def hmm():
states = []
states.append(stocPy.stocPrim("categorical", (sProbs,), obs=True, part=pind))
for i in range(1,17):
#print states
states.append(stocPy.stocPrim("categorical", (tProbs[states[i-1]],), obs=True, part=pind))
stocPy.normal(eMeans[states[i]], 1, cond=obs[i-1])
def marsaglia(mean, var):
x = stocPy.stocPrim("uniform", (-1, 2), part=pind) #ss params are start and length of interval
y = stocPy.stocPrim("uniform", (-1, 2), part=pind)
s = x*x + y*y
#print sampleInd, depth, x, y, s
if s < 1:
return mean + (math.sqrt(var) * (x * math.sqrt(-2 * (math.log(s) / s))))
else:
return marsaglia(mean, var)
def dpmLazy():
crp = stocPy.crp(1.72)
sds = {}
ms = {}
for i in range(len(obs)):
c = crp(i)
if c not in ms:
sds[c] = math.sqrt(10 * stocPy.stocPrim("invgamma", (1, 0, 10), part=4))
ms[c] = stocPy.stocPrim("normal", (0, sds[c]), part=4)
stocPy.normal(ms[c], sds[c], obs[i])
obsLens.append(len(ms))
def dpmEager():
crp = stocPy.crp(1.72, 10)
sds = {}
ms = {}
cs = {}
for ps in range(len(crp)):
sds[ps] = math.sqrt(10 * stocPy.stocPrim("invgamma", (1, 0, 10), part=2))
ms[ps] = stocPy.stocPrim("normal", (0, sds[ps]), part=2)
for p in crp[ps]:
cs[p] = ps
for i in range(len(obs)):
stocPy.normal(ms[cs[i]], sds[cs[i]], obs[i])
obsLens.append(len(ms))
def categorical(ps): #scipy.stats has no categorical. Should implement this in StocPy eventually.
assert(abs(sum(ps) - 1) < 0.0001)
c = stocPy.stocPrim("uniform", (0,1), part=pind)
s = 0
for i in range(len(ps)):
s += ps[i]
if s > c:
return i
assert(False) #shouldn't ever get here
def normal8DecU20():
n = stocPy.stocPrim("randint", (0, 21))
var = 1.0
ms = []
for i in range(n):
ms.append(stocPy.normal(0, math.sqrt(var/(2**(i+1))), obs=True))
ms.append(stocPy.normal(0, math.sqrt(var/(2**n)), obs=True))
m = 10000 * ss.norm.cdf(sum(ms), loc = 0, scale = 1)
for datum in normalData:
stocPy.normal(m, 1, datum)
def lettersPerDayPart10(): # generative model
expectedLetters = stocPy.stocPrim("uniform",(0, 20), obs=True, part=10) # prior on the number of letters
for datum in noLetters:
stocPy.poisson(expectedLetters, cond=datum) # conditioning on the data we have
def normal8Part2():
m = stocPy.stocPrim("uniform", (0, 10000), obs=True, part=10)
for datum in normalData:
stocPy.normal(m, 1, datum)
def normal8Part():
m = stocPy.stocPrim("uniform", (0, 10000), obs=True, part=stocPy.stocPrim("randint", (0, 21)))
for datum in normalData:
stocPy.normal(m, 1, datum)
def normal8():
m = stocPy.stocPrim("uniform", (0, 10000), obs=True)
for datum in normalData:
stocPy.normal(m, 1, datum)
