def mixture_loglike(data, thetas, covs, labels):
n = len(data)
likes = pdfs.mvnpdf(data, thetas, covs)
loglike = likes.ravel('F').take(labels * n + np.arange(n)).sum()
if np.isnan(loglike):
return -1e300
return loglike
if np.isnan(likes).any():
loglike = 0.
for j, (theta, cov) in enumerate(zip(thetas, covs)):
this_data = data[labels == j]
ch = chol(cov)
loglike += pm.mv_normal_chol_like(this_data, theta, ch)
return loglike
def mixture_loglike(data, thetas, covs, labels):
n = len(data)
likes = pdfs.mvnpdf(data, thetas, covs)
loglike = likes.ravel('F').take(labels * n + np.arange(n)).sum()
if np.isnan(loglike):
return -1e300
return loglike
if np.isnan(likes).any():
loglike = 0.
for j, (theta, cov) in enumerate(zip(thetas, covs)):
this_data = data[labels == j]
ch = chol(cov)
loglike += pm.mv_normal_chol_like(this_data, theta, ch)
return loglike
def mixture_loglike2(data, thetas, taus, weights):
n = len(data)
covs = [inv(tau) for tau in taus]
likes = pdfs.mvnpdf(data, thetas, covs)
loglike = (likes * weights).sum()
# loglike = likes.ravel('F').take(labels * n + np.arange(n)).sum()
if np.isnan(loglike):
st()
return -1e300
return loglike
if np.isnan(likes).any():
loglike = 0.
for j, (theta, cov) in enumerate(zip(thetas, covs)):
this_data = data[labels == j]
loglike += pm.mv_normal_chol_like(this_data, theta, ch)
return loglike
def mixture_loglike2(data, thetas, taus, weights):
n = len(data)
covs = [inv(tau) for tau in taus]
likes = pdfs.mvnpdf(data, thetas, covs)
loglike = (likes * weights).sum()
# loglike = likes.ravel('F').take(labels * n + np.arange(n)).sum()
if np.isnan(loglike):
st()
return -1e300
return loglike
if np.isnan(likes).any():
loglike = 0.
for j, (theta, cov) in enumerate(zip(thetas, covs)):
this_data = data[labels == j]
loglike += pm.mv_normal_chol_like(this_data, theta, ch)
return loglike
