Ejemplo n.º 1
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def net() -> BayesNet:
    with Model() as m:
        m.gamma = Gamma(1., 1.)
        m.exp = Exponential(1.)
        m.cauchy = Cauchy(m.gamma, m.exp)

    return m.to_bayes_net()
Ejemplo n.º 2
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def model() -> Model:

    with Model() as m:
        m.temperature = Uniform(0., 100.)
        m.thermometer_one = Gaussian(m.temperature, 1.0)
        m.thermometer_two = Gaussian(m.temperature, 1.0)

    return m
Ejemplo n.º 3
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def model() -> Model:
    with Model() as m:
        m.a = Gaussian(0., 50.)
        m.b = Gaussian(0., 50.)
        m.c = m.a + m.b
        m.d = Gaussian(m.c, 1.)
        m.d.observe(20.0)
    return m
Ejemplo n.º 4
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def tensor_net() -> BayesNet:
    with Model() as m:
        m.gamma = Gamma(
            np.array([1., 1., 1., 1.]).reshape((2, 2)),
            np.array([2., 2., 2., 2.]).reshape((2, 2)))
        m.exp = Exponential(np.array([1., 1., 1., 1.]).reshape((2, 2)))
        m.add = m.gamma + m.exp

    return m.to_bayes_net()
def model() -> Model:
    KeanuRandom.set_default_random_seed(1)

    with Model() as m:
        m.a = Gaussian(0., 50.)
        m.b = Gaussian(0., 50.)
        m.c = m.a + m.b
        m.d = Gaussian(m.c, 1.)
        m.d.observe(20.0)
    return m
Ejemplo n.º 6
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def test_autocorr_returns_ndarray_of_correct_dtype() -> None:
    with Model() as m:
        m.uniform = Uniform(0, 1000)
    net = m.to_bayes_net()
    samples = sample(net=net, sample_from=net.iter_latent_vertices(), draws=10)
    valid_key = list(samples.keys())[0]
    sample_ = samples.get(valid_key)
    assert sample_ is not None
    autocorr = stats.autocorrelation(sample_)
    assert type(autocorr) == np.ndarray
Ejemplo n.º 7
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def test_to_bayes_net_excludes_non_vertices() -> None:
    with Model() as m:
        m.not_a_vertex = 1
        m.vertex = Gamma(0.5, 0.1)

    net = m.to_bayes_net()

    assert isinstance(net, BayesNet)

    net_vertex_ids = [vertex.get_id() for vertex in net.iter_latent_or_observed_vertices()]

    assert len(net_vertex_ids) == 1
    assert m.vertex.get_id() in net_vertex_ids
Ejemplo n.º 8
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def autocorrelation_example_nd():
    with Model() as m:
        m.a = Gaussian(np.array([[20., 30.], [40., 60.]]),
                       np.array([[1., 1.], [1., 1.]]))
    bayes_net = m.to_bayes_net()
    # %%SNIPPET_START%% PythonNdAutocorrelation
    algo = MetropolisHastingsSampler()
    posterior_samples = sample(net=bayes_net,
                               sample_from=bayes_net.get_latent_vertices(),
                               sampling_algorithm=algo,
                               draws=100)
    vertex_samples = posterior_samples.get('a')
    ac = stats.autocorrelation(vertex_samples, (0, 1))
Ejemplo n.º 9
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    def model(self) -> Model:
        start_year, end_year = (self._data.index.min(), self._data.index.max())

        with Model() as m:
            m.switchpoint = UniformInt(start_year, end_year + 1)

            m.early_rate = Exponential(1.0)
            m.late_rate = Exponential(1.0)

            m.years = np.array(self._data.index)
            m.rates = If(m.switchpoint > m.years, m.early_rate, m.late_rate)
            m.disasters = Poisson(m.rates)

        return m
Ejemplo n.º 10
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def test_thermometers_example():
    # %%SNIPPET_START%% PythonTwoThermometers
    with Model() as m:
        m.temperature = Uniform(20., 30.)
        m.first_thermometer = Gaussian(m.temperature, 2.5)
        m.second_thermometer = Gaussian(m.temperature, 5.)

    m.first_thermometer.observe(25.)
    m.second_thermometer.observe(30.)

    bayes_net = m.to_bayes_net()
    optimizer = GradientOptimizer(bayes_net)
    optimizer.max_a_posteriori()

    calculated_temperature = m.temperature.get_value()
Ejemplo n.º 11
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def autocorrelation_example_scalar():
    with Model() as m:
        m.a = Gaussian(20, 1.)
        m.b = Gaussian(20, 1.)
        m.c = Gaussian(m.a + m.b, 1.)
    m.c.observe(43.)
    m.a.set_value(20.)
    m.b.set_value(20.)
    bayes_net = m.to_bayes_net()
    # %%SNIPPET_START%% PythonScalarAutocorrelation
    algo = MetropolisHastingsSampler()
    posterior_samples = sample(net=bayes_net,
                               sample_from=bayes_net.get_latent_vertices(),
                               sampling_algorithm=algo,
                               draws=100)
    vertex_samples = posterior_samples.get('a')
    ac = stats.autocorrelation(vertex_samples)
Ejemplo n.º 12
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def test_autocorrelation_same_for_streaming_as_batch() -> None:
    with Model() as model:
        model.uniform = Uniform(0, 1000)
    net = model.to_bayes_net()
    draws = 15
    set_starting_state(model)
    samples = sample(net=net, sample_from=net.get_latent_vertices(), algo="metropolis", draws=draws)
    set_starting_state(model)
    iter_samples = generate_samples(net=net, sample_from=net.get_latent_vertices(), algo="metropolis")

    samples_dataframe = pd.DataFrame()
    for next_sample in islice(iter_samples, draws):
        samples_dataframe = samples_dataframe.append(next_sample, ignore_index=True)

    for vertex_id in samples_dataframe:
        autocorr_streaming = stats.autocorrelation(list(samples_dataframe[vertex_id].values))
        autocorr_batch = stats.autocorrelation(samples[vertex_id])
        np.testing.assert_array_equal(autocorr_batch, autocorr_streaming)
Ejemplo n.º 13
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def inference_example_metropolis():
    # %%SNIPPET_START%% PythonMetropolisExample
    with Model() as m:
        m.a = Gaussian(20., 1.)
        m.b = Gaussian(20., 1.)
        m.c = Gaussian(m.a + m.b, 1.)
    m.c.observe(43.)
    m.a.set_value(20.)
    m.b.set_value(20.)
    bayes_net = m.to_bayes_net()
    posterior_samples = sample(net=bayes_net,
                               sample_from=bayes_net.get_latent_vertices(),
                               algo="metropolis",
                               draws=100000)

    average_posterior_a = np.average(posterior_samples.get('a'))
    average_posterior_b = np.average(posterior_samples.get('b'))

    actual = average_posterior_a + average_posterior_b
Ejemplo n.º 14
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def test_to_bayes_net() -> None:
    with Model() as m:
        m.mu = Exponential(1.)
        m.sigma = Gamma(0.5, 0.1)

        m.gaussian = Gaussian(m.mu, m.sigma)

    net = m.to_bayes_net()

    assert isinstance(net, BayesNet)

    net_vertex_ids = [
        vertex.get_id() for vertex in net.get_latent_or_observed_vertices()
    ]

    assert len(net_vertex_ids) == 3
    assert m.mu.get_id() in net_vertex_ids
    assert m.sigma.get_id() in net_vertex_ids
    assert m.gaussian.get_id() in net_vertex_ids
Ejemplo n.º 15
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def inference_example_hmc_nuts():
    with Model() as m:
        m.a = Gaussian(20., 1.)
        m.b = Gaussian(20., 1.)
        m.c = Gaussian(m.a + m.b, 1.)
    m.c.observe(43.)
    m.a.set_value(20.)
    m.b.set_value(20.)
    bayes_net = m.to_bayes_net()
    # %%SNIPPET_START%% PythonHamiltonianExample
    posterior_samples = sample(net=bayes_net,
                               sample_from=bayes_net.get_latent_vertices(),
                               algo="hamiltonian",
                               draws=2000)
    # %%SNIPPET_END%% PythonHamiltonianExample
    # %%SNIPPET_START%% PythonNUTSExample
    posterior_samples = sample(net=bayes_net,
                               sample_from=bayes_net.get_latent_vertices(),
                               algo="NUTS",
                               draws=2000)
Ejemplo n.º 16
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def net() -> BayesNet:
    with Model() as m:
        m.gamma = Gamma(1., 1.)
        m.gaussian = Gaussian(0., m.gamma)

    return m.to_bayes_net()
Ejemplo n.º 17
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def build_model():
    with Model() as m:
        m.temperature = Uniform(20., 30.)
        m.first_thermometer = Gaussian(m.temperature, 2.5)
        m.second_thermometer = Gaussian(m.temperature, 5.)
    return m