def __init__(self, graph, predictorList, penList = [], rounds = 500,
eventFunction = lambda : Induction.getRandomEvent(2.0/3.0)):
self.graph = graph
self.rounds = rounds
self.predictorList = copy.deepcopy(predictorList)
self.world = Induction.World(eventFunction)
for predictor in self.predictorList:
self.world.register(predictor)
penBox = copy.copy(penList)
for predictor in self.predictorList:
pen = penBox[0]; del penBox[0]
pen.lineWidth = Gfx.MEDIUM
self.graph.addPen(str(predictor), pen, updateCaption = False)
if SHOW_MI_MEAN:
pen = Gfx.Pen((0.0, 0.0, 0.0), lineWidth=Gfx.THICK,
linePattern=Gfx.CONTINUOUS)
self.graph.addPen("miMean", pen, updateCaption = False)
if SHOW_NON_MI_MEAN:
pen = Gfx.Pen((0.3, 0.0, 0.6), lineWidth=Gfx.THICK,
linePattern=Gfx.CONTINUOUS)
self.graph.addPen("non_miMean", pen, updateCaption = False)
self.graph.redrawCaption()
self.interrupt = False
self.isRunning = False
self.last_xPixel = -1
bluePens = [Gfx.Pen(c) for c in filter(bright,filter(Colors.BlueFilter, Colors.colors))]
greenPens = [Gfx.Pen(c) for c in filter(bright,filter(Colors.GreenFilter, Colors.colors))]
yellowPens = [Gfx.Pen(c) for c in filter(bright,filter(Colors.YellowFilter, Colors.colors))]
emptyPens = [Graph.DONT_DRAW_PEN]*10
exampleList = {}
exampleList["01(MI): MI + Forecasters"] = \
("Example 01(MI): MI + Forecasters",
[Induction.DelayedForecaster(0.7, 40, "Forecaster 1 (success; delay)"),
Induction.ForecasterFromBottom(0.90, "Forecaster 2 (success; recovers from zero)"),
Induction.MetaInductivist("Meta-Inductivist"),
Induction.ObjectInductivist("Object-Inductivist")],
bluePens[:2] + redPens[:1] + greenPens,
500,
lambda : Induction.getRandomEvent(2.0/3.0))
exampleList["02(MI): MI + Amplitude-Oscillator + OI"] = \
("Example 02(MI): MI + Amplitude-Oscillator + OI",
[Induction.AmplitudeOscillator(0.3, 0.85, "AmpOsc"),
Induction.MetaInductivist("Meta-Inductivist"),
Induction.ObjectInductivist("Object-Inductivist")],
bluePens[:1] + redPens[:1] + greenPens,
500,
lambda : Induction.getRandomEvent(2.0/3.0))
exampleList["03(MI): MI + 1 Systematic Oscillator + OI"] = \
("Example 03(MI): MI + 1 Systematic Oscillator + OI",
[Induction.SystOscillator("SystOscillator"),
Induction.MetaInductivist("Meta-Inductivist"),
Induction.ObjectInductivist("Object-Inductivist")],
def Sim(self, title, predictorList, penList = [], rounds = 500,
eventFunction = lambda : Induction.getRandomEvent(2.0/3.0)):
# die Axen wird entsprechend den Menüvorgaben angepasst
self.graph.reset(1, 0.0, rounds, 1.0)
if self.win.absoluteSuccess:
self.graph.setLabels(yaxis="Absolute Success")
else:
self.graph.setLabels(yaxis="Success Rate")
self.graph.setTitle(title)
self.zoomlist = [(1, 0.0, rounds, 1.0)]
self.zoomPos = 0
self.predictorList = copy.deepcopy(predictorList)
#nun wird die Klasse World aus Induction.py initialisiert bzw. erzeugt; siehe Step 6
self.world = Induction.World(eventFunction)
#nun wird jeder predictor der predictorList aus dem Example bei der Klasse world
#angemeldet, das führt zu Step 6 in Induction.py
for predictor in self.predictorList:
self.world.register(predictor)
if self.worldDeception:
self.world.worldDeceived = self.world.miList[0]
# Hier oben bei [0] moegliche Veraenderung des Betrogenen MI eintragen
#"0" ist die erste Stelle der miList; der wird betrogen, falls
# worldDeception an ist
#Step7: nun werden den predictors die entprechenden zeichenfarben zugeordnet
penBox = ProxyPenGenerator(penList)
for predictor in self.predictorList:
pen = penBox.next()
pen.lineWidth = Gfx.MEDIUM
if self.win.blackwhite: p = greyscale(pen)
else: p = pen
# print pen.lineWidth
self.graph.addPen(str(predictor), p)
pen = penBox.next()
pen.lineWidth = Gfx.MEDIUM
#if self.win.blackwhite: greyscale(pen)
#if self.win.hiddenOI:
# self.graph.addPen(str(self.world.OI), pen)
Np = len(self.world.getPredictorList())
Nmi = len(self.world.miList)
Nn_mi = len(self.world.non_miList)
#Step7-Fortsetzung: Hier wird die Legende der Logging-Ausgabe erzeugt,
#falls Logging an ist. Danach folgt Mittelwertfarbeneinstellung.
self.win.clearLog()
self.fmtStr = "%5i:| " + \
"%1.2f "*(Np) + \
"|" + " %4s"*Nmi + \
" ||" + "%4s|"*Nn_mi + "\n"
s0 = "Round | "
s0+= ("Success Rates"+" "*100)[:5*Np] + "|"
s0+= (" Favorites"+" "*100)[:5*Nmi] + " ||"
s0+= ("Deceivers"+" "*100)[:5*Nn_mi]
self.win.logAppend(s0+"\n")
s = " | "
for p in self.world.getPredictorList():
s += "%4s " % self.shortName(p)
s += "|"
for mi in self.world.miList:
s += " %4s" % self.shortName(mi)
s += " ||"
for p in self.world.non_miList:
s += "%4s|" % self.shortName(p)
self.win.logAppend(s+"\n")
self.win.logAppend("-"*len(s)+"\n")
# Zur Mittelwertberechnung
# p1 ist der Farbenwert von non_miMean; und p2 der von miMean
if self.win.showMeanValues:
p1 = Gfx.Pen((0.3, 0.0, 0.6), lineWidth=Gfx.THICK,
linePattern=Gfx.CONTINUOUS)
p2 = Gfx.Pen((0.0, 0.0, 0.0), lineWidth=Gfx.THICK,
linePattern=Gfx.CONTINUOUS)
if self.win.blackwhite:
p1 = greyscaleMean(p1); p1.lineWidth = Gfx.THICK
p2 = greyscaleMean(p2); p2.lineWidth = Gfx.THICK
# Non-MI-Mean-Out: folgende Zeile auskommentieren, um non-MI-mean anzuzeigen
p1 = Graph.DONT_DRAW_PEN
self.graph.addPen("non_miMean", p1)
self.graph.addPen("miMean", p2)
# Mittelwerberechnung Ende
#Option 6: PrintEventfrequency
# p = Gfx.Pen((0.8, 0.5, 0.0), lineWidth=Gfx.MEDIUM, \
# linePattern=Gfx.CONTINUOUS)
# self.graph.addPen("Event Frequency", p)
#Step8: Jetzt erst beginnt die HAUPTschleife
self.calcNdraw(1, rounds)
def Sim(self, title, predictorList, penList = [], rounds = 500,
eventFunction = lambda : Induction.getRandomEvent(2.0/3.0)):
self.graph.reset(1, 0.0, rounds, 1.0)
if self.win.absoluteSuccess:
self.graph.setLabels(yaxis="Absolute Success")
else:
self.graph.setLabels(yaxis="Success Rate")
self.graph.setTitle(title)
self.zoomlist = [(1, 0.0, rounds, 1.0)]
self.zoomPos = 0
self.predictorList = copy.deepcopy(predictorList)
self.world = Induction.World(eventFunction)
for predictor in self.predictorList:
self.world.register(predictor)
if self.worldDeception:
# Hier bei [0] moegliche Veraenderung des Betrogenen MI eintragen
self.world.worldDeceived = self.world.miList[0]
penBox = ProxyPenGenerator(penList)
for predictor in self.predictorList:
pen = penBox.next()
pen.lineWidth = Gfx.MEDIUM
if self.win.blackwhite: p = greyscale(pen)
else: p = pen
# print pen.lineWidth
self.graph.addPen(str(predictor), p)
pen = penBox.next()
pen.lineWidth = Gfx.MEDIUM
#if self.win.blackwhite: greyscale(pen)
Np = len(self.world.getPredictorList())
Nmi = len(self.world.miList)
Nn_mi = len(self.world.non_miList)
self.win.clearLog()
self.fmtStr = "%5i:| " + \
"%1.2f "*(Np) + \
"|" + " %4s"*Nmi + \
" ||" + "%4s|"*Nn_mi + "\n"
s0 = "Round | "
s0+= ("Success Rates"+" "*100)[:5*Np] + "|"
s0+= (" Favorites"+" "*100)[:5*Nmi] + " ||"
s0+= ("Deceivers"+" "*100)[:5*Nn_mi]
self.win.logAppend(s0+"\n")
s = " | "
for p in self.world.getPredictorList():
s += "%4s " % self.shortName(p)
s += "|"
for mi in self.world.miList:
s += " %4s" % self.shortName(mi)
s += " ||"
for p in self.world.non_miList:
s += "%4s|" % self.shortName(p)
self.win.logAppend(s+"\n")
self.win.logAppend("-"*len(s)+"\n")
# Zur Mittelwertberechnung
if self.win.showMeanValues:
p1 = Gfx.Pen((0.3, 0.0, 0.6), lineWidth=Gfx.THICK,
linePattern=Gfx.CONTINUOUS)
p2 = Gfx.Pen((0.0, 0.0, 0.0), lineWidth=Gfx.THICK,
linePattern=Gfx.CONTINUOUS)
if self.win.blackwhite:
p1 = greyscaleMean(p1); p1.lineWidth = Gfx.THICK
p2 = greyscaleMean(p2); p2.lineWidth = Gfx.THICK
# Non-MI-Mean-Out: folgende Zeile auskommentieren, um non-MI-mean anzuzeigen
p1 = Graph.DONT_DRAW_PEN
self.graph.addPen("non_miMean", p1)
self.graph.addPen("miMean", p2)
# Mittelwerberechnung Ende
#Option 6:
# p = Gfx.Pen((0.8, 0.5, 0.0), lineWidth=Gfx.MEDIUM, \
# linePattern=Gfx.CONTINUOUS)
# self.graph.addPen("Event Frequency", p)
self.calcNdraw(1, rounds)
###############################################################################
#
# list of examples
#
###############################################################################
exampleList = {}
exampleList["A: MI Demonstration"] = \
("Example 01(MI): MI + Forecasters",
[Induction.DelayedForecaster(0.7, 40, "Forecaster 1 (success; delay)"),
Induction.ForecasterFromBottom(0.90, "Forecaster 2 (success; recovers from zero)"),
Induction.MetaInductivist("Meta-Inductivist"),
Induction.ObjectInductivist("Object-Inductivist")],
bluePens[:2] + redPens[:1] + greenPens,
500,
lambda : Induction.getRandomEvent(2.0/3.0),
""" "MI Demonstration" demonstrates how a meta-inductivist works:
The meta-inductivist simply follows the predictor that had the highest
success rate so far. This predictor is called its "favorite". If another
predictor becomes better than the favorite, the meta-inductivists choses this
predictor as its new favorite.
""")
exampleList["B: Amp. Oscillators"] = \
("Example 02(MI): MI + Amplitude-Oscillator + OI",
[Induction.AmplitudeOscillator(0.3, 0.85, "AmpOsc"),
Induction.MetaInductivist("Meta-Inductivist"),
Induction.ObjectInductivist("Object-Inductivist")],
bluePens[:1] + redPens[:1] + greenPens,
500,
lambda : Induction.getRandomEvent(2.0/3.0),
"""Hier noch eine Beschreibung einfügen...
