Python KalmanFilter.bind方法代码示例

# 需要导入模块: from statsmodels.tsa.statespace.kalman_filter import KalmanFilter [as 别名]
# 或者: from statsmodels.tsa.statespace.kalman_filter.KalmanFilter import bind [as 别名]
def test_slice_notation():
    endog = np.arange(10)*1.0
    mod = KalmanFilter(k_endog=1, k_states=2)
    mod.bind(endog)

    # Test invalid __setitem__
    def set_designs():
        mod['designs'] = 1
    def set_designs2():
        mod['designs',0,0] = 1
    def set_designs3():
        mod[0] = 1
    assert_raises(IndexError, set_designs)
    assert_raises(IndexError, set_designs2)
    assert_raises(IndexError, set_designs3)

    # Test invalid __getitem__
    assert_raises(IndexError, lambda: mod['designs'])
    assert_raises(IndexError, lambda: mod['designs',0,0,0])
    assert_raises(IndexError, lambda: mod[0])

    # Test valid __setitem__, __getitem__
    assert_equal(mod.design[0,0,0], 0)
    mod['design',0,0,0] = 1
    assert_equal(mod['design'].sum(), 1)
    assert_equal(mod.design[0,0,0], 1)
    assert_equal(mod['design',0,0,0], 1)

    # Test valid __setitem__, __getitem__ with unspecified time index
    mod['design'] = np.zeros(mod['design'].shape)
    assert_equal(mod.design[0,0], 0)
    mod['design',0,0] = 1
    assert_equal(mod.design[0,0], 1)
    assert_equal(mod['design',0,0], 1)

# 需要导入模块: from statsmodels.tsa.statespace.kalman_filter import KalmanFilter [as 别名]
# 或者: from statsmodels.tsa.statespace.kalman_filter.KalmanFilter import bind [as 别名]
class Options(object):
    def __init__(self, *args, **kwargs):

        # Dummy data
        endog = np.arange(10)
        k_states = 1

        self.model = KalmanFilter(k_endog=1, k_states=k_states, *args, **kwargs)
        self.model.bind(endog)

# 需要导入模块: from statsmodels.tsa.statespace.kalman_filter import KalmanFilter [as 别名]
# 或者: from statsmodels.tsa.statespace.kalman_filter.KalmanFilter import bind [as 别名]
def test_cython():
    # Test the cython _kalman_filter creation, re-creation, calling, etc.

    # Check that datatypes are correct:
    for prefix, dtype in tools.prefix_dtype_map.items():
        endog = np.array(1.0, ndmin=2, dtype=dtype)
        mod = KalmanFilter(k_endog=1, k_states=1, dtype=dtype)

        # Bind data and initialize the ?KalmanFilter object
        mod.bind(endog)
        mod._initialize_filter()

        # Check that the dtype and prefix are correct
        assert_equal(mod.prefix, prefix)
        assert_equal(mod.dtype, dtype)

        # Test that a dKalmanFilter instance was created
        assert_equal(prefix in mod._kalman_filters, True)
        kf = mod._kalman_filters[prefix]
        assert_equal(isinstance(kf, tools.prefix_kalman_filter_map[prefix]), True)

        # Test that the default returned _kalman_filter is the above instance
        assert_equal(mod._kalman_filter, kf)

    # Check that upcasting datatypes / ?KalmanFilter works (e.g. d -> z)
    mod = KalmanFilter(k_endog=1, k_states=1)

    # Default dtype is float
    assert_equal(mod.prefix, "d")
    assert_equal(mod.dtype, np.float64)

    # Prior to initialization, no ?KalmanFilter exists
    assert_equal(mod._kalman_filter, None)

    # Bind data and initialize the ?KalmanFilter object
    endog = np.ascontiguousarray(np.array([1.0, 2.0], dtype=np.float64))
    mod.bind(endog)
    mod._initialize_filter()
    kf = mod._kalman_filters["d"]

    # Rebind data, still float, check that we haven't changed
    mod.bind(endog)
    mod._initialize_filter()
    assert_equal(mod._kalman_filter, kf)

    # Force creating new ?Statespace and ?KalmanFilter, by changing the
    # time-varying character of an array
    mod.design = np.zeros((1, 1, 2))
    mod._initialize_filter()
    assert_equal(mod._kalman_filter == kf, False)
    kf = mod._kalman_filters["d"]

    # Rebind data, now complex, check that the ?KalmanFilter instance has
    # changed
    endog = np.ascontiguousarray(np.array([1.0, 2.0], dtype=np.complex128))
    mod.bind(endog)
    assert_equal(mod._kalman_filter == kf, False)

# 需要导入模块: from statsmodels.tsa.statespace.kalman_filter import KalmanFilter [as 别名]
# 或者: from statsmodels.tsa.statespace.kalman_filter.KalmanFilter import bind [as 别名]
def test_kalman_filter_pickle(data):
    # Construct the statespace representation
    true = results_kalman_filter.uc_uni
    k_states = 4
    model = KalmanFilter(k_endog=1, k_states=k_states)
    model.bind(data['lgdp'].values)

    model.design[:, :, 0] = [1, 1, 0, 0]
    model.transition[([0, 0, 1, 1, 2, 3],
                      [0, 3, 1, 2, 1, 3],
                      [0, 0, 0, 0, 0, 0])] = [1, 1, 0, 0, 1, 1]
    model.selection = np.eye(model.k_states)

    # Update matrices with given parameters
    (sigma_v, sigma_e, sigma_w, phi_1, phi_2) = np.array(
        true['parameters']
    )
    model.transition[([1, 1], [1, 2], [0, 0])] = [phi_1, phi_2]
    model.state_cov[
        np.diag_indices(k_states) + (np.zeros(k_states, dtype=int),)] = [
        sigma_v ** 2, sigma_e ** 2, 0, sigma_w ** 2
    ]

    # Initialization
    initial_state = np.zeros((k_states,))
    initial_state_cov = np.eye(k_states) * 100

    # Initialization: modification
    initial_state_cov = np.dot(
        np.dot(model.transition[:, :, 0], initial_state_cov),
        model.transition[:, :, 0].T
    )
    model.initialize_known(initial_state, initial_state_cov)
    pkl_mod = cPickle.loads(cPickle.dumps(model))

    results = model.filter()
    pkl_results = pkl_mod.filter()

    assert_allclose(results.llf_obs[true['start']:].sum(),
                    pkl_results.llf_obs[true['start']:].sum())
    assert_allclose(results.filtered_state[0][true['start']:],
                    pkl_results.filtered_state[0][true['start']:])
    assert_allclose(results.filtered_state[1][true['start']:],
                    pkl_results.filtered_state[1][true['start']:])
    assert_allclose(results.filtered_state[3][true['start']:],
                    pkl_results.filtered_state[3][true['start']:])

# 需要导入模块: from statsmodels.tsa.statespace.kalman_filter import KalmanFilter [as 别名]
# 或者: from statsmodels.tsa.statespace.kalman_filter.KalmanFilter import bind [as 别名]
def test_slice_notation():
    # Test setting and getting state space representation matrices using the
    # slice notation.

    endog = np.arange(10) * 1.0
    mod = KalmanFilter(k_endog=1, k_states=2)
    mod.bind(endog)

    # Test invalid __setitem__
    def set_designs():
        mod["designs"] = 1

    def set_designs2():
        mod["designs", 0, 0] = 1

    def set_designs3():
        mod[0] = 1

    assert_raises(IndexError, set_designs)
    assert_raises(IndexError, set_designs2)
    assert_raises(IndexError, set_designs3)

    # Test invalid __getitem__
    assert_raises(IndexError, lambda: mod["designs"])
    assert_raises(IndexError, lambda: mod["designs", 0, 0, 0])
    assert_raises(IndexError, lambda: mod[0])

    # Test valid __setitem__, __getitem__
    assert_equal(mod.design[0, 0, 0], 0)
    mod["design", 0, 0, 0] = 1
    assert_equal(mod["design"].sum(), 1)
    assert_equal(mod.design[0, 0, 0], 1)
    assert_equal(mod["design", 0, 0, 0], 1)

    # Test valid __setitem__, __getitem__ with unspecified time index
    mod["design"] = np.zeros(mod["design"].shape)
    assert_equal(mod.design[0, 0], 0)
    mod["design", 0, 0] = 1
    assert_equal(mod.design[0, 0], 1)
    assert_equal(mod["design", 0, 0], 1)

# 需要导入模块: from statsmodels.tsa.statespace.kalman_filter import KalmanFilter [as 别名]
# 或者: from statsmodels.tsa.statespace.kalman_filter.KalmanFilter import bind [as 别名]
class Clark1987(object):
    """
    Clark's (1987) univariate unobserved components model of real GDP (as
    presented in Kim and Nelson, 1999)

    Test data produced using GAUSS code described in Kim and Nelson (1999) and
    found at http://econ.korea.ac.kr/~cjkim/SSMARKOV.htm

    See `results.results_kalman_filter` for more information.
    """
    def __init__(self, dtype=float, **kwargs):
        self.true = results_kalman_filter.uc_uni
        self.true_states = pd.DataFrame(self.true['states'])

        # GDP, Quarterly, 1947.1 - 1995.3
        data = pd.DataFrame(
            self.true['data'],
            index=pd.date_range('1947-01-01', '1995-07-01', freq='QS'),
            columns=['GDP']
        )
        data['lgdp'] = np.log(data['GDP'])

        # Construct the statespace representation
        k_states = 4
        self.model = KalmanFilter(k_endog=1, k_states=k_states, **kwargs)
        self.model.bind(data['lgdp'].values)

        self.model.design[:, :, 0] = [1, 1, 0, 0]
        self.model.transition[([0, 0, 1, 1, 2, 3],
                               [0, 3, 1, 2, 1, 3],
                               [0, 0, 0, 0, 0, 0])] = [1, 1, 0, 0, 1, 1]
        self.model.selection = np.eye(self.model.k_states)

        # Update matrices with given parameters
        (sigma_v, sigma_e, sigma_w, phi_1, phi_2) = np.array(
            self.true['parameters']
        )
        self.model.transition[([1, 1], [1, 2], [0, 0])] = [phi_1, phi_2]
        self.model.state_cov[
            np.diag_indices(k_states)+(np.zeros(k_states, dtype=int),)] = [
            sigma_v**2, sigma_e**2, 0, sigma_w**2
        ]

        # Initialization
        initial_state = np.zeros((k_states,))
        initial_state_cov = np.eye(k_states)*100

        # Initialization: modification
        initial_state_cov = np.dot(
            np.dot(self.model.transition[:, :, 0], initial_state_cov),
            self.model.transition[:, :, 0].T
        )
        self.model.initialize_known(initial_state, initial_state_cov)

    def run_filter(self):
        # Filter the data
        self.results = self.model.filter()

    def test_loglike(self):
        assert_almost_equal(
            self.results.llf_obs[self.true['start']:].sum(),
            self.true['loglike'], 5
        )

    def test_filtered_state(self):
        assert_almost_equal(
            self.results.filtered_state[0][self.true['start']:],
            self.true_states.iloc[:, 0], 4
        )
        assert_almost_equal(
            self.results.filtered_state[1][self.true['start']:],
            self.true_states.iloc[:, 1], 4
        )
        assert_almost_equal(
            self.results.filtered_state[3][self.true['start']:],
            self.true_states.iloc[:, 2], 4
        )

# 需要导入模块: from statsmodels.tsa.statespace.kalman_filter import KalmanFilter [as 别名]
# 或者: from statsmodels.tsa.statespace.kalman_filter.KalmanFilter import bind [as 别名]
class Clark1989(object):
    """
    Clark's (1989) bivariate unobserved components model of real GDP (as
    presented in Kim and Nelson, 1999)

    Tests two-dimensional observation data.

    Test data produced using GAUSS code described in Kim and Nelson (1999) and
    found at http://econ.korea.ac.kr/~cjkim/SSMARKOV.htm

    See `results.results_kalman_filter` for more information.
    """
    def __init__(self, dtype=float, **kwargs):
        self.true = results_kalman_filter.uc_bi
        self.true_states = pd.DataFrame(self.true['states'])

        # GDP and Unemployment, Quarterly, 1948.1 - 1995.3
        data = pd.DataFrame(
            self.true['data'],
            index=pd.date_range('1947-01-01', '1995-07-01', freq='QS'),
            columns=['GDP', 'UNEMP']
        )[4:]
        data['GDP'] = np.log(data['GDP'])
        data['UNEMP'] = (data['UNEMP']/100)

        k_states = 6
        self.model = KalmanFilter(k_endog=2, k_states=k_states, **kwargs)
        self.model.bind(np.ascontiguousarray(data.values))

        # Statespace representation
        self.model.design[:, :, 0] = [[1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1]]
        self.model.transition[
            ([0, 0, 1, 1, 2, 3, 4, 5],
             [0, 4, 1, 2, 1, 2, 4, 5],
             [0, 0, 0, 0, 0, 0, 0, 0])
        ] = [1, 1, 0, 0, 1, 1, 1, 1]
        self.model.selection = np.eye(self.model.k_states)

        # Update matrices with given parameters
        (sigma_v, sigma_e, sigma_w, sigma_vl, sigma_ec,
         phi_1, phi_2, alpha_1, alpha_2, alpha_3) = np.array(
            self.true['parameters'],
        )
        self.model.design[([1, 1, 1], [1, 2, 3], [0, 0, 0])] = [
            alpha_1, alpha_2, alpha_3
        ]
        self.model.transition[([1, 1], [1, 2], [0, 0])] = [phi_1, phi_2]
        self.model.obs_cov[1, 1, 0] = sigma_ec**2
        self.model.state_cov[
            np.diag_indices(k_states)+(np.zeros(k_states, dtype=int),)] = [
            sigma_v**2, sigma_e**2, 0, 0, sigma_w**2, sigma_vl**2
        ]

        # Initialization
        initial_state = np.zeros((k_states,))
        initial_state_cov = np.eye(k_states)*100

        # Initialization: self.modelification
        initial_state_cov = np.dot(
            np.dot(self.model.transition[:, :, 0], initial_state_cov),
            self.model.transition[:, :, 0].T
        )
        self.model.initialize_known(initial_state, initial_state_cov)

    def run_filter(self):
        # Filter the data
        self.results = self.model.filter()

    def test_loglike(self):
        assert_almost_equal(
            # self.results.llf_obs[self.true['start']:].sum(),
            self.results.llf_obs[0:].sum(),
            self.true['loglike'], 2
        )

    def test_filtered_state(self):
        assert_almost_equal(
            self.results.filtered_state[0][self.true['start']:],
            self.true_states.iloc[:, 0], 4
        )
        assert_almost_equal(
            self.results.filtered_state[1][self.true['start']:],
            self.true_states.iloc[:, 1], 4
        )
        assert_almost_equal(
            self.results.filtered_state[4][self.true['start']:],
            self.true_states.iloc[:, 2], 4
        )
        assert_almost_equal(
            self.results.filtered_state[5][self.true['start']:],
            self.true_states.iloc[:, 3], 4
        )