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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()

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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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Sklearn/treeData.png Normal file

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@ -10,7 +10,7 @@
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;
@ -22,6 +22,8 @@ struct cell
};
char pole[27][27][2];
int stan[27][27][2];
string polecenie = "python injectCode.py 1 ";
int pozycjaTraktoraX = 1, pozycjaTraktoraY = 1;
char currentWay = 'S';
@ -98,7 +100,6 @@ void color(string foregroundColor, string backgroundColor)
backgroundCode = 15;
SetConsoleTextAttribute(hOut, foregroundCode + backgroundCode * 16);
}
void SetWindow(int Width, int Height)
{
_COORD coord;
@ -115,7 +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");
@ -154,41 +154,40 @@ void updatePola()
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;
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;
}
}
}
@ -197,56 +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] = '.';
pozycjaTraktoraY--;
pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = 'T';
}
updatePola();
}break;
//dół-(s)
case 's':
correctMovement('N');
pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = '.';
pozycjaTraktoraY--;
pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = 'T';
}
updatePola();
}break;
//dół-(s)
case 's':
{
if (pole[pozycjaTraktoraY + 1][pozycjaTraktoraX][0] != '#')
{
if (pole[pozycjaTraktoraY + 1][pozycjaTraktoraX][0] != '#')
{
correctMovement('S');
pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = '.';
pozycjaTraktoraY++;
pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = 'T';
}
updatePola();
}break;
//lewo-(a)
case 'a':
correctMovement('S');
pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = '.';
pozycjaTraktoraY++;
pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = 'T';
}
updatePola();
}break;
//lewo-(a)
case 'a':
{
if (pole[pozycjaTraktoraY][pozycjaTraktoraX - 1][0] != '#')
{
if (pole[pozycjaTraktoraY][pozycjaTraktoraX - 1][0] != '#')
{
correctMovement('W');
pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = '.';
pozycjaTraktoraX--;
pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = 'T';
}
updatePola();
}break;
//prawo-(d)
case 'd':
correctMovement('W');
pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = '.';
pozycjaTraktoraX--;
pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = 'T';
}
updatePola();
}break;
//prawo-(d)
case 'd':
{
if (pole[pozycjaTraktoraY][pozycjaTraktoraX + 1][0] != '#')
{
if (pole[pozycjaTraktoraY][pozycjaTraktoraX + 1][0] != '#')
{
correctMovement('E');
pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = '.';
pozycjaTraktoraX++;
pole[pozycjaTraktoraY][pozycjaTraktoraX][0] = 'T';
}
updatePola();
}break;
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] != '#')
@ -255,7 +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)
{
@ -298,14 +296,14 @@ void tracePath(cell cellDetails[][COL], Pair dest)
Move('s');
if (p.second < pozycjaTraktoraY)
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));
@ -330,7 +328,7 @@ void aStarSearch(int grid[][COL],Pair src, Pair dest)
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;
@ -345,7 +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)
@ -479,11 +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++)
{
@ -497,6 +494,33 @@ 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';
@ -549,9 +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++)
{
@ -581,7 +660,26 @@ int main()
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---------//
bool traktorDziala = true;

View File

@ -0,0 +1,31 @@

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BIN
decisionTree.sav Normal file

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BIN
images/Tree/StanPola.jpg Normal file

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27
injectCode.py Normal file
View File

@ -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:])

View File

@ -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
użyty zawsze (ale max 1)
środek sprawia że roślina starzejąca uzdatnia roślinę do zbioru poleInt[0]=2
używany tylko jak starzejąca (ale max 1)
użyty środek i nawóz poleInt[0]=3