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2022-01-26 01:46:04 +01:00 · 2022-01-26 01:45:06 +01:00 · 2022-01-26 01:41:46 +01:00 · 2022-01-26 00:53:07 +01:00 · 2020-06-09 19:11:55 +00:00 · 2020-06-09 19:10:26 +00:00
62 changed files with 7439 additions and 114 deletions
--- a/Main.cpp
+++ b/Main.cpp
--- a/RaportJaroslawZbaski.md
+++ b/RaportJaroslawZbaski.md
@ -0,0 +1,209 @@
+##Jarosław Zbąski – raport z podprojektu
+---
+   #Wybrana metoda: 
+   ---
+Do realizacji podprojektu wykorzystano drzewa decyzyjne wskazujące którą roślinę (jeśli w ogóle) należy posadzić na danym polu. Drzewo decyzję podejmuje na podstawie poszczególnych parametrów gleby:
+
+-żyzność (‘z’-żyzna, ‘j’-jałowa)
+
+-nawodnienie (‘n’ - nawodniona, ‘s’ - sucha)
+
+-nasłonecznienie (‘s’ - w słońcu, ‘c’ – w cieniu)
+
+-kwasowość gleby (‘k’ – kwasowa, ‘n’ – neutralna, ‘z’ - zasadowa)
+
+
+   #Uczenie modelu: 
+   ---
+Dane treningowe:
+```
+training_data = [
+    #zyznosc, nawodnienie, cien, kwasowość, grupa
+    ['z', 'n', 's', 'z', 1],
+    ['z', 'n', 's', 'n', 1],
+    ['j', 'n', 's', 'z', 1],
+    ['z', 's', 's', 'n', 1],
+    ['j', 'n', 'c', 'n', 1],
+    ['z', 'n', 's', 'k', 1],
+    ['z', 'n', 'c', 'k', 2],
+    ['z', 's', 's', 'k', 2],
+    ['z', 's', 'c', 'k', 2],
+    ['j', 'n', 's', 'k', 2],
+    ['z', 's', 'c', 'z', 3],
+    ['j', 'n', 's', 'n', 3]
+]
+```
+Budowanie drzewa decyzyjnego opiera się na podziale gałęzi względem algorytmu CART. Ma ono postać ciągu pytań, na które odpowiedzi determinują kolejne pytania, bądź kończą etap. W wyniku otrzymujemy strukturę drzewa, która w węzłach końcowych nie zawiera już pytań, lecz same odpowiedzi. Dodatkowo wypisuje zestaw danych pasujących do liścia, z zestawu treningowego.
+```
+def build_tree(rows):
+    gain, question = find_best_split(rows)
+    if gain == 0:
+        return Leaf(rows)
+    true_rows, false_rows = partition(rows, question)
+    true_branch = build_tree(true_rows)
+    false_branch = build_tree(false_rows)
+    return Decision_Node(question, true_branch, false_branch)
+```
+Znajdowanie najlepszego podziału opiera się głównie na Współczynniku Giniego, który mierzy stopień niejednorodności i dzieli ją przez ilość pozostałych zestawów testowych (entropia), co daje nam przyrost informacji.
+```
+def find_best_split(rows):
+    best_gain = 0
+    best_question = None
+    current_uncertainty = gini(rows)
+    n_features = len(rows[0]) - 1
+    for col in range(n_features):
+        values = set([row[col] for row in rows])
+        for val in values:
+            question = Question(col, val)
+            true_rows, false_rows = partition(rows, question)
+            if len(true_rows) == 0 or len(false_rows) == 0:
+                continue
+            gain = info_gain(true_rows, false_rows, current_uncertainty)
+            if gain >= best_gain:
+                best_gain, best_question = gain, question
+    return best_gain, best_question
+```
+
+Drzewo powstałe poprzez wykonanie metody print_tree(node,spacing) na zestawie testowym:
+```
+Czy kwasowosc == k?
+--> True:
+  Czy cien == s?
+  --> True:
+    Czy nawodnienie == n?
+    --> True:
+      Czy zyznosc == j?
+      --> True:
+        Predict {2: 1}
+      --> False:
+        Predict {1: 1}
+    --> False:
+      Predict {2: 1}
+  --> False:
+    Predict {2: 2}
+--> False:
+  Czy cien == s?
+  --> True:
+    Czy zyznosc == j?
+    --> True:
+      Czy kwasowosc == n?
+      --> True:
+        Predict {3: 1}
+      --> False:
+        Predict {1: 1}
+    --> False:
+      Predict {1: 3}
+  --> False:
+    Czy kwasowosc == n?
+    --> True:
+      Predict {1: 1}
+    --> False:
+      Predict {3: 1}
+```
+#Implementacja w C++:
+---
+Komunikacja między pythonem a cpp zachodzi przez pliki dane.txt i decyzje.txt. W pliku dane.txt cpp wypisuje stan całego pola w oddzielonych spacją kolumnach począwszy od indeksu x=1,y=1 aż po x=25,y=25. Decyzje podjęte przez drzewo decyzyjne wypisane w pliku decyzje.txt zawierają symbol rośliny lub pola jakie mają się znajdować na polu (również w całej przestrzeni pola).
+
+Zestaw testowych danych:
+```
+void testSI1()
+{
+	for (int i = 1; i < 26; i++)
+	{
+		for (int j = 1; j < 26; j++)
+		{
+			if (j % 3 == 0)
+			{
+				pole[i][j][2] = 'z';	//zyzne
+				pole[i][j][3] = 'n';	//nawodnione
+				pole[i][j][4] = 'c';	//w cieniu
+				pole[i][j][5] = 'k';	//kwasne
+			}
+			else
+			{
+				if (j % 3 == 1)
+				{
+					pole[i][j][2] = 'j';	//jalowe
+					pole[i][j][3] = 'n';	//nawodnione
+					pole[i][j][4] = 's';	//w sloncu
+					pole[i][j][5] = 'n';	//neutralne
+				}
+				else
+				{
+					pole[i][j][2] = 'z';	//zyzne
+					pole[i][j][3] = 's';	//suche
+					pole[i][j][4] = 's';	//sloneczne
+					pole[i][j][5] = 'z';	//zasadowe
+				}
+			}
+		}
+	}
+}
+```
+
+Funkcja wysyłająca stan pola:
+```
+void sendState()
+{
+	ofstream write("dane.txt");
+	for (int i = 1; i < 26; i++)
+	{
+		for (int j = 1; j < 26; j++)
+		{
+			string a;
+			a += pole[i][j][2];
+			a += ' ';
+			a += pole[i][j][3];
+			a += ' ';
+			a += pole[i][j][4];
+			a += ' ';
+			a += pole[i][j][5];
+			write << a << endl;
+		}
+	}
+	write.close();
+}
+```
+
+Funkcja kierująca traktorem (decyzja co zasiać):
+```
+void reciveState()
+{
+	ifstream read("decyzje.txt");
+	if (read.is_open())
+	{
+		char plant;
+		int i = 1;
+		int j = 1;
+		while (read >> plant)
+		{
+			if (j == 25)
+			{
+				gogo(1, i+1);
+			}
+			else
+			{
+				gogo(j+1 , i );
+			}
+			pole[i][j][0] = plant;
+			if (plant == '.')
+			{
+				pole[i][j][1] = '1';
+			}
+			else
+			{
+				pole[i][j][1] = '9';
+			}
+			if (j == 25)
+			{
+				j = 1;
+				i += 1;
+			}
+			else
+			{
+				j += 1;
+			}
+		}
+	}
+}
+```
--- a/RaportLauraPiotrowska.md
+++ b/RaportLauraPiotrowska.md
@ -0,0 +1,47 @@
+# Opis dokumentu  
+Ten dokument to raport z wykonanego podprojektu na przedmiot Sztuczna Inteligencja. Celem
+zadania jest implementacja algorytmu genetycznego w projekcie o tematyce inteligentny traktor.
+
+# Zastosowanie algorytmu
+Algorytm został wykorzystany do wygenerowania zbioru roślin do zasadzenia na bazie zebranych wcześniej roślin
+
+# Skrócony opis implementacji w projekcie wspólnym
+* Na początku dla każdego pola zawierającego buraki, zostanie wygenerowany kod genetyczny roślin z tego pola.
+
+![](images/test2_generowanie_burakow.png)
+
+* Funkcja Move teraz zajmuje się również zbieraniem i wywoływaniem sadzenia roślin
+
+![](images/move_zbieranie.png)
+
+* Funkcja przypiszKod decyduje czy należy zasadzić jedną z modyfikowanych genetycznie roślin, czy należy zasadzić nową (losową).
+
+![](images/przypiszKod.png)
+
+* Gdy liczba zebranych buraków osiągnie określoną wartość, zostanie przeprowadzony algorytm genetyczny i powstanie tablica roślin do zasadzenia
+
+![](images/wykonanie_algorytmu_gen.png)
+
+# Opis algorytmu genetycznego
+* Algorytm wykonuje 5 iteracji, w których dokonuje selekcji osobników populacji, które zostaną poddane rozrodowi i wytwarza kolejne pokolenie populacji. Po wyjściu z pętli metodą rankingu selekcjonuje najlepszą część ostatniego pokolenia.
+
+![](images/genetic_algorithm.png)
+
+* Selekcja odbywa się metodą ruletki. Obliczana jest wartość funkcji dostosowania dla każdego osobnika. Im lepsza wartość, tym większa szansa na wylosowanie.
+
+![](images/selection.png)
+
+* Funkcja dostosowania polega na wyliczeniu średniej arytmetycznej trzech wartości: smaku, rozmiaru i koloru rośliny, które są zakodowane w łańcuchu znaków.
+
+![](images/fitness.png)
+
+* Funkcja crossover zajmuje się wywołaniem krzyżowania odpowiedniej liczby osobników
+
+![](images/crossover.png)
+
+* Zastosowana została metoda krzyżowania równomiernego (uniform crossover) z małą modyfikacją, zmniejszającą szansę na to, że dziecko będzie "klonem" rodzica. Istnieje również mała szansa, że dojdzie do równomiernej mutacji (uniform mutation), w której losowa cecha dziecka zostaje wygenerowana na nowo.
+
+![](images/cross.png)
+
+ 
+
--- a/RaportSzymonParafiński.md
+++ b/RaportSzymonParafiński.md
@ -0,0 +1,211 @@
+# Podprojekt indywidualny - Szymon Parafiński
+---
+W folderze Sklearn znajduje się wykorzystana baza, wygenerowane drzewo oraz reszta plików związana z podprojektem.
+
+## Wykorzystana metoda uczenia
+
+Do realizacji podprojektu wykorzystano drzewa decyzyjne do decydowania, co należy zrobić po najechaniu na konkretne pole. 
+Drzewo decyduje na jakim etapie jest roślina analizując poszczególne stany danego pola: 
+- Dopiero co zasiana (kiełek)
+    - 0: "Nie_podejmuj_działania"
+- Roślinka kiełkująca (młoda)
+    - 1: "Zastosuj_nawóz"
+- Roślina starzejąca się, bez środka ochrony
+    - 2: "Zastosuj_środek"
+- Roślinka dojrzałam gotowa do zbioru
+    - 4: "Zbierz"
+- Roślina zepsuta, nie nadaje się do użytku
+    - 5: "Roślina_już_zgniła-zbierz_i_wyrzuć".
+	
+ Do implementacji drzew decyzyjnych w Pythonie wykorzystane zostały biblioteki 
+ **sklearn** , **pandas**,**sys** oraz **pickle**.
+ 
+## Uczenie modelu
+
+#### loadLearningBase():  [python]
+Metoda **loadLearningBase** rozpoczyna od utworzenia zbioru uczącego na podstawie tabeli zawierającej informacje wszystkich możliwych stanach roślinki. 
+
+* *col_names* -> zawiera nagłówki poszczególnych kolumn
+* *feature_cols* -> zawiera nagłówki z kolumnami w których znajdują się dane do analizy
+```
+    col_names = ['Warzywo', 'Nawoz', 'Srodek', 'Stan', 'Dzialanie']
+    base = pd.read_csv("Database.csv", header=None, names=col_names)
+    feature_cols = ['Warzywo', 'Nawoz', 'Srodek', 'Stan']
+    """ print dataset"""
+    # print(base.head())
+```
+ Tutaj dzielimy podane kolumny na dwa typy zmiennych:
+* zmienne docelowe ---> y
+*  i zmienne funkcyjne ---> X
+
+Aby móc sprawdzić wydajność modelu, dzielę zestaw danych na zestaw szkoleniowy i zestaw testowy --->  za pomocą funkcji train_test_split (). 
+
+```
+    X = base[feature_cols]  # Features
+    y = base.Dzialanie  # Target variable
+
+    # Split dataset into training set and test set
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,
+                                                        random_state=1)  # 70% training and 30% test
+```
+Wywołanie funkcji odpowiedzialnej za wygenerowanie drzewa. 
+```                                                    
+    data = generateDecisionTree(X_train, X_test, y_train, y_test)
+```
+Dodatkowe elementy pozwalające na wizualizację stworzonego drzewa decyzyjnego poprzez wygenerowanie drzewa zależności if/else lub najpierw do pliku .dot, który następnie poddany odpowiedniej 'obróbce' utworzy obraz.
+```
+    """generate data for image"""
+    # tree.export_graphviz(data, out_file='treeData.dot', filled=True, rounded=True, special_characters=True,
+    #                      feature_names=feature_cols)
+
+    """Printing if_styled tree to console"""
+    # tree_to_code(data, feature_cols)
+
+    return data
+```
+--- 
+#### generateDecisionTree():  [python]
+Metoda **generateDecisionTree** generuje drzewo decyzyjne na podstawie dostarczonej bazy danych. 
+
+Do zmiennej *clf* zapisujemy drzewo decyzyjne z biblioteki **sklearn** utworzone za pomocą metody **DecisionTreeClassifier** z parametrem **criterion** ustawionym na **"entropy"**, który pozwala na uzyskiwanie informacji. 
+Na drzewie wywołujemy metodę **fit**, która dopasowuje do drzewa zbiór uczący zadany w tablicach **X_train** i **y_train**.
+Po dopasowaniu danych możemy przewidzieć stan nowych przykładów, co robimy wywołując na drzewie metodę **predict** z parametrami, które zawierają informację o stanie danego pola. 
+```
+def generateDecisionTree(X_train, X_test, y_train, y_test):
+    # Create Decision Tree classifer object
+    clf = DecisionTreeClassifier(criterion="entropy")
+
+    # Train Decision Tree Classifer
+    clf = clf.fit(X_train, y_train)
+```
+Aby ocenić dokładność naszego modelu przewidujemy odpowiedzi dla naszego zestawu testowego, aby móc go porównać z zestawem y_test i otrzymać dokładność wygenerowanego modelu.
+```
+	# Predict the response for test dataset
+    y_pred = clf.predict(X_test)
+
+    """Model Accuracy, how often is the classifier correct """
+    # print("Accuracy:", metrics.accuracy_score(y_test, y_pred))
+```
+---
+#### main():  [python]
+Metoda main wywołuje pozostałe metody oraz zapisuje wygenerowany model do pliku .sav aby nie było trzeba ponownie generować drzewa, tylko wczytać już te wygenerowane.
+```
+generated = loadLearningBase()
+
+    # Save generated tree
+    filename = 'decisionTree.sav'
+    pickle.dump(generated, open(filename, 'wb'))
+```
+
+## Implementacja w projekcie całościowym
+
+Klasa wywoływana w **C++** nazywa się *injectCode*. 
+Działanie polega na tym że, funkcja **stanPola** sprawdza jakie wartości ma dane pole i generuje odpowiednie polecenie do wykonania.
+#### stanPola():  [C++]
+
+```
+void stanPola(int x, int y) {
+	//[x][x][0] = 0 - brak chemii
+	//[x][x][0] = 1 - tylko nawóz
+	//[x][x][0] = 2 - tylko środek
+	//[x][x][0] = 3 - środek i nawóz
+	//[x][x][1] - wartość wzrostu rośliny 
+
+	polecenie = "python pliki/injectCode.py 1 ";
+
+	if (stan[x][y][0] == 0)
+		polecenie.append("0 0 ");
+	if (stan[x][y][0] == 1)
+		polecenie.append("1 0 ");
+	if (stan[x][y][0] == 2)
+		polecenie.append("0 1 ");
+	if (stan[x][y][0] == 3)
+		polecenie.append("1 1 ");
+	int w = (stan[x][y][1]);
+	std::string s = std::to_string(w);
+	polecenie.append(s);
+
+	decisionTree(polecenie);
+}
+```
+
+Następnie funckja **decisionTree** wykonuje wygenerowane zapytanie.
+#### decisionTree():  [C++]
+
+```
+void decisionTree(string polecenie) {
+
+	std::string str = polecenie;
+	const char* c = str.c_str();
+	system(c);
+
+	int line = 0;
+	ifstream inFile;
+	inFile.open("pliki/dec.txt");
+	if (!inFile) {
+		cout << "Unable to open file";
+		exit(1); // terminate with error
+	}
+
+	while (inFile >> line) {
+		decyzja = line;
+	}
+
+	inFile.close();
+	akcja();
+}
+```
+#### injectCode(): [python]
+
+```
+import pickle
+import sys
+
+
+def prediction(warzywo, nawoz ,srodek, stan_wzrostu):
+	filename = 'pliki/decisionTree.sav'
+	tree = pickle.load(open(filename, 'rb'))
+	val = (tree.predict([[warzywo, nawoz, srodek, stan_wzrostu]]))
+	save(val)
+    
+def save(prediction):
+	pred = str(prediction)
+	plik = open('pliki/dec.txt', 'w')
+	plik.write(pred[1])
+	plik.close()
+
+def decision(prediction):
+	if prediction == 0:
+		return "Nie_podejmuj_dzialania"
+	elif prediction == 1:
+		return "Zastosuj_nawoz"
+	elif prediction == 2:
+		return "Zastosuj_srodek"
+	elif prediction == 4:
+		return "Zbierz"
+	elif prediction == 5:
+		return "Roslina_juz_zgnila__zbierz_i_wyrzuc"
+
+
+
+if __name__ == '__main__':
+	# Map command line arguments to function arguments.
+	prediction(*sys.argv[1:])
+```
+Generowane polecenie wygląda w ten sposób:  
+```
+python injectCode.py a b c d
+```
+Gdzie:
+* a -> rodzaj warzywa 
+* b -> czy roślina była nawożona 
+	* 0: "nie"
+	* 1: "tak" 
+* c ->  czy na roślinie był stosowany środek ochronny
+	* 0: "nie"
+	* 1: "tak" 
+* d -> stan wzrostu w jakim znajduje się roślina
+	* [1,20) - kiełek,
+	* [20,45) - młoda roślina,
+	* [45,85) - dojrzała,
+	* [80,100] - starzejąca się.
--- a/RaportTomaszDzierzbicki.md
+++ b/RaportTomaszDzierzbicki.md
@ -0,0 +1,105 @@
+# Lista funkcji
+
+![](images/13_05/1.png)
+![](images/13_05/2.png)
+
+-----
+# sigmoid, pSigmoid
+funkcja sigmoid przetwarzająca podaną liczbę na liczbę z przedziału [0 ; 1]
+funkcja psigmoid jest pochodną sigmoid
+
+![](images/13_05/3.png)
+
+-----
+# lookOFVege
+funkcja przemieniająca stan rośliny na to 'jak wygląda'
+
+![](images/13_05/4.png)
+
+-----
+# setValusesRange
+funkcja przygotowująca dane pod sigmoid
+
+![](images/13_05/5.png)
+
+-----
+# gradient
+funkcja gradientu przechowująca wzrosty połączeń neuronów
+
+![](images/13_05/6.png)
+
+-----
+# buildMatrix
+funkcja tworząca czystą macierz połączeń neuronów
+
+![](images/13_05/7.png)
+
+-----
+# buildAvrGrad
+funkcja tworząca czystą macierz średnich gradientów
+
+![](images/13_05/8.png)
+
+-----
+# neuronsInputBuild
+funkcja uzupełniająca dane wejściowe neuronów, uzupełniająca wyjścia na podstawie wag macierzy połączeń oraz zwracająca koszt dla wybranego oczekiwanego rozwiązania
+
+![](images/13_05/9.png)
+![](images/13_05/10.png)
+![](images/13_05/11.png)
+
+-----
+# backProp
+funkcja wstecznej propagacji obniżająca koszt conajmniej 50 oraz zapisująca gradient tego kosztu
+
+![](images/13_05/12.png)
+
+-----
+# network
+funkcja przekazująca dane oczekiwane o wstecznej propagacji
+
+![](images/13_05/13.png)
+
+-----
+# bestMatrixBuild
+funkcja budująca najlepszą macierz wag na podstawie średnich gradientów
+
+![](images/13_05/14.png)
+
+-----
+# tests
+zbiory testowe
+## neuroTest1
+test z jednym konkrenym warzywem na polu
+
+![](images/13_05/15.png)
+
+## neuroTest2
+test z warzywami blokującymi oczekiwane warzywo
+
+![](images/13_05/16.png)
+
+-----
+# neuroStart1
+funkcja inicjalizująca naukę traktora
+
+![](images/13_05/17.png)
+
+-----
+# chousePath
+funkcja poruszająca traktor do najlepszego pola
+
+![](images/13_05/18.png)
+![](images/13_05/19.png)
+
+-----
+# testOfNeuroMove
+test do sprawdzenia działania traktora po nauce
+
+![](images/13_05/20.png)
+
+-----
+# main
+inicjalizacja nauki oraz użycia jej
+
+![](images/13_05/21.png)
--- a/backups/Main.cpp
+++ b/backups/Main.cpp
--- a/backups/MainPure.cpp
+++ b/backups/MainPure.cpp
@ -0,0 +1,606 @@
+#include<iostream>
+#include<stdlib.h>
+#include<windows.h>
+#include<conio.h>
+#include<string>
+#include<list>
+#include<set>
+#include<math.h>
+#include<stack>
+
+using namespace std;
+
+const float maxFloat=FLT_MAX;
+const int ROW = 27;
+const int COL = 27;
+typedef pair<int, int> Pair;
+typedef pair<double, pair<int, int>> pPair;
+struct cell
+{
+	int parent_i, parent_j;
+	double f, g, h;
+};
+
+char pole[27][27][2];
+int pozycjaTraktoraX = 1, pozycjaTraktoraY = 1;
+char currentWay = 'S';
+
+void color(string foregroundColor, string backgroundColor)
+{
+	HANDLE hOut;
+	hOut = GetStdHandle(STD_OUTPUT_HANDLE);
+	int foregroundCode = 15;
+	if (foregroundColor == "black")
+		foregroundCode = 0;
+	if (foregroundColor == "dark_blue")
+		foregroundCode = 1;
+	if (foregroundColor == "green")
+		foregroundCode = 2;
+	if (foregroundColor == "cyan")
+		foregroundCode = 3;
+	if (foregroundColor == "dark_red")
+		foregroundCode = 4;
+	if (foregroundColor == "purple")
+		foregroundCode = 5;
+	if (foregroundColor == "dark_yellow")
+		foregroundCode = 6;
+	if (foregroundColor == "light_gray")
+		foregroundCode = 7;
+	if (foregroundColor == "gray")
+		foregroundCode = 8;
+	if (foregroundColor == "blue")
+		foregroundCode = 9;
+	if (foregroundColor == "lime")
+		foregroundCode = 10;
+	if (foregroundColor == "light_blue")
+		foregroundCode = 11;
+	if (foregroundColor == "red")
+		foregroundCode = 12;
+	if (foregroundColor == "magenta")
+		foregroundCode = 13;
+	if (foregroundColor == "yellow")
+		foregroundCode = 14;
+	if (foregroundColor == "white")
+		foregroundCode = 15;
+
+	int backgroundCode = 0;
+	if (backgroundColor == "black")
+		backgroundCode = 0;
+	if (backgroundColor == "dark_blue")
+		backgroundCode = 1;
+	if (backgroundColor == "green")
+		backgroundCode = 2;
+	if (backgroundColor == "cyan")
+		backgroundCode = 3;
+	if (backgroundColor == "dark_red")
+		backgroundCode = 4;
+	if (backgroundColor == "purple")
+		backgroundCode = 5;
+	if (backgroundColor == "dark_yellow")
+		backgroundCode = 6;
+	if (backgroundColor == "light_gray")
+		backgroundCode = 7;
+	if (backgroundColor == "gray")
+		backgroundCode = 8;
+	if (backgroundColor == "blue")
+		backgroundCode = 9;
+	if (backgroundColor == "lime")
+		backgroundCode = 10;
+	if (backgroundColor == "light_blue")
+		backgroundCode = 11;
+	if (backgroundColor == "red")
+		backgroundCode = 12;
+	if (backgroundColor == "magenta")
+		backgroundCode = 13;
+	if (backgroundColor == "yellow")
+		backgroundCode = 14;
+	if (backgroundColor == "white")
+		backgroundCode = 15;
+	SetConsoleTextAttribute(hOut, foregroundCode + backgroundCode * 16);
+}
+
+void SetWindow(int Width, int Height)
+{
+	_COORD coord;
+	coord.X = Width;
+	coord.Y = Height;
+
+	_SMALL_RECT Rect;
+	Rect.Top = 0;
+	Rect.Left = 0;
+	Rect.Bottom = Height - 1;
+	Rect.Right = Width - 1;
+
+	HANDLE Handle = GetStdHandle(STD_OUTPUT_HANDLE);      // Get Handle 
+	SetConsoleScreenBufferSize(Handle, coord);            // Set Buffer Size 
+	SetConsoleWindowInfo(Handle, TRUE, &Rect);            // Set Window Size 
+}
+
+void updatePola()
+{
+	system("cls");
+	for (int i = 0; i < 27; i++)
+	{
+		for (int j = 0; j < 27; j++)
+		{
+			char item = pole[i][j][0];
+			switch (item)
+			{
+			case 'B':
+			{
+				color("purple", "dark_yellow");
+			}break;
+			case 'T':
+			{
+				color("red", "dark_yellow");
+			}break;
+			case 'G':
+			{
+				color("lime", "dark_yellow");
+			}break;
+			case '.':
+			{
+				color("yellow", "dark_yellow");
+			}break;
+			case '#':
+			{
+				color("light_gray", "gray");
+			}break;
+			}
+			cout << pole[i][j][0];
+
+		}
+		cout << endl;
+		color("white", "black");
+	}
+}
+
+void correctMovement(char wantedWay)
+{
+	while (currentWay != wantedWay)
+	{
+		switch (currentWay)
+		{
+			case 'N':
+			{
+				if (wantedWay == 'S')
+					currentWay = wantedWay;
+				else
+					currentWay = 'W';
+			}break;
+			case 'S':
+			{
+				if (wantedWay == 'N')
+					currentWay = wantedWay;
+				else
+					currentWay = 'W';
+			}break;
+			case 'W':
+			{
+				if (wantedWay == 'E')
+					currentWay = wantedWay;
+				else
+					currentWay = 'N';
+			}break;
+			case 'E':
+			{
+				if (wantedWay == 'W')
+					currentWay = wantedWay;
+				else
+					currentWay = 'N';
+			}break;
+		}
+	}
+}
+void Move(char kierunek)
+{
+	switch (kierunek)
+	{
+		//góra-(w)
+		case 'w':
+		{
+			if (pole[pozycjaTraktoraY - 1][pozycjaTraktoraX][0] != '#')
+			{
+				correctMovement('N');
+				pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = '.';
+				pozycjaTraktoraY--;
+				pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = 'T';
+			}
+			updatePola();
+		}break;
+		//dół-(s)
+		case 's':
+		{
+			if (pole[pozycjaTraktoraY + 1][pozycjaTraktoraX][0] != '#')
+			{
+				correctMovement('S');
+				pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = '.';
+				pozycjaTraktoraY++;
+				pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = 'T';
+			}
+			updatePola();
+		}break;
+		//lewo-(a)
+		case 'a':
+		{
+			if (pole[pozycjaTraktoraY][pozycjaTraktoraX - 1][0] != '#')
+			{
+				correctMovement('W');
+				pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = '.';
+				pozycjaTraktoraX--;
+				pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = 'T';
+			}
+			updatePola();
+		}break;
+		//prawo-(d)
+		case 'd':
+		{
+			if (pole[pozycjaTraktoraY][pozycjaTraktoraX + 1][0] != '#')
+			{
+				correctMovement('E');
+				pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = '.';
+				pozycjaTraktoraX++;
+				pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = 'T';
+			}
+			updatePola();
+		}break;
+	}
+}
+
+bool isValid(int x, int y)
+{
+	if (pole[x][y][0] != '#')
+	{
+		return true;
+	}
+	return false;
+}
+bool isDestination(int x, int y,Pair dest)
+{
+	if (dest.first == x && dest.second == y)
+	{
+		return true;
+	}
+	return false;
+}
+double calculateHValue(int x, int y, Pair dest)
+{
+	return abs(x - dest.first) + abs(y - dest.second);
+}
+void tracePath(cell cellDetails[][COL], Pair dest)
+{
+	//printf("\nThe Path is "); //----start info
+	int row = dest.first;
+	int col = dest.second;
+
+	stack<Pair> Path;
+
+	while (!(cellDetails[row][col].parent_i == row
+		&& cellDetails[row][col].parent_j == col))
+	{
+		Path.push(make_pair(row, col));
+		int temp_row = cellDetails[row][col].parent_i;
+		int temp_col = cellDetails[row][col].parent_j;
+		row = temp_row;
+		col = temp_col;
+	}
+
+	Path.push(make_pair(row, col));
+	while (!Path.empty())
+	{
+		pair<int, int> p = Path.top();
+		Path.pop();
+		if (p.first > pozycjaTraktoraX)
+			Move('d');
+		if (p.first < pozycjaTraktoraX)
+			Move('a');
+		if (p.second > pozycjaTraktoraY)
+			Move('s');
+		if (p.second < pozycjaTraktoraY)
+			Move('w');
+		
+		//printf("-> (%d,%d) ", p.first, p.second);  //---- informacja wierzchołku
+		Sleep(1000);
+	}
+
+	return;
+}
+void aStarSearch(int grid[][COL],Pair src, Pair dest)
+{
+	bool closedList[ROW][COL];
+	memset(closedList, false, sizeof(closedList));
+
+	cell cellDetails[ROW][COL];
+
+	int i, j;
+	for (i = 0; i < ROW; i++)
+	{
+		for (j = 0; j < COL; j++)
+		{
+			cellDetails[i][j].f = maxFloat;
+			cellDetails[i][j].g = maxFloat;
+			cellDetails[i][j].h = maxFloat;
+			cellDetails[i][j].parent_i = -1;
+			cellDetails[i][j].parent_j = -1;
+		}
+	}
+	i = src.first, j = src.second;
+	cellDetails[i][j].f = 0.0;
+	cellDetails[i][j].g = 0.0;
+	cellDetails[i][j].h = 0.0;
+	cellDetails[i][j].parent_i = i;
+	cellDetails[i][j].parent_j = j;
+	
+	set<pPair> openList;
+	openList.insert(make_pair(0.0, make_pair(i, j)));
+	bool foundDest = false;
+
+	while (!openList.empty())
+	{
+		pPair p = *openList.begin();
+		openList.erase(openList.begin());
+		i = p.second.first;
+		j = p.second.second;
+		closedList[i][j] = true;
+
+		double gNew, hNew, fNew;
+		double waga = 1.0;
+		waga = ((double)pole[j][i][1] - 48)*1.0;//----waga
+
+		//----------- 1st Successor (North) ------------ 
+		if (isValid(i - 1, j) == true)
+		{
+			if (isDestination(i - 1, j, dest) == true)
+			{
+				cellDetails[i - 1][j].parent_i = i;
+				cellDetails[i - 1][j].parent_j = j;
+				//printf("The destination cell is found\n");
+				tracePath(cellDetails, dest);
+				foundDest = true;
+				return;
+			}
+			else if (closedList[i - 1][j] == false)
+			{
+				gNew = cellDetails[i][j].g + waga;
+				hNew = calculateHValue(i - 1, j, dest);
+				fNew = gNew + hNew;
+
+				if (cellDetails[i - 1][j].f == maxFloat ||
+					cellDetails[i - 1][j].f > fNew)
+				{
+					openList.insert(make_pair(fNew,
+						make_pair(i - 1, j)));
+
+
+					cellDetails[i - 1][j].f = fNew;
+					cellDetails[i - 1][j].g = gNew;
+					cellDetails[i - 1][j].h = hNew;
+					cellDetails[i - 1][j].parent_i = i;
+					cellDetails[i - 1][j].parent_j = j;
+				}
+			}
+		}
+
+		//----------- 2nd Successor (South) ------------ 
+		if (isValid(i + 1, j) == true)
+		{
+			if (isDestination(i + 1, j, dest) == true)
+			{
+				cellDetails[i + 1][j].parent_i = i;
+				cellDetails[i + 1][j].parent_j = j;
+				//printf("The destination cell is found\n");
+				tracePath(cellDetails, dest);
+				foundDest = true;
+				return;
+			}
+			else if (closedList[i + 1][j] == false)
+			{
+				gNew = cellDetails[i][j].g + waga;
+				hNew = calculateHValue(i + 1, j, dest);
+				fNew = gNew + hNew;
+				if (cellDetails[i + 1][j].f == maxFloat ||
+					cellDetails[i + 1][j].f > fNew)
+				{
+					openList.insert(make_pair(fNew, make_pair(i + 1, j)));
+					cellDetails[i + 1][j].f = fNew;
+					cellDetails[i + 1][j].g = gNew;
+					cellDetails[i + 1][j].h = hNew;
+					cellDetails[i + 1][j].parent_i = i;
+					cellDetails[i + 1][j].parent_j = j;
+				}
+			}
+		}
+
+		//----------- 3rd Successor (East) ------------ 
+		if (isValid(i, j + 1) == true)
+		{
+			if (isDestination(i, j + 1, dest) == true)
+			{
+				cellDetails[i][j + 1].parent_i = i;
+				cellDetails[i][j + 1].parent_j = j;
+				//printf("The destination cell is found\n");
+				tracePath(cellDetails, dest);
+				foundDest = true;
+				return;
+			}
+			else if (closedList[i][j + 1] == false)
+			{
+				gNew = cellDetails[i][j].g + waga;
+				hNew = calculateHValue(i, j + 1, dest);
+				fNew = gNew + hNew;
+				if (cellDetails[i][j + 1].f == maxFloat ||
+					cellDetails[i][j + 1].f > fNew)
+				{
+					openList.insert(make_pair(fNew,
+						make_pair(i, j + 1)));
+					cellDetails[i][j + 1].f = fNew;
+					cellDetails[i][j + 1].g = gNew;
+					cellDetails[i][j + 1].h = hNew;
+					cellDetails[i][j + 1].parent_i = i;
+					cellDetails[i][j + 1].parent_j = j;
+				}
+			}
+		}
+
+		//----------- 4th Successor (West) ------------ 
+		if (isValid(i, j - 1) == true)
+		{
+			if (isDestination(i, j - 1, dest) == true)
+			{
+				cellDetails[i][j - 1].parent_i = i;
+				cellDetails[i][j - 1].parent_j = j;
+				//printf("The destination cell is found\n");
+				tracePath(cellDetails, dest);
+				foundDest = true;
+				return;
+			}
+			else if (closedList[i][j - 1] == false)
+			{
+				gNew = cellDetails[i][j].g + waga;
+				hNew = calculateHValue(i, j - 1, dest);
+				fNew = gNew + hNew;
+				if (cellDetails[i][j - 1].f == maxFloat ||
+					cellDetails[i][j - 1].f > fNew)
+				{
+					openList.insert(make_pair(fNew,
+						make_pair(i, j - 1)));
+					cellDetails[i][j - 1].f = fNew;
+					cellDetails[i][j - 1].g = gNew;
+					cellDetails[i][j - 1].h = hNew;
+					cellDetails[i][j - 1].parent_i = i;
+					cellDetails[i][j - 1].parent_j = j;
+				}
+			}
+		}
+	}
+
+	/*if (foundDest == false)
+		printf("Failed to find the Destination Cell\n");*/
+
+	return;
+}
+
+void gogo(int endX,int endY)
+{
+	updatePola();
+	Sleep(1000);
+	int grid[27][27];
+	for (int i = 0; i < 27; i++)
+	{
+		for (int j = 0; j < 27; j++)
+		{
+			grid[i][j] = 0;
+		}
+	}
+	Pair src = make_pair(pozycjaTraktoraX, pozycjaTraktoraY);
+	Pair dest = make_pair(endX, endY);
+	aStarSearch(grid, src, dest);
+}
+
+void test1()
+{
+	pole[1][3][0] = 'B';
+	pole[1][3][1] = '9';
+	pole[3][1][0] = 'B';
+	pole[3][1][1] = '9';
+}
+void test2()
+{
+	for (int i = 1; i < 26; i++)
+	{
+		for (int j = 1; j < i; j++)
+		{
+			pole[i][j][0] = 'B';
+			pole[i][j][1] = '9';
+		}
+	}
+	test1();
+	updatePola();
+}
+
+void start1()
+{
+	int goalX = 3, goalY = 4;
+	test1();
+	pole[1][1][0] = 'T';
+	pole[1][1][1] = '1';
+	pole[goalY][goalX][0] = 'G';
+	pole[goalY][goalX][1] = '9';
+	gogo(goalX, goalY);
+}
+void start2()
+{
+	int goalX = 6, goalY = 6;
+	test2();
+	pole[1][1][0] = 'T';
+	pole[1][1][1] = '1';
+	pole[goalY][goalX][0] = 'G';
+	pole[goalY][goalX][1] = '9';
+	gogo(goalX, goalY);
+}
+void start3()
+{
+	int goalX = 6, goalY = 9;
+	test2();
+	pole[1][1][0] = 'T';
+	pole[1][1][1] = '1';
+	pole[goalY][goalX][0] = 'G';
+	pole[goalY][goalX][1] = '9';
+	gogo(goalX, goalY);
+}
+
+int main()
+{
+	SetWindow(50, 30);
+	//create pola//
+	for (int i = 0; i < 27; i++)
+	{
+		pole[i][0][0] = '#';
+		pole[0][i][0] = '#';
+		pole[26][i][0] = '#';
+		pole[i][26][0] = '#';
+		pole[i][0][1] = '9';
+		pole[0][i][1] = '9';
+		pole[26][i][1] = '9';
+		pole[i][26][1] = '9';
+	}
+	for (int i = 1; i < 26; i++)
+	{
+		for (int j = 1; j < 26; j++)
+		{
+			pole[i][j][0] = '.';
+			pole[i][j][1] = '1';
+		}
+	}
+
+	for (int i = 0; i < 25; i++)
+	{
+		pole[i + 1][i + 1][0] = 'B';
+		pole[i + 1][i + 1][1] = '9';
+	}
+
+	updatePola();
+
+	start3();  // testy start 1-3
+
+	//---------start---------//
+	bool traktorDziala = true;
+
+	char akcja;
+
+	do
+	{
+		akcja = _getch();
+		if (akcja == 'w' || akcja == 's' || akcja == 'a' || akcja == 'd')
+		{
+			Move(akcja);
+		}
+		if (akcja == '0')
+		{
+			traktorDziala = false;
+		}
+	} while (traktorDziala);
+	//---------end---------//
+
+	return 0;
+}
--- a/backups/Main_drzewa1.cpp
+++ b/backups/Main_drzewa1.cpp
@ -0,0 +1,712 @@
+#include<iostream>
+#include<stdlib.h>
+#include<windows.h>
+#include<conio.h>
+#include<string>
+#include<list>
+#include<set>
+#include<math.h>
+#include<stack>
+#include<fstream>
+
+using namespace std;
+
+const float maxFloat=FLT_MAX;
+const int ROW = 27;
+const int COL = 27;
+typedef pair<int, int> Pair;
+typedef pair<double, pair<int, int>> pPair;
+struct cell
+{
+	int parent_i, parent_j;
+	double f, g, h;
+};
+
+char pole[27][27][6];
+int pozycjaTraktoraX = 1, pozycjaTraktoraY = 1;
+char currentWay = 'S';
+
+void color(string foregroundColor, string backgroundColor)
+{
+	HANDLE hOut;
+	hOut = GetStdHandle(STD_OUTPUT_HANDLE);
+	int foregroundCode = 15;
+	if (foregroundColor == "black")
+		foregroundCode = 0;
+	if (foregroundColor == "dark_blue")
+		foregroundCode = 1;
+	if (foregroundColor == "green")
+		foregroundCode = 2;
+	if (foregroundColor == "cyan")
+		foregroundCode = 3;
+	if (foregroundColor == "dark_red")
+		foregroundCode = 4;
+	if (foregroundColor == "purple")
+		foregroundCode = 5;
+	if (foregroundColor == "dark_yellow")
+		foregroundCode = 6;
+	if (foregroundColor == "light_gray")
+		foregroundCode = 7;
+	if (foregroundColor == "gray")
+		foregroundCode = 8;
+	if (foregroundColor == "blue")
+		foregroundCode = 9;
+	if (foregroundColor == "lime")
+		foregroundCode = 10;
+	if (foregroundColor == "light_blue")
+		foregroundCode = 11;
+	if (foregroundColor == "red")
+		foregroundCode = 12;
+	if (foregroundColor == "magenta")
+		foregroundCode = 13;
+	if (foregroundColor == "yellow")
+		foregroundCode = 14;
+	if (foregroundColor == "white")
+		foregroundCode = 15;
+
+	int backgroundCode = 0;
+	if (backgroundColor == "black")
+		backgroundCode = 0;
+	if (backgroundColor == "dark_blue")
+		backgroundCode = 1;
+	if (backgroundColor == "green")
+		backgroundCode = 2;
+	if (backgroundColor == "cyan")
+		backgroundCode = 3;
+	if (backgroundColor == "dark_red")
+		backgroundCode = 4;
+	if (backgroundColor == "purple")
+		backgroundCode = 5;
+	if (backgroundColor == "dark_yellow")
+		backgroundCode = 6;
+	if (backgroundColor == "light_gray")
+		backgroundCode = 7;
+	if (backgroundColor == "gray")
+		backgroundCode = 8;
+	if (backgroundColor == "blue")
+		backgroundCode = 9;
+	if (backgroundColor == "lime")
+		backgroundCode = 10;
+	if (backgroundColor == "light_blue")
+		backgroundCode = 11;
+	if (backgroundColor == "red")
+		backgroundCode = 12;
+	if (backgroundColor == "magenta")
+		backgroundCode = 13;
+	if (backgroundColor == "yellow")
+		backgroundCode = 14;
+	if (backgroundColor == "white")
+		backgroundCode = 15;
+	SetConsoleTextAttribute(hOut, foregroundCode + backgroundCode * 16);
+}
+
+void SetWindow(int Width, int Height)
+{
+	_COORD coord;
+	coord.X = Width;
+	coord.Y = Height;
+
+	_SMALL_RECT Rect;
+	Rect.Top = 0;
+	Rect.Left = 0;
+	Rect.Bottom = Height - 1;
+	Rect.Right = Width - 1;
+
+	HANDLE Handle = GetStdHandle(STD_OUTPUT_HANDLE);      // Get Handle 
+	SetConsoleScreenBufferSize(Handle, coord);            // Set Buffer Size 
+	SetConsoleWindowInfo(Handle, TRUE, &Rect);            // Set Window Size 
+}
+
+void updatePola()
+{
+	system("cls");
+	for (int i = 0; i < 27; i++)
+	{
+		for (int j = 0; j < 27; j++)
+		{
+			char item = pole[i][j][0];
+			switch (item)
+			{
+			case 'B':
+			{
+				color("purple", "dark_yellow");
+			}break;
+			case 'Z':
+			{
+				color("cyan", "dark_yellow");
+			}break;
+			case 'T':
+			{
+				color("red", "dark_yellow");
+			}break;
+			case 'G':
+			{
+				color("lime", "dark_yellow");
+			}break;
+			case '.':
+			{
+				color("yellow", "dark_yellow");
+			}break;
+			case '#':
+			{
+				color("light_gray", "gray");
+			}break;
+			}
+			cout << pole[i][j][0];
+
+		}
+		cout << endl;
+		color("white", "black");
+	}
+}
+
+void correctMovement(char wantedWay)
+{
+	while (currentWay != wantedWay)
+	{
+		switch (currentWay)
+		{
+			case 'N':
+			{
+				if (wantedWay == 'S')
+					currentWay = wantedWay;
+				else
+					currentWay = 'W';
+			}break;
+			case 'S':
+			{
+				if (wantedWay == 'N')
+					currentWay = wantedWay;
+				else
+					currentWay = 'W';
+			}break;
+			case 'W':
+			{
+				if (wantedWay == 'E')
+					currentWay = wantedWay;
+				else
+					currentWay = 'N';
+			}break;
+			case 'E':
+			{
+				if (wantedWay == 'W')
+					currentWay = wantedWay;
+				else
+					currentWay = 'N';
+			}break;
+		}
+	}
+}
+void Move(char kierunek)
+{
+	switch (kierunek)
+	{
+		//góra-(w)
+		case 'w':
+		{
+			if (pole[pozycjaTraktoraY - 1][pozycjaTraktoraX][0] != '#')
+			{
+				correctMovement('N');
+				pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = '.';
+				pozycjaTraktoraY--;
+				pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = 'T';
+			}
+			updatePola();
+		}break;
+		//dół-(s)
+		case 's':
+		{
+			if (pole[pozycjaTraktoraY + 1][pozycjaTraktoraX][0] != '#')
+			{
+				correctMovement('S');
+				pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = '.';
+				pozycjaTraktoraY++;
+				pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = 'T';
+			}
+			updatePola();
+		}break;
+		//lewo-(a)
+		case 'a':
+		{
+			if (pole[pozycjaTraktoraY][pozycjaTraktoraX - 1][0] != '#')
+			{
+				correctMovement('W');
+				pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = '.';
+				pozycjaTraktoraX--;
+				pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = 'T';
+			}
+			updatePola();
+		}break;
+		//prawo-(d)
+		case 'd':
+		{
+			if (pole[pozycjaTraktoraY][pozycjaTraktoraX + 1][0] != '#')
+			{
+				correctMovement('E');
+				pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = '.';
+				pozycjaTraktoraX++;
+				pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = 'T';
+			}
+			updatePola();
+		}break;
+	}
+}
+
+bool isValid(int x, int y)
+{
+	if (pole[x][y][0] != '#')
+	{
+		return true;
+	}
+	return false;
+}
+bool isDestination(int x, int y,Pair dest)
+{
+	if (dest.first == x && dest.second == y)
+	{
+		return true;
+	}
+	return false;
+}
+double calculateHValue(int x, int y, Pair dest)
+{
+	return abs(x - dest.first) + abs(y - dest.second);
+}
+void tracePath(cell cellDetails[][COL], Pair dest)
+{
+	//printf("\nThe Path is "); //----start info
+	int row = dest.first;
+	int col = dest.second;
+
+	stack<Pair> Path;
+
+	while (!(cellDetails[row][col].parent_i == row
+		&& cellDetails[row][col].parent_j == col))
+	{
+		Path.push(make_pair(row, col));
+		int temp_row = cellDetails[row][col].parent_i;
+		int temp_col = cellDetails[row][col].parent_j;
+		row = temp_row;
+		col = temp_col;
+	}
+
+	Path.push(make_pair(row, col));
+	while (!Path.empty())
+	{
+		pair<int, int> p = Path.top();
+		Path.pop();
+		if (p.first > pozycjaTraktoraX)
+			Move('d');
+		if (p.first < pozycjaTraktoraX)
+			Move('a');
+		if (p.second > pozycjaTraktoraY)
+			Move('s');
+		if (p.second < pozycjaTraktoraY)
+			Move('w');
+		
+		//printf("-> (%d,%d) ", p.first, p.second);  //---- informacja wierzchołku
+		Sleep(1000);
+	}
+
+	return;
+}
+void aStarSearch(int grid[][COL],Pair src, Pair dest)
+{
+	bool closedList[ROW][COL];
+	memset(closedList, false, sizeof(closedList));
+
+	cell cellDetails[ROW][COL];
+
+	int i, j;
+	for (i = 0; i < ROW; i++)
+	{
+		for (j = 0; j < COL; j++)
+		{
+			cellDetails[i][j].f = maxFloat;
+			cellDetails[i][j].g = maxFloat;
+			cellDetails[i][j].h = maxFloat;
+			cellDetails[i][j].parent_i = -1;
+			cellDetails[i][j].parent_j = -1;
+		}
+	}
+	i = src.first, j = src.second;
+	cellDetails[i][j].f = 0.0;
+	cellDetails[i][j].g = 0.0;
+	cellDetails[i][j].h = 0.0;
+	cellDetails[i][j].parent_i = i;
+	cellDetails[i][j].parent_j = j;
+	
+	set<pPair> openList;
+	openList.insert(make_pair(0.0, make_pair(i, j)));
+	bool foundDest = false;
+
+	while (!openList.empty())
+	{
+		pPair p = *openList.begin();
+		openList.erase(openList.begin());
+		i = p.second.first;
+		j = p.second.second;
+		closedList[i][j] = true;
+
+		double gNew, hNew, fNew;
+		double waga = 1.0;
+		waga = ((double)pole[j][i][1] - 48)*1.0;//----waga
+
+		//----------- 1st Successor (North) ------------ 
+		if (isValid(i - 1, j) == true)
+		{
+			if (isDestination(i - 1, j, dest) == true)
+			{
+				cellDetails[i - 1][j].parent_i = i;
+				cellDetails[i - 1][j].parent_j = j;
+				//printf("The destination cell is found\n");
+				tracePath(cellDetails, dest);
+				foundDest = true;
+				return;
+			}
+			else if (closedList[i - 1][j] == false)
+			{
+				gNew = cellDetails[i][j].g + waga;
+				hNew = calculateHValue(i - 1, j, dest);
+				fNew = gNew + hNew;
+
+				if (cellDetails[i - 1][j].f == maxFloat ||
+					cellDetails[i - 1][j].f > fNew)
+				{
+					openList.insert(make_pair(fNew,
+						make_pair(i - 1, j)));
+
+
+					cellDetails[i - 1][j].f = fNew;
+					cellDetails[i - 1][j].g = gNew;
+					cellDetails[i - 1][j].h = hNew;
+					cellDetails[i - 1][j].parent_i = i;
+					cellDetails[i - 1][j].parent_j = j;
+				}
+			}
+		}
+
+		//----------- 2nd Successor (South) ------------ 
+		if (isValid(i + 1, j) == true)
+		{
+			if (isDestination(i + 1, j, dest) == true)
+			{
+				cellDetails[i + 1][j].parent_i = i;
+				cellDetails[i + 1][j].parent_j = j;
+				//printf("The destination cell is found\n");
+				tracePath(cellDetails, dest);
+				foundDest = true;
+				return;
+			}
+			else if (closedList[i + 1][j] == false)
+			{
+				gNew = cellDetails[i][j].g + waga;
+				hNew = calculateHValue(i + 1, j, dest);
+				fNew = gNew + hNew;
+				if (cellDetails[i + 1][j].f == maxFloat ||
+					cellDetails[i + 1][j].f > fNew)
+				{
+					openList.insert(make_pair(fNew, make_pair(i + 1, j)));
+					cellDetails[i + 1][j].f = fNew;
+					cellDetails[i + 1][j].g = gNew;
+					cellDetails[i + 1][j].h = hNew;
+					cellDetails[i + 1][j].parent_i = i;
+					cellDetails[i + 1][j].parent_j = j;
+				}
+			}
+		}
+
+		//----------- 3rd Successor (East) ------------ 
+		if (isValid(i, j + 1) == true)
+		{
+			if (isDestination(i, j + 1, dest) == true)
+			{
+				cellDetails[i][j + 1].parent_i = i;
+				cellDetails[i][j + 1].parent_j = j;
+				//printf("The destination cell is found\n");
+				tracePath(cellDetails, dest);
+				foundDest = true;
+				return;
+			}
+			else if (closedList[i][j + 1] == false)
+			{
+				gNew = cellDetails[i][j].g + waga;
+				hNew = calculateHValue(i, j + 1, dest);
+				fNew = gNew + hNew;
+				if (cellDetails[i][j + 1].f == maxFloat ||
+					cellDetails[i][j + 1].f > fNew)
+				{
+					openList.insert(make_pair(fNew,
+						make_pair(i, j + 1)));
+					cellDetails[i][j + 1].f = fNew;
+					cellDetails[i][j + 1].g = gNew;
+					cellDetails[i][j + 1].h = hNew;
+					cellDetails[i][j + 1].parent_i = i;
+					cellDetails[i][j + 1].parent_j = j;
+				}
+			}
+		}
+
+		//----------- 4th Successor (West) ------------ 
+		if (isValid(i, j - 1) == true)
+		{
+			if (isDestination(i, j - 1, dest) == true)
+			{
+				cellDetails[i][j - 1].parent_i = i;
+				cellDetails[i][j - 1].parent_j = j;
+				//printf("The destination cell is found\n");
+				tracePath(cellDetails, dest);
+				foundDest = true;
+				return;
+			}
+			else if (closedList[i][j - 1] == false)
+			{
+				gNew = cellDetails[i][j].g + waga;
+				hNew = calculateHValue(i, j - 1, dest);
+				fNew = gNew + hNew;
+				if (cellDetails[i][j - 1].f == maxFloat ||
+					cellDetails[i][j - 1].f > fNew)
+				{
+					openList.insert(make_pair(fNew,
+						make_pair(i, j - 1)));
+					cellDetails[i][j - 1].f = fNew;
+					cellDetails[i][j - 1].g = gNew;
+					cellDetails[i][j - 1].h = hNew;
+					cellDetails[i][j - 1].parent_i = i;
+					cellDetails[i][j - 1].parent_j = j;
+				}
+			}
+		}
+	}
+
+	/*if (foundDest == false)
+		printf("Failed to find the Destination Cell\n");*/
+
+	return;
+}
+
+void gogo(int endX,int endY)
+{
+	updatePola();
+	Sleep(1000);
+	int grid[27][27];
+	for (int i = 0; i < 27; i++)
+	{
+		for (int j = 0; j < 27; j++)
+		{
+			grid[i][j] = 0;
+		}
+	}
+	Pair src = make_pair(pozycjaTraktoraX, pozycjaTraktoraY);
+	Pair dest = make_pair(endX, endY);
+	aStarSearch(grid, src, dest);
+}
+
+void test1()
+{
+	pole[1][3][0] = 'B';
+	pole[1][3][1] = '9';
+	pole[3][1][0] = 'Z';
+	pole[3][1][1] = '9';
+}
+void test2()
+{
+	for (int i = 1; i < 26; i++)
+	{
+		for (int j = 1; j < i; j++)
+		{
+			pole[i][j][0] = 'B';
+			pole[i][j][1] = '9';
+		}
+	}
+	test1();
+	updatePola();
+}
+
+void testSI1()
+{
+	for (int i = 1; i < 26; i++)
+	{
+		for (int j = 1; j < 26; j++)
+		{
+			if (j % 3 == 0)
+			{
+				pole[i][j][2] = 'z';	//zyzne
+				pole[i][j][3] = 'n';	//nawodnione
+				pole[i][j][4] = 'c';	//w cieniu
+				pole[i][j][5] = 'k';	//kwasne
+			}
+			else
+			{
+				if (j % 3 == 1)
+				{
+					pole[i][j][2] = 'j';	//jalowe
+					pole[i][j][3] = 'n';	//nawodnione
+					pole[i][j][4] = 's';	//w sloncu
+					pole[i][j][5] = 'n';	//neutralne
+				}
+				else
+				{
+					pole[i][j][2] = 'z';	//zyzne
+					pole[i][j][3] = 's';	//suche
+					pole[i][j][4] = 's';	//sloneczne
+					pole[i][j][5] = 'z';	//zasadowe
+				}
+			}
+		}
+	}
+}
+
+void sendState()
+{
+	ofstream write("dane.txt");
+	for (int i = 1; i < 26; i++)
+	{
+		for (int j = 1; j < 26; j++)
+		{
+			string a;
+			a += pole[i][j][2];
+			a += ' ';
+			a += pole[i][j][3];
+			a += ' ';
+			a += pole[i][j][4];
+			a += ' ';
+			a += pole[i][j][5];
+			write << a << endl;
+		}
+	}
+	write.close();
+}
+void reciveState()
+{
+	ifstream read("decyzje.txt");
+	if (read.is_open())
+	{
+		char plant;
+		int i = 1;
+		int j = 1;
+		while (read >> plant)
+		{
+			if (j == 25)
+			{
+				gogo(1, i+1);
+			}
+			else
+			{
+				gogo(j+1 , i );
+			}
+			pole[i][j][0] = plant;
+			if (plant == '.')
+			{
+				pole[i][j][1] = '1';
+			}
+			else
+			{
+				pole[i][j][1] = '9';
+			}
+			if (j == 25)
+			{
+				j = 1;
+				i += 1;
+			}
+			else
+			{
+				j += 1;
+			}
+		}
+	}
+}
+
+void start1()
+{
+	int goalX = 3, goalY = 4;
+	test1();
+	testSI1();
+	pole[1][1][0] = 'T';
+	pole[1][1][1] = '1';
+	pole[goalY][goalX][0] = 'G';
+	pole[goalY][goalX][1] = '9';
+	gogo(goalX, goalY);
+	gogo(goalX - 1, goalY);
+	pole[goalY][goalX][0] = 'Z';
+	pole[goalY][goalX][1] = '9';
+	updatePola();
+	//sendState();		//trzeba ręcznie zmieniać między wysyłaniem stanu a pobieraniem stanu pola
+	reciveState();
+}
+void start2()
+{
+	int goalX = 6, goalY = 6;
+	test2();
+	pole[1][1][0] = 'T';
+	pole[1][1][1] = '1';
+	pole[goalY][goalX][0] = 'G';
+	pole[goalY][goalX][1] = '9';
+	gogo(goalX, goalY);
+}
+void start3()
+{
+	int goalX = 6, goalY = 9;
+	test2();
+	pole[1][1][0] = 'T';
+	pole[1][1][1] = '1';
+	pole[goalY][goalX][0] = 'G';
+	pole[goalY][goalX][1] = '9';
+	gogo(goalX, goalY);
+}
+
+int main()
+{
+	SetWindow(50, 30);
+	//create pola//
+	for (int i = 0; i < 27; i++)
+	{
+		pole[i][0][0] = '#';
+		pole[0][i][0] = '#';
+		pole[26][i][0] = '#';
+		pole[i][26][0] = '#';
+		pole[i][0][1] = '9';
+		pole[0][i][1] = '9';
+		pole[26][i][1] = '9';
+		pole[i][26][1] = '9';
+	}
+	for (int i = 1; i < 26; i++)
+	{
+		for (int j = 1; j < 26; j++)
+		{
+			pole[i][j][0] = '.';
+			pole[i][j][1] = '1';
+		}
+	}
+
+	for (int i = 0; i < 25; i++)
+	{
+		pole[i + 1][i + 1][0] = 'B';
+		pole[i + 1][i + 1][1] = '9';
+	}
+
+	updatePola();
+
+	start1();  // testy start 1-3
+
+	//---------start---------//
+	bool traktorDziala = true;
+
+	char akcja;
+
+	do
+	{
+		akcja = _getch();
+		if (akcja == 'w' || akcja == 's' || akcja == 'a' || akcja == 'd')
+		{
+			Move(akcja);
+		}
+		if (akcja == '0')
+		{
+			traktorDziala = false;
+		}
+	} while (traktorDziala);
+	//---------end---------//
+
+	return 0;
+}
--- a/backups/Main_drzewa1_algorytmGenetyczny.cpp
+++ b/backups/Main_drzewa1_algorytmGenetyczny.cpp
--- a/dane.txt
+++ b/dane.txt
@ -0,0 +1,625 @@
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
+z s s z
+z n c k
+j n s n
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+j n s n
--- a/decisionTree.sav
+++ b/decisionTree.sav
--- a/decyzje.txt
+++ b/decyzje.txt
@ -0,0 +1,625 @@
+.
+B
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--- a/drzewaDecyzyjne.py
+++ b/drzewaDecyzyjne.py
@ -0,0 +1,165 @@
+training_data = [
+    #zyznosc, nawodnienie, cien, kwasowosc
+    ['z', 'n', 's', 'z', 1],
+    ['z', 'n', 's', 'n', 1],
+    ['j', 'n', 's', 'z', 1],
+    ['z', 's', 's', 'n', 1],
+    ['j', 'n', 'c', 'n', 1],
+    ['z', 'n', 's', 'k', 1],
+    ['z', 'n', 'c', 'k', 2],
+    ['z', 's', 's', 'k', 2],
+    ['z', 's', 'c', 'k', 2],
+    ['j', 'n', 's', 'k', 2],
+    ['z', 's', 'c', 'z', 3],
+    ['j', 'n', 's', 'n', 3]
+]
+
+header = ["zyznosc", "nawodnienie", "cien", "kwasowosc", "wybor"]
+
+def class_counts(rows):
+    counts = {}
+    for row in rows:
+        label = row[-1]
+        if label not in counts:
+            counts[label] = 0
+        counts[label] += 1
+    return counts
+
+
+def is_numeric(value):
+    return isinstance(value, int) or isinstance(value, float)
+
+
+class Question:
+    def __init__(self, column, value):
+        self.column = column
+        self.value = value
+
+    def match(self, example):
+        val = example[self.column]
+        if is_numeric(val):
+            return val >= self.value
+        else:
+            return val == self.value
+
+    def __repr__(self):
+        condition = "=="
+        if is_numeric(self.value):
+            condition = ">="
+        return "Czy %s %s %s?" % (
+            header[self.column], condition, str(self.value))
+
+def partition(rows, question):
+    true_rows, false_rows = [], []
+    for row in rows:
+        if question.match(row):
+            true_rows.append(row)
+        else:
+            false_rows.append(row)
+    return true_rows, false_rows
+
+
+def gini(rows):
+    counts = class_counts(rows)
+    impurity = 1
+    for lbl in counts:
+        prob_of_lbl = counts[lbl] / float(len(rows))
+        impurity -= prob_of_lbl**2
+    return impurity
+
+
+def info_gain(left, right, current_uncertainty):
+    p = float(len(left)) / (len(left) + len(right))
+    return current_uncertainty - p * gini(left) - (1 - p) * gini(right)
+
+
+def find_best_split(rows):
+    best_gain = 0
+    best_question = None
+    current_uncertainty = gini(rows)
+    n_features = len(rows[0]) - 1
+    for col in range(n_features):
+        values = set([row[col] for row in rows])
+        for val in values:
+            question = Question(col, val)
+            true_rows, false_rows = partition(rows, question)
+            if len(true_rows) == 0 or len(false_rows) == 0:
+                continue
+            gain = info_gain(true_rows, false_rows, current_uncertainty)
+            if gain >= best_gain:
+                best_gain, best_question = gain, question
+    return best_gain, best_question
+
+
+class Leaf:
+    def __init__(self, rows):
+        self.predictions = class_counts(rows)
+
+class Decision_Node:
+    def __init__(self,
+                 question,
+                 true_branch,
+                 false_branch):
+        self.question = question
+        self.true_branch = true_branch
+        self.false_branch = false_branch
+
+def build_tree(rows):
+    gain, question = find_best_split(rows)
+    if gain == 0:
+        return Leaf(rows)
+    true_rows, false_rows = partition(rows, question)
+    true_branch = build_tree(true_rows)
+    false_branch = build_tree(false_rows)
+    return Decision_Node(question, true_branch, false_branch)
+
+
+def print_tree(node, spacing=""):
+    if isinstance(node, Leaf):
+        print (spacing + "Predict", node.predictions)
+        return
+    print (spacing + str(node.question))
+    print (spacing + '--> True:')
+    print_tree(node.true_branch, spacing + "  ")
+    print (spacing + '--> False:')
+    print_tree(node.false_branch, spacing + "  ")
+
+
+my_tree = build_tree(training_data)
+
+print_tree(my_tree)
+
+def classify(row, node):
+    if isinstance(node, Leaf):
+        return node.predictions
+    if node.question.match(row):
+        return classify(row, node.true_branch)
+    else:
+        return classify(row, node.false_branch)
+
+def print_leaf(counts):
+    total = sum(counts.values()) * 1.0
+    probs = {}
+    for lbl in counts.keys():
+        probs[lbl] = str(int(counts[lbl] / total * 100)) + "%"
+    return probs
+
+
+with open( 'dane.txt', "r" ) as f:
+    testing_data = [ line.split() for line in f ]
+
+
+file = open("decyzje.txt", "w")
+file.write("")
+file.close()
+
+for row in testing_data:
+    pom = print_leaf(classify(row, my_tree))
+    f = open("decyzje.txt", "a")
+    if pom == {1: '100%'}:
+        f.write("B\n")
+    if pom == {2: '100%'}:
+        f.write("Z\n")
+    if pom == {3: '100%'}:
+        f.write(".\n")
+    f.close()
--- a/environment.md
+++ b/environment.md
@ -8,7 +8,7 @@ którym będzie poruszał się agent.
 W skład zespołu wchodzą:  
 Tomasz Dzierzbicki,   
 Szymon Parafiński,    
-Karol Piotrowski,    
+Laura Piotrowska,    
 Jarosław Zbąski.  

 # Temat projektu i wybrane rozwiązanie   
--- a/final-evaluation.md
+++ b/final-evaluation.md
@ -0,0 +1,52 @@
+# Szymon Parafiński
+implementacja drzew decyzyjnych z nauczonego pliku
+
+![](images/last_images/1.png)
+
+implementacja funkcji która decyduje co zrobić z obecnym polem
+
+![](images/last_images/2.png)
+
+Funkcja jest zastosowana podczas osiągniecia celu ruchu
+
+![](images/last_images/3.png)
+# Tomasz Dzierzbicki
+implementacja sieci neuronowych z nauczonego pliku
+
+![](images/last_images/4.png)
+
+implementacja funkcji która szuka nalepszego pola do zastosowania na nim działań
+
+![](images/last_images/5.png)
+
+Funkcja jest używana w nieskończoność po uruchomieniu programu
+
+![](images/last_images/6.png)
+
+# Jarosław Zbąski
+implementacja drzew decyzyjnych z nauczonego pliku
+
+![](images/last_images/7.png)
+
+implementacja funkcji która decyduje co posadzić
+
+![](images/last_images/8.png)
+
+Funkcja jest zastosowana po zebraniu na tym samym polu
+
+![](images/last_images/9.png)
+
+# Laura Piotrowska
+implementacja algorytmu genetycznego
+
+![](images/last_images/10.png)
+
+Zaimplementowane zostało zbieranie warzyw, a algorytm przygotowuje porcję nowych roślin do zasadzenia
+
+Algorytm jest wywoływany co ustaloną liczbę zebranych buraków lub ziemniaków
+
+![](images/last_images/obsluga.png)
+
+Implementacja zbierania
+
+![](images/last_images/11.png)
--- a/images/13_05/1.png
+++ b/images/13_05/1.png
--- a/images/13_05/10.png
+++ b/images/13_05/10.png
--- a/images/13_05/11.png
+++ b/images/13_05/11.png
--- a/images/13_05/12.png
+++ b/images/13_05/12.png
--- a/images/13_05/13.png
+++ b/images/13_05/13.png
--- a/images/13_05/14.png
+++ b/images/13_05/14.png
--- a/images/13_05/15.png
+++ b/images/13_05/15.png
--- a/images/13_05/16.png
+++ b/images/13_05/16.png
--- a/images/13_05/17.png
+++ b/images/13_05/17.png
--- a/images/13_05/18.png
+++ b/images/13_05/18.png
--- a/images/13_05/19.png
+++ b/images/13_05/19.png
--- a/images/13_05/2.png
+++ b/images/13_05/2.png
--- a/images/13_05/20.png
+++ b/images/13_05/20.png
--- a/images/13_05/21.png
+++ b/images/13_05/21.png
--- a/images/13_05/3.png
+++ b/images/13_05/3.png
--- a/images/13_05/4.png
+++ b/images/13_05/4.png
--- a/images/13_05/5.png
+++ b/images/13_05/5.png
--- a/images/13_05/6.png
+++ b/images/13_05/6.png
--- a/images/13_05/7.png
+++ b/images/13_05/7.png
--- a/images/13_05/8.png
+++ b/images/13_05/8.png
--- a/images/13_05/9.png
+++ b/images/13_05/9.png
--- a/images/cross.png
+++ b/images/cross.png
--- a/images/crossover.png
+++ b/images/crossover.png
--- a/images/fitness.png
+++ b/images/fitness.png
--- a/images/genetic_algorithm.png
+++ b/images/genetic_algorithm.png
--- a/images/last_images/1.png
+++ b/images/last_images/1.png
--- a/images/last_images/10.png
+++ b/images/last_images/10.png
--- a/images/last_images/11.png
+++ b/images/last_images/11.png
--- a/images/last_images/2.png
+++ b/images/last_images/2.png
--- a/images/last_images/3.png
+++ b/images/last_images/3.png
--- a/images/last_images/4.png
+++ b/images/last_images/4.png
--- a/images/last_images/5.png
+++ b/images/last_images/5.png
--- a/images/last_images/6.png
+++ b/images/last_images/6.png
--- a/images/last_images/7.png
+++ b/images/last_images/7.png
--- a/images/last_images/8.png
+++ b/images/last_images/8.png
--- a/images/last_images/9.png
+++ b/images/last_images/9.png
--- a/images/last_images/obsluga.png
+++ b/images/last_images/obsluga.png
--- a/images/move_zbieranie.png
+++ b/images/move_zbieranie.png
--- a/images/obsluga.png
+++ b/images/obsluga.png
--- a/images/przypiszKod.png
+++ b/images/przypiszKod.png
--- a/images/selection.png
+++ b/images/selection.png
--- a/images/test2_generowanie_burakow.png
+++ b/images/test2_generowanie_burakow.png
--- a/images/wykonanie_algorytmu_gen.png
+++ b/images/wykonanie_algorytmu_gen.png
--- a/initial.txt
+++ b/initial.txt
--- a/injectCode.py
+++ b/injectCode.py
@ -0,0 +1,33 @@
+import pickle
+import sys
+
+
+def prediction(warzywo, nawoz ,srodek, stan_wzrostu):
+	filename = 'decisionTree.sav'
+	tree = pickle.load(open(filename, 'rb'))
+	val = (tree.predict([[warzywo, nawoz, srodek, stan_wzrostu]]))
+	save(val)
+    
+def save(prediction):
+	pred = str(prediction)
+	plik = open('dec.txt', 'w')
+	plik.write(pred[1])
+	plik.close()
+
+def decision(prediction):
+	if prediction == 0:
+		return "Nie_podejmuj_dzialania"
+	elif prediction == 1:
+		return "Zastosuj_nawoz"
+	elif prediction == 2:
+		return "Zastosuj_srodek"
+	elif prediction == 4:
+		return "Zbierz"
+	elif prediction == 5:
+		return "Roslina_juz_zgnila__zbierz_i_wyrzuc"
+
+
+
+if __name__ == '__main__':
+	# Map command line arguments to function arguments.
+	prediction(*sys.argv[1:])
--- a/matrix.txt
+++ b/matrix.txt
@ -0,0 +1,625 @@
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--- a/route-planning.md
+++ b/route-planning.md
@ -5,7 +5,7 @@ Ten dokument to raport z wykonanego drugiego zadania projektu zespołowego na pr
 W skład zespołu wchodzą:  
 Tomasz Dzierzbicki,   
 Szymon Parafiński,    
-Karol Piotrowski,    
+Laura Piotrowska,    
 Jarosław Zbąski.  

 # Zasady poruszania się agenta po planszy
Author	SHA1	Message	Date
Laura Piotrowska	9b0017e911	Zaktualizuj 'environment.md'	2022-01-26 01:46:04 +01:00
Laura Piotrowska	0f0fda7ea1	Zaktualizuj 'route-planning.md'	2022-01-26 01:45:06 +01:00
Laura Piotrowska	f5860fd5ff	Zaktualizuj 'final-evaluation.md'	2022-01-26 01:41:46 +01:00
Laura Piotrowska	e9723b798a	Zaktualizuj 'RaportLauraPiotrowska.md'	2022-01-26 00:53:07 +01:00
Szymon Parafiński	f71fee9980	Zaktualizuj 'RaportSzymonParafiński.md'	2020-06-09 19:11:55 +00:00
Tomasz Dzierzbicki	eccc3b22f5	Prześlij pliki do 'images/13_05'	2020-06-09 19:10:26 +00:00
Tomasz Dzierzbicki	d3d24b390d	Prześlij pliki do 'images/13_05'	2020-06-09 19:10:20 +00:00
Tomasz Dzierzbicki	273120323a	Prześlij pliki do 'images/13_05'	2020-06-09 19:10:11 +00:00
Tomasz Dzierzbicki	0b497a0ffd	Prześlij pliki do 'images/13_05'	2020-06-09 19:10:04 +00:00
Tomasz Dzierzbicki	1b77e170f2	Prześlij pliki do 'images/13_05'	2020-06-09 19:09:53 +00:00
Tomasz Dzierzbicki	7b52100b89	Zaktualizuj 'RaportSzymonParafiński.md'	2020-06-09 19:07:13 +00:00
Tomasz Dzierzbicki	c801cc9b26	Zaktualizuj 'RaportTomaszDzierzbicki.md'	2020-06-09 19:06:26 +00:00
Karol Piotrowski	b57f666197	Usuń 'algorytm_genetyczny.md'	2020-06-09 19:06:03 +00:00
Karol Piotrowski	a23cf34f6c	Zaktualizuj 'RaportKarolPiotrowski.md'	2020-06-09 19:05:28 +00:00
Karol Piotrowski	d3ebf4b7a7	Prześlij pliki do 'images'	2020-06-09 19:04:10 +00:00
Karol Piotrowski	2384f973f1	Prześlij pliki do 'images'	2020-06-09 19:03:34 +00:00
Karol Piotrowski	11c1e105f5	Prześlij pliki do 'images'	2020-06-09 19:01:25 +00:00
Tomasz Dzierzbicki	ce8b08e577	Dodaj 'RaportSzymonParafiński.md'	2020-06-09 18:59:50 +00:00
Tomasz Dzierzbicki	ef4bc0a433	Dodaj 'RaportKarolPiotrowski.md'	2020-06-09 18:59:17 +00:00
Tomasz Dzierzbicki	a6162997d1	Dodaj 'RaportTomaszDzierzbicki.md'	2020-06-09 18:58:44 +00:00
Karol Piotrowski	e2a47723ba	Zaktualizuj 'final-evaluation.md'	2020-06-09 18:21:30 +00:00
Karol Piotrowski	533941e823	Prześlij pliki do 'images/last_images'	2020-06-09 18:17:56 +00:00
Karol Piotrowski	1feaa4bc98	Zaktualizuj 'final-evaluation.md'	2020-06-09 18:15:03 +00:00
Karol Piotrowski	8c900dad64	Prześlij pliki do 'images/last_images'	2020-06-09 18:14:25 +00:00
Karol Piotrowski	6be799e401	Zaktualizuj 'final-evaluation.md'	2020-06-09 18:14:01 +00:00
Karol Piotrowski	d9318afc47	Zaktualizuj 'final-evaluation.md'	2020-06-09 18:13:21 +00:00
Karol Piotrowski	740f4279ad	Prześlij pliki do 'images'	2020-06-09 18:12:55 +00:00
Karol Piotrowski	5997d00515	Zaktualizuj 'final-evaluation.md'	2020-06-09 18:01:03 +00:00
Tomasz Dzierzbicki	5f7e089494	Zaktualizuj 'final-evaluation.md'	2020-06-09 17:53:50 +00:00
Tomasz Dzierzbicki	493704882e	Zaktualizuj 'final-evaluation.md'	2020-06-09 17:46:20 +00:00
Tomasz Dzierzbicki	1a3ec8f890	Zaktualizuj 'final-evaluation.md'	2020-06-09 17:46:07 +00:00
Tomasz Dzierzbicki	574c34785e	Zaktualizuj 'final-evaluation.md'	2020-06-09 17:44:18 +00:00
Tomasz Dzierzbicki	59d275388b	Prześlij pliki do 'images/last_images'	2020-06-09 17:42:59 +00:00
Tomasz Dzierzbicki	385a2b81ee	Prześlij pliki do 'images/last_images'	2020-06-09 17:42:46 +00:00
Tomasz Dzierzbicki	4e4c81dac6	Prześlij pliki do 'images/last_images'	2020-06-09 17:35:48 +00:00
Tomasz Dzierzbicki	5d1f5387b4	Dodaj 'final-evaluation.md'	2020-06-09 17:33:03 +00:00
Tomasz Dzierzbicki	60f4bd437d	ostateczna wersja	2020-06-09 17:13:10 +00:00
Tomasz Dzierzbicki	a9cf31e294	ostateczna wersja	2020-06-09 17:12:50 +00:00
Tomasz Dzierzbicki	bedbdbf1e2	ostateczna wersja	2020-06-09 17:12:24 +00:00
Szymon Parafiński	5978098747	Main_podVS Drzewo - Jarek Genetyczny	2020-06-08 21:41:38 +00:00
Tomasz Dzierzbicki	6f016b84a7	wersja pod VS	2020-06-08 21:19:05 +00:00
Karol Piotrowski	3eaa94999a	Zaktualizuj 'backups/Main_drzewa1_algorytmGenetyczny.cpp'	2020-06-08 15:11:16 +00:00
Karol Piotrowski	88f3f1bd17	Zaktualizuj 'Main.cpp'	2020-06-08 15:09:13 +00:00
Karol Piotrowski	44345c162c	Zaktualizuj 'backups/Main_drzewa1_algorytmGenetyczny.cpp'	2020-06-07 16:10:05 +00:00
Karol Piotrowski	6f0b782f0a	Prześlij pliki do 'backups'	2020-06-07 16:08:58 +00:00
Karol Piotrowski	efdb69843d	Prześlij pliki do ''	2020-06-07 13:24:23 +00:00
Karol Piotrowski	a1976880c9	Zaktualizuj 'Main.cpp'	2020-06-06 23:04:18 +00:00
Karol Piotrowski	d0efae4171	Prześlij pliki do ''	2020-06-06 21:27:31 +00:00
Karol Piotrowski	4675500a89	Prześlij pliki do ''	2020-06-06 20:21:12 +00:00
Karol Piotrowski	42af7cf229	Zaktualizuj 'backups/Main_drzewa1.cpp'	2020-06-06 20:20:36 +00:00
Karol Piotrowski	730b21e122	Prześlij pliki do 'backups'	2020-06-06 20:20:02 +00:00
Karol Piotrowski	5e2638f65a	Usuń 'gmo.cpp'	2020-06-06 16:21:06 +00:00
Jarosław Zbąski	30a7cf434c	Prześlij pliki do ''	2020-06-06 16:07:06 +00:00
Jarosław Zbąski	efe3723b57	Prześlij pliki do ''	2020-06-06 16:04:24 +00:00
Karol Piotrowski	75ae2eeeb2	Zaktualizuj 'backups/MainPure.cpp'	2020-06-06 15:51:52 +00:00
Karol Piotrowski	9b9046531e	Prześlij pliki do 'backups'	2020-06-06 15:49:34 +00:00
Jarosław Zbąski	7ad0c77062	Prześlij pliki do ''	2020-06-06 08:00:53 +00:00
Karol Piotrowski	ffb1c10a03	Prześlij pliki do ''	2020-05-26 22:05:43 +00:00
Karol Piotrowski	4856536a29	Prześlij pliki do ''	2020-05-26 21:34:44 +00:00
Karol Piotrowski	666ab0c3dd	Merge branch 'algorytm_genetyczny_Karol_Piotrowski' of s444459/SI2020 into master	2020-05-24 11:59:10 +00:00