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18 Commits
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Author SHA1 Message Date
Szymon Parafiński
3a6d0b9465 Zaktualizuj 'Raport- drzewa decyzyjne.md' 2020-05-13 03:33:34 +00:00
Szymon Parafiński
f9505a932a Zaktualizuj 'Raport- drzewa decyzyjne.md' 2020-05-13 03:26:11 +00:00
Szymon Parafiński
cf7bb6590d Zaktualizuj 'Raport- drzewa decyzyjne.md' 2020-05-13 03:23:10 +00:00
Szymon Parafiński
77f8acbdd8 Zaktualizuj 'Raport- drzewa decyzyjne.md' 2020-05-13 03:22:42 +00:00
Szymon Parafiński
81977d051f Prześlij pliki do 'images/Tree' 2020-05-13 03:09:27 +00:00
Szymon Parafiński
e5ad6c7ab2 Usuń 'Main.cpp' 2020-05-13 03:06:42 +00:00
Szymon Parafiński
bf2253580d Working decision tree in main 2020-05-13 03:06:13 +00:00
Szymon Parafiński
4917bd457d Zaktualizuj 'notatki/11_05B' 2020-05-13 01:52:55 +00:00
Szymon Parafiński
95df84c69e Zaktualizuj 'Raport- drzewa decyzyjne.md' 2020-05-13 01:19:31 +00:00
Szymon Parafiński
2388576cc1 Zaktualizuj 'Raport- drzewa decyzyjne.md' 2020-05-13 01:19:02 +00:00
Szymon Parafiński
85eefa0954 Prześlij pliki do 'Sklearn' 2020-05-13 01:16:47 +00:00
Szymon Parafiński
fd5f23258b Prześlij pliki do 'Sklearn/.idea' 2020-05-13 01:16:18 +00:00
Szymon Parafiński
d99f57ce41 Prześlij pliki do 'Sklearn/.idea/inspectionProfiles' 2020-05-13 01:16:05 +00:00
Szymon Parafiński
92b10ab523 Prześlij pliki do 'Sklearn' 2020-05-13 01:15:10 +00:00
Szymon Parafiński
e1336ab5c9 Prześlij pliki do 'Sklearn' 2020-05-13 01:14:09 +00:00
Szymon Parafiński
70908a51ba Usuń 'initial.txt' 2020-05-13 01:09:56 +00:00
Szymon Parafiński
1dbb64001f Zaktualizuj 'Raport- drzewa decyzyjne.md' 2020-05-13 01:09:33 +00:00
Szymon Parafiński
115670987d Raport- drzewa decyzyjne 2020-05-13 01:06:51 +00:00
78 changed files with 1685 additions and 7765 deletions
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1587
Main.cpp
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30
RaportSzymonParafiński.md → Raport- drzewa decyzyjne.md
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@ -111,8 +111,6 @@ void stanPola(int x, int y) {
//[x][x][0] = 3 - środek i nawóz //[x][x][0] = 3 - środek i nawóz
//[x][x][1] - wartość wzrostu rośliny //[x][x][1] - wartość wzrostu rośliny
polecenie = "python pliki/injectCode.py 1 ";
if (stan[x][y][0] == 0) if (stan[x][y][0] == 0)
polecenie.append("0 0 "); polecenie.append("0 0 ");
if (stan[x][y][0] == 1) if (stan[x][y][0] == 1)
@ -124,8 +122,6 @@ void stanPola(int x, int y) {
int w = (stan[x][y][1]); int w = (stan[x][y][1]);
std::string s = std::to_string(w); std::string s = std::to_string(w);
polecenie.append(s); polecenie.append(s);
decisionTree(polecenie);
} }
``` ```
@ -138,21 +134,6 @@ void decisionTree(string polecenie) {
std::string str = polecenie; std::string str = polecenie;
const char* c = str.c_str(); const char* c = str.c_str();
system(c); 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] #### injectCode(): [python]
@ -163,16 +144,10 @@ import sys
def prediction(warzywo, nawoz ,srodek, stan_wzrostu): 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): def decision(prediction):
if prediction == 0: if prediction == 0:
@ -198,6 +173,7 @@ python injectCode.py a b c d
``` ```
Gdzie: Gdzie:
* a -> rodzaj warzywa * a -> rodzaj warzywa
* 1: "burak"
* b -> czy roślina była nawożona * b -> czy roślina była nawożona
* 0: "nie" * 0: "nie"
* 1: "tak" * 1: "tak"

209
RaportJaroslawZbaski.md
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@ -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;
}
}
}
}
```

47
RaportLauraPiotrowska.md
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@ -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)

105
RaportTomaszDzierzbicki.md
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@ -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)

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415
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357 1 1 0 97 2
358 1 1 0 98 2
359 1 1 0 99 2
360 1 1 0 100 2
361 1 0 1 81 5
362 1 0 1 82 5
363 1 0 1 83 5
364 1 0 1 84 5
365 1 0 1 85 5
366 1 0 1 86 5
367 1 0 1 87 5
368 1 0 1 88 5
369 1 0 1 89 5
370 1 0 1 90 5
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372 1 0 1 92 5
373 1 0 1 93 5
374 1 0 1 94 5
375 1 0 1 95 5
376 1 0 1 96 5
377 1 0 1 97 5
378 1 0 1 98 5
379 1 0 1 99 5
380 1 0 1 100 5
381 1 1 1 81 5
382 1 1 1 82 5
383 1 1 1 83 5
384 1 1 1 84 5
385 1 1 1 85 5
386 1 1 1 86 5
387 1 1 1 87 5
388 1 1 1 88 5
389 1 1 1 89 5
390 1 1 1 90 5
391 1 1 1 91 5
392 1 1 1 92 5
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394 1 1 1 94 5
395 1 1 1 95 5
396 1 1 1 96 5
397 1 1 1 97 5
398 1 1 1 98 5
399 1 1 1 99 5
400 1 1 1 100 5

86
Sklearn/Generate.py Normal file
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@ -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'))

36
Sklearn/Test.py Normal file
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@ -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()

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14
Sklearn/injectCode.py Normal file
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@ -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:])

25
Sklearn/treeData.dot Normal file
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@ -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 ;
}

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236
backups/Main_drzewa1.cpp → Traktor_DrzewaDecyzyjne.cpp
View File

@ -7,11 +7,10 @@
#include<set> #include<set>
#include<math.h> #include<math.h>
#include<stack> #include<stack>
#include<fstream>
using namespace std; using namespace std;
const float maxFloat=FLT_MAX; const float maxFloat = FLT_MAX;
const int ROW = 27; const int ROW = 27;
const int COL = 27; const int COL = 27;
typedef pair<int, int> Pair; typedef pair<int, int> Pair;
@ -22,7 +21,9 @@ struct cell
double f, g, h; 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; int pozycjaTraktoraX = 1, pozycjaTraktoraY = 1;
char currentWay = 'S'; char currentWay = 'S';
@ -99,7 +100,6 @@ void color(string foregroundColor, string backgroundColor)
backgroundCode = 15; backgroundCode = 15;
SetConsoleTextAttribute(hOut, foregroundCode + backgroundCode * 16); SetConsoleTextAttribute(hOut, foregroundCode + backgroundCode * 16);
} }
void SetWindow(int Width, int Height) void SetWindow(int Width, int Height)
{ {
_COORD coord; _COORD coord;
@ -116,7 +116,6 @@ void SetWindow(int Width, int Height)
SetConsoleScreenBufferSize(Handle, coord); // Set Buffer Size SetConsoleScreenBufferSize(Handle, coord); // Set Buffer Size
SetConsoleWindowInfo(Handle, TRUE, &Rect); // Set Window Size SetConsoleWindowInfo(Handle, TRUE, &Rect); // Set Window Size
} }
void updatePola() void updatePola()
{ {
system("cls"); system("cls");
@ -131,10 +130,6 @@ void updatePola()
{ {
color("purple", "dark_yellow"); color("purple", "dark_yellow");
}break; }break;
case 'Z':
{
color("cyan", "dark_yellow");
}break;
case 'T': case 'T':
{ {
color("red", "dark_yellow"); color("red", "dark_yellow");
@ -159,7 +154,6 @@ void updatePola()
color("white", "black"); color("white", "black");
} }
} }
void correctMovement(char wantedWay) void correctMovement(char wantedWay)
{ {
while (currentWay != wantedWay) while (currentWay != wantedWay)
@ -251,7 +245,6 @@ void Move(char kierunek)
}break; }break;
} }
} }
bool isValid(int x, int y) bool isValid(int x, int y)
{ {
if (pole[x][y][0] != '#') if (pole[x][y][0] != '#')
@ -260,7 +253,7 @@ bool isValid(int x, int y)
} }
return false; 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) if (dest.first == x && dest.second == y)
{ {
@ -305,12 +298,12 @@ void tracePath(cell cellDetails[][COL], Pair dest)
Move('w'); Move('w');
//printf("-> (%d,%d) ", p.first, p.second); //---- informacja wierzchołku //printf("-> (%d,%d) ", p.first, p.second); //---- informacja wierzchołku
Sleep(1000); //Sleep(1000);
} }
return; return;
} }
void aStarSearch(int grid[][COL],Pair src, Pair dest) void aStarSearch(int grid[][COL], Pair src, Pair dest)
{ {
bool closedList[ROW][COL]; bool closedList[ROW][COL];
memset(closedList, false, sizeof(closedList)); memset(closedList, false, sizeof(closedList));
@ -350,7 +343,7 @@ void aStarSearch(int grid[][COL],Pair src, Pair dest)
double gNew, hNew, fNew; double gNew, hNew, fNew;
double waga = 1.0; 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) ------------ //----------- 1st Successor (North) ------------
if (isValid(i - 1, j) == true) if (isValid(i - 1, j) == true)
@ -484,11 +477,10 @@ void aStarSearch(int grid[][COL],Pair src, Pair dest)
return; return;
} }
void gogo(int endX, int endY)
void gogo(int endX,int endY)
{ {
updatePola(); updatePola();
Sleep(1000); //Sleep(1000);
int grid[27][27]; int grid[27][27];
for (int i = 0; i < 27; i++) for (int i = 0; i < 27; i++)
{ {
@ -502,11 +494,38 @@ void gogo(int endX,int endY)
aStarSearch(grid, src, dest); 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() void test1()
{ {
pole[1][3][0] = 'B'; pole[1][3][0] = 'B';
pole[1][3][1] = '9'; pole[1][3][1] = '9';
pole[3][1][0] = 'Z'; pole[3][1][0] = 'B';
pole[3][1][1] = '9'; pole[3][1][1] = '9';
} }
void test2() void test2()
@ -523,116 +542,15 @@ void test2()
updatePola(); 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() void start1()
{ {
int goalX = 3, goalY = 4; int goalX = 3, goalY = 4;
test1(); test1();
testSI1();
pole[1][1][0] = 'T'; pole[1][1][0] = 'T';
pole[1][1][1] = '1'; pole[1][1][1] = '1';
pole[goalY][goalX][0] = 'G'; pole[goalY][goalX][0] = 'G';
pole[goalY][goalX][1] = '9'; pole[goalY][goalX][1] = '9';
gogo(goalX, goalY); 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() void start2()
{ {
@ -655,9 +573,64 @@ void start3()
gogo(goalX, goalY); 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() int main()
{ {
SetWindow(50, 30); //SetWindow(50, 30);
//create pola// //create pola//
for (int i = 0; i < 27; i++) for (int i = 0; i < 27; i++)
{ {
@ -687,7 +660,26 @@ int main()
updatePola(); 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---------// //---------start---------//
bool traktorDziala = true; bool traktorDziala = true;

31
Traktor_DrzewaDecyzyjne.sln Normal file
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@ -0,0 +1,31 @@

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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
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GlobalSection(SolutionProperties) = preSolution
HideSolutionNode = FALSE
EndGlobalSection
GlobalSection(ExtensibilityGlobals) = postSolution
SolutionGuid = {891B470F-58C4-41E4-A102-A1D8CBB6BD64}
EndGlobalSection
EndGlobal

163
Traktor_DrzewaDecyzyjne.vcxproj Normal file
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@ -0,0 +1,163 @@
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<VCProjectVersion>16.0</VCProjectVersion>
<ProjectGuid>{C2FF0872-AC9D-4BD9-A46D-DC65A728BFF5}</ProjectGuid>
<Keyword>Win32Proj</Keyword>
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1166
backups/Main.cpp
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backups/MainPure.cpp
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#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;
}

1163
backups/Main_drzewa1_algorytmGenetyczny.cpp
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decyzje.txt
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165
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()

2
environment.md
View File

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

52
final-evaluation.md
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@ -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)

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8
injectCode.py
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@ -6,13 +6,7 @@ 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): def decision(prediction):
if prediction == 0: if prediction == 0:

625
matrix.txt
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@ -1,625 +0,0 @@
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7
notatki/11_05B
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@ -5,10 +5,11 @@
[80,100] - przejrzała [80,100] - przejrzała
nawóz przyśpiesza wzrost (czas * stałaPrzyrosuNawozu) poleInt[0]=1 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 użyty środek i nawóz poleInt[0]=3

2
route-planning.md
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@ -5,7 +5,7 @@ Ten dokument to raport z wykonanego drugiego zadania projektu zespołowego na pr
W skład zespołu wchodzą: W skład zespołu wchodzą:
Tomasz Dzierzbicki, Tomasz Dzierzbicki,
Szymon Parafiński, Szymon Parafiński,
Laura Piotrowska, Karol Piotrowski,
Jarosław Zbąski. Jarosław Zbąski.
# Zasady poruszania się agenta po planszy # Zasady poruszania się agenta po planszy