Zaktualizuj 'Raport- drzewa decyzyjne.md'

Raport- drzewa decyzyjne.md

@@ -111,8 +111,6 @@ void stanPola(int x, int y) {
 	//[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)
@@ -124,8 +122,6 @@ void stanPola(int x, int y) {
 	int w = (stan[x][y][1]);
 	std::string s = std::to_string(w);
 	polecenie.append(s);
-
-	decisionTree(polecenie);
 }
 ```
 
@@ -138,21 +134,6 @@ 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]
@@ -163,34 +144,28 @@ import sys
 
 
 def prediction(warzywo, nawoz ,srodek, stan_wzrostu):
-	filename = 'pliki/decisionTree.sav'
+    filename = 'decisionTree.sav'
-	tree = pickle.load(open(filename, 'rb'))
+    tree = pickle.load(open(filename, 'rb'))
-	val = (tree.predict([[warzywo, nawoz, srodek, stan_wzrostu]]))
+    val = (tree.predict([[warzywo, nawoz, srodek, stan_wzrostu]]))
-	save(val)
+    print(decision(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:
+    if prediction == 0:
-		return "Nie_podejmuj_dzialania"
+        return "Nie_podejmuj_dzialania"
-	elif prediction == 1:
+    elif prediction == 1:
-		return "Zastosuj_nawoz"
+        return "Zastosuj_nawoz"
-	elif prediction == 2:
+    elif prediction == 2:
-		return "Zastosuj_srodek"
+        return "Zastosuj_srodek"
-	elif prediction == 4:
+    elif prediction == 4:
-		return "Zbierz"
+        return "Zbierz"
-	elif prediction == 5:
+    elif prediction == 5:
-		return "Roslina_juz_zgnila__zbierz_i_wyrzuc"
+        return "Roslina_juz_zgnila__zbierz_i_wyrzuc"
 
 
 
 if __name__ == '__main__':
-	# Map command line arguments to function arguments.
+    # Map command line arguments to function arguments.
-	prediction(*sys.argv[1:])
+    prediction(*sys.argv[1:])
 ```
 Generowane polecenie wygląda w ten sposób:  
 ```
@@ -198,6 +173,7 @@ python injectCode.py a b c d
 ```
 Gdzie:
 * a -> rodzaj warzywa 
+    * 1: "burak"
 * b -> czy roślina była nawożona 
 	* 0: "nie"
 	* 1: "tak" 

RaportJaroslawZbaski.md

@@ -1,209 +0,0 @@
-##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;
-			}
-		}
-	}
-}
-```

RaportLauraPiotrowska.md

@@ -1,47 +0,0 @@
-# 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)
-
- 
-

RaportTomaszDzierzbicki.md

@@ -1,105 +0,0 @@
-# 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)

Sklearn/.idea/Sklearn.iml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="Python 3.8 (2)" jdkType="Python SDK" />
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+</module>

Sklearn/.idea/inspectionProfiles/profiles_settings.xml

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+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
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Sklearn/.idea/misc.xml

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+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (2)" project-jdk-type="Python SDK" />
+</project>

Sklearn/.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/Sklearn.iml" filepath="$PROJECT_DIR$/.idea/Sklearn.iml" />
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+  </component>
+</project>

Sklearn/.idea/workspace.xml

@@ -0,0 +1,136 @@
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Sklearn/Generate.py

@@ -0,0 +1,86 @@
+# Load libraries
+import pickle
+import pandas as pd
+from sklearn import tree, metrics
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier, _tree
+
+
+def tree_to_code(tree, feature_names):
+    # f = open('generatedTree.py', 'w')
+    tree_ = tree.tree_
+    feature_name = [
+        feature_names[i] if i != _tree.TREE_UNDEFINED else "undefined!"
+        for i in tree_.feature
+    ]
+    # print("def tree({}):".format(", ".join(feature_names)), file=f)
+    print("def tree({}):".format(", ".join(feature_names)))
+
+    def recurse(node, depth):
+        indent = "  " * depth
+        if tree_.feature[node] != _tree.TREE_UNDEFINED:
+            name = feature_name[node]
+            threshold = tree_.threshold[node]
+            # print("{}if {} <= {}:".format(indent, name, threshold), file=f)
+            print("{}if {} <= {}:".format(indent, name, threshold))
+            recurse(tree_.children_left[node], depth + 1)
+            # print("{}else:  # if {} > {}".format(indent, name, threshold), file=f)
+            print("{}else:  # if {} > {}".format(indent, name, threshold))
+            recurse(tree_.children_right[node], depth + 1)
+        else:
+            # print("{}return {}".format(indent, tree_.value[node],), file=f)
+            print("{}return {}".format(indent, tree_.value[node]))
+
+    recurse(0, 1)
+    # f.close()
+
+
+def loadLearningBase():
+    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())
+
+    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
+
+    data = generateDecisionTree(X_train, X_test, y_train, y_test)
+
+    """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
+
+
+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)
+
+    # 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))
+
+    return clf
+
+
+if __name__ == '__main__':
+    generated = loadLearningBase()
+
+    # Save generated tree
+    filename = 'decisionTree.sav'
+    pickle.dump(generated, open(filename, 'wb'))

Sklearn/Test.py

@@ -0,0 +1,36 @@
+import pickle
+
+
+def decision(prediction):
+    if prediction == 0:
+        return "Nie_podejmuj_działania"
+    elif prediction == 1:
+        return "Zastosuj_nawóz"
+    elif prediction == 2:
+        return "Zastosuj_środek"
+    elif prediction == 4:
+        return "Zbierz"
+    elif prediction == 5:
+        return "Roślina_już_zgniła-zbierz_i_wyrzuć"
+
+
+def test():
+    for n in range(0, 2):
+        if n == 0:
+            print("############# Nie ma nawozu #############")
+        else:
+            print("############# Zastosowano nawóz #############")
+        for s in range(0, 2):
+            if s == 0:
+                print("############# Nie ma środka ochrony #############")
+            else:
+                print("############# Zastosowano środek ochrony #############")
+            for st in range(0, 101):
+                val = tree.predict([[1, n, s, st]])
+                print("Stan roślinki: ", st, " ", decision(val))
+
+
+if __name__ == '__main__':
+    filename = 'decisionTree.sav'
+    tree = pickle.load(open(filename, 'rb'))
+    test()

Sklearn/injectCode.py

@@ -0,0 +1,14 @@
+import pickle
+import sys
+
+
+def prediction(warzywo, nawoz ,srodek, stan_wzrostu):
+    filename = 'decisionTree.sav'
+    tree = pickle.load(open(filename, 'rb'))
+    print(tree.predict([[warzywo, nawoz, srodek, stan_wzrostu]]))
+
+
+
+if __name__ == '__main__':
+    # Map command line arguments to function arguments.
+    prediction(*sys.argv[1:])

Sklearn/treeData.dot

@@ -0,0 +1,25 @@
+digraph Tree {
+node [shape=box, style="filled, rounded", color="black", fontname=helvetica] ;
+edge [fontname=helvetica] ;
+0 [label=<Stan &le; 40.5<br/>entropy = 2.092<br/>samples = 280<br/>value = [78, 41, 29, 108, 24]>, fillcolor="#e2e2fb"] ;
+1 [label=<Nawoz &le; 0.5<br/>entropy = 0.929<br/>samples = 119<br/>value = [78, 41, 0, 0, 0]>, fillcolor="#f3c3a1"] ;
+0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
+2 [label=<Stan &le; 10.5<br/>entropy = 0.796<br/>samples = 54<br/>value = [13, 41, 0, 0, 0]>, fillcolor="#a5ed78"] ;
+1 -> 2 ;
+3 [label=<entropy = 0.0<br/>samples = 13<br/>value = [13, 0, 0, 0, 0]>, fillcolor="#e58139"] ;
+2 -> 3 ;
+4 [label=<entropy = 0.0<br/>samples = 41<br/>value = [0, 41, 0, 0, 0]>, fillcolor="#7be539"] ;
+2 -> 4 ;
+5 [label=<entropy = 0.0<br/>samples = 65<br/>value = [65, 0, 0, 0, 0]>, fillcolor="#e58139"] ;
+1 -> 5 ;
+6 [label=<Stan &le; 80.5<br/>entropy = 1.241<br/>samples = 161<br/>value = [0, 0, 29, 108, 24]>, fillcolor="#8a88ef"] ;
+0 -> 6 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
+7 [label=<entropy = 0.0<br/>samples = 108<br/>value = [0, 0, 0, 108, 0]>, fillcolor="#3c39e5"] ;
+6 -> 7 ;
+8 [label=<Srodek &le; 0.5<br/>entropy = 0.994<br/>samples = 53<br/>value = [0, 0, 29, 0, 24]>, fillcolor="#ddfbf5"] ;
+6 -> 8 ;
+9 [label=<entropy = 0.0<br/>samples = 29<br/>value = [0, 0, 29, 0, 0]>, fillcolor="#39e5c5"] ;
+8 -> 9 ;
+10 [label=<entropy = 0.0<br/>samples = 24<br/>value = [0, 0, 0, 0, 24]>, fillcolor="#e539c0"] ;
+8 -> 10 ;
+}

Traktor_DrzewaDecyzyjne.cpp

@@ -7,11 +7,10 @@
 #include<set>
 #include<math.h>
 #include<stack>
-#include<fstream>
 
 using namespace std;
 
-const float maxFloat=FLT_MAX;
+const float maxFloat = FLT_MAX;
 const int ROW = 27;
 const int COL = 27;
 typedef pair<int, int> Pair;
@@ -22,7 +21,9 @@ struct cell
 	double f, g, h;
 };
 
-char pole[27][27][6];
+char pole[27][27][2];
+int stan[27][27][2];
+string polecenie = "python injectCode.py 1 ";
 int pozycjaTraktoraX = 1, pozycjaTraktoraY = 1;
 char currentWay = 'S';
 
@@ -99,7 +100,6 @@ void color(string foregroundColor, string backgroundColor)
 		backgroundCode = 15;
 	SetConsoleTextAttribute(hOut, foregroundCode + backgroundCode * 16);
 }
-
 void SetWindow(int Width, int Height)
 {
 	_COORD coord;
@@ -116,7 +116,6 @@ void SetWindow(int Width, int Height)
 	SetConsoleScreenBufferSize(Handle, coord);            // Set Buffer Size 
 	SetConsoleWindowInfo(Handle, TRUE, &Rect);            // Set Window Size 
 }
-
 void updatePola()
 {
 	system("cls");
@@ -131,10 +130,6 @@ void updatePola()
 			{
 				color("purple", "dark_yellow");
 			}break;
-			case 'Z':
-			{
-				color("cyan", "dark_yellow");
-			}break;
 			case 'T':
 			{
 				color("red", "dark_yellow");
@@ -159,41 +154,40 @@ void updatePola()
 		color("white", "black");
 	}
 }
-
 void correctMovement(char wantedWay)
 {
 	while (currentWay != wantedWay)
 	{
 		switch (currentWay)
 		{
-			case 'N':
+		case 'N':
-			{
+		{
-				if (wantedWay == 'S')
+			if (wantedWay == 'S')
-					currentWay = wantedWay;
+				currentWay = wantedWay;
-				else
+			else
-					currentWay = 'W';
+				currentWay = 'W';
-			}break;
+		}break;
-			case 'S':
+		case 'S':
-			{
+		{
-				if (wantedWay == 'N')
+			if (wantedWay == 'N')
-					currentWay = wantedWay;
+				currentWay = wantedWay;
-				else
+			else
-					currentWay = 'W';
+				currentWay = 'W';
-			}break;
+		}break;
-			case 'W':
+		case 'W':
-			{
+		{
-				if (wantedWay == 'E')
+			if (wantedWay == 'E')
-					currentWay = wantedWay;
+				currentWay = wantedWay;
-				else
+			else
-					currentWay = 'N';
+				currentWay = 'N';
-			}break;
+		}break;
-			case 'E':
+		case 'E':
-			{
+		{
-				if (wantedWay == 'W')
+			if (wantedWay == 'W')
-					currentWay = wantedWay;
+				currentWay = wantedWay;
-				else
+			else
-					currentWay = 'N';
+				currentWay = 'N';
-			}break;
+		}break;
 		}
 	}
 }
@@ -202,56 +196,55 @@ void Move(char kierunek)
 	switch (kierunek)
 	{
 		//góra-(w)
-		case 'w':
+	case 'w':
+	{
+		if (pole[pozycjaTraktoraY - 1][pozycjaTraktoraX][0] != '#')
 		{
-			if (pole[pozycjaTraktoraY - 1][pozycjaTraktoraX][0] != '#')
+			correctMovement('N');
-			{
+			pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = '.';
-				correctMovement('N');
+			pozycjaTraktoraY--;
-				pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = '.';
+			pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = 'T';
-				pozycjaTraktoraY--;
+		}
-				pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = 'T';
+		updatePola();
-			}
+	}break;
-			updatePola();
+	//dół-(s)
-		}break;
+	case 's':
-		//dół-(s)
+	{
-		case 's':
+		if (pole[pozycjaTraktoraY + 1][pozycjaTraktoraX][0] != '#')
 		{
-			if (pole[pozycjaTraktoraY + 1][pozycjaTraktoraX][0] != '#')
+			correctMovement('S');
-			{
+			pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = '.';
-				correctMovement('S');
+			pozycjaTraktoraY++;
-				pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = '.';
+			pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = 'T';
-				pozycjaTraktoraY++;
+		}
-				pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = 'T';
+		updatePola();
-			}
+	}break;
-			updatePola();
+	//lewo-(a)
-		}break;
+	case 'a':
-		//lewo-(a)
+	{
-		case 'a':
+		if (pole[pozycjaTraktoraY][pozycjaTraktoraX - 1][0] != '#')
 		{
-			if (pole[pozycjaTraktoraY][pozycjaTraktoraX - 1][0] != '#')
+			correctMovement('W');
-			{
+			pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = '.';
-				correctMovement('W');
+			pozycjaTraktoraX--;
-				pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = '.';
+			pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = 'T';
-				pozycjaTraktoraX--;
+		}
-				pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = 'T';
+		updatePola();
-			}
+	}break;
-			updatePola();
+	//prawo-(d)
-		}break;
+	case 'd':
-		//prawo-(d)
+	{
-		case 'd':
+		if (pole[pozycjaTraktoraY][pozycjaTraktoraX + 1][0] != '#')
 		{
-			if (pole[pozycjaTraktoraY][pozycjaTraktoraX + 1][0] != '#')
+			correctMovement('E');
-			{
+			pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = '.';
-				correctMovement('E');
+			pozycjaTraktoraX++;
-				pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = '.';
+			pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = 'T';
-				pozycjaTraktoraX++;
+		}
-				pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = 'T';
+		updatePola();
-			}
+	}break;
-			updatePola();
-		}break;
 	}
 }
-
 bool isValid(int x, int y)
 {
 	if (pole[x][y][0] != '#')
@@ -260,7 +253,7 @@ bool isValid(int x, int y)
 	}
 	return false;
 }
-bool isDestination(int x, int y,Pair dest)
+bool isDestination(int x, int y, Pair dest)
 {
 	if (dest.first == x && dest.second == y)
 	{
@@ -305,12 +298,12 @@ void tracePath(cell cellDetails[][COL], Pair dest)
 			Move('w');
 
 		//printf("-> (%d,%d) ", p.first, p.second);  //---- informacja wierzchołku
-		Sleep(1000);
+		//Sleep(1000);
 	}
 
 	return;
 }
-void aStarSearch(int grid[][COL],Pair src, Pair dest)
+void aStarSearch(int grid[][COL], Pair src, Pair dest)
 {
 	bool closedList[ROW][COL];
 	memset(closedList, false, sizeof(closedList));
@@ -350,7 +343,7 @@ void aStarSearch(int grid[][COL],Pair src, Pair dest)
 
 		double gNew, hNew, fNew;
 		double waga = 1.0;
-		waga = ((double)pole[j][i][1] - 48)*1.0;//----waga
+		waga = ((double)pole[j][i][1] - 48) * 1.0;//----waga
 
 		//----------- 1st Successor (North) ------------ 
 		if (isValid(i - 1, j) == true)
@@ -484,11 +477,10 @@ void aStarSearch(int grid[][COL],Pair src, Pair dest)
 
 	return;
 }
-
+void gogo(int endX, int endY)
-void gogo(int endX,int endY)
 {
 	updatePola();
-	Sleep(1000);
+	//Sleep(1000);
 	int grid[27][27];
 	for (int i = 0; i < 27; i++)
 	{
@@ -502,11 +494,38 @@ void gogo(int endX,int endY)
 	aStarSearch(grid, src, dest);
 }
 
+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 
+
+	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);
+}
+
+void decisionTree(string polecenie) {
+
+	std::string str = polecenie;
+	const char* c = str.c_str();
+	system(c);
+}
+
 void test1()
 {
 	pole[1][3][0] = 'B';
 	pole[1][3][1] = '9';
-	pole[3][1][0] = 'Z';
+	pole[3][1][0] = 'B';
 	pole[3][1][1] = '9';
 }
 void test2()
@@ -523,116 +542,15 @@ void test2()
 	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()
 {
@@ -655,9 +573,64 @@ void start3()
 	gogo(goalX, goalY);
 }
 
+void testTree() {
+	int x, y;
+	x = 3;
+	y = 3;
+	//Nie podejmuj
+	stan[x][y][0] = 0;
+	stan[x][y][1] = 10;
+	stanPola(x, y);
+}
+void testTree1() {
+	int x, y;
+	x = 3;
+	y = 3;
+	//Nawoz
+	stan[x][y][0] = 0;
+	stan[x][y][1] = 15;
+	stanPola(x, y);
+}
+void testTree2() {
+	int x, y;
+	x = 3;
+	y = 3;
+	//Nie podejmuj
+	stan[x][y][0] = 1;
+	stan[x][y][1] = 20;
+	stanPola(x, y);
+}
+void testTree3() {
+	int x, y;
+	x = 3;
+	y = 3;
+	//Zbierz
+	stan[x][y][0] = 1;
+	stan[x][y][1] = 41;
+	stanPola(x, y);
+}
+void testTree4() {
+	int x, y;
+	x = 3;
+	y = 3;
+	//Nie podejmuj
+	stan[x][y][0] = 1;
+	stan[x][y][1] = 90;
+	stanPola(x, y);
+}
+void testTree5() {
+	int x, y;
+	x = 3;
+	y = 3;
+	//Srodek
+	stan[x][y][0] = 3;
+	stan[x][y][1] = 90;
+	stanPola(x, y);
+}
+
 int main()
 {
-	SetWindow(50, 30);
+	//SetWindow(50, 30);
 	//create pola//
 	for (int i = 0; i < 27; i++)
 	{
@@ -687,7 +660,26 @@ int main()
 
 	updatePola();
 
-	start1();  // testy start 1-3
+	//start3();  // testy start 1-3
+	testTree();
+	decisionTree(polecenie);
+	polecenie = "python injectCode.py 1 ";
+	testTree1();
+	decisionTree(polecenie);
+	polecenie = "python injectCode.py 1 ";
+	testTree2();
+	decisionTree(polecenie);
+	polecenie = "python injectCode.py 1 ";
+	testTree3();
+	decisionTree(polecenie);
+	polecenie = "python injectCode.py 1 ";
+	testTree4();
+	decisionTree(polecenie);
+	polecenie = "python injectCode.py 1 ";
+	testTree5();
+	decisionTree(polecenie);
+
+
 
 	//---------start---------//
 	bool traktorDziala = true;

Traktor_DrzewaDecyzyjne.sln

@@ -0,0 +1,31 @@
+
+Microsoft Visual Studio Solution File, Format Version 12.00
+# Visual Studio Version 16
+VisualStudioVersion = 16.0.29503.13
+MinimumVisualStudioVersion = 10.0.40219.1
+Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "Traktor_DrzewaDecyzyjne", "Traktor_DrzewaDecyzyjne.vcxproj", "{C2FF0872-AC9D-4BD9-A46D-DC65A728BFF5}"
+EndProject
+Global
+	GlobalSection(SolutionConfigurationPlatforms) = preSolution
+		Debug|x64 = Debug|x64
+		Debug|x86 = Debug|x86
+		Release|x64 = Release|x64
+		Release|x86 = Release|x86
+	EndGlobalSection
+	GlobalSection(ProjectConfigurationPlatforms) = postSolution
+		{C2FF0872-AC9D-4BD9-A46D-DC65A728BFF5}.Debug|x64.ActiveCfg = Debug|x64
+		{C2FF0872-AC9D-4BD9-A46D-DC65A728BFF5}.Debug|x64.Build.0 = Debug|x64
+		{C2FF0872-AC9D-4BD9-A46D-DC65A728BFF5}.Debug|x86.ActiveCfg = Debug|Win32
+		{C2FF0872-AC9D-4BD9-A46D-DC65A728BFF5}.Debug|x86.Build.0 = Debug|Win32
+		{C2FF0872-AC9D-4BD9-A46D-DC65A728BFF5}.Release|x64.ActiveCfg = Release|x64
+		{C2FF0872-AC9D-4BD9-A46D-DC65A728BFF5}.Release|x64.Build.0 = Release|x64
+		{C2FF0872-AC9D-4BD9-A46D-DC65A728BFF5}.Release|x86.ActiveCfg = Release|Win32
+		{C2FF0872-AC9D-4BD9-A46D-DC65A728BFF5}.Release|x86.Build.0 = Release|Win32
+	EndGlobalSection
+	GlobalSection(SolutionProperties) = preSolution
+		HideSolutionNode = FALSE
+	EndGlobalSection
+	GlobalSection(ExtensibilityGlobals) = postSolution
+		SolutionGuid = {891B470F-58C4-41E4-A102-A1D8CBB6BD64}
+	EndGlobalSection
+EndGlobal

Traktor_DrzewaDecyzyjne.vcxproj

@@ -0,0 +1,163 @@
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+      <Configuration>Debug</Configuration>
+      <Platform>Win32</Platform>
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+    <ProjectConfiguration Include="Release|Win32">
+      <Configuration>Release</Configuration>
+      <Platform>Win32</Platform>
+    </ProjectConfiguration>
+    <ProjectConfiguration Include="Debug|x64">
+      <Configuration>Debug</Configuration>
+      <Platform>x64</Platform>
+    </ProjectConfiguration>
+    <ProjectConfiguration Include="Release|x64">
+      <Configuration>Release</Configuration>
+      <Platform>x64</Platform>
+    </ProjectConfiguration>
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+    <VCProjectVersion>16.0</VCProjectVersion>
+    <ProjectGuid>{C2FF0872-AC9D-4BD9-A46D-DC65A728BFF5}</ProjectGuid>
+    <Keyword>Win32Proj</Keyword>
+    <RootNamespace>TraktorDrzewaDecyzyjne</RootNamespace>
+    <WindowsTargetPlatformVersion>10.0</WindowsTargetPlatformVersion>
+  </PropertyGroup>
+  <Import Project="$(VCTargetsPath)\Microsoft.Cpp.Default.props" />
+  <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'" Label="Configuration">
+    <ConfigurationType>Application</ConfigurationType>
+    <UseDebugLibraries>true</UseDebugLibraries>
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+    <CharacterSet>Unicode</CharacterSet>
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+  <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|x64'" Label="Configuration">
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+  <ImportGroup Label="ExtensionSettings">
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+  <PropertyGroup Label="UserMacros" />
+  <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'">
+    <LinkIncremental>true</LinkIncremental>
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+  <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">
+    <LinkIncremental>true</LinkIncremental>
+  </PropertyGroup>
+  <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|Win32'">
+    <LinkIncremental>false</LinkIncremental>
+  </PropertyGroup>
+  <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|x64'">
+    <LinkIncremental>false</LinkIncremental>
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+  <ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='Debug|Win32'">
+    <ClCompile>
+      <PrecompiledHeader>
+      </PrecompiledHeader>
+      <WarningLevel>Level3</WarningLevel>
+      <Optimization>Disabled</Optimization>
+      <SDLCheck>true</SDLCheck>
+      <PreprocessorDefinitions>WIN32;_DEBUG;_CONSOLE;%(PreprocessorDefinitions)</PreprocessorDefinitions>
+      <ConformanceMode>true</ConformanceMode>
+    </ClCompile>
+    <Link>
+      <SubSystem>Console</SubSystem>
+      <GenerateDebugInformation>true</GenerateDebugInformation>
+    </Link>
+  </ItemDefinitionGroup>
+  <ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='Debug|x64'">
+    <ClCompile>
+      <PrecompiledHeader>
+      </PrecompiledHeader>
+      <WarningLevel>Level3</WarningLevel>
+      <Optimization>Disabled</Optimization>
+      <SDLCheck>true</SDLCheck>
+      <PreprocessorDefinitions>_DEBUG;_CONSOLE;%(PreprocessorDefinitions)</PreprocessorDefinitions>
+      <ConformanceMode>true</ConformanceMode>
+    </ClCompile>
+    <Link>
+      <SubSystem>Console</SubSystem>
+      <GenerateDebugInformation>true</GenerateDebugInformation>
+    </Link>
+  </ItemDefinitionGroup>
+  <ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='Release|Win32'">
+    <ClCompile>
+      <PrecompiledHeader>
+      </PrecompiledHeader>
+      <WarningLevel>Level3</WarningLevel>
+      <Optimization>MaxSpeed</Optimization>
+      <FunctionLevelLinking>true</FunctionLevelLinking>
+      <IntrinsicFunctions>true</IntrinsicFunctions>
+      <SDLCheck>true</SDLCheck>
+      <PreprocessorDefinitions>WIN32;NDEBUG;_CONSOLE;%(PreprocessorDefinitions)</PreprocessorDefinitions>
+      <ConformanceMode>true</ConformanceMode>
+    </ClCompile>
+    <Link>
+      <SubSystem>Console</SubSystem>
+      <EnableCOMDATFolding>true</EnableCOMDATFolding>
+      <OptimizeReferences>true</OptimizeReferences>
+      <GenerateDebugInformation>true</GenerateDebugInformation>
+    </Link>
+  </ItemDefinitionGroup>
+  <ItemDefinitionGroup Condition="'$(Configuration)|$(Platform)'=='Release|x64'">
+    <ClCompile>
+      <PrecompiledHeader>
+      </PrecompiledHeader>
+      <WarningLevel>Level3</WarningLevel>
+      <Optimization>MaxSpeed</Optimization>
+      <FunctionLevelLinking>true</FunctionLevelLinking>
+      <IntrinsicFunctions>true</IntrinsicFunctions>
+      <SDLCheck>true</SDLCheck>
+      <PreprocessorDefinitions>NDEBUG;_CONSOLE;%(PreprocessorDefinitions)</PreprocessorDefinitions>
+      <ConformanceMode>true</ConformanceMode>
+    </ClCompile>
+    <Link>
+      <SubSystem>Console</SubSystem>
+      <EnableCOMDATFolding>true</EnableCOMDATFolding>
+      <OptimizeReferences>true</OptimizeReferences>
+      <GenerateDebugInformation>true</GenerateDebugInformation>
+    </Link>
+  </ItemDefinitionGroup>
+  <ItemGroup>
+    <ClCompile Include="Traktor_DrzewaDecyzyjne.cpp" />
+  </ItemGroup>
+  <ItemGroup>
+    <None Include="..\AI_Traktor\decisionTree.sav" />
+    <None Include="..\AI_Traktor\injectCode.py" />
+  </ItemGroup>
+  <Import Project="$(VCTargetsPath)\Microsoft.Cpp.targets" />
+  <ImportGroup Label="ExtensionTargets">
+  </ImportGroup>
+</Project>

backups/MainPure.cpp

@@ -1,606 +0,0 @@
-#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;
-}

dane.txt

@@ -1,625 +0,0 @@
-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
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decyzje.txt

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drzewaDecyzyjne.py

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-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()

environment.md

@@ -8,7 +8,7 @@ którym będzie poruszał się agent.
 W skład zespołu wchodzą:  
 Tomasz Dzierzbicki,   
 Szymon Parafiński,    
-Laura Piotrowska,    
+Karol Piotrowski,    
 Jarosław Zbąski.  
 
 # Temat projektu i wybrane rozwiązanie   

final-evaluation.md

@@ -1,52 +0,0 @@
-# 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)

injectCode.py

@@ -3,31 +3,25 @@ import sys
 
 
 def prediction(warzywo, nawoz ,srodek, stan_wzrostu):
-	filename = 'decisionTree.sav'
+    filename = 'decisionTree.sav'
-	tree = pickle.load(open(filename, 'rb'))
+    tree = pickle.load(open(filename, 'rb'))
-	val = (tree.predict([[warzywo, nawoz, srodek, stan_wzrostu]]))
+    val = (tree.predict([[warzywo, nawoz, srodek, stan_wzrostu]]))
-	save(val)
+    print(decision(val))
-    
-def save(prediction):
-	pred = str(prediction)
-	plik = open('dec.txt', 'w')
-	plik.write(pred[1])
-	plik.close()
 
 def decision(prediction):
-	if prediction == 0:
+    if prediction == 0:
-		return "Nie_podejmuj_dzialania"
+        return "Nie_podejmuj_dzialania"
-	elif prediction == 1:
+    elif prediction == 1:
-		return "Zastosuj_nawoz"
+        return "Zastosuj_nawoz"
-	elif prediction == 2:
+    elif prediction == 2:
-		return "Zastosuj_srodek"
+        return "Zastosuj_srodek"
-	elif prediction == 4:
+    elif prediction == 4:
-		return "Zbierz"
+        return "Zbierz"
-	elif prediction == 5:
+    elif prediction == 5:
-		return "Roslina_juz_zgnila__zbierz_i_wyrzuc"
+        return "Roslina_juz_zgnila__zbierz_i_wyrzuc"
 
 
 
 if __name__ == '__main__':
-	# Map command line arguments to function arguments.
+    # Map command line arguments to function arguments.
-	prediction(*sys.argv[1:])
+    prediction(*sys.argv[1:])

matrix.txt

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notatki/11_05B

@@ -5,10 +5,11 @@
 [80,100] - przejrzała
 
 nawóz przyśpiesza wzrost (czas * stałaPrzyrosuNawozu) poleInt[0]=1
-użyty tylko kiedy kiełek lub młoda  (ale max 1)
+brak nawozu poleInt[0]=0
+użyty tylko kiedy młoda  (ale max 1)
 
-środek zmienia granice rośliny poleInt[0]=2
+środek sprawia że roślina starzejąca uzdatnia roślinę do zbioru poleInt[0]=2
-użyty zawsze  (ale max 1)
+używany tylko jak starzejąca  (ale max 1)
 
 użyty środek i nawóz poleInt[0]=3
 

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,    
-Laura Piotrowska,    
+Karol Piotrowski,    
 Jarosław Zbąski.  
 
 # Zasady poruszania się agenta po planszy