Python slice_sample Examples

Example #1

File: test_util.py Project: jhamrick/bayesian-quadrature

def test_slice_sample_uniform():
    tutil.npseed()

    def logpdf(x):
        if x > 1 or x < 0:
            return -np.inf
        return 0

    w = np.array([0.5])
    x0 = np.array([0.0])
    samples = util.slice_sample(logpdf, 10000, w, xval=x0, nburn=10, freq=1)
    hist, bins = np.histogram(samples, bins=5, normed=True, range=[0, 1])

    assert (np.abs(hist - 1) < 0.05).all()

Example #2

File: test_util.py Project: jhamrick/bayesian-quadrature

def test_slice_sample_uniform():
    tutil.npseed()

    def logpdf(x):
        if x > 1 or x < 0:
            return -np.inf
        return 0

    w = np.array([0.5])
    x0 = np.array([0.0])
    samples = util.slice_sample(logpdf, 10000, w, xval=x0, nburn=10, freq=1)
    hist, bins = np.histogram(samples, bins=5, normed=True, range=[0, 1])

    assert (np.abs(hist - 1) < 0.05).all()

Example #3

File: test_util.py Project: jhamrick/bayesian-quadrature

def test_slice_sample_normal():
    tutil.npseed()

    def logpdf(x):
        return (-(x ** 2) / 2.) - 0.5 * np.log(2 * np.pi)
        
    w = np.array([1.0])
    x0 = np.array([0.0])
    samples = util.slice_sample(logpdf, 10000, w, xval=x0, nburn=10, freq=1)
    hist, bins = np.histogram(samples, bins=10, normed=True)
    centers = (bins[:-1] + bins[1:]) / 2.
    bin_pdf = np.exp(logpdf(centers))

    assert (np.abs(bin_pdf - hist) < 0.02).all()

Example #4

File: test_util.py Project: jhamrick/bayesian-quadrature

def test_slice_sample_normal():
    tutil.npseed()

    def logpdf(x):
        return (-(x**2) / 2.) - 0.5 * np.log(2 * np.pi)

    w = np.array([1.0])
    x0 = np.array([0.0])
    samples = util.slice_sample(logpdf, 10000, w, xval=x0, nburn=10, freq=1)
    hist, bins = np.histogram(samples, bins=10, normed=True)
    centers = (bins[:-1] + bins[1:]) / 2.
    bin_pdf = np.exp(logpdf(centers))

    assert (np.abs(bin_pdf - hist) < 0.02).all()