drzewo impl klasy do podlewania
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@ -1,8 +1,7 @@
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{
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"ExpandedNodes": [
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"",
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"\\decisionTree"
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""
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],
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"SelectedNode": "\\decisionTree\\decisionTree.sav",
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"SelectedNode": "\\C:\\Users\\zmysz\\Desktop\\nowy-inteligentny-traktor",
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"PreviewInSolutionExplorer": false
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}
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.vs/nowy-inteligentny-traktor/v17/.wsuo
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.vs/nowy-inteligentny-traktor/v17/.wsuo
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.vs/slnx.sqlite
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.vs/slnx.sqlite
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13
classes.py
13
classes.py
@ -8,6 +8,7 @@ class Field:
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self.fertilizedTime = fertilizedTime # number
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class Plant:
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def __init__(self, plantType, growthState):
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self.plantType = plantType # wheat/carrot/cabbage
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@ -23,3 +24,15 @@ class Player:
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x = 0
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y = 0
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rotation = 0
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class Watering:
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def __init__(self, rain, planted, temperature, sunny, snowy, moist, rotten, dayTime ):
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self.rain = rain # yes/no
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self.planted = planted # yes/no
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self.temperature = temperature # good/bad
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self.sunny = sunny
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self.snowy = snowy # yes/no
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self.moist = moist # yes/no
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self.rotten = rotten # yes/no
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self.dayTime = dayTime # 1 2 3 4
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@ -1,3 +1,6 @@
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# -*- coding: utf-8 -*-
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# from sklearn.datasets import load_iris
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from sklearn.tree import export_text
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@ -6,7 +9,7 @@ import joblib
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X1 = []
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view = []
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with open("decisionTree/database.txt", 'r') as f:
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with open("database.txt", 'r') as f:
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for line in f:
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line = line.strip()
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test_list = [int(i) for i in line]
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@ -50,14 +53,14 @@ with open("decisionTree/database.txt", 'r') as f:
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view.append(x)
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X1.append(test_list)
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f = open("decisionTree/learning_set.txt", "w") # zapisuje atrybuty s³ownie
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f = open("learning_set.txt", "w") # zapisuje atrybuty s³ownie
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for i in view:
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f.write(str(i)+"\n")
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f.close()
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Y1 = []
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with open("decisionTree/decissions.txt", 'r') as f: # czyta decyzje
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with open("decissions.txt", 'r') as f: # czyta decyzje
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for line in f:
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line = line.strip()
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test = int(line)
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@ -67,7 +70,7 @@ dataset = X1
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decision = Y1
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labels = ['Rain', 'Plant', 'Temperature', 'Sun', 'Snow', 'Moisture', 'Rotten', 'Time']
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model = DecisionTreeClassifier(random_state=0, max_depth=20).fit(dataset, decision)
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filename = 'decisionTree/decisionTree.sav'
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filename = 'decisionTree.sav'
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print("Model trained")
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print("Decision tree:")
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print(export_text(model, feature_names=labels))
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31
main.py
31
main.py
@ -1,3 +1,5 @@
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import joblib
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import numpy as np
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import pygame
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import random
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@ -10,7 +12,7 @@ from torchvision.transforms import Lambda
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from PIL import Image
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import astar
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from classes import Field, Player
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from classes import Field, Player, Watering
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from bfs import Istate, succ
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from bfs import graphsearch
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from board import Grid, Box, Obstacle, getGridBoxes, gridObjects
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@ -406,6 +408,26 @@ def eventHandler(kbdObj, mouseObj):
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goalNode = [int(posX/50), int(posY/50)]
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# drzewo decyzyjne:
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W = np.random.randint(2, size=(10, 10, 8))
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# Wczytywanie modelu z pliku
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labels = ['Rain', 'Planted', 'Temperature', 'Sun', 'Snow', 'Moisture', 'Rotten', 'Time']
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loaded_model = joblib.load('decisionTree/decisionTree.sav')
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sample = W[goalNode[0]-1][goalNode[1]-1]
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# Klasyfikacja przy użyciu wczytanego modelu
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predicted_class = loaded_model.predict([sample])
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print(labels)
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print(sample)
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print('Predicted class:', predicted_class)
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# Decyzja dotycząca podlania grządek na podstawie przewidzianej etykiety
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if predicted_class == [1]:
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print('Podlej grządkę')
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else:
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print('Nie podlewaj grządki')
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print('goalNode x = ', goalNode[0], 'goalNode y = ', goalNode[1])
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# Delay to avoid multiple spawning of objects
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@ -694,6 +716,7 @@ T = [[Field(1,0,0,0,0,0),Field(0,0,1,0,0,0),Field(1,2,1,0,0,0),Field(1,3,0,0,0,0
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[Field(1,0,0,0,0,0),Field(0,2,0,0,0,0),Field(1,1,0,0,0,0),Field(1,0,1,0,0,0),Field(0,2,1,0,0,0),Field(0,3,0,0,0,0),Field(0,0,0,0,0,0),Field(1,0,1,0,0,0),Field(1,0,0,0,0,0),Field(1,0,1,0,0,0)],
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[Field(1,0,1,0,0,0),Field(0,0,0,0,0,0),Field(1,1,1,0,0,0),Field(1,0,0,0,0,0),Field(0,1,1,0,0,0),Field(0,0,1,0,0,0),Field(0,0,0,0,0,0),Field(1,0,1,0,0,0),Field(1,0,0,0,0,0),Field(1,2,1,0,0,0)]]
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# =========================================================================================
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# no i tutaj mamy główna pętlę programu
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@ -753,6 +776,10 @@ while running:
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j = j + 1
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i = i + 1
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font = pygame.font.SysFont('comicsans', 22)
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labelx = font.render('temp:22 |rain:none |snow:none |sun:cloudy |time:evening', True, (0, 0, 0))
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SCREEN.blit(labelx, (10, 10))
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i = 0
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while i < len(T)+1:
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pygame.draw.line(SCREEN, (0, 0, 0), (50 + i * 50, 50), (50 + i * 50, 50 + len(T) * 50), 1)
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@ -775,7 +802,7 @@ while running:
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# player seen at the beginning
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SCREEN.blit(tmpImg, (55 + 50 * player.x, 55 + 50 * player.y))
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font = pygame.font.SysFont('comicsans', 22)
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label = font.render('F - cel | X - drzewo', True, (0, 0, 0))
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label1 = font.render('ARROWS - ręczne poruszanie', True, (0, 0, 0))
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label2 = font.render('A - lewo | D - prawo | W - ruch', True, (0, 0, 0))
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