def runKsTest(mi): mi = str(mi) expDir = stocPy.getCurDir(__file__) + "/" paths = ["normal" + mi + "Met3600", "normal" + mi + "Dec5Met600", "normal" + mi + "Dec10Met600", "normal" + mi + "Dec15Met600"] titles = ["Metropolis 60m", "Metropolis Dec5 10m", "Metropolis Dec10 10m", "Metropolis Dec15 10m"] paths = [expDir + path for path in paths] #stocPy.calcKSTest(expDir + "normal" + mi + "Post", paths, names = titles) stocPy.calcKSTests(expDir + "normal" + mi + "Post", paths, aggFreq=np.logspace(1,math.log(50000,10),10), burnIn=1000, single=True, alpha=1, names=titles, ylim=[0.0225, 1.5], title="Single Run performance on NormalMean" + mi +"-prior = Unif(0,10000)")
def genRuns(model, noRuns, time, fn, alg="met", autoNames=True):
runs = []
for i in range(noRuns):
print str(model), "Run", i
samples = stocPy.getTimedSamples(model, time, alg=alg, autoNames=autoNames)
assert(len(samples.keys()) == 1)
samples = samples[samples.keys()[0]]
runs.append(samples)
cd = stocPy.getCurDir(__file__)
with open(cd + "/" + fn, 'w') as f:
cPickle.dump(runs, f)
def genRuns(model, noRuns, time, fn, alg="met", autoNames=True):
global obsMean
runs = []
for i in range(noRuns):
print str(model), "Run", i
samples, traceAcc = stocPy.getTimedSamples(model, time, alg=alg, autoNames=autoNames, outTraceAcc=True)
runs.append(stocPy.procUserSamples(obsMean, traceAcc))
obsMean = []
cd = stocPy.getCurDir(__file__)
print map(lambda run: (min(run.values()), max(run.values())), runs)
with open(cd + "/" + fn, 'w') as f:
cPickle.dump(runs, f)
def runKsRuns(mi, term = "MetRuns", paths = None, titles = None):
mi = str(mi)
expDir = stocPy.getCurDir(__file__) + "/"
if not paths:
paths = ["normal" + mi + term, "normal" + mi + "Dec5" + term, "normal" + mi + "Dec10" + term, "normal" + mi + "Dec15" + term]
if not titles:
titles = ["Metropolis", "Metropolis Dec5", "Metropolis Dec10", "Metropolis Dec15"]
paths = [expDir + path for path in paths]
stocPy.calcKSSumms(expDir + "normal" + mi + "Post", paths, aggFreq=np.logspace(1,math.log(1000000,10),10), burnIn=0, names=titles, modelName = "NormalMean" + mi, postXlim=[float("-inf"), float("inf")])
def genPost(ps, data, fn=None, show=False, prior=ss.uniform(0,20).pdf, xlim=None, title=None):
post = anlPost(data, ps, prior)
xs, ys = zip(*sorted(post.items()))
if fn:
cd = stocPy.getCurDir(__file__)
with open(cd + "/" + fn, 'w') as f:
cPickle.dump((xs,ys), f)
if show:
plt.plot(xs, ys)
if xlim:
plt.xlim(xlim)
if title:
plt.title(title)
plt.show()
def genRuns(model, noRuns, runTime, fn, alg, autoNames=False, discAll=False):
global startTime
global stateHist
global singleStateHist
runs = []
for i in range(noRuns):
print str(model), "Run", i
#startTime = time.time()
samples = stocPy.getTimedSamples(model, runTime, alg=alg, autoNames=autoNames, discAll=discAll, orderNames=True)
#print map(lambda x: dict([(k,v/float(len(stateHist))) for (k,v) in sorted(dict(Counter(x)).items())]), zip(*stateHist))
runs.append(samples)#copy.deepcopy(stateHist))#singleStateHist))#map(lambda x: dict([(k,v/float(len(stateHist))) for (k,v) in sorted(dict(Counter(x)).items())]), zip(*stateHist)))
#stateHist = []
#singleStateHist = {}
cd = stocPy.getCurDir(__file__)
#print runs
with open(cd + "/" + fn, 'w') as f:
cPickle.dump(runs, f)
plt.title(title)
plt.show()
if __name__ == "__main__":
#for i in [0,1,2,4,8,16,32,64,128,256]:
# genPost(np.arange(0,10,0.01), prior=ss.uniform(0,10).pdf, data=[4]*i, show=True, xlim=(-1,11), title="data = " + str(i) + " 4s")
#assert(False)
no = "1"
iters = 5
time = 3
term = "_" + str(iters) + "_" + str(time)
global noLetters
noLetters = getattr(sys.modules[__name__], "noLetters" + no)
#met = stocPy.extractDict(stocPy.getTimedSamples(lettersPerDay, 10, alg="met"))
#metExp = stocPy.extractDict(stocPy.getTimedSamples(lettersPerDayExp, 10, alg="met", autoNames=False))
#slic = stocPy.extractDict(stocPy.getTimedSamples(lettersPerDay, 10, alg="sliceTD"))
#slicExp = stocPy.extractDict(stocPy.getTimedSamples(lettersPerDayExp, 10, alg="sliceTD", autoNames=False))
#print len(met)/float(len(metExp)), len(slic)/float(len(slicExp))
#assert(False)
#genPost(np.arange(0,20,0.01), noLetters, show=True, fn = "post" + no)
#genRuns(lettersPerDayExp, iters, time, "runs" + no + "Exp" + term, autoNames=False)
#genRuns(lettersPerDay, iters, time, "runs" + no + term)
#genRuns(lettersPerDayPart10, iters, time, "runs" + no + "Part10" + term, alg="met")
#genRuns(lettersPerDayPart10, iters, time, "runs" + no + "SlicePart10" + term, alg="sliceTD")
#genRuns(lettersPerDayPartU20, iters, time, "runs" + no + "PartU20" + term)
cd = stocPy.getCurDir(__file__)
#stocPy.calcKSTests(cd + "/post" + no , [cd + "/runs" + no + term, cd + "/runs" + no + "Part10" +term, cd + "/runs" + no + "PartU20" + term], names = ["Depth0", "Depth10", "Depth_U20"], burnIn=100, aggFreq=np.logspace(1,math.log(1000000,10),10), modelName="simplePoisson" + no)
stocPy.calcKSSumms(cd + "/post" + no , [cd + "/runs" + no + term, cd + "/runs" + no + "Part10" + term, cd + "/runs" + no + "SlicePart10" + term], names = ["Depth0", "Depth10", "SliceDepth10"], burnIn=0, aggFreq=np.logspace(1,math.log(1000000,10),10), modelName="simplePoisson" + no)
#stocPy.calcKSSumms(cd + "/post" + no , [cd + "/runs" + no + term, cd + "/runs" + no + "Exp" + term, cd + "/runs" + no + "PartU20" + term], names = ["AutoNames", "ExpNames", "Auto-U20"], burnIn=100, aggFreq=np.logspace(1,math.log(1000000,10),10), modelName="simplePoisson" + no)
ax4, = plt.plot(ds, dr2, 'gd')
ax5, = plt.plot(ds, dr3, 'm*')
ax6, = plt.plot(ds, dr4, 'co')
plt.xscale("log")
plt.yscale("log")
plt.legend([ax1,ax2,ax3,ax4,ax5,ax6],["Optimal Depth", "Depth 0", "Depth 5", "Depth 10", "Depth 20", "Depth = 2^1..2^6"], loc=3)
plt.xlabel("PriorStd / PostStd")
plt.ylabel("Movement / PostStd")
plt.title("Expected movement for optimal depth and several fixed depths")
plt.show()
if __name__ == "__main__":
approxOptimalDepth(np.logspace(0,9,100))
assert(False)
global normalData
normalData = loadData(stocPy.getCurDir(__file__) + "/normalData_2_001_1000")
#genRuns(normal8, alg="sliceTD", noRuns = 5, time=10, fn="normal8SliceTimedSmall")
#genRuns(normal4, alg="met", noRuns = 10, time=6, fn="normal4MetTimed1Small")
#genRuns(normal4, alg="slice", noRuns = 10, time=6, fn="normal4SliceTimed1Small")
#genRuns(normal4, alg="sliceTD", noRuns = 10, time=6, fn="normal4SliceTDTimed1Small")
runKsRuns(8, paths=["normal8SliceTimedSmall", "normal8MetRuns","normal8Dec5MetRuns", "normal8Dec15MetRuns"], titles=["Slice", "Met", "Met_Part5", "Met_Part15"])
assert(False)
#getPost(9, -15, 15, 0.001, fn="normal9Post")
#samples = stocPy.aggDecomp(stocPy.getTimedSamples(normal9Dec5, 10, alg="met", thresh=0.1), func= lambda xs: 10000 * ss.norm.cdf(sum(xs)))
#samples = stocPy.getTimedSamples(normal8Part, 5, alg="met", thresh=0.1)
#print samples
#samples = stocPy.aggDecomp(stocPy.getTimedSamples(normal9Dec15, 1, alg="met", thresh=0.1))
#stocPy.plotSamples(samples)
#print samples
#stocPy.saveRun(samples, "normal8U20_600")
