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transform.py
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from pyTsetlinMachineParallel.tm import MultiClassTsetlinMachine
from pyTsetlinMachineParallel.tm import MultiClassConvolutionalTsetlinMachine2D
import numpy as np
import time as stime
import tm_predict as predict
machine = "TM"
name = "Trond"
#name = "Kristoffer"
#dim = "9x9Natsukaze_"
#dim = "90_9x9Aya_"
#loadfile = "0302-1057"
#loadfile = "0304-1027"
dim = "90_100T_9x9Aya_"
#loadfile = "0310-1211"
loadfile = "0310-1342"
inndata = "Draw"
numb = "0"
###################
#numbboard = 654
#numbboard = 566
#numbboard = 868
numbboard = 91
depth = 8 #number of moves
tree_width = 3
#save_file = "91-5-4"
save_file = str(numbboard)+"-"+str(depth)+"-"+str(tree_width)
##################
weights = []
clauses= 0
global X_train,Y_train,X_test,Y_test,m, loadedstate
def init(dim, machine, loadfile):
global X_train, Y_train, X_test, Y_test, m, loadedstate, weights, clauses
inndata = "Draw"
boost = 1
with open("Results/" + name + "/" + machine + "/" + machine + dim + loadfile + ".csv", 'r') as file:
loadarray = []
for line in file.readlines():
lineds = [str(x) for x in line.strip().split(',')]
if lineds[-1] == "":
loadarray.append(lineds[:-1])
else:
loadarray.append(lineds)
if loadarray[0][0][10] == "T":
machine = "TM"
else:
machine = "cTM"
clauses = int(loadarray[2][1][:-2])
Threshold = int(loadarray[3][1][:-2])
S = int(loadarray[4][1][:-2])
if machine == "cTM":
Window_X = int(loadarray[5][1][:-2])
Window_Y = int(loadarray[6][1][:-2])
Shape_X = int(loadarray[7][1][:-2])
Shape_Y = int(loadarray[8][1][:-2])
Shape_Z = int(loadarray[9][1][:-2])
train_data = np.loadtxt("Data/" + dim + inndata + numb + "train", delimiter=",")
test_data = np.loadtxt("Data/" + dim + inndata + numb + "test", delimiter=",")
if machine == "TM":
X_train = train_data[:, 0:-1]
Y_train = train_data[:, -1]
X_test = test_data[:, 0:-1]
Y_test = test_data[:, -1]
m = MultiClassTsetlinMachine(clauses, Threshold, S, boost_true_positive_feedback=boost, weighted_clauses=True)
if machine == "cTM":
X_train = train_data[:, 0:-1].reshape(train_data.shape[0], Shape_X, Shape_Y, Shape_Z)
Y_train = train_data[:, -1]
X_test = test_data[:, 0:-1].reshape(test_data.shape[0], Shape_X, Shape_Y, Shape_Z)
Y_test = test_data[:, -1]
m = MultiClassConvolutionalTsetlinMachine2D(clauses, Threshold, S, (Window_X, Window_Y), boost_true_positive_feedback=boost, weighted_clauses=True)
loadedstate = np.load("Results/" + "Trond" + "/" + machine + "/" + machine + dim + loadfile +"kFold"+numb + ".npy", allow_pickle=True)
m.fit(X_train, Y_train, epochs=0, incremental=True)
m.set_state(loadedstate)
weights = m.get_state()
def transform(table,size):
black = table[:int(len(table)/2)]
white = table[int(len(table)/2):]
bwtable = []
for i in range(size*size):
if table[i] == table[i+size*size]:
bwtable.append(".")
elif table[i] == 1:
bwtable.append("b")
elif table[i+size*size]:
bwtable.append("w")
else:
print("Something went wrong!")
return bwtable
def printableTable(table, size):
underline = ["A","B","C","D","E","F","G","H","I "]
mellomrom = " "
printTable = []
printTable.append("-------------------------------------------")
#print(table[4][-1])
#print(table[4][-1][0])
printTable.append("Correct outcome: %i Predicted outcome: %i " % (Y_train[numbboard], table[4][-1][0]))
for i in range(len(table[3])):
scoreLine= "%s Move: %s Score: %i (%s)(%s) " % (table[3][i], table[2][i], table[5][i],table[4][i][0],table[4][i][1])
printTable.append(scoreLine+length(table[5][i],6)+length(table[4][i][1],3))
for column in range(size):
start = str(size-column)+mellomrom
tableLine = start
for row in range(size):
tableLine += table[1][size*column+row] + mellomrom
tableLine+= mellomrom+mellomrom+mellomrom+mellomrom+mellomrom+mellomrom+mellomrom
printTable.append(tableLine)
uLine = " "
for i in range(size):
uLine +=underline[i] + mellomrom
printTable.append(uLine)
return printTable
def length(numb,length): #alters space depending on length of score
mellomrom = ""
for i in range(length-int(len(str(numb)))):
mellomrom += " "
return mellomrom
def printTable(table,position):
mellomrom = " "
line = ""
for i in range(position):
line +=mellomrom
for row in table:
print(line+row)
results.write(line+row + "\n")
counter = 0
def moveTransform(number,size): #change the moves into letter/number variation
if(number < 0): return "Start "
Alphabet = ["A", "B", "C", "D", "E", "F", "G", "H", "I"]
alphabet = ["a", "b", "c", "d", "e", "f", "g", "h", "i"]
a = int(number/size)
b = number%size
return alphabet[b]+str(size-a)+" "
def findEmpty(table, player,size,width):
global counter
alteredTables = []
for i in range(len(table[1])):
if table[1][i] == ".":
tempTable = np.copy(table[0])
tempTable2 = tableCopy(table[1])
tempTable2[i] = player
if player == "B":
tempTable[i] = 1
else:
tempTable[i + 81] = 1
outcome, score, go_outcome, lossresult, winresult, drawresult, retTable, retBw = predict.predictSum(tempTable2)
newM = tableCopy(table[2])
newM.append(moveTransform(i,9))
newP = tableCopy(table[3])
newO = tableCopy(table[4])
newS = tableCopy(table[5])
newP.append(player)
newO.append([outcome,go_outcome])
newS.append(score)
alteredTables.append([retTable,retBw,newM,newP, newO, newS])
#print(alteredTables)
alteredTables = topFive(alteredTables,player, width)
for i in range(len(alteredTables)):
alteredTables[i].append(printableTable(
[alteredTables[i][0], alteredTables[i][1], alteredTables[i][2], alteredTables[i][3], alteredTables[i][4],
alteredTables[i][5]], size))
return alteredTables
def tableCopy(table):
newTable = []
for i in range(len(table)):
newTable.append(table[i])
return newTable
end_table = []
def recursive(bwtable,player,size,moves,width):
if moves == 0: end_table.append(bwtable)
if moves == 0: return bwtable
moves -= 1
newBoards = findEmpty(bwtable,player,size,width)
for i in newBoards:
if i[3][-1] == "B":
nplayer = "W"
else:
nplayer = "B"
i.append(recursive(i, nplayer, size, moves,width))
bwtable.append(newBoards)
#bwtable[0] have bitboard
#bwtable[1] have black/white table
#bwtable[2] have converted moves,
#bwtable[3] have player
#bwtable[4] have outcome
#bwtable[5] have score make as table? tmscore = index 0 go score =index 1 or vice versa
#bwtable[6] have printableoutput
#bwtable[7] have list of top5 children nodes (newBoards)
return bwtable
def topFive(boards, player, width):
number = width #how wide is the search
whiteBoard = []
blackBoard = []
drawBoard = []
for board in boards:
if board[4][-1][0] == 0:
whiteBoard.append(board)
if board[4][-1][0] == 1:
blackBoard.append(board)
if board[4][-1][0] == 2:
drawBoard.append(board)
#whiteBoard = np.random.shuffle(whiteBoard)
#blackBoard = np.random.shuffle(blackBoard)
#drawBoard = np.random.shuffle(drawBoard)
if player == "W":
if len(whiteBoard) >= number:
return topFiveCalculate(whiteBoard, number)
elif len(whiteBoard) < number:
if len(whiteBoard) +len(drawBoard) == number:
return topFiveCalculate(whiteBoard,len(whiteBoard)) + topFiveCalculate(drawBoard,len(drawBoard))
elif len(whiteBoard) + len(drawBoard) > number:
return topFiveCalculate(whiteBoard,len(whiteBoard)) + topFiveCalculate(drawBoard, number-len(whiteBoard))
elif len(whiteBoard) + len(drawBoard) < number:
return topFiveCalculate(whiteBoard,len(whiteBoard)) + topFiveCalculate(drawBoard,len(drawBoard)) + bottomFiveCalculate(blackBoard, number - len(whiteBoard)-len(drawBoard))
if player == "B":
if len(blackBoard) >= number:
return topFiveCalculate(blackBoard,number)
elif len(blackBoard) < number:
if len(blackBoard) +len(drawBoard) == number:
return topFiveCalculate(blackBoard,len(blackBoard)) + topFiveCalculate(drawBoard,len(drawBoard))
elif len(blackBoard) + len(drawBoard) > number:
return topFiveCalculate(blackBoard,len(blackBoard))+ topFiveCalculate(drawBoard, number-len(blackBoard))
elif len(blackBoard) + len(drawBoard) < number:
return topFiveCalculate(blackBoard,len(blackBoard)) + topFiveCalculate(drawBoard,len(drawBoard)) + bottomFiveCalculate(whiteBoard, number - len(blackBoard)-len(drawBoard))
def topFive5(boards, player,number):
whiteBoard = []
blackBoard = []
drawBoard = []
for board in boards:
if board[4][-1][0] == 0:
whiteBoard.append(board)
if board[4][-1][0] == 1:
blackBoard.append(board)
if board[4][-1][0] == 2:
drawBoard.append(board)
if player == "W":
if len(whiteBoard) >= number:
return topFiveCalculate(whiteBoard, number)
elif len(whiteBoard) < number:
if len(whiteBoard) +len(drawBoard) == number:
return topFiveCalculate(whiteBoard,len(whiteBoard)) + topFiveCalculate(drawBoard,len(drawBoard))
elif len(whiteBoard) + len(drawBoard) > number:
return topFiveCalculate(whiteBoard,len(whiteBoard)) + topFiveCalculate(drawBoard, number-len(whiteBoard))
elif len(whiteBoard) + len(drawBoard) < number:
return topFiveCalculate(whiteBoard,len(whiteBoard)) + topFiveCalculate(drawBoard,len(drawBoard)) + bottomFiveCalculate(blackBoard, number - len(whiteBoard)-len(drawBoard))
if player == "B":
if len(blackBoard) >= number:
return topFiveCalculate(blackBoard,number)
elif len(blackBoard) < number:
if len(blackBoard) +len(drawBoard) == number:
return topFiveCalculate(blackBoard,len(blackBoard)) + topFiveCalculate(drawBoard,len(drawBoard))
elif len(blackBoard) + len(drawBoard) > number:
return topFiveCalculate(blackBoard,len(blackBoard))+ topFiveCalculate(drawBoard, number-len(blackBoard))
elif len(blackBoard) + len(drawBoard) < number:
return topFiveCalculate(blackBoard,len(blackBoard)) + topFiveCalculate(drawBoard,len(drawBoard)) + bottomFiveCalculate(whiteBoard, number - len(blackBoard)-len(drawBoard))
def topFiveCalculate(boards, numb):
listallscore = []
list =[]
for i in range(numb):
tempScore = -100000
tempID = 0
for j in range(len(boards)):
listallscore.append(boards[j][5][-1])
#if abs(boards[j][5][0]) > tempScore:
if boards[j][5][-1] > tempScore:
#tempScore = abs(boards[j][5][0])
tempScore = boards[j][5][-1]
tempID = j
if boards[j][5][-1] == tempScore:
np.random.seed()
X1 = np.random.randint(low=0, high=10, size=(1))
if X1[0] > 4:
tempScore = boards[j][5][-1]
tempID = j
boards, list = sortList(boards, list, tempID)
return list
def bottomFiveCalculate(boards,numb):
listallscore = []
list = []
for i in range(numb):
tempScore = 100000
tempID = 0
for j in range(len(boards)):
listallscore.append(boards[j][5][-1])
#if abs(boards[j][5][0]) < tempScore:
#tempScore = abs(boards[j][5][0])
if boards[j][5][-1] < tempScore:
tempScore = boards[j][5][-1]
tempID = j
if boards[j][5][-1] == tempScore:
np.random.seed()
X1 = np.random.randint(low=0, high=10, size=(1))
if X1[0] > 4:
tempScore = boards[j][5][-1]
tempID = j
boards, list = sortList(boards,list,tempID)
return list
def sortList(boards,list,iD):
newList = []
list.append(boards[iD])
for i in range(len(boards)):
if i != iD:
newList.append(boards[i])
return newList, list
def main(moves, width):
#moves = 5
#width= 2
size = 9
player = "B"
timestamp = stime.strftime("%H:%M:%S")
init(dim,machine,loadfile)
predict.init(weights, clauses,m)
timestamp2 = stime.strftime("%H:%M:%S")
initBoard = X_train[numbboard]
bwtable = transform(initBoard, size)
outcome, score, percentage, losstot,wintot,drawtot, newbit,newbw = predict.predictSum(bwtable)
bwTable = [initBoard,bwtable, ["Initial "], [player], [[outcome,percentage]], [score]]
pTable = printableTable(bwTable, size)
bwTable.append(pTable)
tree = recursive(bwTable, player, size, moves,width)
timestamp3 = stime.strftime("%H:%M:%S")
printTree(tree,0,width)
print("Start Time : %s " % (timestamp))
print("Init finished Time: %s " % (timestamp2))
print("Predict done Time : %s " % (timestamp3))
ran =5
if width == 1:
ran = 1
printTop(topFive(end_table,"B",ran),ran)
printTop(bottomFiveCalculate(end_table,ran),ran)
print(predict.getTime())
def printTree(table, pos,width):
printTable(table[6], pos)
for i in range(len(table[7][0][6])):
#print(table[7][0][6][i]+table[7][1][6][i]+table[7][2][6][i]+table[7][3][6][i]+table[7][4][6][i]) #if number = 5
tempTxt = ""
for j in range(width):
tempTxt+= table[7][j][6][i]
print(tempTxt)
results.write(tempTxt+"\n")
for i in range(len(table[7])):
if(len(table[7][i])) == 9:
printTree(table[7][i],i, width)
def printTop(table, width):
for i in range(len(table[0][6])):
tempTxt = ""
#print(table[0][6][i] + table[1][6][i] + table[2][6][i] + table[3][6][i] + table[4][6][i])
for j in range(width):
tempTxt += table[j][6][i]
print(tempTxt)
results.write(tempTxt + "\n")
results = open("Results/" + name + "/" + machine + "/" + machine + dim + loadfile + save_file+".txt", 'w')
main(depth, tree_width)
results.close()