import numpy as np
from emcee import backends, EnsembleSampler
__all__ = ["test_blob_shape"]
class BlobLogProb(object):
def __init__(self, blob_function):
self.blob_function = blob_function
def __call__(self, params):
return -0.5 * np.sum(params**2), self.blob_function(params)
@pytest.mark.parametrize("backend", backends.get_test_backends())
def test_blob_shape(backend):
with backend() as be:
np.random.seed(42)
nblobs = 5
model = BlobLogProb(lambda x: np.random.randn(nblobs))
coords = np.random.randn(32, 3)
nwalkers, ndim = coords.shape
sampler = EnsembleSampler(nwalkers, ndim, model, backend=be)
nsteps = 10
sampler.run_mcmc(coords, nsteps)
assert sampler.get_blobs().shape == (nsteps, nwalkers, nblobs)
model = BlobLogProb(lambda x: np.random.randn())
# -*- coding: utf-8 -*-
from __future__ import division, print_function
import pickle
from itertools import product
import pytest
import numpy as np
from emcee import moves, backends, EnsembleSampler
__all__ = ["test_shapes", "test_errors", "test_thin", "test_vectorize"]
all_backends = backends.get_test_backends()
def normal_log_prob(params):
return -0.5 * np.sum(params**2)
@pytest.mark.parametrize("backend, moves", product(
all_backends,
[
None,
moves.GaussianMove(0.5),
[moves.StretchMove(), moves.GaussianMove(0.5)],
[(moves.StretchMove(), 0.3), (moves.GaussianMove(0.5), 0.1)],
])
)
def test_shapes(backend, moves, nwalkers=32, ndim=3, nsteps=10, seed=1234):
# -*- coding: utf-8 -*-
import os
from itertools import product
import h5py
import numpy as np
import pytest
from emcee import EnsembleSampler, backends
__all__ = ["test_backend", "test_reload"]
all_backends = backends.get_test_backends()
other_backends = all_backends[1:]
dtypes = [None, [("log_prior", float), ("mean", int)]]
def normal_log_prob(params):
return -0.5 * np.sum(params**2)
def normal_log_prob_blobs(params):
return normal_log_prob(params), 0.1, int(5)
def run_sampler(
backend,
nwalkers=32,
ndim=3,
nsteps=25,
from emcee import backends, EnsembleSampler
__all__ = ["test_blob_shape"]
class BlobLogProb(object):
def __init__(self, blob_function):
self.blob_function = blob_function
def __call__(self, params):
return -0.5 * np.sum(params**2), self.blob_function(params)
@pytest.mark.parametrize("backend", backends.get_test_backends())
def test_blob_shape(backend):
with backend() as be:
np.random.seed(42)
nblobs = 5
model = BlobLogProb(lambda x: np.random.randn(nblobs))
coords = np.random.randn(32, 3)
nwalkers, ndim = coords.shape
sampler = EnsembleSampler(nwalkers, ndim, model, backend=be)
nsteps = 10
sampler.run_mcmc(coords, nsteps)
assert sampler.get_blobs().shape == (nsteps, nwalkers, nblobs)
model = BlobLogProb(lambda x: np.random.randn())