Merge remote-tracking branch 'origin/master'

Kamila.py

@@ -1,9 +1,228 @@
+import pandas as pd
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.model_selection import train_test_split
+from sklearn import metrics
+import numpy
+
+header = ["hydration", "weeds", "empty", "ready", "TODO"]
+work = ["Podlac", "Odchwascic", "Zasadzic", "Zebrac"]
+
+
+def check(field):
+    if field == 0:
+        return [[0, 0, 1, 0, "Zasadzic"], [0, 0, 1, 0, "Podlac"]]
+    elif field == 1:
+        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, 1, 0, 0, "Odchwascic"], [0, 1, 0, 0, "Podlac"]]
+    elif field == 4:
+        return [[1, 0, 1, 0, "Zasadzic"]]
+    elif field == 5:
+        return [[1, 1, 1, 0, "Odchwascic"], [1, 1, 1, 0, "Zasadzic"]]
+    elif field == 6:
+        return []
+    elif field == 7:
+        return [[1, 1, 0, 0, "Odchwascic"]]
+    elif field == 8:
+        return [[0, 0, 0, 1, "Zebrac"], [0, 0, 0, 1, "Potem podlac"], [0, 0, 0, 1, "Potem zasadzic"]]
+    else:
+        print("wrong field number")
+
+
+# liczenie ilości prac do wykonania
+def class_counts(rows):
+    counts = {}
+    for row in rows:
+        label = row[-1]
+        if label not in counts:
+            counts[label] = 0
+        counts[label] += 1
+    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
+        self.value = value
+
+    def match(self, example):
+        val = example[self.column]
+        if is_numeric(val):
+            return val == self.value
+
+    # wyświetlenie pytania
+    def __repr__(self):
+        if is_numeric(self.value):
+            condition = "=="
+        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:
+        if question.match(row):
+            true_rows.append(row)
+        else:
+            false_rows.append(row)
+    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
+    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)
+
+
+# znalezienie najlepszego "miejsca" na podział danych
+def find_best_split(rows):
+    best_gain = 0
+    best_question = None
+    current_uncertainty = gini(rows)
+    n_features = len(rows[0]) - 1
+
+    for col in range(n_features):
+
+        values = set([row[col] for row in rows])
+
+        for val in values:
+            question = Question(col, val)
+            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)
+            if gain >= best_gain:
+                best_gain, best_question = gain, question
+
+    return best_gain, best_question
+
+
+class Leaf:
+    def __init__(self, rows):
+        self.predictions = class_counts(rows)
+
+
+class DecisionNode:
+    def __init__(self, question, true_branch, false_branch):
+        self.question = question
+        self.true_branch = true_branch
+        self.false_branch = false_branch
+
+
+# funkcja budująca drzewo
+def build_tree(rows):
+    gain, question = find_best_split(rows)
+    if gain == 0:
+        return Leaf(rows)
+    true_rows, false_rows = partition(rows, question)
+
+    true_branch = build_tree(true_rows)
+    false_branch = build_tree(false_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)
+        return
+
+    print(spacing + str(node.question))
+
+    print(spacing + '--> True: ')
+    print_tree(node.true_branch, spacing + " ")
+
+    print(spacing + '--> False: ')
+    print_tree(node.false_branch, spacing + " ")
+
+
 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):
-        pass
+        # 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],
+                  [5, 5, 5, 5, 5, 5, 5, 5, 5, 5],
+                  [4, 4, 4, 4, 4, 4, 4, 4, 4, 4],
+                  [3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
+                  [2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
+                  [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 (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)
+        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
+                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)
+        if len(testing_data) > 0:
+            x = build_tree(testing_data)
+            print_tree(x)
+            if isinstance(x, Leaf):
+                self.best_action = self.find_remaining_action(matrix_todo)
+                return
+            self.best_action = x.question.column
+            print(header[x.question.column])
+            print(x.question.value)
+        else:
+            self.best_action = self.find_remaining_action(matrix_todo)
+            return
+
+    def do_best_action(self):
+        self.traktor.set_mode(self.best_action)
+        while self.path.pathfinding(self.traktor, self.field, self.ui) != 0:
+            pass
+
+
+    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

decisiontree.md

@@ -0,0 +1,11 @@
+# Sztuczna Inteligencja - Raport
+
+**Członkowie zespołu:** Marcin Kwapisz, Kamila Matysiak, Piotr Rychlicki, Justyna Zarzycka
+
+**Temat podprojektu:** Wybór trybu pracy traktora za pomocą drzewa decyzyjnego
+
+**Autor podprojektu:** Kamila Matysiak
+
+
+### Drzewo Decyzyjne
+