def checkDistRuntime(tp = 0):
times = []
xs = []
for d in np.logspace(-7,2,100):
v = make_church_prime_ripl()
timeStart = time.time()
if tp == 0:
v.assume("y", "(student_t %f)" % d)
elif tp == 1:
v.assume("y", "(uniform_continuous 0 %f)" % d)
samples = pu.posterior_samples(v, "y", no_samples=100, no_burns=0, int_mh=1, silent=True)
vals = map(lambda x:x[1], samples)
times.append(time.time() - timeStart)
xs.append(d)
print d, np.mean(vals), times[-1]
print xs
print times
plt.plot(xs,times, '-x')
if tp == 0:
plt.xlabel("Degrees of freedom")
plt.ylabel("Seconds")
plt.title("Speed of generating 100 unconditioned student_t samples")
plt.ylim([0,max(max(times),1)])
elif tp == 1:
plt.xlabel("Upper limit of interval (x)")
plt.ylabel("Seconds")
plt.title("Speed of generating 100 unconditioned (uniform_continuous 0 x) samples")
plt.xscale("log")
plt.ylim([0,max(max(times),1)])
plt.show()
def testCont(ys, sample, burn, lag):
pals = [0,0.5,1,2,5,10,20]
with open("contConvRT",'w') as f:
for i1 in range(len(pals)):
for i2 in range(len(pals))[i1:]:
for i3 in range(len(pals))[i2:]:
for i4 in range(len(pals))[i3:]:
p1 = pals[i1]
p2 = pals[i2]
p3 = pals[i3]
p4 = pals[i4]
v = make_church_prime_ripl()
timeStart = time.time()
rest = 100.0 - (p1 + p2 + p3 + p4)
if p1 > 0:
v.assume("d1", "(uniform_continuous 0 " + str(p1) + ")")
else:
v.assume("d1", "0")
if p2 > 0:
v.assume("d2", "(uniform_continuous 0 " + str(p2) + ")")
else:
v.assume("d2", "0")
if p3 > 0:
v.assume("d3", "(uniform_continuous 0 " + str(p3) + ")")
else:
v.assume("d3", "0")
if p4 > 0:
v.assume("d4", "(uniform_continuous 0 " + str(p4) + ")")
else:
v.assume("d4", "0")
v.assume("r", "(uniform_continuous 2 " + str(rest) + ")")
v.assume("d", "(+ d1 d2 d3 d4 r)")#(uniform_continuous 0 5) (uniform_continuous 0 1))")
v.assume("y", "(lambda () (student_t d))")
[v.observe("(y)", str(ys[i])) for i in range(len(ys))]
samples = pu.posterior_samples(v, "d", no_samples=sample, no_burns=burn, int_mh=lag)
vals = map(lambda x:x[1], samples)
print rest, p1, p2, p3, p4, np.mean(vals), np.var(vals), time.time() - timeStart
f.write("==== " + str((rest,p1,p2,p3,p4,time.time() - timeStart)) + "\n" + str(vals))
f.flush()
def repeatCont(ys, sample, burn, lag):
with open("contRepeats10F",'w') as f:
means = []
times = []
for j in range(10):
v = make_church_prime_ripl()
timeStart = time.time()
v.assume("d1", "(uniform_continuous 0 99.9978)")
v.assume("d2", "(uniform_continuous 0 0.001)")
v.assume("d3", "(uniform_continuous 0 0.001)")
v.assume("d4", "(uniform_continuous 0 0.0001)")
v.assume("d5", "(uniform_continuous 0 0.0001)")
v.assume("d", "(+ d1 d2 d3 d4 d5)")
v.assume("y", "(lambda () (student_t d))")
[v.observe("(y)", str(ys[i])) for i in range(len(ys))]
samples = pu.posterior_samples(v, "d", no_samples=sample, no_burns=burn, int_mh=lag)
vals = map(lambda x:x[1], samples)
means.append(np.mean(vals))
times.append(time.time() - timeStart)
print j, means[-1], times[-1]
f.write(str((j, means[-1], times[-1])) + "\n")
f.flush()
f.write(str(("Total: ", np.mean(means), np.mean(times))))
def runModel(v, ys, mType, sample, burn, lag, fnAdd = "", timeTest = False, silentSamp = False):
timeStart = time.time()
if mType == "cont":
v.assume("d", "(uniform_continuous 2 100)")
v.assume("y", "(lambda () (student_t d))")
elif mType == "cont5var":
v.assume("d1", "(uniform_continuous 2 100)")
v.assume("d2", "(uniform_continuous 0 0.001)")
v.assume("d3", "(uniform_continuous 0 0.001)")
v.assume("d4", "(uniform_continuous 0 0.0001)")
v.assume("d5", "(uniform_continuous 0 0.0001)")
v.assume("d", "(+ d1 d2 d3 d4 d5)") #(uniform_continuous 0 5) (uniform_continuous 0 1))")
v.assume("y", "(lambda () (student_t d))")
elif mType == "contBin":
v.assume("f1", "(* 0.5 (categorical 0.5 0.5))")
v.assume("r", "(uniform_continuous 0 0.5)")
v.assume("d", "(+ f1 r)")
v.assume("y", "(lambda () (student_t d))")
elif mType == "contBin3":
v.assume("f0", "(* 0.5 (uniform_discrete 0 2))")
v.assume("f1", "(* 0.25 (uniform_discrete 0 2))")
v.assume("f2", "(* 0.125 (uniform_discrete 0 2))")
v.assume("r", "(uniform_continuous 0 0.125)")
v.assume("d", "(+ 2 (* 98 (+ f0 f1 f2 r)))")
v.assume("y", "(lambda () (student_t d))")
elif mType == "contBin7":
v.assume("f0", "(* 0.5 (uniform_discrete 0 2))")
v.assume("f1", "(* 0.25 (uniform_discrete 0 2))")
v.assume("f2", "(* 0.125 (uniform_discrete 0 2))")
v.assume("f3", "(* 0.0625 (uniform_discrete 0 2))")
v.assume("f4", "(* 0.03125 (uniform_discrete 0 2))")
v.assume("f5", "(* 0.015625 (uniform_discrete 0 2))")
v.assume("f6", "(* 0.0078125 (uniform_discrete 0 2))")
v.assume("r", "(uniform_continuous 0 0.0078125)")
v.assume("d", "(+ 2 (* 98 (+ f0 f1 f2 f3 f4 f5 f6 r)))")
v.assume("y", "(lambda () (student_t d))")
elif mType == "contMix":
v.assume("d1", "(uniform_continuous 2 97)")
v.assume("d2", "(uniform_continuous 0 1)")
v.assume("d3", "(uniform_continuous 0 2)")
v.assume("d", "(+ d1 d2 d3)") #(uniform_continuous 0 5) (uniform_continuous 0 1))")
v.assume("y", "(lambda () (student_t d))")
elif mType == "flip":
v.assume("d0", "(uniform_continuous 0 1)")
v.assume("f0", "(uniform_discrete 0 2)")
v.assume("f1", "(* 2 (uniform_discrete 0 2))")
v.assume("f2", "(* 4 (uniform_discrete 0 2))")
v.assume("f3", "(* 8 (uniform_discrete 0 2))")
v.assume("f4", "(* 16 (uniform_discrete 0 2))")
v.assume("f5", "(* 32 (uniform_discrete 0 2))")
v.assume("f6", "(* 64 (uniform_discrete 0 2))")
v.assume("d", "(+ (+ (+ d0 f0) (+ f1 f2)) (+ (+ f3 f4) (+ f5 f6)))") #(uniform_continuous 0 5) (uniform_continuous 0 1))")
v.assume("y", "(lambda () (student_t d))")
elif mType == "disc1":
v.assume("d", "(uniform_discrete 2 50)")
v.assume("y", "(lambda () (student_t d))")
elif mType == "disc2":
v.assume("d", "(/ (uniform_discrete 20 60) 10)")
v.assume("y", "(lambda () (student_t d))")
elif mType == "disc21":
v.assume("d", "(uniform_discrete 20 60)")
v.assume("y", "(lambda () (student_t (/ d 10)))")
else:
raise Exception("Unknown model type: " + mType)
[v.observe("(y)", str(ys[i])) for i in range(len(ys))]
samples = pu.posterior_samples(v, "d", no_samples=sample, no_burns=burn, int_mh=lag, silent = silentSamp)
if timeTest:
return time.time() - timeStart
print '\n'.join([str(samples[i][1]) for i in range(len(samples)) if i%10==0])
#v.infer(11000000)
vals = map(lambda x:x[1], samples)
print "Sample mean: ", np.mean(vals), " Sample Stdev: ", np.std(vals)
pu.save_samples(samples, os.getcwd(), fnAdd+mType)
def genTdf(v):
v.assume("y", "(student_t 4)")
samples = pu.posterior_samples(v, "y", no_samples=1000, no_burns=0, int_mh=1)
