270 lines
7.6 KiB
Python
270 lines
7.6 KiB
Python
import pandas as pd
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.model_selection import train_test_split
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from sklearn import metrics
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import numpy
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header = ["ready", "hydration", "weeds", "empty", "TODO"]
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work = ["Zebrac","Podlac","Odchwascic","Zasadzic"]
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#0 - 3
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#1 - 0
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#2 - 1
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#3 - 2
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def check_p(field):
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if field == 0:
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return [0, 0, 0, 0, "Zasadzic"]
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elif field == 1:
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return [0, 0, 1, 0, "Odchwascic"]
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elif field == 2:
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return [0, 0, 0, 1, "Podlac"]
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elif field == 3:
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return [0, 0, 1, 1, "Odchwascic"]
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elif field == 4:
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return [0, 1, 0, 0, "Zasadzic"]
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elif field == 5:
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return [0, 1, 1, 0, "Odchwascic"]
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elif field == 6:
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return [0, 1, 0, 1, "Ignoruj"]
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elif field == 7:
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return [0, 1, 1, 1, "Odchwascic"]
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elif field == 8:
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return [1, 0, 0, 1, "Zebrac"]
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else:
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print("wrong field number")
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def check(field):
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if field == 0:
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return [[0, 0, 0, 1, "Zasadzic"],[0,0,0,1,"Podlac"]]
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elif field == 1:
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return [[0, 0, 1, 1, "Odchwascic"], [0,0,1,1,"Podlac"], [0,0,1,1,"Zasadzic"]]
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elif field == 2:
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return [[0, 0, 0, 0, "Podlac"]]
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elif field == 3:
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return [[0, 0, 1, 0, "Odchwascic"],[0,0,1,0,"Podlac"]]
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elif field == 4:
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return [[0, 1, 0, 1, "Zasadzic"]]
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elif field == 5:
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return [[0, 1, 1, 1, "Odchwascic"],[0,1,1,1,"Zasadzic"]]
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elif field == 6:
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return []
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elif field == 7:
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return [[0, 1, 1, 0, "Odchwascic"]]
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elif field == 8:
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return [[1, 0, 0, 0, "Zebrac"],[1, 0, 0, 0, "Potem podlac"],[1, 0, 0, 0, "Potem zasadzic"]]
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else:
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print("wrong field number")
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def un_values(rows, col):
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return set([row[col] for row in rows])
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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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def is_numeric(value):
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return isinstance(value, int) or isinstance(value, float)
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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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def __repr__(self):
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condition = "!="
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if is_numeric(self.value):
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condition = "=="
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return "Is %s %s %s?" %(
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header[self.column], condition, str(self.value)
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)
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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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def gini(rows):
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counts = class_counts(rows)
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impurity = 1
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for lbl in counts:
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prob_of_lbl = counts[lbl]/float(len(rows))
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impurity -= prob_of_lbl**2
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return impurity
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def info_gain(left, right, current_uncertainty):
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p = float(len(left))/(len(left) + len(right))
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return current_uncertainty - p*gini(left) - (1-p) * gini(right)
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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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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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def print_tree(node, spacing=""):
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if isinstance(node, Leaf):
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print(spacing + "Predict", node.predictions)
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return
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print(spacing + str(node.question))
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print(spacing + '--> True: ')
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print_tree(node.true_branch, spacing + " ")
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print(spacing + '--> False: ')
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print_tree(node.false_branch, spacing + " ")
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def classify(row, node):
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if isinstance(node, Leaf):
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return node.predictions
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if node.question.match(row):
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return classify(row, node.true_branch)
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else:
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return classify(row,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 lbl in counts.keys():
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probs[lbl] = str(int(counts[lbl]/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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array = ([[8, 8, 8, 8, 8, 8, 8, 8, 8, 8],
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[7, 7, 7, 7, 7, 7, 7, 7, 7, 7],
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[6, 6, 6, 6, 6, 6, 6, 6, 6, 6],
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[5, 5, 5, 5, 5, 5, 5, 5, 5, 5],
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[4, 4, 4, 4, 4, 4, 4, 4, 4, 4],
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[3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
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[2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
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while (self.best_action != -1):
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self.find_best_action()
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self.do_best_action()
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print("Koniec roboty")
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def find_best_action(self):
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testing_data = []
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matrix = self.field.get_matrix()
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matrix_todo = []
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#print(self.field)
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for i in range(10):
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matrix_todo.append([])
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verse = matrix[i]
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for j in range(len(verse)):
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coord = (i, j)
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current_field = check(verse[j]) #czynnosci ktore trzeba jeszcze zrobic na kazdym polu
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matrix_todo[i].append([])
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for action in current_field:
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matrix_todo[i][j].append(action[-1])
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testing_data.extend(current_field)
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#testing_data.append(current_field)
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if len(testing_data) > 0:
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x = build_tree(testing_data)
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print_tree(x)
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if isinstance(x, Leaf):
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self.best_action = self.find_remaining_action(matrix_todo)
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return
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self.best_action = x.question.column
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print(header[x.question.column])
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print(x.question.value)
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else:
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self.best_action = self.find_remaining_action(matrix_todo)
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return
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#for row in testing_data:
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# print("Actual: %s. Predicted %s" %
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# (row[-1], print_leaf(classify(row, x))))
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#for row in matrix_todo:
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# print(row)
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def do_best_action(self):
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self.traktor.set_mode((self.best_action+3) % 4)
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while self.path.pathfinding(self.traktor,self.field,self.ui) != 0:
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pass
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# 0 - 3
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# 1 - 0
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# 2 - 1
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# 3 - 2
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def find_remaining_action(self, matrix_todo):
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for row in matrix_todo:
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for field in row:
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for action in field:
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print(action)
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return work.index(action)
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return -1 |