Gotowy podprojekt
This commit is contained in:
xkamikoo
parent
3666bd3079
commit
89ac7e6da6
120
Kamila.py
120
Kamila.py
@ -4,61 +4,34 @@ from sklearn.model_selection import train_test_split
|
||||
from sklearn import metrics
|
||||
import numpy
|
||||
|
||||
header = ["ready", "hydration", "weeds", "empty", "TODO"]
|
||||
work = ["Zebrac","Podlac","Odchwascic","Zasadzic"]
|
||||
#0 - 3
|
||||
#1 - 0
|
||||
#2 - 1
|
||||
#3 - 2
|
||||
def check_p(field):
|
||||
if field == 0:
|
||||
return [0, 0, 0, 0, "Zasadzic"]
|
||||
elif field == 1:
|
||||
return [0, 0, 1, 0, "Odchwascic"]
|
||||
elif field == 2:
|
||||
return [0, 0, 0, 1, "Podlac"]
|
||||
elif field == 3:
|
||||
return [0, 0, 1, 1, "Odchwascic"]
|
||||
elif field == 4:
|
||||
return [0, 1, 0, 0, "Zasadzic"]
|
||||
elif field == 5:
|
||||
return [0, 1, 1, 0, "Odchwascic"]
|
||||
elif field == 6:
|
||||
return [0, 1, 0, 1, "Ignoruj"]
|
||||
elif field == 7:
|
||||
return [0, 1, 1, 1, "Odchwascic"]
|
||||
elif field == 8:
|
||||
return [1, 0, 0, 1, "Zebrac"]
|
||||
else:
|
||||
print("wrong field number")
|
||||
header = ["hydration", "weeds", "empty", "ready", "TODO"]
|
||||
work = ["Podlac", "Odchwascic", "Zasadzic", "Zebrac"]
|
||||
|
||||
|
||||
def check(field):
|
||||
if field == 0:
|
||||
return [[0, 0, 0, 1, "Zasadzic"],[0,0,0,1,"Podlac"]]
|
||||
return [[0, 0, 1, 0, "Zasadzic"], [0, 0, 1, 0, "Podlac"]]
|
||||
elif field == 1:
|
||||
return [[0, 0, 1, 1, "Odchwascic"], [0,0,1,1,"Podlac"], [0,0,1,1,"Zasadzic"]]
|
||||
return [[0, 1, 1, 0, "Odchwascic"], [0, 1, 1, 0, "Podlac"], [0, 1, 1, 0, "Zasadzic"]]
|
||||
elif field == 2:
|
||||
return [[0, 0, 0, 0, "Podlac"]]
|
||||
elif field == 3:
|
||||
return [[0, 0, 1, 0, "Odchwascic"],[0,0,1,0,"Podlac"]]
|
||||
return [[0, 1, 0, 0, "Odchwascic"], [0, 1, 0, 0, "Podlac"]]
|
||||
elif field == 4:
|
||||
return [[0, 1, 0, 1, "Zasadzic"]]
|
||||
return [[1, 0, 1, 0, "Zasadzic"]]
|
||||
elif field == 5:
|
||||
return [[0, 1, 1, 1, "Odchwascic"],[0,1,1,1,"Zasadzic"]]
|
||||
return [[1, 1, 1, 0, "Odchwascic"], [1, 1, 1, 0, "Zasadzic"]]
|
||||
elif field == 6:
|
||||
return []
|
||||
elif field == 7:
|
||||
return [[0, 1, 1, 0, "Odchwascic"]]
|
||||
return [[1, 1, 0, 0, "Odchwascic"]]
|
||||
elif field == 8:
|
||||
return [[1, 0, 0, 0, "Zebrac"],[1, 0, 0, 0, "Potem podlac"],[1, 0, 0, 0, "Potem zasadzic"]]
|
||||
return [[0, 0, 0, 1, "Zebrac"], [0, 0, 0, 1, "Potem podlac"], [0, 0, 0, 1, "Potem zasadzic"]]
|
||||
else:
|
||||
print("wrong field number")
|
||||
|
||||
def un_values(rows, col):
|
||||
return set([row[col] for row in rows])
|
||||
|
||||
|
||||
# liczenie ilości prac do wykonania
|
||||
def class_counts(rows):
|
||||
counts = {}
|
||||
for row in rows:
|
||||
@ -69,10 +42,12 @@ def class_counts(rows):
|
||||
return counts
|
||||
|
||||
|
||||
# sprawdzenie czy wartość jest liczbą
|
||||
def is_numeric(value):
|
||||
return isinstance(value, int) or isinstance(value, float)
|
||||
|
||||
|
||||
# klasa tworząca zapytanie do podziału danych
|
||||
class Question():
|
||||
def __init__(self, column, value):
|
||||
self.column = column
|
||||
@ -82,18 +57,17 @@ class Question():
|
||||
val = example[self.column]
|
||||
if is_numeric(val):
|
||||
return val == self.value
|
||||
else:
|
||||
return val != self.value
|
||||
|
||||
# wyświetlenie pytania
|
||||
def __repr__(self):
|
||||
condition = "!="
|
||||
if is_numeric(self.value):
|
||||
condition = "=="
|
||||
return "Is %s %s %s?" %(
|
||||
return "Is %s %s %s?" % (
|
||||
header[self.column], condition, str(self.value)
|
||||
)
|
||||
|
||||
|
||||
# podział danych na spełnione i niespełnione wiersze
|
||||
def partition(rows, question):
|
||||
true_rows, false_rows = [], []
|
||||
for row in rows:
|
||||
@ -104,20 +78,22 @@ def partition(rows, question):
|
||||
return true_rows, false_rows
|
||||
|
||||
|
||||
# funkcja implementująca indeks gini
|
||||
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
|
||||
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)
|
||||
p = float(len(left)) / (len(left) + len(right))
|
||||
return current_uncertainty - p * gini(left) - (1 - p) * gini(right)
|
||||
|
||||
|
||||
# znalezienie najlepszego "miejsca" na podział danych
|
||||
def find_best_split(rows):
|
||||
best_gain = 0
|
||||
best_question = None
|
||||
@ -133,7 +109,7 @@ def find_best_split(rows):
|
||||
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)
|
||||
gain = info_gain(true_rows, false_rows, current_uncertainty)
|
||||
if gain >= best_gain:
|
||||
best_gain, best_question = gain, question
|
||||
|
||||
@ -152,6 +128,7 @@ class DecisionNode:
|
||||
self.false_branch = false_branch
|
||||
|
||||
|
||||
# funkcja budująca drzewo
|
||||
def build_tree(rows):
|
||||
gain, question = find_best_split(rows)
|
||||
if gain == 0:
|
||||
@ -164,6 +141,7 @@ def build_tree(rows):
|
||||
return DecisionNode(question, true_branch, false_branch)
|
||||
|
||||
|
||||
# funcka wypisująca drzewo
|
||||
def print_tree(node, spacing=""):
|
||||
if isinstance(node, Leaf):
|
||||
print(spacing + "Predict", node.predictions)
|
||||
@ -178,31 +156,16 @@ def print_tree(node, spacing=""):
|
||||
print_tree(node.false_branch, spacing + " ")
|
||||
|
||||
|
||||
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
|
||||
|
||||
|
||||
class main():
|
||||
def __init__(self,traktor,field,ui,path):
|
||||
def __init__(self, traktor, field, ui, path):
|
||||
self.traktor = traktor
|
||||
self.field = field
|
||||
self.ui = ui
|
||||
self.path = path
|
||||
self.best_action = 0
|
||||
|
||||
def main(self):
|
||||
# dane testowe
|
||||
array = ([[8, 8, 8, 8, 8, 8, 8, 8, 8, 8],
|
||||
[7, 7, 7, 7, 7, 7, 7, 7, 7, 7],
|
||||
[6, 6, 6, 6, 6, 6, 6, 6, 6, 6],
|
||||
@ -213,28 +176,30 @@ class main():
|
||||
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
|
||||
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
|
||||
while (self.best_action != -1):
|
||||
|
||||
while (True):
|
||||
self.find_best_action()
|
||||
if self.best_action == -1:
|
||||
break
|
||||
self.do_best_action()
|
||||
print("Koniec roboty")
|
||||
|
||||
|
||||
def find_best_action(self):
|
||||
testing_data = []
|
||||
matrix = self.field.get_matrix()
|
||||
matrix_todo = []
|
||||
#print(self.field)
|
||||
# print(self.field)
|
||||
for i in range(10):
|
||||
matrix_todo.append([])
|
||||
verse = matrix[i]
|
||||
for j in range(len(verse)):
|
||||
coord = (i, j)
|
||||
current_field = check(verse[j]) #czynnosci ktore trzeba jeszcze zrobic na kazdym polu
|
||||
current_field = check(verse[j]) # czynnosci ktore trzeba jeszcze zrobic na kazdym polu
|
||||
matrix_todo[i].append([])
|
||||
for action in current_field:
|
||||
matrix_todo[i][j].append(action[-1])
|
||||
testing_data.extend(current_field)
|
||||
#testing_data.append(current_field)
|
||||
# testing_data.append(current_field)
|
||||
if len(testing_data) > 0:
|
||||
x = build_tree(testing_data)
|
||||
print_tree(x)
|
||||
@ -247,24 +212,17 @@ class main():
|
||||
else:
|
||||
self.best_action = self.find_remaining_action(matrix_todo)
|
||||
return
|
||||
#for row in testing_data:
|
||||
# print("Actual: %s. Predicted %s" %
|
||||
# (row[-1], print_leaf(classify(row, x))))
|
||||
#for row in matrix_todo:
|
||||
# print(row)
|
||||
|
||||
def do_best_action(self):
|
||||
self.traktor.set_mode((self.best_action+3) % 4)
|
||||
while self.path.pathfinding(self.traktor,self.field,self.ui) != 0:
|
||||
self.traktor.set_mode(self.best_action)
|
||||
while self.path.pathfinding(self.traktor, self.field, self.ui) != 0:
|
||||
pass
|
||||
# 0 - 3
|
||||
# 1 - 0
|
||||
# 2 - 1
|
||||
# 3 - 2
|
||||
|
||||
|
||||
def find_remaining_action(self, matrix_todo):
|
||||
for row in matrix_todo:
|
||||
for field in row:
|
||||
for action in field:
|
||||
print(action)
|
||||
return work.index(action)
|
||||
return -1
|
||||
return -1
|
||||
|
||||
Loading…
Reference in New Issue
Block a user