform = transforms.Compose([
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transforms.Resize(size=(100, 100)),
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transforms.RandomHorizontalFlip(p=0.5),
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transforms.ToTensor(),
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Lambda(lambda x: x.flatten())
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])
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train_data = datasets.ImageFolder(root="./datasets",
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transform=data_transform,
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target_transform=None)
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model1 = nn.Sequential(nn.Linear(30000, 10000), nn.ReLU(), nn.Linear(10000, 10000), nn.ReLU(), nn.Linear(10000, 0000), nn.Linear(10000, 4), nn.LogSoftmax(dim=-1)).to(device)
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model1.load_state_dict(torch.load("./trained"))
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train(model1, train_data)
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torch.save(model1.state_dict(), "./trained")
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32
astar.py
@ -1,6 +1,7 @@
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from operator import itemgetter
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import cart
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import copy
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from classes import Field
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class Istate:
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@ -67,23 +68,23 @@ class Node:
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self.y = y
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def fieldCost(T, node):
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def fieldCost(T,node):
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c = 0
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if T[node.x-1][node.y-1].plantType == 1:
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c = 2
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c =2
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elif T[node.x-1][node.y-1].plantType == 2:
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c = 5
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c =5
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elif T[node.x-1][node.y-1].plantType == 3:
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c = 13
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c =13
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elif T[node.x-1][node.y-1].plantType == 4:
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c = 100000
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c =100000
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else:
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c = 0
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c=0
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if T[node.x-1][node.y-1].isWet == 1:
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c = c + 4
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else:
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c = c+1
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c=c+1
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return c
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@ -91,7 +92,7 @@ def fieldCost(T, node):
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def cost(T, node):
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cost = 0
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while node.get_parent() is not None:
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while (node.get_parent() != None):
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cost = cost + fieldCost(T, node)
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node = node.get_parent()
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@ -102,7 +103,7 @@ def f(goaltest, map, node):
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return cost(map, node) + heuristic(goaltest, node)
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def goal_test(elem, goaltest):
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def goal_test(elem,goaltest):
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if elem.get_x() == goaltest[0] and elem.get_y() == goaltest[1]:
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return True
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else:
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@ -131,7 +132,7 @@ def graphsearch(explored, f, fringe, goaltest, istate, map, succ): # przeszukiw
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explored_tuple.append((x.get_direction(), x.get_x(), x.get_y()))
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x = Node(action, state[0], elem[0], state[1], state[2]) # stworzenie nowego wierzchołka, którego rodzicem jest elem
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p = f(goaltest, map, x) # liczy priorytet
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# print('Koszt =', p)
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#print('Koszt =', p)
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if state not in fringe_tuple and state not in explored_tuple: # jeżeli stan nie znajduje się na fringe oraz nie znajduje się w liście wierzchołków odwiedzonych
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fringe.append((x, p)) # dodanie wierzchołka na fringe
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fringe = sorted(fringe, key=itemgetter(1)) # sortowanie fringe'a według priorytetu
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@ -140,7 +141,7 @@ def graphsearch(explored, f, fringe, goaltest, istate, map, succ): # przeszukiw
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for (state_prio, r) in fringe_tuple_prio:
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if str(state_prio) == str(state):
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if r > p:
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fringe.insert(i, (x, p)) # zamiana state, który należy do fringe z priorytetem r na state z priorytetem p (niższym)
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fringe.insert(i, (x,p)) # zamiana state, który należy do fringe z priorytetem r na state z priorytetem p (niższym)
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fringe.pop(i + 1)
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fringe = sorted(fringe, key=itemgetter(1)) # sortowanie fringe'a według priorytetu
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break
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@ -153,7 +154,7 @@ def heuristic(goaltest, node):
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def print_moves(elem):
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moves_list = []
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while elem.get_parent() is not None:
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while (elem.get_parent() != None):
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moves_list.append(elem.get_action())
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elem = elem.get_parent()
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moves_list.reverse()
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@ -183,6 +184,7 @@ def succ(elem):
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temp_move_east = elem.get_x() + 1
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temp_move_north = elem.get_y() + 1
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if cart.Cart.is_move_allowed_succ(elem) == "x + 1":
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actions_list.append(("move", (elem.get_direction(), temp_move_east, elem.get_y())))
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elif cart.Cart.is_move_allowed_succ(elem) == "y + 1":
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@ -192,4 +194,8 @@ def succ(elem):
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elif cart.Cart.is_move_allowed_succ(elem) == "x - 1":
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actions_list.append(("move", (elem.get_direction(), temp_move_west, elem.get_y())))
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return actions_list
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return actions_list
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18
bfs.py
@ -1,3 +1,4 @@
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import sys
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import cart
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import copy
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@ -66,19 +67,23 @@ class Node:
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self.y = y
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def goal_test(goaltest, elem):
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def goal_test(goaltest,elem):
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if elem.get_x() == goaltest[0] and elem.get_y() == goaltest[1]:
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return True
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else:
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return False
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def graphsearch(goaltest, istate): # przeszukiwanie grafu wszerz
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# def graphsearch(explored, fringe, goaltest, istate, succ): # przeszukiwanie grafu wszerz
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def graphsearch(explored, fringe, goaltest, istate): # przeszukiwanie grafu wszerz
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node = Node(None, istate.get_direction(), None, istate.get_x(), istate.get_y())
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fringe = []
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#elem = []
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explored = []
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#action = []
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fringe.append(node) # wierzchołki do odwiedzenia
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# fringe = [node]
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while True:
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if not fringe:
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return False
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@ -104,7 +109,7 @@ def graphsearch(goaltest, istate): # przeszukiwanie grafu wszerz
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def print_moves(elem):
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moves_list = []
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while elem.get_parent() is not None:
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while (elem.get_parent() != None):
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moves_list.append(elem.get_action())
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elem = elem.get_parent()
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moves_list.reverse()
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@ -134,6 +139,7 @@ def succ(elem):
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temp_move_east = elem.get_x() + 1
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temp_move_north = elem.get_y() + 1
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if cart.Cart.is_move_allowed_succ(elem) == "x + 1":
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actions_list.append(("move", (elem.get_direction(), temp_move_east, elem.get_y())))
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elif cart.Cart.is_move_allowed_succ(elem) == "y + 1":
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@ -143,4 +149,8 @@ def succ(elem):
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elif cart.Cart.is_move_allowed_succ(elem) == "x - 1":
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actions_list.append(("move", (elem.get_direction(), temp_move_west, elem.get_y())))
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return actions_list
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return actions_list
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14
board.py
@ -1,5 +1,6 @@
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import pygame
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from screen import SCREEN
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global BLACK
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# global SCREEN
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global BLACK
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@ -7,8 +8,8 @@ global gridObjects
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global imgTree
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global imgTree
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imgTree = pygame.image.load('img/tree.png')
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gridObjects = {} # Store grid-box objects from Grid Class
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gridObjects = {} # Store grid-box objects from Grid Class
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class Grid(object):
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# ta klasa rysuje kratę na ekranie
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@ -26,7 +27,6 @@ class Grid(object):
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BLACK = (0, 0, 0)
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pygame.draw.rect(SCREEN, BLACK, (self.x, self.y, self.sx, self.sy), self.width)
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class Box(object):
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# global SCREEN
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@ -43,7 +43,6 @@ class Box(object):
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# global BLACK
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pygame.draw.rect(SCREEN, self.color, pygame.Rect(self.x, self.y, self.sx, self.sy))
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class Obstacle(object):
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def __init__(self, mouseObj):
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self.mseX = mouseObj[0]
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@ -55,8 +54,8 @@ class Obstacle(object):
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self.y = g.y
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self.sx = g.sx
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self.sy = g.sy
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if self.x < self.mseX < self.x + self.sx:
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if self.y < self.mseY < self.y + self.sy:
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if self.mseX > self.x and self.mseX < self.x + self.sx:
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if self.mseY > self.y and self.mseY < self.y + self.sy:
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self.posX = self.x
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self.posY = self.y
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self.gridBox = grid
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@ -67,7 +66,6 @@ class Obstacle(object):
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SCREEN.blit(imgTree, (self.posX, self.posY))
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# pygame.display.update()
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def getGridBoxes(grid_box):
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global gridObjects
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return gridObjects[grid_box]
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global gridObjects
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return gridObjects[grid_box]
|
5
cart.py
@ -25,7 +25,9 @@ class Cart:
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def set_y(self, y):
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self.y = y
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def is_move_allowed(self, cart_rect): # sprawdza czy dany ruch, który chce wykonać wózek jest możliwy, zwraca prawdę lub fałsz
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def is_move_allowed(self,
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cart_rect): # sprawdza czy dany ruch, który chce wykonać wózek jest możliwy, zwraca prawdę lub fałsz
|
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if self.direction == definitions.CART_DIRECTION_EAST and cart_rect.x + definitions.BLOCK_SIZE < definitions.WIDTH_MAP:
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return True
|
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elif self.direction == definitions.CART_DIRECTION_SOUTH and cart_rect.y - definitions.BLOCK_SIZE > 0:
|
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@ -72,3 +74,4 @@ class Cart:
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self.direction = 1
|
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else:
|
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self.direction = self.direction + 1
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|
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|
@ -1,6 +1,5 @@
|
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import random
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|
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|
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# Generowanie unikalnej losowej linii tekstu
|
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def generate_unique_line(existing_lines):
|
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while True:
|
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@ -10,7 +9,6 @@ def generate_unique_line(existing_lines):
|
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if line not in existing_lines:
|
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return line
|
||||
|
||||
|
||||
# Generowanie 200 unikalnych linii tekstu
|
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lines = []
|
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while len(lines) < 200:
|
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@ -20,4 +18,4 @@ while len(lines) < 200:
|
||||
# Zapisywanie linii tekstu do pliku
|
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with open('decisionTree/database.txt', 'w') as file:
|
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for line in lines:
|
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file.write(line + '\n')
|
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file.write(line + '\n')
|
@ -53,7 +53,7 @@ with open("database.txt", 'r') as f:
|
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view.append(x)
|
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X1.append(test_list)
|
||||
|
||||
f = open("learning_set.txt", "w") # zapisuje atrybuty s這wnie
|
||||
f = open("learning_set.txt", "w") # zapisuje atrybuty s?ownie
|
||||
for i in view:
|
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f.write(str(i)+"\n")
|
||||
f.close()
|
||||
|
@ -1,3 +1,5 @@
|
||||
# definicje
|
||||
import os
|
||||
import pygame
|
||||
|
||||
pygame.init()
|
||||
@ -19,4 +21,4 @@ HEIGHT_AMOUNT, WIDTH_AMOUNT = 11, 11
|
||||
HEIGHT_MAP, WIDTH_MAP = BLOCK_SIZE * HEIGHT_AMOUNT, BLOCK_SIZE * WIDTH_AMOUNT
|
||||
HEIGHT, WIDTH = HEIGHT_MAP + BLOCK_SIZE, WIDTH_MAP
|
||||
IMAGE_SIZE_NEURAL_NETWORK = 16
|
||||
WINDOW = pygame.display.set_mode((WIDTH, HEIGHT))
|
||||
WINDOW = pygame.display.set_mode((WIDTH, HEIGHT))
|
@ -1,90 +0,0 @@
|
||||
import random
|
||||
import math
|
||||
|
||||
|
||||
def create_initial_population(population_size, new_list, player):
|
||||
population = []
|
||||
for _ in range(population_size):
|
||||
chromosome = new_list.copy()
|
||||
chromosome.remove((player.x+1, player.y+1))
|
||||
random.shuffle(chromosome)
|
||||
chromosome.insert(0, (player.x+1, player.y+1))
|
||||
population.append(chromosome)
|
||||
return population
|
||||
|
||||
|
||||
def calculate_distance(node1, node2):
|
||||
x1, y1 = node1
|
||||
x2, y2 = node2
|
||||
distance = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
|
||||
return distance
|
||||
|
||||
|
||||
def calculate_fitness(individual):
|
||||
total_distance = 0
|
||||
num_nodes = len(individual)
|
||||
|
||||
for i in range(num_nodes - 1):
|
||||
node1 = individual[i]
|
||||
node2 = individual[i + 1]
|
||||
distance = calculate_distance(node1, node2)
|
||||
|
||||
total_distance += distance
|
||||
|
||||
if total_distance == 0:
|
||||
fitness = float('inf')
|
||||
return fitness
|
||||
|
||||
fitness = 1 / total_distance
|
||||
return fitness
|
||||
|
||||
|
||||
def crossover(parent1, parent2, player):
|
||||
child = [(player.x+1, player.y+1)] + [None] * (len(parent1) - 1)
|
||||
start_index = random.randint(1, len(parent1) - 1)
|
||||
end_index = random.randint(start_index + 1, len(parent1))
|
||||
child[start_index:end_index] = parent1[start_index:end_index]
|
||||
remaining_nodes = [node for node in parent2 if node not in child]
|
||||
child[1:start_index] = remaining_nodes[:start_index - 1]
|
||||
child[end_index:] = remaining_nodes[start_index - 1:]
|
||||
return child
|
||||
|
||||
|
||||
def mutate(individual, mutation_rate):
|
||||
for i in range(1, len(individual)):
|
||||
if random.random() < mutation_rate:
|
||||
j = random.randint(1, len(individual) - 1)
|
||||
individual[i], individual[j] = individual[j], individual[i]
|
||||
return individual
|
||||
|
||||
|
||||
def genetic_algorithm(new_list, player):
|
||||
max_generations = 200
|
||||
population_size = 200
|
||||
mutation_rate = 0.1
|
||||
population = create_initial_population(population_size, new_list, player)
|
||||
best_individual = None
|
||||
best_fitness = float('-inf')
|
||||
|
||||
for generation in range(max_generations):
|
||||
fitness_values = [calculate_fitness(individual) for individual in population]
|
||||
population = [x for _, x in sorted(zip(fitness_values, population), reverse=True)]
|
||||
fitness_values.sort(reverse=True)
|
||||
best_individuals = population[:10]
|
||||
new_population = best_individuals.copy()
|
||||
|
||||
while len(new_population) < population_size:
|
||||
parent1, parent2 = random.choices(best_individuals, k=2)
|
||||
child = crossover(parent1, parent2, player)
|
||||
child = mutate(child, mutation_rate)
|
||||
new_population.append(child)
|
||||
|
||||
for individual in best_individuals:
|
||||
fitness = calculate_fitness(individual)
|
||||
if fitness > best_fitness:
|
||||
best_fitness = fitness
|
||||
best_individual = individual
|
||||
|
||||
population = new_population[:population_size]
|
||||
|
||||
return best_individual
|
BIN
img/player.png
Before Width: | Height: | Size: 2.9 KiB After Width: | Height: | Size: 16 KiB |
BIN
img/tree.png
Before Width: | Height: | Size: 3.9 KiB After Width: | Height: | Size: 2.0 KiB |
2
main.py
@ -413,7 +413,7 @@ def eventHandler(kbdObj, mouseObj):
|
||||
|
||||
# Wczytywanie modelu z pliku
|
||||
labels = ['Rain', 'Planted', 'Temperature', 'Sun', 'Snow', 'Moisture', 'Rotten', 'Time']
|
||||
loaded_model = joblib.load('decisionTree/decisionTree.sav')
|
||||
loaded_model = joblib.load('decisionTree/decisionTreeFinal.sav')
|
||||
sample = W[goalNode[0]-1][goalNode[1]-1]
|
||||
|
||||
# Klasyfikacja przy użyciu wczytanego modelu
|
||||
|
@ -1,2 +1,2 @@
|
||||
import pygame
|
||||
SCREEN = pygame.display.set_mode([600, 690])
|
||||
SCREEN = pygame.display.set_mode([600,665])
|
BIN
test/00/test.png
Before Width: | Height: | Size: 3.1 KiB |