Merge pull request 'drzewo_wiki' (#19) from drzewo_wiki into master

Reviewed-on: #19

.vscode/settings.json

@@ -0,0 +1,5 @@
+{
+    "python.analysis.extraPaths": [
+        "./DecisionTree"
+    ]
+}

DecisionTree/drzewo_decyzyjne.py

@@ -1,87 +0,0 @@
-import graphviz
-import pandas as pd
-from sklearn.tree import DecisionTreeClassifier
-from sklearn.tree import export_graphviz
-
-# def make_tree():
-#     plikZPrzecinkami = open("training_data.txt", 'w')
-
-#     with open('DecisionTree/200permutations_table.txt', 'r') as plik:
-#         for linia in plik:
-#             liczby = linia.strip()
-#             wiersz = ""
-#             licznik = 0
-#             for liczba in liczby:
-#                 wiersz += liczba
-#                 wiersz += ";"
-#             wiersz = wiersz[:-1]
-#             wiersz += '\n'
-#             plikZPrzecinkami.write(wiersz)
-
-#     plikZPrzecinkami.close()
-
-#     x = pd.read_csv('DecisionTree/training_data.txt', delimiter=';',
-#                     names=['wielkosc', 'waga,', 'priorytet', 'ksztalt', 'kruchosc', 'dolna', 'gorna', 'g > d'])
-#     y = pd.read_csv('DecisionTree/decisions.txt', names=['polka'])
-#     # X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=1)  # 70% treningowe and 30% testowe
-
-#     # Tworzenie instancji klasyfikatora ID3
-#     clf = DecisionTreeClassifier(criterion='entropy')
-
-#     # Trenowanie klasyfikatora
-#     clf.fit(x.values, y.values)
-#     # clf.fit(X_train, y_train)
-#     return clf
-
-plikZPrzecinkami = open("training_data.txt", 'w')
-
-with open('200permutations_table.txt', 'r') as plik:
-    for linia in plik:
-        liczby = linia.strip()
-        wiersz = ""
-        licznik = 0
-        for liczba in liczby:
-            wiersz += liczba
-            wiersz += ";"
-        wiersz = wiersz[:-1]
-        wiersz += '\n'
-        plikZPrzecinkami.write(wiersz)
-
-plikZPrzecinkami.close()
-
-x = pd.read_csv('training_data.txt', delimiter=';',
-                names=['wielkosc', 'waga,', 'priorytet', 'ksztalt', 'kruchosc', 'dolna', 'gorna', 'g > d'])
-y = pd.read_csv('decisions.txt', names=['polka'])
-# X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=1)  # 70% treningowe and 30% testowe
-
-# Tworzenie instancji klasyfikatora ID3
-clf = DecisionTreeClassifier(criterion='entropy')
-
-# Trenowanie klasyfikatora
-clf.fit(x.values, y.values)
-# clf.fit(X_train, y_train)
-
-
-# Predykcja na nowych danych
-new_data = [[2, 2, 1, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]]
-predictions = clf.predict(new_data)
-# y_pred = clf.predict(X_test)
-
-
-print(predictions)
-# print("Accuracy:", clf.score(new_data, predictions))
-# print("Accuracy:", metrics.accuracy_score(y_test, y_pred))
-
-
-# Wygenerowanie pliku .dot reprezentującego drzewo
-dot_data = export_graphviz(clf, out_file=None, feature_names=list(x.columns), class_names=['0', '1'], filled=True,
-                           rounded=True)
-
-# Tworzenie obiektu graphviz z pliku .dot
-graph = graphviz.Source(dot_data)
-
-# Wyświetlanie drzewa
-graph.view()
-
-z = pd.concat([x, y], axis=1)
-z.to_csv('dane.csv', index=False)

Source.gv

@@ -0,0 +1,197 @@
+digraph Tree {
+node [shape=box, style="filled, rounded", color="black", fontname="helvetica"] ;
+edge [fontname="helvetica"] ;
+0 [label="g > d <= 0.5\nentropy = 0.997\nsamples = 200\nvalue = [94, 106]\nclass = 1", fillcolor="#e9f4fc"] ;
+1 [label="waga, <= 0.5\nentropy = 0.803\nsamples = 98\nvalue = [74, 24]\nclass = 0", fillcolor="#edaa79"] ;
+0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
+2 [label="wielkosc <= 1.5\nentropy = 0.998\nsamples = 34\nvalue = [16, 18]\nclass = 1", fillcolor="#e9f4fc"] ;
+1 -> 2 ;
+3 [label="priorytet <= 0.5\nentropy = 0.887\nsamples = 23\nvalue = [7, 16]\nclass = 1", fillcolor="#90c8f0"] ;
+2 -> 3 ;
+4 [label="kruchosc <= 0.5\nentropy = 0.439\nsamples = 11\nvalue = [1, 10]\nclass = 1", fillcolor="#4da7e8"] ;
+3 -> 4 ;
+5 [label="entropy = 0.0\nsamples = 7\nvalue = [0, 7]\nclass = 1", fillcolor="#399de5"] ;
+4 -> 5 ;
+6 [label="ksztalt <= 0.5\nentropy = 0.811\nsamples = 4\nvalue = [1, 3]\nclass = 1", fillcolor="#7bbeee"] ;
+4 -> 6 ;
+7 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
+6 -> 7 ;
+8 [label="dolna <= 0.5\nentropy = 0.918\nsamples = 3\nvalue = [1, 2]\nclass = 1", fillcolor="#9ccef2"] ;
+6 -> 8 ;
+9 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
+8 -> 9 ;
+10 [label="gorna <= 0.5\nentropy = 1.0\nsamples = 2\nvalue = [1, 1]\nclass = 0", fillcolor="#ffffff"] ;
+8 -> 10 ;
+11 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
+10 -> 11 ;
+12 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
+10 -> 12 ;
+13 [label="kruchosc <= 0.5\nentropy = 1.0\nsamples = 12\nvalue = [6, 6]\nclass = 0", fillcolor="#ffffff"] ;
+3 -> 13 ;
+14 [label="entropy = 0.0\nsamples = 5\nvalue = [5, 0]\nclass = 0", fillcolor="#e58139"] ;
+13 -> 14 ;
+15 [label="wielkosc <= 0.5\nentropy = 0.592\nsamples = 7\nvalue = [1, 6]\nclass = 1", fillcolor="#5aade9"] ;
+13 -> 15 ;
+16 [label="entropy = 0.0\nsamples = 4\nvalue = [0, 4]\nclass = 1", fillcolor="#399de5"] ;
+15 -> 16 ;
+17 [label="ksztalt <= 0.5\nentropy = 0.918\nsamples = 3\nvalue = [1, 2]\nclass = 1", fillcolor="#9ccef2"] ;
+15 -> 17 ;
+18 [label="entropy = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = 1", fillcolor="#399de5"] ;
+17 -> 18 ;
+19 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
+17 -> 19 ;
+20 [label="ksztalt <= 0.5\nentropy = 0.684\nsamples = 11\nvalue = [9, 2]\nclass = 0", fillcolor="#eb9d65"] ;
+2 -> 20 ;
+21 [label="kruchosc <= 0.5\nentropy = 1.0\nsamples = 4\nvalue = [2, 2]\nclass = 0", fillcolor="#ffffff"] ;
+20 -> 21 ;
+22 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
+21 -> 22 ;
+23 [label="dolna <= 0.5\nentropy = 0.918\nsamples = 3\nvalue = [1, 2]\nclass = 1", fillcolor="#9ccef2"] ;
+21 -> 23 ;
+24 [label="entropy = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = 1", fillcolor="#399de5"] ;
+23 -> 24 ;
+25 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
+23 -> 25 ;
+26 [label="entropy = 0.0\nsamples = 7\nvalue = [7, 0]\nclass = 0", fillcolor="#e58139"] ;
+20 -> 26 ;
+27 [label="gorna <= 0.5\nentropy = 0.449\nsamples = 64\nvalue = [58, 6]\nclass = 0", fillcolor="#e88e4d"] ;
+1 -> 27 ;
+28 [label="entropy = 0.0\nsamples = 33\nvalue = [33, 0]\nclass = 0", fillcolor="#e58139"] ;
+27 -> 28 ;
+29 [label="wielkosc <= 1.5\nentropy = 0.709\nsamples = 31\nvalue = [25, 6]\nclass = 0", fillcolor="#eb9f69"] ;
+27 -> 29 ;
+30 [label="ksztalt <= 0.5\nentropy = 0.918\nsamples = 18\nvalue = [12, 6]\nclass = 0", fillcolor="#f2c09c"] ;
+29 -> 30 ;
+31 [label="kruchosc <= 0.5\nentropy = 1.0\nsamples = 10\nvalue = [5, 5]\nclass = 0", fillcolor="#ffffff"] ;
+30 -> 31 ;
+32 [label="waga, <= 1.5\nentropy = 0.722\nsamples = 5\nvalue = [4, 1]\nclass = 0", fillcolor="#eca06a"] ;
+31 -> 32 ;
+33 [label="entropy = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = 0", fillcolor="#e58139"] ;
+32 -> 33 ;
+34 [label="dolna <= 0.5\nentropy = 1.0\nsamples = 2\nvalue = [1, 1]\nclass = 0", fillcolor="#ffffff"] ;
+32 -> 34 ;
+35 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
+34 -> 35 ;
+36 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
+34 -> 36 ;
+37 [label="priorytet <= 0.5\nentropy = 0.722\nsamples = 5\nvalue = [1, 4]\nclass = 1", fillcolor="#6ab6ec"] ;
+31 -> 37 ;
+38 [label="entropy = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = 1", fillcolor="#399de5"] ;
+37 -> 38 ;
+39 [label="dolna <= 0.5\nentropy = 1.0\nsamples = 2\nvalue = [1, 1]\nclass = 0", fillcolor="#ffffff"] ;
+37 -> 39 ;
+40 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
+39 -> 40 ;
+41 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
+39 -> 41 ;
+42 [label="waga, <= 1.5\nentropy = 0.544\nsamples = 8\nvalue = [7, 1]\nclass = 0", fillcolor="#e99355"] ;
+30 -> 42 ;
+43 [label="entropy = 0.0\nsamples = 4\nvalue = [4, 0]\nclass = 0", fillcolor="#e58139"] ;
+42 -> 43 ;
+44 [label="wielkosc <= 0.5\nentropy = 0.811\nsamples = 4\nvalue = [3, 1]\nclass = 0", fillcolor="#eeab7b"] ;
+42 -> 44 ;
+45 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
+44 -> 45 ;
+46 [label="dolna <= 0.5\nentropy = 0.918\nsamples = 3\nvalue = [2, 1]\nclass = 0", fillcolor="#f2c09c"] ;
+44 -> 46 ;
+47 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
+46 -> 47 ;
+48 [label="kruchosc <= 0.5\nentropy = 1.0\nsamples = 2\nvalue = [1, 1]\nclass = 0", fillcolor="#ffffff"] ;
+46 -> 48 ;
+49 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
+48 -> 49 ;
+50 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
+48 -> 50 ;
+51 [label="entropy = 0.0\nsamples = 13\nvalue = [13, 0]\nclass = 0", fillcolor="#e58139"] ;
+29 -> 51 ;
+52 [label="wielkosc <= 1.5\nentropy = 0.714\nsamples = 102\nvalue = [20, 82]\nclass = 1", fillcolor="#69b5eb"] ;
+0 -> 52 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
+53 [label="waga, <= 0.5\nentropy = 0.469\nsamples = 70\nvalue = [7, 63]\nclass = 1", fillcolor="#4fa8e8"] ;
+52 -> 53 ;
+54 [label="entropy = 0.0\nsamples = 21\nvalue = [0, 21]\nclass = 1", fillcolor="#399de5"] ;
+53 -> 54 ;
+55 [label="ksztalt <= 0.5\nentropy = 0.592\nsamples = 49\nvalue = [7, 42]\nclass = 1", fillcolor="#5aade9"] ;
+53 -> 55 ;
+56 [label="wielkosc <= 0.5\nentropy = 0.25\nsamples = 24\nvalue = [1, 23]\nclass = 1", fillcolor="#42a1e6"] ;
+55 -> 56 ;
+57 [label="entropy = 0.0\nsamples = 15\nvalue = [0, 15]\nclass = 1", fillcolor="#399de5"] ;
+56 -> 57 ;
+58 [label="kruchosc <= 0.5\nentropy = 0.503\nsamples = 9\nvalue = [1, 8]\nclass = 1", fillcolor="#52a9e8"] ;
+56 -> 58 ;
+59 [label="gorna <= 0.5\nentropy = 0.722\nsamples = 5\nvalue = [1, 4]\nclass = 1", fillcolor="#6ab6ec"] ;
+58 -> 59 ;
+60 [label="dolna <= 0.5\nentropy = 0.918\nsamples = 3\nvalue = [1, 2]\nclass = 1", fillcolor="#9ccef2"] ;
+59 -> 60 ;
+61 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
+60 -> 61 ;
+62 [label="waga, <= 1.5\nentropy = 1.0\nsamples = 2\nvalue = [1, 1]\nclass = 0", fillcolor="#ffffff"] ;
+60 -> 62 ;
+63 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
+62 -> 63 ;
+64 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
+62 -> 64 ;
+65 [label="entropy = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = 1", fillcolor="#399de5"] ;
+59 -> 65 ;
+66 [label="entropy = 0.0\nsamples = 4\nvalue = [0, 4]\nclass = 1", fillcolor="#399de5"] ;
+58 -> 66 ;
+67 [label="kruchosc <= 0.5\nentropy = 0.795\nsamples = 25\nvalue = [6, 19]\nclass = 1", fillcolor="#78bced"] ;
+55 -> 67 ;
+68 [label="priorytet <= 0.5\nentropy = 0.98\nsamples = 12\nvalue = [5, 7]\nclass = 1", fillcolor="#c6e3f8"] ;
+67 -> 68 ;
+69 [label="dolna <= 0.5\nentropy = 0.764\nsamples = 9\nvalue = [2, 7]\nclass = 1", fillcolor="#72b9ec"] ;
+68 -> 69 ;
+70 [label="entropy = 0.0\nsamples = 5\nvalue = [0, 5]\nclass = 1", fillcolor="#399de5"] ;
+69 -> 70 ;
+71 [label="gorna <= 0.5\nentropy = 1.0\nsamples = 4\nvalue = [2, 2]\nclass = 0", fillcolor="#ffffff"] ;
+69 -> 71 ;
+72 [label="entropy = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = 0", fillcolor="#e58139"] ;
+71 -> 72 ;
+73 [label="entropy = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = 1", fillcolor="#399de5"] ;
+71 -> 73 ;
+74 [label="entropy = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = 0", fillcolor="#e58139"] ;
+68 -> 74 ;
+75 [label="gorna <= 0.5\nentropy = 0.391\nsamples = 13\nvalue = [1, 12]\nclass = 1", fillcolor="#49a5e7"] ;
+67 -> 75 ;
+76 [label="priorytet <= 0.5\nentropy = 0.65\nsamples = 6\nvalue = [1, 5]\nclass = 1", fillcolor="#61b1ea"] ;
+75 -> 76 ;
+77 [label="entropy = 0.0\nsamples = 4\nvalue = [0, 4]\nclass = 1", fillcolor="#399de5"] ;
+76 -> 77 ;
+78 [label="wielkosc <= 0.5\nentropy = 1.0\nsamples = 2\nvalue = [1, 1]\nclass = 0", fillcolor="#ffffff"] ;
+76 -> 78 ;
+79 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
+78 -> 79 ;
+80 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
+78 -> 80 ;
+81 [label="entropy = 0.0\nsamples = 7\nvalue = [0, 7]\nclass = 1", fillcolor="#399de5"] ;
+75 -> 81 ;
+82 [label="gorna <= 0.5\nentropy = 0.974\nsamples = 32\nvalue = [13, 19]\nclass = 1", fillcolor="#c0e0f7"] ;
+52 -> 82 ;
+83 [label="kruchosc <= 0.5\nentropy = 0.65\nsamples = 12\nvalue = [10, 2]\nclass = 0", fillcolor="#ea9a61"] ;
+82 -> 83 ;
+84 [label="entropy = 0.0\nsamples = 7\nvalue = [7, 0]\nclass = 0", fillcolor="#e58139"] ;
+83 -> 84 ;
+85 [label="priorytet <= 0.5\nentropy = 0.971\nsamples = 5\nvalue = [3, 2]\nclass = 0", fillcolor="#f6d5bd"] ;
+83 -> 85 ;
+86 [label="waga, <= 1.5\nentropy = 0.918\nsamples = 3\nvalue = [1, 2]\nclass = 1", fillcolor="#9ccef2"] ;
+85 -> 86 ;
+87 [label="entropy = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = 1", fillcolor="#399de5"] ;
+86 -> 87 ;
+88 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
+86 -> 88 ;
+89 [label="entropy = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = 0", fillcolor="#e58139"] ;
+85 -> 89 ;
+90 [label="dolna <= 0.5\nentropy = 0.61\nsamples = 20\nvalue = [3, 17]\nclass = 1", fillcolor="#5caeea"] ;
+82 -> 90 ;
+91 [label="entropy = 0.0\nsamples = 11\nvalue = [0, 11]\nclass = 1", fillcolor="#399de5"] ;
+90 -> 91 ;
+92 [label="kruchosc <= 0.5\nentropy = 0.918\nsamples = 9\nvalue = [3, 6]\nclass = 1", fillcolor="#9ccef2"] ;
+90 -> 92 ;
+93 [label="waga, <= 0.5\nentropy = 0.811\nsamples = 4\nvalue = [3, 1]\nclass = 0", fillcolor="#eeab7b"] ;
+92 -> 93 ;
+94 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
+93 -> 94 ;
+95 [label="entropy = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = 0", fillcolor="#e58139"] ;
+93 -> 95 ;
+96 [label="entropy = 0.0\nsamples = 5\nvalue = [0, 5]\nclass = 1", fillcolor="#399de5"] ;
+92 -> 96 ;
+}

drzewo_decyzyjne.py

@@ -0,0 +1,58 @@
+import graphviz
+import pandas as pd
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.tree import export_graphviz
+
+def make_tree():
+    plikZPrzecinkami = open("training_data.txt", 'w')
+
+    with open('DecisionTree/200permutations_table.txt', 'r') as plik:
+        for linia in plik:
+            liczby = linia.strip()
+            wiersz = ""
+            licznik = 0
+            for liczba in liczby:
+                wiersz += liczba
+                wiersz += ";"
+            wiersz = wiersz[:-1]
+            wiersz += '\n'
+            plikZPrzecinkami.write(wiersz)
+
+    plikZPrzecinkami.close()
+
+    x = pd.read_csv('DecisionTree/training_data.txt', delimiter=';',
+                    names=['wielkosc', 'waga,', 'priorytet', 'ksztalt', 'kruchosc', 'dolna', 'gorna', 'g > d'])
+    y = pd.read_csv('DecisionTree/decisions.txt', names=['polka'])
+    # X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=1)  # 70% treningowe and 30% testowe
+
+    # Tworzenie instancji klasyfikatora ID3
+    clf = DecisionTreeClassifier(criterion='entropy')
+
+    # Trenowanie klasyfikatora
+    clf.fit(x.values, y.values)
+    # clf.fit(X_train, y_train)
+    return clf
+
+# # Predykcja na nowych danych
+# new_data = [[2, 2, 1, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]]
+# predictions = clf.predict(new_data)
+# # y_pred = clf.predict(X_test)
+
+
+# print(predictions)
+# # print("Accuracy:", clf.score(new_data, predictions))
+# # print("Accuracy:", metrics.accuracy_score(y_test, y_pred))
+
+
+# Wygenerowanie pliku .dot reprezentującego drzewo
+# dot_data = export_graphviz(clf, out_file=None, feature_names=list(x.columns), class_names=['0', '1'], filled=True,
+#                            rounded=True)
+
+# # Tworzenie obiektu graphviz z pliku .dot
+# graph = graphviz.Source(dot_data)
+
+# # Wyświetlanie drzewa
+# graph.view()
+
+# z = pd.concat([x, y], axis=1)
+# z.to_csv('dane.csv', index=False)

ekran.py

@@ -1,10 +1,7 @@
 import pygame
-
 from plansza import x1, y1, x2, y2, x3, y3, x4, y4, a_pix, b_pix
 from regal import Regal, obliczPixeleNaPodstawieKratek
-# import wozek
 from packageList import *
-# from paczka import Paczka
 
 EKRAN_SZEROKOSC = 770
 EKRAN_WYSOKOSC = 770
@@ -15,9 +12,6 @@ pygame.display.set_caption("Inteligentny wozek")
 icon = pygame.image.load('images/icon.png')
 pygame.display.set_icon(icon)
 
-# lista_paczek = packageList.zainicjuj_liste_paczek(a_pix, b_pix)
-
-#lista_paczek = listOfPackages()
 lista_paczek = []
 lista_paczek_na_regalach = []
         
@@ -52,11 +46,6 @@ def zwroc_regaly_kategoria(kategoria):
             lista_reg.append(reg)
     return lista_reg
 
-# def zwroc_regaly_wspolrzedne(x_reg, y_reg):
-#     for regal in lista_regalow:
-#         if regal.wiersz == obliczPixeleNaPodstawieKratek(x_reg) and regal.kolumna == obliczPixeleNaPodstawieKratek(y_reg):
-#             return regal 
-
 def narysuj_siatke():
     blockSize = 70  # Set the size of the grid block
     WHITE = (200, 200, 200)
@@ -94,13 +83,16 @@ def sprawdz_ktora_kolumna(y):
 
 
 def narysuj_paczki(wozek):
-    #if wozek.ln == 0:
-        #for paczka in lista_paczek.list:
-    #if len(lista_paczek_na_regalach) == 0:
     for paczka in lista_paczek:
         paczka.narysuj(paczka.x, paczka.y, screen)
 
 def narysuj_paczke_na_regale():
     for paczka in lista_paczek_na_regalach:
         if paczka.is_in_move is False:
-            paczka.narysuj(paczka.x, paczka.y, screen)
+            paczka.narysuj(paczka.x, paczka.y, screen)
+
+def dodaj_paczki_na_rampe(p1, p2):
+    lista_paczek.append(p1)
+    lista_paczek.append(p2)
+    p1.update_position(a_pix, b_pix)
+    p2.update_position(a_pix, b_pix)

main.py

@@ -7,6 +7,7 @@ import ekran
 from grid import GridCellType, SearchGrid
 from sklearn.tree import DecisionTreeClassifier
 import pandas as pd
+import drzewo_decyzyjne
 
 from plansza import a_pix, b_pix
 pygame.init()
@@ -14,15 +15,11 @@ pygame.init()
 
 def main():
     wozek = Wozek()
-
     p1 = Paczka('duzy', 12, 'narzedzia', False, True, False, any, any, any, any, any)
     p2 = Paczka('maly', 1, 'ogród', False, True, False, any, any, any, any, any)
-    ekran.lista_paczek.append(p1)
-    ekran.lista_paczek.append(p2)
-    p1.update_position(a_pix, b_pix)
-    p2.update_position(a_pix, b_pix)
-
+    ekran.dodaj_paczki_na_rampe(p1, p2)
     grid_points = SearchGrid()
+    drzewo = drzewo_decyzyjne.make_tree()
 
     while True:
         for event in pygame.event.get():
@@ -52,34 +49,14 @@ def main():
 
                             array, reg = przenoszona_paczka.tablica_do_drzewa(przenoszona_paczka.kategoria)
 
-                            plikZPrzecinkami = open("DecisionTree/training_data.txt", 'w')
+                            predictions = drzewo.predict([array])
 
-                            with open('DecisionTree/200permutations_table.txt', 'r') as plik:
-                                for linia in plik:
-                                    liczby = linia.strip()
-                                    wiersz = ""
-                                    licznik = 0
-                                    for liczba in liczby:
-                                        wiersz += liczba
-                                        wiersz += ";"
-                                    wiersz = wiersz[:-1]
-                                    wiersz += '\n'
-                                    plikZPrzecinkami.write(wiersz)
-
-                            plikZPrzecinkami.close()
-                            x = pd.read_csv('DecisionTree/training_data.txt', delimiter=';',
-                            names=['wielkosc', 'waga,', 'priorytet', 'ksztalt', 'kruchosc', 'dolna', 'gorna', 'g > d'])
-                            y = pd.read_csv('DecisionTree/decisions.txt', names=['polka'])
-                            clf = DecisionTreeClassifier(criterion='entropy')
-                            clf.fit(x.values, y.values)
-
-                            predictions = clf.predict([array])
                             if predictions == 0:
                                 print('odklada na dolna polke!')
                             else:
                                 print('odklada na gorna polke!')
 
-                            docelowy_stan = wyszukiwanie.Stan(reg.numerWiersza * 70, reg.numerKolumny * 70, 1)  # x1 i y1 bo to są regału kategorii ogród
+                            docelowy_stan = wyszukiwanie.Stan(reg.numerWiersza * 70, reg.numerKolumny * 70, 1)
                             wezel = wyszukiwanie.wyszukiwanie_a_star(wozek.obecnyStan, docelowy_stan, grid_points)
                             sciezka = wyszukiwanie.znajdz_sciezke(wezel)
                             wozek.przemiesc_wozek_po_sciezce(sciezka)

paczka.py

@@ -89,36 +89,36 @@ class Paczka(pygame.sprite.Sprite):
 
         # piorytet
         if self.priorytet is True:
-            tablica.append(0)
-        else: tablica.append(1)
+            tablica.append(1)
+        else: tablica.append(0)
 
         # kształt
         if self.ksztalt is True:
-            tablica.append(0)
-        else: tablica.append(1)           
+            tablica.append(1)
+        else: tablica.append(0)           
 
         # kruchość
         if self.kruchosc is True:
-            tablica.append(0)
-        else: tablica.append(1)
+            tablica.append(1)
+        else: tablica.append(0)
 
         reg = ekran.zwroc_regaly_kategoria(kategoria)
         # czy dolna wolna
         if reg[0].is_dolna_free() is True:
-            tablica.append(0)
-        else: 
             tablica.append(1)
+        else: 
+            tablica.append(0)
 
         #czy górna wolna
         if reg[0].is_dolna_free() is True:
-            tablica.append(0)
-        else: 
             tablica.append(1)
+        else: 
+            tablica.append(0)
         
         # czy na górnej więcej miejsca
-        if reg[0].is_dolna_free() is True:
-            tablica.append(0)
-        else: 
+        if reg[0].czy_na_gornej_wiecej_miejsca() is True:
             tablica.append(1)
+        else: 
+            tablica.append(0)
 
         return tablica, reg[0]