Merge branch 'master' into genetic_algorithm

decision_tree/tree_as_txt.txt

@@ -1,3 +1,96 @@
+<<<<<<< HEAD
+|--- feature_2 <= 3.50
+|   |--- feature_4 <= 3.50
+|   |   |--- feature_0 <= 1.50
+|   |   |   |--- class: 0
+|   |   |--- feature_0 >  1.50
+|   |   |   |--- feature_3 <= 3.50
+|   |   |   |   |--- feature_4 <= 2.50
+|   |   |   |   |   |--- class: 1
+|   |   |   |   |--- feature_4 >  2.50
+|   |   |   |   |   |--- feature_2 <= 2.50
+|   |   |   |   |   |   |--- class: 1
+|   |   |   |   |   |--- feature_2 >  2.50
+|   |   |   |   |   |   |--- class: 0
+|   |   |   |--- feature_3 >  3.50
+|   |   |   |   |--- feature_3 <= 4.50
+|   |   |   |   |   |--- feature_1 <= 2.50
+|   |   |   |   |   |   |--- feature_0 <= 2.50
+|   |   |   |   |   |   |   |--- feature_1 <= 1.50
+|   |   |   |   |   |   |   |   |--- feature_4 <= 2.50
+|   |   |   |   |   |   |   |   |   |--- class: 1
+|   |   |   |   |   |   |   |   |--- feature_4 >  2.50
+|   |   |   |   |   |   |   |   |   |--- feature_2 <= 2.00
+|   |   |   |   |   |   |   |   |   |   |--- class: 1
+|   |   |   |   |   |   |   |   |   |--- feature_2 >  2.00
+|   |   |   |   |   |   |   |   |   |   |--- class: 0
+|   |   |   |   |   |   |   |--- feature_1 >  1.50
+|   |   |   |   |   |   |   |   |--- class: 0
+|   |   |   |   |   |   |--- feature_0 >  2.50
+|   |   |   |   |   |   |   |--- feature_2 <= 2.50
+|   |   |   |   |   |   |   |   |--- class: 1
+|   |   |   |   |   |   |   |--- feature_2 >  2.50
+|   |   |   |   |   |   |   |   |--- feature_4 <= 2.50
+|   |   |   |   |   |   |   |   |   |--- class: 1
+|   |   |   |   |   |   |   |   |--- feature_4 >  2.50
+|   |   |   |   |   |   |   |   |   |--- class: 0
+|   |   |   |   |   |--- feature_1 >  2.50
+|   |   |   |   |   |   |--- feature_1 <= 3.50
+|   |   |   |   |   |   |   |--- feature_0 <= 3.50
+|   |   |   |   |   |   |   |   |--- class: 0
+|   |   |   |   |   |   |   |--- feature_0 >  3.50
+|   |   |   |   |   |   |   |   |--- feature_4 <= 2.50
+|   |   |   |   |   |   |   |   |   |--- class: 1
+|   |   |   |   |   |   |   |   |--- feature_4 >  2.50
+|   |   |   |   |   |   |   |   |   |--- feature_2 <= 2.00
+|   |   |   |   |   |   |   |   |   |   |--- class: 1
+|   |   |   |   |   |   |   |   |   |--- feature_2 >  2.00
+|   |   |   |   |   |   |   |   |   |   |--- class: 0
+|   |   |   |   |   |   |--- feature_1 >  3.50
+|   |   |   |   |   |   |   |--- class: 0
+|   |   |   |   |--- feature_3 >  4.50
+|   |   |   |   |   |--- class: 0
+|   |--- feature_4 >  3.50
+|   |   |--- feature_2 <= 1.50
+|   |   |   |--- feature_4 <= 4.50
+|   |   |   |   |--- feature_3 <= 3.50
+|   |   |   |   |   |--- feature_0 <= 1.50
+|   |   |   |   |   |   |--- class: 0
+|   |   |   |   |   |--- feature_0 >  1.50
+|   |   |   |   |   |   |--- class: 1
+|   |   |   |   |--- feature_3 >  3.50
+|   |   |   |   |   |--- feature_1 <= 2.50
+|   |   |   |   |   |   |--- feature_0 <= 2.50
+|   |   |   |   |   |   |   |--- class: 0
+|   |   |   |   |   |   |--- feature_0 >  2.50
+|   |   |   |   |   |   |   |--- feature_3 <= 4.50
+|   |   |   |   |   |   |   |   |--- class: 1
+|   |   |   |   |   |   |   |--- feature_3 >  4.50
+|   |   |   |   |   |   |   |   |--- class: 0
+|   |   |   |   |   |--- feature_1 >  2.50
+|   |   |   |   |   |   |--- class: 0
+|   |   |   |--- feature_4 >  4.50
+|   |   |   |   |--- class: 0
+|   |   |--- feature_2 >  1.50
+|   |   |   |--- class: 0
+|--- feature_2 >  3.50
+|   |--- feature_1 <= 1.50
+|   |   |--- feature_4 <= 1.50
+|   |   |   |--- feature_2 <= 4.50
+|   |   |   |   |--- feature_0 <= 1.50
+|   |   |   |   |   |--- class: 0
+|   |   |   |   |--- feature_0 >  1.50
+|   |   |   |   |   |--- feature_3 <= 4.50
+|   |   |   |   |   |   |--- class: 1
+|   |   |   |   |   |--- feature_3 >  4.50
+|   |   |   |   |   |   |--- class: 0
+|   |   |   |--- feature_2 >  4.50
+|   |   |   |   |--- class: 0
+|   |   |--- feature_4 >  1.50
+|   |   |   |--- class: 0
+|   |--- feature_1 >  1.50
+|   |   |--- class: 0
+=======
 |--- feature_2 <= 3.50
 |   |--- feature_4 <= 3.50
 |   |   |--- feature_0 <= 1.50
@@ -89,3 +182,4 @@
 |   |   |   |--- class: 0
 |   |--- feature_1 >  1.50
 |   |   |--- class: 0
+>>>>>>> genetic_algorithm

main_test.py

@@ -0,0 +1,10 @@
+from map import map_utils
+from path_search_algorthms import a_star, a_star_utils
+import numpy
+
+file = open('last_map.nparr', 'rb')
+array = numpy.load(file)
+file.close
+
+actions = a_star.search_path(12, 0, a_star_utils.Rotation.UP, 1, 5, array)
+print(actions)

neurone/main.py

@@ -0,0 +1,71 @@
+import pybrain3
+import pickle
+import matplotlib.pylab as plt
+from numpy import ravel
+from pybrain3.tools.shortcuts import buildNetwork
+from pybrain3.datasets import SupervisedDataSet
+from pybrain3.supervised.trainers import BackpropTrainer 
+from pybrain3.tools.xml.networkwriter import NetworkWriter
+from pybrain3.tools.xml.networkreader import NetworkReader
+
+# https://www.machinelearningmastery.ru/how-to-configure-the-number-of-layers-and-nodes-in-a-neural-network/
+class SupervisedDataSetModel():
+    def __init__(self, metrics:int = 4, 
+                 predictions:int = 1, 
+                 input_layer:int = 4, 
+                 hidden_layer:int = 3,
+                 output_layer:int = 1):
+        # 4 метрики, 1 предикшн
+        self.metrics = metrics
+        self.predictions = predictions
+        self.input_layer = input_layer
+        self.hidden_layer = hidden_layer
+        self.output_layer = output_layer
+        self.ds = SupervisedDataSet(metrics, predictions)
+        
+    
+    def activateModel(self):
+        self.net = buildNetwork(self.input_layer, self.hidden_layer, self.output_layer, bias=True)
+        self.trainer = BackpropTrainer(self.net, dataset=self.ds, momentum=0.1, learningrate=0.01, verbose=True, weightdecay=0.01)
+
+        self.trnerr, self.valerr = self.trainer.trainUntilConvergence()
+        
+        plt.plot(self.trnerr, 'b', self.valerr, 'r')
+        plt.show()
+
+    def addDataToModel(self, input:list, target:list):
+        self.ds.addSample(inp=input, target=target)
+    
+    def predict(self, data:list):
+        y = self.net.activate(data)
+        print(y)
+        return y
+    
+    def saveModel(self):
+        fileObject = open('model.txt', 'wb') 
+        pickle.dump(self.net, fileObject) 
+        fileObject.close()
+        
+
+def getModel():
+        fileObject = open('model.txt', 'rb') 
+        net2 = pickle.load(fileObject) 
+        fileObject.close()
+        return net2
+    
+model = SupervisedDataSetModel()
+
+# model.addDataToModel([2, 3, 80, 1], [5])
+# model.addDataToModel([5, 5, 50, 2], [4])
+# model.addDataToModel([10, 7, 40, 3], [3])
+# model.addDataToModel([15, 9, 20, 4], [2])
+# model.addDataToModel([20, 11, 10, 5], [1])
+
+# model.activateModel()
+
+# model.saveModel()
+
+# USE MODEL - >
+model = getModel()
+print(model.activate([2, 3, 80, 1]))
+

path_search_algorthms/a_star_utils.py

@@ -3,6 +3,7 @@ from enum import Enum
 from map import map_utils
 from settings import *
 
+
 class Rotation(Enum):
     UP = 0
     RIGHT = 1
@@ -13,34 +14,37 @@ class Rotation(Enum):
     def __int__(self):
         return self.value
 
+
 class Node:
     def __init__(self, x: int, y: int, rotation: Rotation):
         self.x = x
         self.y = y
         self.g_cost = 0
         self.h_cost = 0
-        self.parent = 0
+        self.parent: Node | bool = False
-        self.rotation = rotation
+        self.rotation: Rotation = rotation
 
     def f_cost(self):
         return self.g_cost + self.h_cost
 
+
 def get_neighbours(node, searched_list, array):
     neighbours = []
-    for offset_x in range (-1, 2):
+    for offset_x in range(-1, 2):
-        for offset_y in range (-1, 2):
+        for offset_y in range(-1, 2):
             # don't look for cross neighbours
-            if(abs(offset_x) + abs(offset_y) == 1):
+            if (abs(offset_x) + abs(offset_y) == 1):
                 x = node.x + offset_x
                 y = node.y + offset_y
                 # prevent out of map coords
                 if (x >= 0 and x < MAP_WIDTH and y >= 0 and y < MAP_HEIGHT):
-                    if(map_utils.isRoadTile(array[y][x]) and (x, y) not in searched_list):
+                    if (map_utils.isRoadTile(array[y][x]) and (x, y) not in searched_list):
                         neighbour = Node(x, y, Rotation.NONE)
                         neighbour.rotation = get_needed_rotation(node, neighbour)
                         neighbours.append(neighbour)
     return neighbours
 
+
 # move cost schema:
 # - move from tile to tile: 10
 # - add extra 10 (1 rotation) if it exists
@@ -49,11 +53,12 @@ def get_h_cost(start_node: Node, target_node: Node):
     distance_y = abs(start_node.y - target_node.y)
     cost = (distance_x + distance_y) * 10
 
-    if(distance_x > 0 and distance_y > 0):
+    if (distance_x > 0 and distance_y > 0):
         cost += 10
 
     return cost
 
+
 # move cost schema:
 # - move from tile to tile: 10
 # - every rotation 90*: 10
@@ -67,6 +72,7 @@ def get_neighbour_cost(start_node: Node, target_node: Node) -> int:
     else:
         return 30
 
+
 # translate rotation change to move
 def get_move(start_node: Node, target_node: Node):
     rotate_change = get_rotate_change(start_node.rotation, target_node.rotation)
@@ -79,10 +85,12 @@ def get_move(start_node: Node, target_node: Node):
     else:
         return ["left", "forward"]
 
+
 # simple calc func
 def get_rotate_change(rotationA: Rotation, rotationB: Rotation) -> int:
     return int(rotationA) - int(rotationB)
 
+
 # get new rotation for target_node as neighbour of start_node
 def get_needed_rotation(start_node: Node or bool, target_node: Node) -> Rotation:
     if(start_node == False):
@@ -100,4 +108,3 @@ def get_needed_rotation(start_node: Node or bool, target_node: Node) -> Rotation
 
 
 
-