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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
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187
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# Podprojekt indywidualny - Szymon Parafiński
---
W folderze Sklearn znajduje się wykorzystana baza, wygenerowane drzewo oraz reszta plików związana z podprojektem.
## Wykorzystana metoda uczenia
Do realizacji podprojektu wykorzystano drzewa decyzyjne do decydowania, co należy zrobić po najechaniu na konkretne pole.
Drzewo decyduje na jakim etapie jest roślina analizując poszczególne stany danego pola:
- Dopiero co zasiana (kiełek)
- 0: "Nie_podejmuj_działania"
- Roślinka kiełkująca (młoda)
- 1: "Zastosuj_nawóz"
- Roślina starzejąca się, bez środka ochrony
- 2: "Zastosuj_środek"
- Roślinka dojrzałam gotowa do zbioru
- 4: "Zbierz"
- Roślina zepsuta, nie nadaje się do użytku
- 5: "Roślina_już_zgniła-zbierz_i_wyrzuć".
Do implementacji drzew decyzyjnych w Pythonie wykorzystane zostały biblioteki
**sklearn** , **pandas**,**sys** oraz **pickle**.
## Uczenie modelu
#### loadLearningBase(): [python]
Metoda **loadLearningBase** rozpoczyna od utworzenia zbioru uczącego na podstawie tabeli zawierającej informacje wszystkich możliwych stanach roślinki.
* *col_names* -> zawiera nagłówki poszczególnych kolumn
* *feature_cols* -> zawiera nagłówki z kolumnami w których znajdują się dane do analizy
```
col_names = ['Warzywo', 'Nawoz', 'Srodek', 'Stan', 'Dzialanie']
base = pd.read_csv("Database.csv", header=None, names=col_names)
feature_cols = ['Warzywo', 'Nawoz', 'Srodek', 'Stan']
""" print dataset"""
# print(base.head())
```
Tutaj dzielimy podane kolumny na dwa typy zmiennych:
* zmienne docelowe ---> y
* i zmienne funkcyjne ---> X
Aby móc sprawdzić wydajność modelu, dzielę zestaw danych na zestaw szkoleniowy i zestaw testowy ---> za pomocą funkcji train_test_split ().
```
X = base[feature_cols] # Features
y = base.Dzialanie # Target variable
# Split dataset into training set and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,
random_state=1) # 70% training and 30% test
```
Wywołanie funkcji odpowiedzialnej za wygenerowanie drzewa.
```
data = generateDecisionTree(X_train, X_test, y_train, y_test)
```
Dodatkowe elementy pozwalające na wizualizację stworzonego drzewa decyzyjnego poprzez wygenerowanie drzewa zależności if/else lub najpierw do pliku .dot, który następnie poddany odpowiedniej 'obróbce' utworzy obraz.
```
"""generate data for image"""
# tree.export_graphviz(data, out_file='treeData.dot', filled=True, rounded=True, special_characters=True,
# feature_names=feature_cols)
"""Printing if_styled tree to console"""
# tree_to_code(data, feature_cols)
return data
```
---
#### generateDecisionTree(): [python]
Metoda **generateDecisionTree** generuje drzewo decyzyjne na podstawie dostarczonej bazy danych.
Do zmiennej *clf* zapisujemy drzewo decyzyjne z biblioteki **sklearn** utworzone za pomocą metody **DecisionTreeClassifier** z parametrem **criterion** ustawionym na **"entropy"**, który pozwala na uzyskiwanie informacji.
Na drzewie wywołujemy metodę **fit**, która dopasowuje do drzewa zbiór uczący zadany w tablicach **X_train** i **y_train**.
Po dopasowaniu danych możemy przewidzieć stan nowych przykładów, co robimy wywołując na drzewie metodę **predict** z parametrami, które zawierają informację o stanie danego pola.
```
def generateDecisionTree(X_train, X_test, y_train, y_test):
# Create Decision Tree classifer object
clf = DecisionTreeClassifier(criterion="entropy")
# Train Decision Tree Classifer
clf = clf.fit(X_train, y_train)
```
Aby ocenić dokładność naszego modelu przewidujemy odpowiedzi dla naszego zestawu testowego, aby móc go porównać z zestawem y_test i otrzymać dokładność wygenerowanego modelu.
```
# Predict the response for test dataset
y_pred = clf.predict(X_test)
"""Model Accuracy, how often is the classifier correct """
# print("Accuracy:", metrics.accuracy_score(y_test, y_pred))
```
---
#### main(): [python]
Metoda main wywołuje pozostałe metody oraz zapisuje wygenerowany model do pliku .sav aby nie było trzeba ponownie generować drzewa, tylko wczytać już te wygenerowane.
```
generated = loadLearningBase()
# Save generated tree
filename = 'decisionTree.sav'
pickle.dump(generated, open(filename, 'wb'))
```
## Implementacja w projekcie całościowym
Klasa wywoływana w **C++** nazywa się *injectCode*.
Działanie polega na tym że, funkcja **stanPola** sprawdza jakie wartości ma dane pole i generuje odpowiednie polecenie do wykonania.
#### stanPola(): [C++]
```
void stanPola(int x, int y) {
//[x][x][0] = 0 - brak chemii
//[x][x][0] = 1 - tylko nawóz
//[x][x][0] = 2 - tylko środek
//[x][x][0] = 3 - środek i nawóz
//[x][x][1] - wartość wzrostu rośliny
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);
}
```
Następnie funckja **decisionTree** wykonuje wygenerowane zapytanie.
#### decisionTree(): [C++]
```
void decisionTree(string polecenie) {
std::string str = polecenie;
const char* c = str.c_str();
system(c);
}
```
#### injectCode(): [python]
```
import pickle
import sys
def prediction(warzywo, nawoz ,srodek, stan_wzrostu):
filename = 'decisionTree.sav'
tree = pickle.load(open(filename, 'rb'))
val = (tree.predict([[warzywo, nawoz, srodek, stan_wzrostu]]))
print(decision(val))
def decision(prediction):
if prediction == 0:
return "Nie_podejmuj_dzialania"
elif prediction == 1:
return "Zastosuj_nawoz"
elif prediction == 2:
return "Zastosuj_srodek"
elif prediction == 4:
return "Zbierz"
elif prediction == 5:
return "Roslina_juz_zgnila__zbierz_i_wyrzuc"
if __name__ == '__main__':
# Map command line arguments to function arguments.
prediction(*sys.argv[1:])
```
Generowane polecenie wygląda w ten sposób:
```
python injectCode.py a b c d
```
Gdzie:
* a -> rodzaj warzywa
* 1: "burak"
* b -> czy roślina była nawożona
* 0: "nie"
* 1: "tak"
* c -> czy na roślinie był stosowany środek ochronny
* 0: "nie"
* 1: "tak"
* d -> stan wzrostu w jakim znajduje się roślina
* [1,20) - kiełek,
* [20,45) - młoda roślina,
* [45,85) - dojrzała,
* [80,100] - starzejąca się.

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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()

BIN
Sklearn/decisionTree.sav Normal file
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Binary file not shown.

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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272
Main.cpp → Traktor_DrzewaDecyzyjne.cpp
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@ -10,7 +10,7 @@
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;
@ -22,6 +22,8 @@ struct cell
}; };
char pole[27][27][2]; 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';
@ -98,7 +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;
@ -115,7 +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");
@ -154,41 +154,40 @@ void updatePola()
color("white", "black"); color("white", "black");
} }
} }
void correctMovement(char wantedWay) void correctMovement(char wantedWay)
{ {
while (currentWay != wantedWay) while (currentWay != wantedWay)
{ {
switch (currentWay) 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;
} }
} }
} }
@ -197,56 +196,55 @@ void Move(char kierunek)
switch (kierunek) switch (kierunek)
{ {
//góra-(w) //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) bool isValid(int x, int y)
{ {
if (pole[x][y][0] != '#') if (pole[x][y][0] != '#')
@ -255,7 +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)
{ {
@ -298,14 +296,14 @@ void tracePath(cell cellDetails[][COL], Pair dest)
Move('s'); Move('s');
if (p.second < pozycjaTraktoraY) if (p.second < pozycjaTraktoraY)
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));
@ -330,7 +328,7 @@ void aStarSearch(int grid[][COL],Pair src, Pair dest)
cellDetails[i][j].h = 0.0; cellDetails[i][j].h = 0.0;
cellDetails[i][j].parent_i = i; cellDetails[i][j].parent_i = i;
cellDetails[i][j].parent_j = j; cellDetails[i][j].parent_j = j;
set<pPair> openList; set<pPair> openList;
openList.insert(make_pair(0.0, make_pair(i, j))); openList.insert(make_pair(0.0, make_pair(i, j)));
bool foundDest = false; bool foundDest = false;
@ -345,7 +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)
@ -479,11 +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++)
{ {
@ -497,6 +494,33 @@ 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';
@ -549,9 +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++)
{ {
@ -581,7 +660,26 @@ int main()
updatePola(); updatePola();
start3(); // 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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163
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0
initial.txt
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injectCode.py Normal file
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@ -0,0 +1,27 @@
import pickle
import sys
def prediction(warzywo, nawoz ,srodek, stan_wzrostu):
filename = 'decisionTree.sav'
tree = pickle.load(open(filename, 'rb'))
val = (tree.predict([[warzywo, nawoz, srodek, stan_wzrostu]]))
print(decision(val))
def decision(prediction):
if prediction == 0:
return "Nie_podejmuj_dzialania"
elif prediction == 1:
return "Zastosuj_nawoz"
elif prediction == 2:
return "Zastosuj_srodek"
elif prediction == 4:
return "Zbierz"
elif prediction == 5:
return "Roslina_juz_zgnila__zbierz_i_wyrzuc"
if __name__ == '__main__':
# Map command line arguments to function arguments.
prediction(*sys.argv[1:])

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