257 lines
7.5 KiB
Python
257 lines
7.5 KiB
Python
import time
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header = ["hydration", "weeds", "empty", "ready", "TODO"]
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work = ["Podlac", "Odchwascic", "Zasadzic", "Zebrac"]
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# ustawienie kolejnosci trybow na podstawie pogody
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# 3 - zebranie
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# 1 - odchwaszczenie
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# 2 - zasadzenie
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# 0 - podlanie
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def set_order(pogoda):
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if pogoda == 1:
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order = [3, 1, 2]
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else:
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order = [3, 1, 2, 0]
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return order
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# przetłumaczenie numerka (0-8)
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# nawodnienie, chwasty, puste_pole, gotowe_do_zbioru
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def translate(field):
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if field == 0:
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return [0, 0, 1, 0]
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elif field == 1:
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return [0, 1, 1, 0]
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elif field == 2:
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return [0, 0, 0, 0]
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elif field == 3:
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return [0, 1, 0, 0]
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elif field == 4:
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return [1, 0, 1, 0]
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elif field == 5:
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return [1, 1, 1, 0]
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elif field == 6:
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return [1, 0, 0, 0]
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elif field == 7:
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return [1, 1, 0, 0]
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elif field == 8:
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return [0, 0, 0, 1]
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else:
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print("Błąd: Zły numer pola.")
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# TWORZENIE DRZEWA
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# liczenie ilości prac do wykonania
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def class_counts(rows):
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counts = {}
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for row in rows:
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label = row[-1]
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if label not in counts:
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counts[label] = 0
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counts[label] += 1
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return counts
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# sprawdzenie czy wartość jest liczbą
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def is_numeric(value):
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return isinstance(value, int) or isinstance(value, float)
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# klasa tworząca zapytanie do podziału danych
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class Question():
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def __init__(self, column, value):
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self.column = column
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self.value = value
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def match(self, example):
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val = example[self.column]
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if is_numeric(val):
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return val >= self.value
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else:
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return val == self.value
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# wyświetlenie pytania
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def __repr__(self):
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if is_numeric(self.value):
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condition = "=="
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return "Czy %s %s %s?" % (
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header[self.column], condition, str(self.value)
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)
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# podział danych na spełnione i niespełnione wiersze
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def partition(rows, question):
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true_rows, false_rows = [], []
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for row in rows:
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if question.match(row):
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true_rows.append(row)
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else:
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false_rows.append(row)
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return true_rows, false_rows
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# funkcja implementująca indeks gini
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def gini(rows):
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counts = class_counts(rows)
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impurity = 1
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for label in counts:
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prob_of_label = counts[label] / float(len(rows))
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impurity -= prob_of_label ** 2
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return impurity
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def info_gain(true, false, current_uncertainty):
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p = float(len(true)) / (len(true) + len(false))
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return current_uncertainty - p * gini(true) - (1 - p) * gini(false)
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# znalezienie najlepszego "miejsca" na podział danych
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def find_best_split(rows):
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best_gain = 0
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best_question = None
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current_uncertainty = gini(rows)
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n_features = len(rows[0]) - 1
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for col in range(n_features):
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values = set([row[col] for row in rows])
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for val in values:
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question = Question(col, val)
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true_rows, false_rows = partition(rows, question)
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if len(true_rows) == 0 or len(false_rows) == 0:
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continue
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gain = info_gain(true_rows, false_rows, current_uncertainty)
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if gain >= best_gain:
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best_gain, best_question = gain, question
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return best_gain, best_question
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class Leaf:
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def __init__(self, rows):
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self.predictions = class_counts(rows)
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class DecisionNode:
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def __init__(self, question, true_branch, false_branch):
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self.question = question
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self.true_branch = true_branch
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self.false_branch = false_branch
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# funkcja budująca drzewo
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def build_tree(rows):
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gain, question = find_best_split(rows)
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if gain == 0:
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return Leaf(rows)
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true_rows, false_rows = partition(rows, question)
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true_branch = build_tree(true_rows)
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false_branch = build_tree(false_rows)
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return DecisionNode(question, true_branch, false_branch)
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# funkcja wypisująca drzewo
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def print_tree(node, spacing=""):
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if isinstance(node, Leaf):
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print(spacing + "Przewidywana czynność:", node.predictions)
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return
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print(spacing + str(node.question))
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print(spacing + '--> Prawda: ')
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print_tree(node.true_branch, spacing + " ")
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print(spacing + '--> Fałsz: ')
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print_tree(node.false_branch, spacing + " ")
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def classify(field, node):
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if isinstance(node, Leaf):
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return node.predictions
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if node.question.match(field):
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return classify(field, node.true_branch)
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else:
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return classify(field, node.false_branch)
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def print_leaf(counts):
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total = sum(counts.values()) * 1.0
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probs = {}
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for label in counts.keys():
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probs[label] = str(int(counts[label] / total * 100)) + "%"
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return probs
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class main():
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def __init__(self, traktor, field, ui, path):
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self.traktor = traktor
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self.field = field
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self.ui = ui
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self.path = path
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self.best_action = 0
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def main(self):
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# tworzymy zbior uczacy, w ktorym podajemy wszystkie mozliwe pola i czynnosci
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training_data = [[0, 0, 1, 0, "Zasadzic"],
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[0, 1, 1, 0, "Odchwascic"],
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[0, 0, 0, 0, "Podlac"],
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[0, 1, 0, 0, "Odchwascic"],
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# [1, 0, 1, 0, "Zasadzic"],
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# [1, 1, 1, 0, "Odchwascic"],
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[1, 0, 0, 0, "Czekac"],
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# [1, 1, 0, 0, "Odchwascic"],
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[0, 0, 0, 1, "Zebrac"]]
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self.tree = build_tree(training_data)
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print_tree(self.tree)
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print("TEST:")
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print("Przewidziania czynnosc: %s Czynnosc: Zasadzic"
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% print_leaf(classify(translate(4), self.tree)))
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print("Przewidziania czynnosc: %s Czynnosc: Odchwascic"
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% print_leaf(classify(translate(5), self.tree)))
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print("Przewidziania czynnosc: %s Czynnosc: Odchwascic"
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% print_leaf(classify(translate(7), self.tree)))
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# ustalamy kolejnosc
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order = set_order(self.field.get_pogoda_value())
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for action in order:
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self.traktor.set_mode(action) # ustawiamy tryb traktorowi
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self.search_field() # szukamy pól
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print("Koniec roboty")
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def work_field(self, labels):
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works = []
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for label in labels:
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if labels[label] > 0:
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works.append(label)
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return works
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def search_field(self):
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matrix = self.field.get_matrix() # pobieramy pole
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for i in range(len(matrix)):
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for j in range(len(matrix[i])):
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print("Pole (%d,%d) Przewidziania czynnosc: %s"
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% (i, j, print_leaf(classify(translate(matrix[i][j]), self.tree)))) # przewidujemy czynność za pomocą drzewa
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if work[self.traktor.get_mode()] in self.work_field(classify(translate(matrix[i][j]), self.tree)): # jezeli zgadza sie z aktualnym trybem:
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print("Zgodna z aktualnym trybem, czynnosc wykonywana")
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self.path.find_path(self.traktor, self.field, self.ui, [j, i]) # szukamy sciezki
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self.ui.update() # update'ujemy UI
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time.sleep(0.5)
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