Zaktualizuj 'src/decisionTree.py'

src/decisionTree.py

@@ -2,6 +2,7 @@ import numpy as np
 import pandas as pd
 import pprint
 
+from .matrix import Matrix
 from src.graphics import *
 from .waiter import Waiter
 
@@ -9,15 +10,17 @@ eps = np.finfo(float).eps
 tasksList = []
 tasksQueue = []
 
+
 class DecisionTree:
     def __init__(self):
         graphics = Graphics()
         self.waiter = Waiter(graphics)
+        self.matrix = Matrix(graphics=graphics)
 
     def BuildDf(self):
         actionName = 'order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check'.split(',')
         distance = '1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27'.split(',')
-        priority = '1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,3,3,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,2,2,2,2,2,2,2,3,3,3,3,3,3,3,4,4,4,4,4,4,4'.split(',')
+        priority = '1,1,1,1,1,1,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,3,3,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,2,2,2,3,3,3,3,3,3,3,3,4,4,4,4,4,4,4,4,4,4'.split(',')
 
         dataset ={'actionName':actionName,'distance':distance,'priority':priority}
         df = pd.DataFrame(dataset,columns=['actionName','distance','priority'])
@@ -81,18 +84,40 @@ class DecisionTree:
         return tree
 
     
+
     #Dodawanie zadan do listy zadan
     def TasksList(self, name, coordinate):
-        waiterNode = self.waiter.Node()
+        distance = []
-        distance = abs(waiterNode[0] - coordinate[0]) + abs(waiterNode[1] - coordinate[1])
+        waiterNode = [self.waiter.X, self.waiter.Y]
+        if name != "goToBar":
+            if self.matrix.matrix[coordinate[0] - 1][coordinate[1] - 1].walk_through == 1:
+                distance.append([abs(waiterNode[0] - (coordinate[0] - 1)) + abs(waiterNode[1] - (coordinate[1] - 1)), [coordinate[0] - 1, coordinate[1] - 1]])
+            if self.matrix.matrix[coordinate[0] + 1][coordinate[1] - 1].walk_through == 1:
+                distance.append([abs(waiterNode[0] - (coordinate[0] + 1)) + abs(waiterNode[1] - (coordinate[1] - 1)), [coordinate[0] + 1, coordinate[1] - 1]])
+            if self.matrix.matrix[coordinate[0] + 1][coordinate[1]].walk_through == 1:
+                distance.append([abs(coordinate[0] - (coordinate[0] + 1)) + abs(waiterNode[1] - coordinate[1]), [coordinate[0] + 1, coordinate[1] ]])
+            if self.matrix.matrix[coordinate[0] + 1][coordinate[1] - 1].walk_through == 1:
+                distance.append([abs(waiterNode[0] - (coordinate[0] + 1)) + abs(waiterNode[1] - (coordinate[1] - 1)), [coordinate[0] + 1, coordinate[1] - 1]])
+            if self.matrix.matrix[coordinate[0] - 1][coordinate[1] + 1].walk_through == 1:
+                distance.append([abs(waiterNode[0] - (coordinate[0] - 1)) + abs(waiterNode[1] - (coordinate[1] + 1)), [coordinate[0] - 1, coordinate[1] + 1]])
+
+        else:
+            distance.append([abs(waiterNode[0] - 0) + abs(waiterNode[1] - 12), [0, 12]])
+            distance.append([abs(waiterNode[0] - 1) + abs(waiterNode[1] - 12), [1, 12]])
+            distance.append([abs(waiterNode[0] - 2) + abs(waiterNode[1] - 12), [2, 12]])
+            distance.append([abs(waiterNode[0] - 3) + abs(waiterNode[1] - 12), [3, 12]])
+            distance.append([abs(waiterNode[0] - 4) + abs(waiterNode[1] - 12), [4, 12]])
+            distance.append([abs(waiterNode[0] - 5) + abs(waiterNode[1] - 13), [5, 13]])
+            distance.append([abs(waiterNode[0] - 5) + abs(waiterNode[1] - 14), [5, 14]])
+
+        distance.sort(key=lambda x: x[0])
+        tasksList.append([name, distance[0][0], distance[0][1]])
 
-        tasksList.append([name, distance])
 
     #Kolejkowanie zadan
     def Queue(self, tasksList):
         df = self.BuildDf()
         tree = self.BuildTree(df)
-
         winnerNode = self.FindWinner(df)
 
         for i in tasksList:
@@ -100,26 +125,34 @@ class DecisionTree:
 
                 subtable = tree[winnerNode][i[0]]
                 if subtable in ['0','1','2','3']:
-                    tasksQueue.append([i[0], i[1], subtable])
+                    tasksQueue.append([i[0], i[1], i[2], subtable])
                 else:
-                    tasksQueue.append([i[0], i[1], tree[winnerNode][i[0]]['distance'][str(i[1])]])
+                    tasksQueue.append([i[0], i[1], i[2], tree[winnerNode][i[0]]['distance'][str(i[1])]])
             elif winnerNode is "distance":
 
                 subtable = tree[winnerNode][i[1]]
                 if subtable in ['0','1','2','3']:
-                    tasksQueue.append([i[0], i[1], subtable])
+                    tasksQueue.append([i[0], i[1], i[2], subtable])
                 else:
-                    tasksQueue.append([i[0], i[1], tree[winnerNode][i[1]]['actionName'][str(i[0])]])
+                    tasksQueue.append([i[0], i[1], i[2], tree[winnerNode][i[1]]['actionName'][str(i[0])]])
 
 
-        tasksQueue.sort(key=lambda x: x[2])
+        tasksQueue.sort(key=lambda x: x[3])
-        print(tasksQueue)
+        return tasksQueue
+
+
+    def ReturnQueueList(self):
+        if tasksQueue == []:
+            queue = self.Queue(tasksList)
+        else:
+            queue = tasksQueue
+
+        while queue[0][3] == '0':
+            queue.pop(0)
+
+        print(queue)
+        return queue
 
     def print(self):
-        df = self.BuildDf()
-        #a_entropy = {k:self.FindAttributesEntropy(df,k) for k in df.keys()[:-1]}
-        #print(a_entropy)
-        #print('\n Info Gain: ', self.FindWinner(df))
         print(tasksList)
-        self.Queue(tasksList)