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Gotowy podprojekt

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xkamikoo 2020-05-18 17:13:09 +02:00
parent 3666bd3079
commit 89ac7e6da6
1 changed files with 39 additions and 81 deletions
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Kamila.py
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@ -4,61 +4,34 @@ from sklearn.model_selection import train_test_split
from sklearn import metrics from sklearn import metrics
import numpy import numpy
header = ["ready", "hydration", "weeds", "empty", "TODO"] header = ["hydration", "weeds", "empty", "ready", "TODO"]
work = ["Zebrac","Podlac","Odchwascic","Zasadzic"] work = ["Podlac", "Odchwascic", "Zasadzic", "Zebrac"]
#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")
def check(field): def check(field):
if field == 0: 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: 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: elif field == 2:
return [[0, 0, 0, 0, "Podlac"]] return [[0, 0, 0, 0, "Podlac"]]
elif field == 3: 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: elif field == 4:
return [[0, 1, 0, 1, "Zasadzic"]] return [[1, 0, 1, 0, "Zasadzic"]]
elif field == 5: 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: elif field == 6:
return [] return []
elif field == 7: elif field == 7:
return [[0, 1, 1, 0, "Odchwascic"]] return [[1, 1, 0, 0, "Odchwascic"]]
elif field == 8: 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: else:
print("wrong field number") 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): def class_counts(rows):
counts = {} counts = {}
for row in rows: for row in rows:
@ -69,10 +42,12 @@ def class_counts(rows):
return counts return counts
# sprawdzenie czy wartość jest liczbą
def is_numeric(value): def is_numeric(value):
return isinstance(value, int) or isinstance(value, float) return isinstance(value, int) or isinstance(value, float)
# klasa tworząca zapytanie do podziału danych
class Question(): class Question():
def __init__(self, column, value): def __init__(self, column, value):
self.column = column self.column = column
@ -82,18 +57,17 @@ class Question():
val = example[self.column] val = example[self.column]
if is_numeric(val): if is_numeric(val):
return val == self.value return val == self.value
else:
return val != self.value
# wyświetlenie pytania
def __repr__(self): def __repr__(self):
condition = "!="
if is_numeric(self.value): if is_numeric(self.value):
condition = "==" condition = "=="
return "Is %s %s %s?" %( return "Is %s %s %s?" % (
header[self.column], condition, str(self.value) header[self.column], condition, str(self.value)
) )
# podział danych na spełnione i niespełnione wiersze
def partition(rows, question): def partition(rows, question):
true_rows, false_rows = [], [] true_rows, false_rows = [], []
for row in rows: for row in rows:
@ -104,20 +78,22 @@ def partition(rows, question):
return true_rows, false_rows return true_rows, false_rows
# funkcja implementująca indeks gini
def gini(rows): def gini(rows):
counts = class_counts(rows) counts = class_counts(rows)
impurity = 1 impurity = 1
for lbl in counts: for lbl in counts:
prob_of_lbl = counts[lbl]/float(len(rows)) prob_of_lbl = counts[lbl] / float(len(rows))
impurity -= prob_of_lbl**2 impurity -= prob_of_lbl ** 2
return impurity return impurity
def info_gain(left, right, current_uncertainty): def info_gain(left, right, current_uncertainty):
p = float(len(left))/(len(left) + len(right)) p = float(len(left)) / (len(left) + len(right))
return current_uncertainty - p*gini(left) - (1-p) * gini(right) return current_uncertainty - p * gini(left) - (1 - p) * gini(right)
# znalezienie najlepszego "miejsca" na podział danych
def find_best_split(rows): def find_best_split(rows):
best_gain = 0 best_gain = 0
best_question = None best_question = None
@ -133,7 +109,7 @@ def find_best_split(rows):
true_rows, false_rows = partition(rows, question) true_rows, false_rows = partition(rows, question)
if len(true_rows) == 0 or len(false_rows) == 0: if len(true_rows) == 0 or len(false_rows) == 0:
continue continue
gain = info_gain(true_rows,false_rows,current_uncertainty) gain = info_gain(true_rows, false_rows, current_uncertainty)
if gain >= best_gain: if gain >= best_gain:
best_gain, best_question = gain, question best_gain, best_question = gain, question
@ -152,6 +128,7 @@ class DecisionNode:
self.false_branch = false_branch self.false_branch = false_branch
# funkcja budująca drzewo
def build_tree(rows): def build_tree(rows):
gain, question = find_best_split(rows) gain, question = find_best_split(rows)
if gain == 0: if gain == 0:
@ -164,6 +141,7 @@ def build_tree(rows):
return DecisionNode(question, true_branch, false_branch) return DecisionNode(question, true_branch, false_branch)
# funcka wypisująca drzewo
def print_tree(node, spacing=""): def print_tree(node, spacing=""):
if isinstance(node, Leaf): if isinstance(node, Leaf):
print(spacing + "Predict", node.predictions) print(spacing + "Predict", node.predictions)
@ -178,31 +156,16 @@ def print_tree(node, spacing=""):
print_tree(node.false_branch, 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(): class main():
def __init__(self,traktor,field,ui,path): def __init__(self, traktor, field, ui, path):
self.traktor = traktor self.traktor = traktor
self.field = field self.field = field
self.ui = ui self.ui = ui
self.path = path self.path = path
self.best_action = 0 self.best_action = 0
def main(self): def main(self):
# dane testowe
array = ([[8, 8, 8, 8, 8, 8, 8, 8, 8, 8], array = ([[8, 8, 8, 8, 8, 8, 8, 8, 8, 8],
[7, 7, 7, 7, 7, 7, 7, 7, 7, 7], [7, 7, 7, 7, 7, 7, 7, 7, 7, 7],
[6, 6, 6, 6, 6, 6, 6, 6, 6, 6], [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], [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],
[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() self.find_best_action()
if self.best_action == -1:
break
self.do_best_action() self.do_best_action()
print("Koniec roboty") print("Koniec roboty")
def find_best_action(self): def find_best_action(self):
testing_data = [] testing_data = []
matrix = self.field.get_matrix() matrix = self.field.get_matrix()
matrix_todo = [] matrix_todo = []
#print(self.field) # print(self.field)
for i in range(10): for i in range(10):
matrix_todo.append([]) matrix_todo.append([])
verse = matrix[i] verse = matrix[i]
for j in range(len(verse)): for j in range(len(verse)):
coord = (i, j) 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([]) matrix_todo[i].append([])
for action in current_field: for action in current_field:
matrix_todo[i][j].append(action[-1]) matrix_todo[i][j].append(action[-1])
testing_data.extend(current_field) testing_data.extend(current_field)
#testing_data.append(current_field) # testing_data.append(current_field)
if len(testing_data) > 0: if len(testing_data) > 0:
x = build_tree(testing_data) x = build_tree(testing_data)
print_tree(x) print_tree(x)
@ -247,24 +212,17 @@ class main():
else: else:
self.best_action = self.find_remaining_action(matrix_todo) self.best_action = self.find_remaining_action(matrix_todo)
return 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): def do_best_action(self):
self.traktor.set_mode((self.best_action+3) % 4) self.traktor.set_mode(self.best_action)
while self.path.pathfinding(self.traktor,self.field,self.ui) != 0: while self.path.pathfinding(self.traktor, self.field, self.ui) != 0:
pass pass
# 0 - 3
# 1 - 0
# 2 - 1
# 3 - 2
def find_remaining_action(self, matrix_todo): def find_remaining_action(self, matrix_todo):
for row in matrix_todo: for row in matrix_todo:
for field in row: for field in row:
for action in field: for action in field:
print(action) print(action)
return work.index(action) return work.index(action)
return -1 return -1