Merge pull request 'drzewo impl klasy do podlewania' (#6) from tree_classes into master

Reviewed-on: #6

.vs/VSWorkspaceState.json

@@ -1,8 +1,7 @@
 {
   "ExpandedNodes": [
-    "",
-    "\\decisionTree"
+    ""
   ],
-  "SelectedNode": "\\decisionTree\\decisionTree.sav",
+  "SelectedNode": "\\C:\\Users\\zmysz\\Desktop\\nowy-inteligentny-traktor",
   "PreviewInSolutionExplorer": false
 }

classes.py

@@ -8,6 +8,7 @@ class Field:
         self.fertilizedTime = fertilizedTime    # number
 
 
+
 class Plant:
     def __init__(self, plantType, growthState):
         self.plantType = plantType              # wheat/carrot/cabbage
@@ -23,3 +24,15 @@ class Player:
     x = 0
     y = 0
     rotation = 0
+
+
+class Watering:
+    def __init__(self, rain, planted, temperature, sunny, snowy, moist, rotten, dayTime ):
+        self.rain = rain                    # yes/no
+        self.planted = planted              # yes/no
+        self.temperature = temperature      # good/bad
+        self.sunny = sunny
+        self.snowy = snowy                  # yes/no
+        self.moist = moist                  # yes/no
+        self.rotten = rotten                # yes/no
+        self.dayTime = dayTime              # 1 2 3 4

decisionTree/treemaker.py

@@ -1,3 +1,6 @@
+# -*- coding: utf-8 -*-
+
+
 # from sklearn.datasets import load_iris
 from sklearn.tree import export_text
 
@@ -6,7 +9,7 @@ import joblib
 
 X1 = []
 view = []
-with open("decisionTree/database.txt", 'r') as f:
+with open("database.txt", 'r') as f:
     for line in f:
         line = line.strip()
         test_list = [int(i) for i in line]
@@ -50,14 +53,14 @@ with open("decisionTree/database.txt", 'r') as f:
         view.append(x)
         X1.append(test_list)
 
-f = open("decisionTree/learning_set.txt", "w")              # zapisuje atrybuty s³ownie
+f = open("learning_set.txt", "w")              # zapisuje atrybuty s³ownie
 for i in view:
     f.write(str(i)+"\n")
 f.close()
 
 
 Y1 = []
-with open("decisionTree/decissions.txt", 'r') as f:         # czyta decyzje
+with open("decissions.txt", 'r') as f:         # czyta decyzje
     for line in f:
         line = line.strip()
         test = int(line)
@@ -67,7 +70,7 @@ dataset = X1
 decision = Y1
 labels = ['Rain', 'Plant', 'Temperature', 'Sun', 'Snow', 'Moisture', 'Rotten', 'Time']
 model = DecisionTreeClassifier(random_state=0, max_depth=20).fit(dataset, decision)
-filename = 'decisionTree/decisionTree.sav'
+filename = 'decisionTree.sav'
 print("Model trained")
 print("Decision tree:")
 print(export_text(model, feature_names=labels))

main.py

@@ -1,3 +1,5 @@
+import joblib
+import numpy as np
 import pygame
 import random
 
@@ -10,7 +12,7 @@ from torchvision.transforms import Lambda
 from PIL import Image
 
 import astar
-from classes import Field, Player
+from classes import Field, Player, Watering
 from bfs import Istate, succ
 from bfs import graphsearch
 from board import Grid, Box, Obstacle, getGridBoxes, gridObjects
@@ -406,6 +408,26 @@ def eventHandler(kbdObj, mouseObj):
 
             goalNode = [int(posX/50), int(posY/50)]
 
+            # drzewo decyzyjne:
+            W = np.random.randint(2, size=(10, 10, 8))
+
+            # Wczytywanie modelu z pliku
+            labels = ['Rain', 'Planted', 'Temperature', 'Sun', 'Snow', 'Moisture', 'Rotten', 'Time']
+            loaded_model = joblib.load('decisionTree/decisionTree.sav')
+            sample = W[goalNode[0]-1][goalNode[1]-1]
+
+            # Klasyfikacja przy użyciu wczytanego modelu
+            predicted_class = loaded_model.predict([sample])
+            print(labels)
+            print(sample)
+            print('Predicted class:', predicted_class)
+
+            # Decyzja dotycząca podlania grządek na podstawie przewidzianej etykiety
+            if predicted_class == [1]:
+                print('Podlej grządkę')
+            else:
+                print('Nie podlewaj grządki')
+
             print('goalNode x = ', goalNode[0], 'goalNode y = ', goalNode[1])
 
             # Delay to avoid multiple spawning of objects
@@ -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
      [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)],
      [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)]]
 
+
 # =========================================================================================
 # no i tutaj mamy główna pętlę programu
 
@@ -753,6 +776,10 @@ while running:
             j = j + 1
         i = i + 1
 
+    font = pygame.font.SysFont('comicsans', 22)
+    labelx = font.render('temp:22 |rain:none |snow:none |sun:cloudy |time:evening', True, (0, 0, 0))
+    SCREEN.blit(labelx, (10, 10))
+
     i = 0
     while i < len(T)+1:
         pygame.draw.line(SCREEN, (0, 0, 0), (50 + i * 50, 50), (50 + i * 50, 50 + len(T) * 50), 1)
@@ -775,7 +802,7 @@ while running:
     # player seen at the beginning
     SCREEN.blit(tmpImg, (55 + 50 * player.x, 55 + 50 * player.y))
 
-    font = pygame.font.SysFont('comicsans', 22)
+
     label = font.render('F - cel | X - drzewo', True, (0, 0, 0))
     label1 = font.render('ARROWS - ręczne poruszanie', True, (0, 0, 0))
     label2 = font.render('A - lewo | D - prawo | W - ruch', True, (0, 0, 0))