def __init__(self,
inputs,
labels,
featureName,
featureNames,
featureTypes,
subset=[],
boundary=None,
operator=''):
#Find the actual feature array based on the feature name
colIndex = np.where(featureName == featureNames)[0][0]
features = inputs[:, colIndex]
#The key feature of noonterminal nodes is that they have a decision
if featureTypes[colIndex] == 'string':
#then the feature is of type categorical and the decision must be categorical
self.decision = Decision.Categorical(featureName, subset)
else:
#else the feature is of type numerical and decision must be numerical
self.decision = Decision.Numerical(featureName, operator, boundary)
#create the true and false nodes, both unexplored, by filtering the
#data to each node based on the decision
trueIndices = np.vectorize(self.decision.function)(features)
falseIndices = np.logical_not(trueIndices)
self.trueNode = Node(inputs[trueIndices], labels[trueIndices], '')
self.falseNode = Node(inputs[falseIndices], labels[falseIndices], '')
#the node will never use its input field again, but the labels field
#is needed for tree pruning
Node.__init__(self, [], labels, self.decision.__str__(), 'decision')
def new_generate_agents(num_new_agent, agents):
"""
new agents generate in Move section
"""
#num_new_agent = rnd.randrange(num_new_generate)
new_agents = [Agent_man() for new_agent_id in range(num_new_agent)]
Move.state_task_for_new_agents(new_agents)
Decision.initial_strategy(new_agents)
for id, agent in enumerate(new_agents):
print(
f'new_agents No.{id}, state:{agent.state}, next_state:{agent.next_state}, strategy:{agent.strategy}, next_strategy:{agent.next_strategy}, task:{agent.task}'
)
agents.extend(new_agents)
def __init__(self):
self.loading = LoadingScreen(self)
self.camera = Camera(self)
self.lighting = Lighting(self)
self.balltable = BallTable(self)
self.menu = Menu(self)
self.decision = Decision(self)
self.mousekey = MouseKey(self)
self.shotStrength = ShotStrength(self)
self.state = GameState.START
self.mode = GameMode.TUTORIAL
self.balls = [Ball(self)] * 16
self.ballinHole = [None] * 16
self.LastFrameTimePoint = 0
def get_fitness(population): def mean_square_error(x,y): return np.sqrt(((x - y) ** 2).mean(axis=0)) fitness = [] for ind in population: ind = get_phenotype(ind,units_by_layer) aux_mean_error = 0 for (x,y) in S: aux_mean_error += mean_square_error(y,Decision.get_output_vector(units_by_layer,x,ind,factivation)) fitness.append(aux_mean_error) return fitness
def mainAI(self):
"""Run AI mode"""
start = time.time()
mainGrid = Grid()
mainGrid.computerAddTile()
aiPlayer = Decision()
while not self.checkGameOver(mainGrid):
print("\nCOMPUTER TURN:")
mainGrid.computerAddTile()
mainGrid.displayGrid()
if not self.checkGameOver(mainGrid):
move = aiPlayer.getMove(mainGrid)
mainGrid.move(move)
print("\nPLAYER TURN:")
mainGrid.displayGrid()
print("\nFINAL GRID:")
mainGrid.displayGrid()
end = time.time()
scores = mainGrid.scores()
print("Max score: ", mainGrid.getMaxTile())
print("Total time: ", end - start)
return scores, end - start
def weaponMenuSelectWeapon(num, menu, char, wmenu):
decision = Decision()
panel = menu.panels[0]
panel.clearOptions()
panel.addOptions([Option("Yes", panel.osize, promptMenuYes, [decision]),\
Option("No", panel.osize, promptMenuNo, [decision])])
menu.addText("Equip?", 0, 0)
try:
menu.run()
except SystemExit:
pass
if decision.bool:
char.equipWeapon(num)
wpanel = wmenu.panels[0]
sel = wpanel.selected
weapon = char.weapons[sel]
weaponMenuPrintWeapon(weapon, wmenu, char)
def armorMenuSelectArmor(num, menu, char, amenu):
decision = Decision()
panel = menu.panels[0]
panel.clearOptions()
panel.addOptions([Option("Yes", panel.osize, promptMenuYes, [decision]),\
Option("No", panel.osize, promptMenuNo, [decision])])
menu.addText("Equip?", 0, 0)
try:
menu.run()
except SystemExit:
pass
if decision.bool:
char.equipArmor(num)
apanel = amenu.panels[0]
sel = apanel.selected
armor = char.armors[sel]
armorMenuPrintArmor(armor, amenu, char)
########
# Aprender vector theta #
rho = 0.5
nu = 0.5
l = 1
theta1 = MLPLearning.back_propagation_batch(S,rho,units_by_layer,factivation,850,50)
theta2 = MLPLearning.back_propagation_online(S,rho,units_by_layer,factivation,850,50)
theta3 = MLPLearning.back_propagation_batch_momentum(S,rho,nu,units_by_layer,factivation,850,50)
theta4 = MLPLearning.back_propagation_batch_buffer(S,rho,l,units_by_layer,factivation,850,50)
theta5 = MLPLearning.back_propagation_online_buffer(S,rho,l,units_by_layer,factivation,850,50)
theta6 = MLPLearning.back_propagation_online_momentum(S,rho,nu,units_by_layer,factivation,850,50)
theta7,fitness = MLPLearning.evolutional(S,units_by_layer,factivation,200,500,-2,2,1.1,0.9)
##############################
# Clasificacion #
logging.info("Clase con theta1: (Backprop batch): "+str(Decision.classify(units_by_layer,[1.0,-6.3,1.0],theta1,factivation)))
logging.info("Clase con theta2: (Backprop online): "+str(Decision.classify(units_by_layer,[1.0,-6.3,1.0],theta2,factivation)))
logging.info("Clase con theta3: (Backprop batch con momentum): "+str(Decision.classify(units_by_layer,[1.0,-6.3,1.0],theta3,factivation)))
logging.info("Clase con theta4: (Backprop batch con amortiguamiento): "+str(Decision.classify(units_by_layer,[1.0,-6.3,1.0],theta4,factivation)))
logging.info("Clase con theta5: (Backprop online con amortiguamiento): "+str(Decision.classify(units_by_layer,[1.0,-6.3,1.0],theta5,factivation)))
logging.info("Clase con theta6: (Backprop online con momentum): "+str(Decision.classify(units_by_layer,[1.0,-6.3,1.0],theta6,factivation)))
logging.info("Clase con theta7: (Algoritmo genetico): "+str(Decision.classify(units_by_layer,[1.0,-5.0,1.0],theta7,factivation))+" fitness: "+str(fitness))
#################
# Regresion #
logging.info("Regresion con theta1: (Backprop batch): "+str(Decision.regression(units_by_layer,[1.0,-6.3,1.0],theta1,factivation)))
logging.info("Regresion con theta2: (Backprop online): "+str(Decision.regression(units_by_layer,[1.0,-6.3,1.0],theta2,factivation)))
logging.info("Regresion con theta3: (Backprop batch con momentum): "+str(Decision.regression(units_by_layer,[1.0,-6.3,1.0],theta3,factivation)))
logging.info("Regresion con theta4: (Backprop batch con amortiguamiento): "+str(Decision.regression(units_by_layer,[1.0,-6.3,1.0],theta4,factivation)))
logging.info("Regresion con theta5: (Backprop online con amortiguamiento): "+str(Decision.regression(units_by_layer,[1.0,-6.3,1.0],theta5,factivation)))
logging.info("Regresion con theta6: (Backprop online con momentum): "+str(Decision.regression(units_by_layer,[1.0,-6.3,1.0],theta6,factivation)))
class Game:
'''
'''
def __init__(self):
self.loading = LoadingScreen(self)
self.camera = Camera(self)
self.lighting = Lighting(self)
self.balltable = BallTable(self)
self.menu = Menu(self)
self.decision = Decision(self)
self.mousekey = MouseKey(self)
self.shotStrength = ShotStrength(self)
self.state = GameState.START
self.mode = GameMode.TUTORIAL
self.balls = [Ball(self)] * 16
self.ballinHole = [None] * 16
self.LastFrameTimePoint = 0
def Load(self):
glutInit(sys.argv)
glutInitDisplayMode(GLUT_DOUBLE | GLUT_RGB | GLUT_DEPTH)
glutInitWindowSize(ParamDef.ScreenResolution[0],
ParamDef.ScreenResolution[1])
glutInitWindowPosition(0, 0)
glutCreateWindow(APP_NAME)
glClearColor(0, 0, 0, 0)
glutIdleFunc(self.loading.Idle)
glutDisplayFunc(self.loading.UpdateGL)
self.currentWindow = glutGetWindow()
glutMainLoop()
def Run(self):
ParamDef.DelayCompensation = 1
glutIgnoreKeyRepeat(1)
glEnable(GL_DEPTH_TEST)
glEnable(GL_CULL_FACE)
glEnable(GL_NORMALIZE)
glEnable(GL_BLEND)
glShadeModel(GL_SMOOTH)
glHint(GL_POLYGON_SMOOTH_HINT, GL_NICEST)
glHint(GL_PERSPECTIVE_CORRECTION_HINT, GL_NICEST)
glutMouseFunc(self.mousekey.MouseClick)
glutMotionFunc(self.mousekey.MouseMove)
glutKeyboardFunc(self.mousekey.KeyPress)
glutKeyboardUpFunc(self.mousekey.KeyRelease)
glutSpecialFunc(self.mousekey.SpecialKeyPress)
glutSpecialUpFunc(self.mousekey.SpecialKeyRelease)
glutVisibilityFunc(self.Visible)
glutIdleFunc(self.timerEvent)
glutDisplayFunc(self.updateGL)
def timerEvent(self):
#print "timerEvent"
ParamDef.FrameTimePoint = glutGet(GLUT_ELAPSED_TIME) / 10
ParamDef.Factor = ParamDef.FrameTimePoint - self.LastFrameTimePoint
if (ParamDef.DelayCompensation != 0):
ParamDef.Factor = 1
ParamDef.DelayCompensation = 0
if (ParamDef.Factor != 0):
if (ParamDef.ShowFPS):
if ((ParamDef.FrameTimePoint % 200) <
(self.LastFrameTimePoint % 200)):
self.menu.SetFPS(Frames / 2)
ParamDef.Frames = 0
else:
ParamDef.Frames += 1
self.menu.Update(ParamDef.Factor)
if self.state == GameState.START:
self.StartHandling()
elif self.state == GameState.VIEWING:
self.ViewingHandling()
elif self.state == GameState.AIMING:
self.AimHandling()
elif self.state == GameState.SWING:
self.BackswingHandling()
elif self.state == GameState.SHOT:
self.ShotHandling()
elif self.state == GameState.NEW_WHITE:
self.NewWhiteHandling()
elif self.state == GameState.JUDGEING:
self.JudgeHandling()
self.camera.Ride(ParamDef.Factor)
self.LastFrameTimePoint = ParamDef.FrameTimePoint
glutPostWindowRedisplay(self.currentWindow)
def updateGL(self):
'''
'''
#print "updateGL"
if ParamDef.ZBufferDelete != 0:
glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)
self.camera.draw()
self.lighting.draw()
self.balltable.drawSurface()
glDisable(GL_DEPTH_TEST)
self.balltable.drawLine()
for ball in self.balls:
ball.drawShadow()
glEnable(GL_DEPTH_TEST)
self.balltable.drawBorder()
distance = 0
#distance
resolution = 1
#Display resolution
for ball in self.balls:
x = ball.Pos_xCM() - self.camera.Pos_x
y = ball.Pos_yCM() - self.camera.Pos_y
z = self.camera.Pos_z
distance = sqrt(x * x + y * y + z * z)
resolution = (400 / distance)
if (resolution < 3):
resolution = 3
resolution = (resolution / 2) * 2 + 1
if (resolution > ParamDef.BallResolution):
resolution = ParamDef.BallResolution
ball.draw(resolution)
glDisable(GL_DEPTH_TEST)
glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA)
glDisable(GL_LIGHTING)
self.menu.draw()
if (self.state != GameState.START):
self.shotStrength.draw()
glEnable(GL_DEPTH_TEST)
glutSwapBuffers()
def Visible(self, visible):
print "Visible"
if (visible == GLUT_VISIBLE):
glutIdleFunc(self.timerEvent)
def StartHandling(self):
self.camera.ScenicFlight(ParamDef.Factor)
self.menu.Update(ParamDef.Factor)
def ViewingHandling(self):
self.menu.Update(ParamDef.Factor)
if (KEY_UP):
self.camera.Move_Front(ParamDef.Factor)
if (KEY_DOWN):
self.camera.Move_Back(ParamDef.Factor)
if (KEY_RIGHT):
self.camera.Move_Right(ParamDef.Factor)
if (KEY_LEFT):
self.camera.Move_Left(ParamDef.Factor)
if (KEY_SHIFT):
self.camera.Move_Up(ParamDef.Factor)
if (KEY_CTRL):
self.camera.Move_Down(ParamDef.Factor)
if (KEY_PAGE_UP):
self.camera.Move_In(ParamDef.Factor)
if (KEY_PAGE_DOWN):
self.camera.Move_Out(ParamDef.Factor)
if (KEY_HOME):
self.camera.Zoom_In(ParamDef.Factor)
if (KEY_END):
self.camera.Zoom_Out(ParamDef.Factor)
'''
* Aim Handling
'''
def AimHandling(self):
if (KEY_UP):
self.camera.Move_In(ParamDef.Factor)
if (KEY_DOWN):
self.camera.Move_Out(ParamDef.Factor)
if (KEY_RIGHT):
self.camera.SwingRight(2 * ParamDef.Factor,
self.balls[0].Pos_xCM(),
self.balls[0].Pos_yCM())
if (KEY_LEFT):
self.camera.SwingLeft(2 * ParamDef.Factor, self.balls[0].Pos_xCM(),
self.balls[0].Pos_yCM())
if (KEY_SHIFT):
self.camera.SwingDown(ParamDef.Factor, self.balls[0].Pos_xCM(),
self.balls[0].Pos_yCM())
if (KEY_CTRL):
self.camera.SwingUp(ParamDef.Factor, self.balls[0].Pos_xCM(),
self.balls[0].Pos_yCM())
if (KEY_PAGE_UP):
self.camera.Move_In(ParamDef.Factor)
if (KEY_PAGE_DOWN):
self.camera.Move_Out(ParamDef.Factor)
if (KEY_HOME):
self.camera.Vertigo_In(ParamDef.Factor)
if (KEY_END):
self.camera.Vertigo_Out(ParamDef.Factor)
def BackswingHandling(self):
'''
'''
if (KEY_UP):
self.camera.Move_In(ParamDef.Factor)
if (KEY_DOWN):
self.camera.Move_Out(ParamDef.Factor)
if (KEY_RIGHT):
self.camera.SwingRight(2 * ParamDef.Factor,
self.balls[0].Pos_xCM(),
self.balls[0].Pos_yCM())
if (KEY_LEFT):
self.camera.SwingLeft(2 * ParamDef.Factor, self.balls[0].Pos_xCM(),
self.balls[0].Pos_yCM())
if (KEY_SHIFT):
self.camera.SwingDown(ParamDef.Factor, self.balls[0].Pos_xCM(),
self.balls[0].Pos_yCM())
if (KEY_CTRL):
self.camera.SwingUp(ParamDef.Factor, self.balls[0].Pos_xCM(),
self.balls[0].Pos_yCM())
if (KEY_PAGE_UP):
self.camera.Move_In(ParamDef.Factor)
if (KEY_PAGE_DOWN):
self.camera.Move_Out(ParamDef.Factor)
if (KEY_HOME):
self.camera.Zoom_In(ParamDef.Factor)
if (KEY_END):
self.camera.Zoom_Out(ParamDef.Factor)
shotStrength = MaxAusholStaerke * (1 - exp(
(-ParamDef.FrameTimePoint + ParamDef.ShotStartTime) / 400.0))
self.ShotStrength.setShockStrength(shotStrength / 3.333)
'''
* 击球处理
*
'''
def ShotHandling(self):
FirstShot = 0
#WeisseVersetzbar=0;
if (KEY_UP):
self.camera.Move_Front(ParamDef.Factor)
if (KEY_DOWN):
self.camera.Move_Back(ParamDef.Factor)
if (KEY_RIGHT):
self.camera.Move_Right(ParamDef.Factor)
if (KEY_LEFT):
self.camera.Move_Left(ParamDef.Factor)
if (KEY_SHIFT):
self.camera.Move_Up(ParamDef.Factor)
if (KEY_CTRL):
self.camera.Move_Down(ParamDef.Factor)
if (KEY_PAGE_UP):
self.camera.Move_In(ParamDef.Factor)
if (KEY_PAGE_DOWN):
self.camera.Move_Out(ParamDef.Factor)
if (KEY_HOME):
self.camera.Zoom_In(ParamDef.Factor)
if (KEY_END):
self.camera.Zoom_Out(ParamDef.Factor)
#Frames++; # F"ur die Frames/sec-Anzeige
# Zeit seit Stossbeginn
time = ParamDef.FrameTimePoint - ParamDef.StartTime
#time
# Letzten Zustand noch zeichnen, wenn Stoss
if (time > ParamDef.ShotTime):
time = ParamDef.ShotTime # eigentlich schon vorbei
#printf("%i-%i=%i: ",FrameZeitpunkt,Startzeit,Zeit);
for i in range(16): # Alle Kugeln neu positionieren
if (LightingList[time][i][2] <= 0):
self.balls[i].newPositionD(LightingList[Zeit][i])
self.ShotStrength.setShockStrength(shotStrength / 3.333 - Zeit / 3.0)
if (not (time & 31)):
neu = 0
for i in range(16):
if (self.ballsInGame[i] and self.ballsInHole[i] != 0
and (self.balls[i].Pos_x() == 3000)):
self.ballsInHole[i] = 1
neu = 1
if (neu):
self.menu.NewMenuState()
if (time == ParamDef.ShotTime
and not (self.mode == GameMode.TUTORIAL and
ParamDef.FrameTimePoint - ParamDef.StartTime < 1900)):
# Animation schon fertig?
##ifndef _WIN32
#printf(" %f Frames/sec\n",(Frames*100.0)/(Stossdauer+1.0));
##endif
for i in range(16):
if (self.ballssInGame[i] and not self.ballssInHole[i]
and (self.balls[i].Pos_x() == 3000)):
self.ballssInHole[i] = 1
if (self.mode == GameMode.TRAINING
or self.mode == GameMode.TUTORIAL):
if (self.balls[0].Pos_x() == 3000):
self.state = GameState.NEW_WHITE
ShotStrength.setShockStrength(0.0)
ParamDef.LageVerbesserung = 1
ParamDef.LageVerbesserungKopffeld = 1
WhiteChosen()
self.menu.NewMenuState()
else:
self.state = GameState.VIEWING
ShotStrength.setShockStrength(0.0)
self.menu.NewMenuState()
elif self.decision.Decisioning() == 0:
self.state = GameState.JUDGEING
ShotStrength.setShockStrength(0.0)
else:
self.state = GameState.VIEWING
self.shotStrength.setShockStrength(0.0)
self.menu.NewMenuState()
'''
* 放置白球
'''
def NewWhiteHandling(self):
if (self.mode == GameMode.TRAINING):
#ParamDef.
LageVerbesserungKopffeld = 0
#Location improving header
LageVerbesserung = 1
x = self.balls[0].Pos_xCM()
y = self.balls[0].Pos_yCM()
self.Factor2 = sqrt((self.balls[0].Pos_xCM() - self.camera.Pos_xCM()) *
(self.balls[0].Pos_xCM() - self.camera.Pos_xCM()) +
(self.balls[0].Pos_yCM() - self.camera.Pos_yCM()) *
(self.balls[0].Pos_yCM() - self.camera.Pos_yCM()) +
self.camera.Pos_zCM() * self.camera.Pos_zCM())
self.Factor2 *= .005
if (KEY_UP):
x += .3 * Factor * Faktor2 * sin(self.camera.Beta * M_PI / 180.0)
y += .3 * Factor * Faktor2 * cos(self.camera.Beta * M_PI / 180.0)
if (KEY_DOWN):
x -= .3 * Factor * Faktor2 * sin(self.camera.Beta * M_PI / 180.0)
y -= .3 * Factor * Faktor2 * cos(self.camera.Beta * M_PI / 180.0)
if (KEY_LEFT):
x -= .3 * Factor * Faktor2 * cos(self.camera.Beta * M_PI / 180.0)
y += .3 * Factor * Faktor2 * sin(self.camera.Beta * M_PI / 180.0)
if (KEY_RIGHT):
x += .3 * Factor * Faktor2 * cos(self.camera.Beta * M_PI / 180.0)
y -= .3 * Factor * Faktor2 * sin(self.camera.Beta * M_PI / 180.0)
invalid = 0
if (x < -124 or x > 124 or (x > -63.5 and LageVerbesserungKopffeld)):
x = self.balls[0].Pos_xCM()
if (y < -60.5 or y > 60.5):
y = self.balls[0].Pos_yCM()
for i in range(16):
if ((self.balls[i].Pos_xCM() - x) * (self.balls[i].Pos_xCM() - x) +
(self.balls[i].Pos_yCM() - y) *
(self.balls[i].Pos_yCM() - y) < 32.7):
invalid = 1
#与其他球太近，无效
#invalid
if (not invalid):
self.self.balls[0].newPositionCM(x, y)
if (KEY_SHIFT):
self.camera.Move_Up(Factor)
if (KEY_CTRL):
self.camera.Move_Down(Factor)
if (KEY_PAGE_UP):
self.camera.Move_In(Factor)
if (KEY_PAGE_DOWN):
self.camera.Move_Out(Factor)
if (KEY_HOME):
self.camera.Zoom_In(Factor)
if (KEY_END):
self.camera.Zoom_Out(Factor)
def JudgeHandling(self):
if (ParamDef.JudgeDecision == -1):
ParamDef.JudgeDecision = Judge.Decisioning()
ParamDef.RecodingChanges = ParamDef.JudgeDecision & 1
ParamDef.Foul = ParamDef.JudgeDecision & 2
ParamDef.LageVerbesserungKopffeld = ParamDef.JudgeDecision & 4
ParamDef.LageVerbesserung = ParamDef.JudgeDecision & 8
ParamDef.NeuAufbauenOderWeiterspielen = ParamDef.JudgeDecision & 16
ParamDef.NeuAufbauenOderAchtEinsetzen = ParamDef.JudgeDecision & 32
ParamDef.Player1Win = ParamDef.JudgeDecision & 64
ParamDef.Player2Win = ParamDef.JudgeDecision & 128
self.menu.NewMenuState()
def back_propagation_online_momentum(S,rho,nu,units_by_layer,factivation,max_it=250,report_it=50):
k = 0 # it
delta = [] # Errores
# Inicializar pesos a 0 #
theta = []
for l in xrange(1,len(units_by_layer)):
theta.append([])
if l-1==0: sm = units_by_layer[l-1]
else: sm = units_by_layer[l-1]+1
for i in xrange(units_by_layer[l]): theta[-1].append(np.zeros(sm))
# Inicializar incr_theta_ant #
incr_theta_ant = []
for l in xrange(1,len(units_by_layer)):
incr_theta_ant.append([])
if l-1==0: sm = units_by_layer[l-1]
else: sm = units_by_layer[l-1]+1
for i in xrange(units_by_layer[l]): incr_theta_ant [-1].append(np.zeros(sm))
# Plot #
if Config.VERBOSE: color_classes = [Config.COLORS[c%len(Config.COLORS)]+Config.STYLE[c%len(Config.STYLE)] for c in xrange(len(S[0][1]))]
# Mientras no converja #
while k<max_it:
# Inicializar delta #
delta = []
for l in xrange(1,len(units_by_layer)):
delta.append([])
for i in xrange(units_by_layer[l]): delta[-1].append(0)
# Para cada muestra #
m = 0 # N muestra
for (xk,tk) in S:
# Inicializar incr_theta a cada muestra #
incr_theta = []
for l in xrange(1,len(units_by_layer)):
incr_theta.append([])
if l-1==0: sm = units_by_layer[l-1]
else: sm = units_by_layer[l-1]+1
for i in xrange(units_by_layer[l]): incr_theta[-1].append(np.zeros(sm))
##########################
phi,s = forward_propagation(units_by_layer,xk,theta,factivation)
# Desde la salida a la entrada #
for l in xrange(len(units_by_layer)-1,0,-1):
# Para cada nodo #
for i in xrange(units_by_layer[l]):
########## Calcular delta ###########
if l==len(units_by_layer)-1: delta[l-1][i] = (factivation[l-1][i][1](phi[l-1][i])*(tk[i]-s[l][i]))
else: delta[l-1][i] += factivation[l-1][i][1](phi[l-1][i])*sum([delta[l][r]*theta[l][r][i+1] for r in xrange(units_by_layer[l+1])])
#####################################
if l==len(units_by_layer)-1:
# Calcular incr_theta (capa salida) #
incr_theta[l-1][i][0] += delta[l-1][i]
for j in xrange(units_by_layer[l-1]): incr_theta[l-1][i][j+1] += delta[l-1][i]*s[l-1][j]
#####################################
else:
# Calcular incr_theta (capas ocultas) #
for j in xrange(units_by_layer[l-1]):
if j==0: incr_theta[l-1][i][j] += delta[l-1][i]
else: incr_theta[l-1][i][j] += delta[l-1][i]*s[l-1][j]
#####################################
# Actualizaciones #
for l in xrange(len(theta)):
for i in xrange(len(theta[l])):
# Actualizar los pesos #
theta[l][i] += (rho*incr_theta[l][i]) + (nu*incr_theta_ant[l][i])
# Actualizar incr_theta_ant #
incr_theta_ant[l][i] = (rho*incr_theta[l][i]) + (nu*incr_theta_ant[l][i])
m += 1
# Plot de las muestras #
if Config.VERBOSE:
if k%report_it==0:
plt.clf()
plt.ylabel("Y")
plt.xlabel("X")
maxX = float("-inf")
maxY = float("-inf")
plt.title("Iteracion backprop online con momentum: "+str(k))
for (xk,tk) in S:
predicted_tk = Decision.classify(units_by_layer,xk,theta,factivation)
plt.plot(xk[1],xk[2],color_classes[predicted_tk])
if xk[1]>maxX: maxX = xk[1]
if xk[2]>maxY: maxY = xk[2]
plt.axis([-maxX,2*maxX,-maxY,2*maxY])
plt.show(block=False)
print "Siguientes ",report_it," iteraciones [Enter]"
raw_input()
plt.close()
k += 1
return theta
theta3 = MLPLearning.back_propagation_batch_momentum(
S, rho, nu, units_by_layer, factivation, 850, 50)
theta4 = MLPLearning.back_propagation_batch_buffer(S, rho, l,
units_by_layer,
factivation, 850, 50)
theta5 = MLPLearning.back_propagation_online_buffer(
S, rho, l, units_by_layer, factivation, 850, 50)
theta6 = MLPLearning.back_propagation_online_momentum(
S, rho, nu, units_by_layer, factivation, 850, 50)
theta7, fitness = MLPLearning.evolutional(S, units_by_layer, factivation,
200, 500, -2, 2, 1.1, 0.9)
##############################
# Clasificacion #
logging.info("Clase con theta1: (Backprop batch): " + str(
Decision.classify(units_by_layer, [1.0, -6.3, 1.0], theta1,
factivation)))
logging.info("Clase con theta2: (Backprop online): " + str(
Decision.classify(units_by_layer, [1.0, -6.3, 1.0], theta2,
factivation)))
logging.info("Clase con theta3: (Backprop batch con momentum): " + str(
Decision.classify(units_by_layer, [1.0, -6.3, 1.0], theta3,
factivation)))
logging.info("Clase con theta4: (Backprop batch con amortiguamiento): " +
str(
Decision.classify(units_by_layer, [1.0, -6.3, 1.0],
theta4, factivation)))
logging.info("Clase con theta5: (Backprop online con amortiguamiento): " +
str(
Decision.classify(units_by_layer, [1.0, -6.3, 1.0],
theta5, factivation)))
logging.info("Clase con theta6: (Backprop online con momentum): " + str(
import numpy as np
import Perception
import Decision
import Memory
import random
import gym
import Learning
import math
import naoenvSimulation
import matplotlib.pyplot as plt
camera = Perception.Camera()
memory = Memory.Memory(camera)
Perc = Perception.Ball(memory)
learning = Learning.Learning(memory)
Dec = Decision.Decision(memory, learning)
def createEnvironment(numberOfPossibleBalls, environment, center):
#create some balls
ball = [0, 0]
gaze = [0, 0]
while gaze[0] == 0 or gaze[1] == 0:
ball[0] = random.randrange(environment[0], environment[1], 1)
ball[1] = random.randrange(environment[2], environment[3], 1)
#create the gaze
gaze = [ball[0] - center[0], ball[1] - center[1]]
#find the direction of the gaze
directionx = 1
directiony = 1
def selectBestFeature(inputs,
labels,
impurityCat,
impurityNum,
featuresToConsider,
featureNames,
featureTypes,
numIntervals,
seed=0):
random.seed(seed)
if featuresToConsider > inputs.shape[1] or featuresToConsider < 0:
Exception('featuresToConsider must be between 0 and {maxx}'.format(
maxx=featuresToConsider))
possibleFeatures = np.random.choice(featureNames, featuresToConsider,
False)
bestImpurity = np.inf
bestSplit = ('', '', ''
) #in the form (featureName, isCategorical, arguements)
for name in possibleFeatures:
colIndex = np.where(name == featureNames)[0][0]
vals = inputs[:, colIndex]
isCategorical = False
if featureTypes[colIndex] == 'string':
isCategorical = True
if isCategorical:
#if the feature is categorical, compute a split for each possible
#value and pick the maximum one
subset = []
splitImpurity = bestImpurity
for cat in np.unique(vals):
dec = Decision.Categorical(name, [cat])
trueIndices = np.vectorize(dec.function)(vals)
falseIndices = np.logical_not(trueIndices)
trueNodeImpurity = impurityResubLabel(
labels[trueIndices], impurityCat, impurityNum,
sum(trueIndices) / len(labels))
falseNodeImpurity = impurityResubLabel(
labels[falseIndices], impurityCat, impurityNum,
sum(falseIndices) / len(labels))
if splitImpurity > trueNodeImpurity + falseNodeImpurity:
subset = [cat]
splitImpurity = trueNodeImpurity + falseNodeImpurity
split = (name, True, subset)
else:
#else the feature is numerical. In that case I discretize the
#continuous features into "numIntervals" bins or
#however many intervals are possible.
def findReImpurityGivenBoundary(bound):
lessBound = labels[vals <= bound]
moreBound = labels[vals > bound]
trueNodeReImpurity = impurityResubLabel(
lessBound, impurityCat, impurityNum,
len(lessBound) / len(labels))
falseNodeReImpurity = impurityResubLabel(
moreBound, impurityCat, impurityNum,
len(moreBound) / len(labels))
return trueNodeReImpurity + falseNodeReImpurity
numIntervalsForContinuousFeat = min(numIntervals, inputs.shape[0])
possibleBounds = [
max(i) for i in np.array_split(np.sort(vals),
numIntervalsForContinuousFeat)
]
possibleBounds = np.array(possibleBounds)
splitImpurity = np.inf
bestBoundary = 0
for bound in possibleBounds:
imp = findReImpurityGivenBoundary(bound)
if splitImpurity > imp:
splitImpurity = imp
bestBoundary = bound
split = (name, False, ('less', bestBoundary))
if bestImpurity > splitImpurity:
bestImpurity = splitImpurity
bestSplit = split
return bestSplit
print "error was ", e
sys.exit(1)
# disable ALAutonomousMoves bug
am = ALProxy("ALAutonomousMoves", ip, port)
am.setExpressiveListeningEnabled(False)
am.setBackgroundStrategy("none")
#######Global classes#######
camera = Perc.Camera()
memory = memo.Memory(camera)
facePerception = Perc.Face(memory)
gazeFollowing = Perc.Gaze(memory)
ballPerception = Perc.Ball(memory)
learning = Learn.Learning(memory)
decision = Deci.Decision(memory, learning)
motivation = Moti.Motivation(memory)
motorControl = MC.MotorControl(motionProxy, memory, facePerception, tts)
################################################################
# Main functions
################################################################
def setup():
say("Setting up")
postureProxy.goToPosture("Crouch", 0.6667)
def say(text):
tts.say(text)
def main():
###Calcualtion setting ###
#low:100, 30
#medium:130, 60
#high: 160, 100
num_agent = 160
"""
average_range
beta
gamma
param_ramge
"""
max_season = 37
list_state = []
list_task = []
result_list_state = []
result_list_task = []
state_to_nextstate = []
list_Gr = []
list_Cl = []
list_Rt = []
list_Cf = []
list_Mg = []
info = pd.DataFrame({
'Gr': [],
'Cl': [],
'Rt': [],
'Cf': [],
'Mg': [],
'New': [],
's0': [],
'Sa': [],
'sA': [],
'sB': [],
'sC': []
})
#initialization of ramdom int
num_new_agent = 0
#Prepare agents & initialize state & task &strategy
agents = Agent_man.generate_agents(num_agent)
agents_store = Agent_store.generate_agent_store(5)
Agent_store.init_agent_store(agents_store)
Agent_store.show_store_info(agents_store)
Move.initialize_state_task(agents)
Decision.initial_strategy(agents)
#Agent_man.show_agent_info(agents)
for season in range(1, max_season):
print('###########################')
print(f'simulate season:{season}')
if season != 1 and season != max_season - 1:
num_new_agent = rnd.randint(1, 100)
#generate new agents
print('==== generate new agents ====')
Agent_man.new_generate_agents(num_new_agent, agents)
#move next_state
print('===== agent move =====')
Move.agents_moves(agents, season)
#divide visitor per stores
list_Gr, list_Cl, list_Rt, list_Cf, list_Mg = Manage_store.divide_visitor(
agents)
if season == 1:
#manage instore & waiting for 1st season
Manage_store.manage_instore_waiting_first(agents, agents_store,
list_Gr, 'Gr')
Manage_store.manage_instore_waiting_first(agents, agents_store,
list_Cl, 'Cl')
Manage_store.manage_instore_waiting_first(agents, agents_store,
list_Rt, 'Rt')
Manage_store.manage_instore_waiting_first(agents, agents_store,
list_Cf, 'Cf')
Manage_store.manage_instore_waiting_first(agents, agents_store,
list_Mg, 'Mg')
if season != 1:
Manage_store.manage_instore_waiting_second(agents, agents_store,
list_Gr, 'Gr')
Manage_store.manage_instore_waiting_second(agents, agents_store,
list_Cl, 'Cl')
Manage_store.manage_instore_waiting_second(agents, agents_store,
list_Rt, 'Rt')
Manage_store.manage_instore_waiting_second(agents, agents_store,
list_Cf, 'Cf')
Manage_store.manage_instore_waiting_second(agents, agents_store,
list_Mg, 'Mg')
#Agent_man.show_agent_info(agents)
#strategy determine next_state
print('==== decide next state ====')
Decision.strategy_determine_state(agents, agents_store)
#agents determine next_strategy
print('==== decide next strategy ====')
Decision.decide_next_strategy(agents)
print('==== insert next strategy into strategy ====')
Decision.insert_strategy(agents)
Gr, Cl, Rt, Cf, Mg, s0 = Move.count_state_num(agents)
Sa = Move.count_store_all(agents)
sA, sB, sC = Decision.count_strategy(agents)
new_info = pd.DataFrame(
[[Gr, Cl, Rt, Cf, Mg, num_new_agent, s0, Sa, sA, sB, sC]],
columns=[
'Gr', 'Cl', 'Rt', 'Cf', 'Mg', 'New', 's0', 'Sa', 'sA', 'sB',
'sC'
])
#new_info = pd.DataFrame([[format(Gr, '.4'),format(Cl,'.4'),format(Rt,'.4'),format(Cf,'.4'),format(Mg,'.4'),num_new_agent,format(s0,'.4'),Sa,sA,sB,sC]],columns=['Gr','Cl','Rt','Cf','Mg','New','s0','Sa','sA','sB','sC'])
info = info.append(new_info)
#print(f'info:{info}')
#infomation of state store result_list_state
for id, agent in enumerate(agents):
list_state.append(agent.state + '_' + agent.status)
list_task.append(agent.task)
#print(f'list_state:{list_state}')
#print(f'result_list_state:{result_list_state}')
result_list_state.append(list_state)
result_list_task.append(list_task)
#print(f'result_list_state:{result_list_state}')
list_state = []
list_task = []
list_Gr = []
list_Cl = []
list_Rt = []
list_Cf = []
list_Mg = []
#print(f'list_state:{list_state}')
state = pd.DataFrame(result_list_state)
task = pd.DataFrame(result_list_task)
print(f'result:{state}')
#ディレクトリの変更必須！！！
state.to_csv(f'result/high/result_list_state_high.csv')
task.to_csv(f'result/high/result_list_task_high.csv')
info.to_csv(f'result/high/result_info_high.csv')