def _map(self, prior, samples, weights, out, initializations):
"""
Emit computation of the estimated maximum-likelihood parameter.
"""
# mise en place
K = self._model._K
N = samples.shape[0]
# generate some initial parameters
self._map_initialize(prior, samples, weights, out, initializations)
# run EM until convergence
total = qy.stack_allocate(float)
component_ll = qy.stack_allocate(float)
this_r_KN = StridedArray.heap_allocated(float, (K, N))
last_r_KN = StridedArray.heap_allocated(float, (K, N))
this_r_KN_data = Variable.set_to(this_r_KN.data)
last_r_KN_data = Variable.set_to(last_r_KN.data)
@qy.for_(self._model._iterations)
def _(i):
# compute responsibilities
r_KN = this_r_KN.using(this_r_KN_data.value)
@qy.for_(N)
def _(n):
sample = samples.at(n)
qy.value_from_any(-numpy.inf).store(total)
@qy.for_(K)
def _(k):
responsibility = r_KN.at(k, n).data
self._sub_emitter.ll(
StridedArray.from_typed_pointer(
out.at(k).data.gep(0, 1)),
StridedArray.from_typed_pointer(sample.data),
responsibility,
)
log_add_double(total.load(),
responsibility.load()).store(total)
total_value = total.load()
@qy.if_else(total_value == -numpy.inf)
def _(then):
if then:
@qy.for_(K)
def _(k):
qy.value_from_any(1.0 / K).store(
r_KN.at(k, n).data)
else:
@qy.for_(K)
def _(k):
responsibility = r_KN.at(k, n).data
qy.exp(responsibility.load() -
total_value).store(responsibility)
# estimate new mixture and component parameters
@qy.for_(K)
def _(k):
component = out.at(k).data
self._sub_emitter.map(
prior.at(k),
samples,
r_KN.at(k),
StridedArray.from_typed_pointer(component.gep(0, 1)),
)
qy.value_from_any(0.0).store(total)
@qy.for_(N)
def _(n):
(total.load() + r_KN.at(k, n).data.load()).store(total)
(total.load() / float(N)).store(component.gep(0, 0))
# check for termination
last_r_KN = this_r_KN.using(last_r_KN_data.value)
@qy.if_(i > 0)
def _():
qy.value_from_any(0.0).store(total)
@qy.for_(K)
def _(k):
@qy.for_(N)
def _(n):
delta = r_KN.at(k, n).data.load() - last_r_KN.at(
k, n).data.load()
(total.load() + abs(delta)).store(total)
@qy.if_(total.load() < 1e-12)
def _():
qy.break_()
total_delta = total.load()
# swap the responsibility matrices
temp_r_KN_data_value = this_r_KN_data.value
this_r_KN_data.set(last_r_KN_data.value)
last_r_KN_data.set(temp_r_KN_data_value)
# compute the ll at this step
@qy.for_(N)
def _(n):
sample = samples.at(n)
total_ll = total.load()
qy.value_from_any(-numpy.inf).store(total)
@qy.for_(K)
def _(k):
self._sub_emitter.ll(
StridedArray.from_typed_pointer(
out.at(k).data.gep(0, 1)),
StridedArray.from_typed_pointer(sample.data),
component_ll,
)
log_add_double(
total.load(),
qy.log(out.at(k).data.gep(0, 0).load()) + component_ll.load(),
) \
.store(total)
(total_ll + total.load()).store(total)
total_ll = total.load()
# be informative
qy.py_printf("after EM step %i: delta %s; ll %s\n", i, total_delta,
total_ll)
# clean up
qy.heap_free(this_r_KN.data)
qy.heap_free(last_r_KN.data)
qy.return_()
def _map(self, prior, samples, weights, out, initializations):
"""
Emit computation of the estimated maximum-likelihood parameter.
"""
# mise en place
K = self._model._K
N = samples.shape[0]
# generate some initial parameters
self._map_initialize(prior, samples, weights, out, initializations)
# run EM until convergence
total = qy.stack_allocate(float)
component_ll = qy.stack_allocate(float)
this_r_KN = StridedArray.heap_allocated(float, (K, N))
last_r_KN = StridedArray.heap_allocated(float, (K, N))
this_r_KN_data = Variable.set_to(this_r_KN.data)
last_r_KN_data = Variable.set_to(last_r_KN.data)
@qy.for_(self._model._iterations)
def _(i):
# compute responsibilities
r_KN = this_r_KN.using(this_r_KN_data.value)
@qy.for_(N)
def _(n):
sample = samples.at(n)
qy.value_from_any(-numpy.inf).store(total)
@qy.for_(K)
def _(k):
responsibility = r_KN.at(k, n).data
self._sub_emitter.ll(
StridedArray.from_typed_pointer(out.at(k).data.gep(0, 1)),
StridedArray.from_typed_pointer(sample.data),
responsibility,
)
log_add_double(total.load(), responsibility.load()).store(total)
total_value = total.load()
@qy.if_else(total_value == -numpy.inf)
def _(then):
if then:
@qy.for_(K)
def _(k):
qy.value_from_any(1.0 / K).store(r_KN.at(k, n).data)
else:
@qy.for_(K)
def _(k):
responsibility = r_KN.at(k, n).data
qy.exp(responsibility.load() - total_value).store(responsibility)
# estimate new mixture and component parameters
@qy.for_(K)
def _(k):
component = out.at(k).data
self._sub_emitter.map(
prior.at(k),
samples,
r_KN.at(k),
StridedArray.from_typed_pointer(component.gep(0, 1)),
)
qy.value_from_any(0.0).store(total)
@qy.for_(N)
def _(n):
(total.load() + r_KN.at(k, n).data.load()).store(total)
(total.load() / float(N)).store(component.gep(0, 0))
# check for termination
last_r_KN = this_r_KN.using(last_r_KN_data.value)
@qy.if_(i > 0)
def _():
qy.value_from_any(0.0).store(total)
@qy.for_(K)
def _(k):
@qy.for_(N)
def _(n):
delta = r_KN.at(k, n).data.load() - last_r_KN.at(k, n).data.load()
(total.load() + abs(delta)).store(total)
@qy.if_(total.load() < 1e-12)
def _():
qy.break_()
total_delta = total.load()
# swap the responsibility matrices
temp_r_KN_data_value = this_r_KN_data.value
this_r_KN_data.set(last_r_KN_data.value)
last_r_KN_data.set(temp_r_KN_data_value)
# compute the ll at this step
@qy.for_(N)
def _(n):
sample = samples.at(n)
total_ll = total.load()
qy.value_from_any(-numpy.inf).store(total)
@qy.for_(K)
def _(k):
self._sub_emitter.ll(
StridedArray.from_typed_pointer(out.at(k).data.gep(0, 1)),
StridedArray.from_typed_pointer(sample.data),
component_ll,
)
log_add_double(
total.load(),
qy.log(out.at(k).data.gep(0, 0).load()) + component_ll.load(),
) \
.store(total)
(total_ll + total.load()).store(total)
total_ll = total.load()
# be informative
qy.py_printf("after EM step %i: delta %s; ll %s\n", i, total_delta, total_ll)
# clean up
qy.heap_free(this_r_KN.data)
qy.heap_free(last_r_KN.data)
qy.return_()
def _map_initialize(self, prior, samples, weights, out, initializations):
"""
Emit parameter initialization for EM.
"""
# generate a random initial component assignment
K = self._model._K
N = samples.shape[0]
total = qy.stack_allocate(float)
best_ll = qy.stack_allocate(float, -numpy.inf)
assigns = StridedArray.heap_allocated(int, (K, ))
best_assigns = StridedArray.heap_allocated(int, (K, ))
@qy.for_(initializations)
def _(i):
@qy.for_(K)
def _(k):
# randomly assign the component
j = qy.random_int(N)
component = StridedArray.from_typed_pointer(
out.at(k).data.gep(0, 1))
j.store(assigns.at(k).data)
self._sub_emitter.map(
prior.at(k),
samples.at(j).envelop(),
weights.at(j).envelop(),
component,
)
# compute our total likelihood
qy.value_from_any(0.0).store(total)
@qy.for_(N)
def _(n):
sample = samples.at(n)
mixture_ll = total.load()
qy.value_from_any(-numpy.inf).store(total)
@qy.for_(K)
def _(k):
component_ll = total.load()
self._sub_emitter.ll(
StridedArray.from_typed_pointer(
out.at(k).data.gep(0, 1)),
sample,
total,
)
log_add_double(component_ll, total.load()).store(total)
(mixture_ll + total.load()).store(total)
# best observed so far?
@qy.if_(total.load() >= best_ll.load())
def _():
total.load().store(best_ll)
@qy.for_(K)
def _(k):
assigns.at(k).data.load().store(best_assigns.at(k).data)
# recompute the best observed assignment
@qy.for_(K)
def _(k):
j = assigns.at(k).data.load()
self._sub_emitter.ml(
samples.at(j).envelop(),
weights.at(j).envelop(),
StridedArray.from_typed_pointer(out.at(k).data.gep(0, 1)),
)
qy.heap_free(assigns.data)
qy.heap_free(best_assigns.data)
# generate random initial component weights
@qy.for_(K)
def _(k):
r = qy.random()
r.store(out.at(k).data.gep(0, 0))
(total.load() + r).store(total)
@qy.for_(K)
def _(k):
p = out.at(k).data.gep(0, 0)
(p.load() / total.load()).store(p)
def _map_initialize(self, prior, samples, weights, out, initializations):
"""
Emit parameter initialization for EM.
"""
# generate a random initial component assignment
K = self._model._K
N = samples.shape[0]
total = qy.stack_allocate(float)
best_ll = qy.stack_allocate(float, -numpy.inf)
assigns = StridedArray.heap_allocated(int, (K,))
best_assigns = StridedArray.heap_allocated(int, (K,))
@qy.for_(initializations)
def _(i):
@qy.for_(K)
def _(k):
# randomly assign the component
j = qy.random_int(N)
component = StridedArray.from_typed_pointer(out.at(k).data.gep(0, 1))
j.store(assigns.at(k).data)
self._sub_emitter.map(
prior.at(k),
samples.at(j).envelop(),
weights.at(j).envelop(),
component,
)
# compute our total likelihood
qy.value_from_any(0.0).store(total)
@qy.for_(N)
def _(n):
sample = samples.at(n)
mixture_ll = total.load()
qy.value_from_any(-numpy.inf).store(total)
@qy.for_(K)
def _(k):
component_ll = total.load()
self._sub_emitter.ll(
StridedArray.from_typed_pointer(out.at(k).data.gep(0, 1)),
sample,
total,
)
log_add_double(component_ll, total.load()).store(total)
(mixture_ll + total.load()).store(total)
# best observed so far?
@qy.if_(total.load() >= best_ll.load())
def _():
total.load().store(best_ll)
@qy.for_(K)
def _(k):
assigns.at(k).data.load().store(best_assigns.at(k).data)
# recompute the best observed assignment
@qy.for_(K)
def _(k):
j = assigns.at(k).data.load()
self._sub_emitter.ml(
samples.at(j).envelop(),
weights.at(j).envelop(),
StridedArray.from_typed_pointer(out.at(k).data.gep(0, 1)),
)
qy.heap_free(assigns.data)
qy.heap_free(best_assigns.data)
# generate random initial component weights
@qy.for_(K)
def _(k):
r = qy.random()
r.store(out.at(k).data.gep(0, 0))
(total.load() + r).store(total)
@qy.for_(K)
def _(k):
p = out.at(k).data.gep(0, 0)
(p.load() / total.load()).store(p)
