gówno
This commit is contained in:
Marcin Dobrowolski
commit
9f44a715cf
32
main.py
32
main.py
@ -26,6 +26,10 @@ if __name__ == "__main__":
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# AStar
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goal = None
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path = ''
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#Dominik
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check = 0
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queue = []
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# Marcin Dobrowolski
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suggestionTreeRoot = SuggestionTree.buildTree(trainingData)
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@ -51,13 +55,15 @@ if __name__ == "__main__":
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break
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if event.key == pygame.K_s:
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tree.TasksList('check', [10, 3])
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tree.TasksList('eat', [5, 12])
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tree.TasksList('order', [12, 4])
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tree.TasksList('goToBar', [10, 10])
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tree.TasksList('check', [3, 4])
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tree.TasksList('eat', [10, 5])
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tree.TasksList('check', [2,3])
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tree.TasksList('eat', [9, 6])
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tree.TasksList('order', [4, 8])
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tree.TasksList('goToBar', [11, 4])
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tree.TasksList('check', [2, 9])
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tree.TasksList('eat', [1, 1])
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queue = tree.ReturnQueueList()
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tree.print()
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check = 1
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if event.key == pygame.K_m:
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@ -126,6 +132,20 @@ if __name__ == "__main__":
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goal = (x, y)
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path = waiter.findPath(goal)
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path = waiter.translatePath(path)
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# Dominik
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if check == 1:
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task = queue.pop(0)
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goal = (task[2][0], task[2][1])
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print(goal)
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path = waiter.findPath(goal)
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print(path)
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path = waiter.translatePath(path)
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print(path)
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check = 0
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if len(queue) != 0 and check == 0 and path == '':
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check = 1
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# AStar
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if path == '' and actions:
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@ -1,125 +1,158 @@
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import numpy as np
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import pandas as pd
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import pprint
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from src.graphics import *
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from .waiter import Waiter
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eps = np.finfo(float).eps
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tasksList = []
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tasksQueue = []
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class DecisionTree:
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def __init__(self):
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graphics = Graphics()
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self.waiter = Waiter(graphics)
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def BuildDf(self):
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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(',')
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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(',')
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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(',')
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dataset ={'actionName':actionName,'distance':distance,'priority':priority}
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df = pd.DataFrame(dataset,columns=['actionName','distance','priority'])
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return df
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#Obliczanie entropii dla calego zestawu
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def FindPriorityEntropy(self,df):
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entropyNode = 0
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values = df.priority.unique()
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for value in values:
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propability = df.priority.value_counts()[value]/len(df.priority)
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entropyNode += -propability*np.log2(propability)
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return entropyNode
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#Obliczanie entropii dla wszystkich atrybut<75>w
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def FindAttributesEntropy(self, df, attribute):
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targetVariables = df.priority.unique()
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variables = df[attribute].unique()
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entropy2 = 0
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for variable in variables:
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entropy = 0
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for targetVariable in targetVariables:
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num = len(df[attribute][df[attribute]==variable][df.priority == targetVariable])
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den = len(df[attribute][df[attribute]==variable])
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propability = num/(den + eps)
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entropy += propability*np.log2(propability+eps)
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propability2 = den/len(df)
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entropy2 += -propability2*entropy
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return abs(entropy2)
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#Znajdowanie wierzcholka o najwyzszym info Gain
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def FindWinner(self, df):
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infoGain = []
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for key in df.keys()[:-1]:
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infoGain.append(self.FindPriorityEntropy(df) - self.FindAttributesEntropy(df, key))
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return df.keys()[:-1][np.argmax(infoGain)]
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def GetSubtable(self, df, node, value):
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return df[df[node] == value].reset_index(drop=True)
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#Budowanie drzewa
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def BuildTree(self, df, tree=None):
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node = self.FindWinner(df)
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attValues = np.unique(df[node])
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if tree is None:
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tree = {}
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tree[node] = {}
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for value in attValues:
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subtable = self.GetSubtable(df, node, value)
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clValue,counts = np.unique(subtable['priority'],return_counts=True)
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if len(counts) == 1:
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tree[node][value] = clValue[0]
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else:
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tree[node][value] = self.BuildTree(subtable)
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return tree
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#Dodawanie zadan do listy zadan
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def TasksList(self, name, coordinate):
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waiterNode = self.waiter.Node()
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distance = abs(waiterNode[0] - coordinate[0]) + abs(waiterNode[1] - coordinate[1])
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tasksList.append([name, distance])
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#Kolejkowanie zadan
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def Queue(self, tasksList):
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df = self.BuildDf()
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tree = self.BuildTree(df)
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winnerNode = self.FindWinner(df)
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for i in tasksList:
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if winnerNode is "actionName":
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subtable = tree[winnerNode][i[0]]
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if subtable in ['0','1','2','3']:
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tasksQueue.append([i[0], i[1], subtable])
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else:
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tasksQueue.append([i[0], i[1], tree[winnerNode][i[0]]['distance'][str(i[1])]])
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elif winnerNode is "distance":
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subtable = tree[winnerNode][i[1]]
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if subtable in ['0','1','2','3']:
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tasksQueue.append([i[0], i[1], subtable])
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else:
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tasksQueue.append([i[0], i[1], tree[winnerNode][i[1]]['actionName'][str(i[0])]])
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tasksQueue.sort(key=lambda x: x[2])
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print(tasksQueue)
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def print(self):
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df = self.BuildDf()
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#a_entropy = {k:self.FindAttributesEntropy(df,k) for k in df.keys()[:-1]}
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#print(a_entropy)
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#print('\n Info Gain: ', self.FindWinner(df))
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print(tasksList)
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self.Queue(tasksList)
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import numpy as np
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import pandas as pd
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import pprint
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from .matrix import Matrix
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from src.graphics import *
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from .waiter import Waiter
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eps = np.finfo(float).eps
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tasksList = []
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tasksQueue = []
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class DecisionTree:
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def __init__(self):
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graphics = Graphics()
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self.waiter = Waiter(graphics)
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self.matrix = Matrix(graphics=graphics)
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def BuildDf(self):
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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(',')
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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(',')
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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(',')
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dataset ={'actionName':actionName,'distance':distance,'priority':priority}
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df = pd.DataFrame(dataset,columns=['actionName','distance','priority'])
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return df
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#Obliczanie entropii dla calego zestawu
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def FindPriorityEntropy(self,df):
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entropyNode = 0
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values = df.priority.unique()
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for value in values:
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propability = df.priority.value_counts()[value]/len(df.priority)
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entropyNode += -propability*np.log2(propability)
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return entropyNode
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#Obliczanie entropii dla wszystkich atrybut<75>w
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def FindAttributesEntropy(self, df, attribute):
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targetVariables = df.priority.unique()
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variables = df[attribute].unique()
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entropy2 = 0
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for variable in variables:
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entropy = 0
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for targetVariable in targetVariables:
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num = len(df[attribute][df[attribute]==variable][df.priority == targetVariable])
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den = len(df[attribute][df[attribute]==variable])
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propability = num/(den + eps)
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entropy += propability*np.log2(propability+eps)
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propability2 = den/len(df)
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entropy2 += -propability2*entropy
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return abs(entropy2)
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#Znajdowanie wierzcholka o najwyzszym info Gain
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def FindWinner(self, df):
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infoGain = []
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for key in df.keys()[:-1]:
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infoGain.append(self.FindPriorityEntropy(df) - self.FindAttributesEntropy(df, key))
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return df.keys()[:-1][np.argmax(infoGain)]
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def GetSubtable(self, df, node, value):
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return df[df[node] == value].reset_index(drop=True)
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#Budowanie drzewa
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def BuildTree(self, df, tree=None):
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node = self.FindWinner(df)
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attValues = np.unique(df[node])
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if tree is None:
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tree = {}
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tree[node] = {}
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for value in attValues:
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subtable = self.GetSubtable(df, node, value)
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clValue,counts = np.unique(subtable['priority'],return_counts=True)
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if len(counts) == 1:
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tree[node][value] = clValue[0]
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else:
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tree[node][value] = self.BuildTree(subtable)
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return tree
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#Dodawanie zadan do listy zadan
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def TasksList(self, name, coordinate):
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distance = []
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waiterNode = [self.waiter.X, self.waiter.Y]
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if name != "goToBar":
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if self.matrix.matrix[coordinate[0] - 1][coordinate[1] - 1].walk_through == 1:
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distance.append([abs(waiterNode[0] - (coordinate[0] - 1)) + abs(waiterNode[1] - (coordinate[1] - 1)), [coordinate[0] - 1, coordinate[1] - 1]])
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if self.matrix.matrix[coordinate[0] + 1][coordinate[1] - 1].walk_through == 1:
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distance.append([abs(waiterNode[0] - (coordinate[0] + 1)) + abs(waiterNode[1] - (coordinate[1] - 1)), [coordinate[0] + 1, coordinate[1] - 1]])
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if self.matrix.matrix[coordinate[0] + 1][coordinate[1]].walk_through == 1:
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distance.append([abs(coordinate[0] - (coordinate[0] + 1)) + abs(waiterNode[1] - coordinate[1]), [coordinate[0] + 1, coordinate[1] ]])
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if self.matrix.matrix[coordinate[0] + 1][coordinate[1] - 1].walk_through == 1:
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distance.append([abs(waiterNode[0] - (coordinate[0] + 1)) + abs(waiterNode[1] - (coordinate[1] - 1)), [coordinate[0] + 1, coordinate[1] - 1]])
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if self.matrix.matrix[coordinate[0] - 1][coordinate[1] + 1].walk_through == 1:
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distance.append([abs(waiterNode[0] - (coordinate[0] - 1)) + abs(waiterNode[1] - (coordinate[1] + 1)), [coordinate[0] - 1, coordinate[1] + 1]])
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else:
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distance.append([abs(waiterNode[0] - 0) + abs(waiterNode[1] - 12), [0, 12]])
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distance.append([abs(waiterNode[0] - 1) + abs(waiterNode[1] - 12), [1, 12]])
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distance.append([abs(waiterNode[0] - 2) + abs(waiterNode[1] - 12), [2, 12]])
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distance.append([abs(waiterNode[0] - 3) + abs(waiterNode[1] - 12), [3, 12]])
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distance.append([abs(waiterNode[0] - 4) + abs(waiterNode[1] - 12), [4, 12]])
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distance.append([abs(waiterNode[0] - 5) + abs(waiterNode[1] - 13), [5, 13]])
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distance.append([abs(waiterNode[0] - 5) + abs(waiterNode[1] - 14), [5, 14]])
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distance.sort(key=lambda x: x[0])
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tasksList.append([name, distance[0][0], distance[0][1]])
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#Kolejkowanie zadan
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def Queue(self, tasksList):
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df = self.BuildDf()
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tree = self.BuildTree(df)
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winnerNode = self.FindWinner(df)
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for i in tasksList:
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if winnerNode is "actionName":
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subtable = tree[winnerNode][i[0]]
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if subtable in ['0','1','2','3']:
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tasksQueue.append([i[0], i[1], i[2], subtable])
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else:
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tasksQueue.append([i[0], i[1], i[2], tree[winnerNode][i[0]]['distance'][str(i[1])]])
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elif winnerNode is "distance":
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subtable = tree[winnerNode][i[1]]
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if subtable in ['0','1','2','3']:
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tasksQueue.append([i[0], i[1], i[2], subtable])
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else:
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tasksQueue.append([i[0], i[1], i[2], tree[winnerNode][i[1]]['actionName'][str(i[0])]])
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tasksQueue.sort(key=lambda x: x[3])
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return tasksQueue
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def ReturnQueueList(self):
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if tasksQueue == []:
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queue = self.Queue(tasksList)
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else:
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queue = tasksQueue
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while queue[0][3] == '0':
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queue.pop(0)
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print(queue)
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return queue
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def print(self):
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print(tasksList)
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