Python multivariate_normal.logpdf方法代码示例

# 需要导入模块: from scipy.stats import multivariate_normal [as 别名]
# 或者: from scipy.stats.multivariate_normal import logpdf [as 别名]
def test_matches_multivariate(self):
        # Check that the pdfs match those obtained by vectorising and
        # treating as a multivariate normal.
        for i in range(1,5):
            for j in range(1,5):
                M = 0.3 * np.ones((i,j))
                U = 0.5 * np.identity(i) + 0.5 * np.ones((i,i))
                V = 0.7 * np.identity(j) + 0.3 * np.ones((j,j))

                frozen = matrix_normal(mean=M, rowcov=U, colcov=V)
                X = frozen.rvs(random_state=1234)
                pdf1 = frozen.pdf(X)
                logpdf1 = frozen.logpdf(X)

                vecX = X.T.flatten()
                vecM = M.T.flatten()
                cov = np.kron(V,U)
                pdf2 = multivariate_normal.pdf(vecX, mean=vecM, cov=cov)
                logpdf2 = multivariate_normal.logpdf(vecX, mean=vecM, cov=cov)

                assert_allclose(pdf1, pdf2, rtol=1E-10)
                assert_allclose(logpdf1, logpdf2, rtol=1E-10) 

# 需要导入模块: from scipy.stats import multivariate_normal [as 别名]
# 或者: from scipy.stats.multivariate_normal import logpdf [as 别名]
def test_array_input(self):
        # Check array of inputs has the same output as the separate entries.
        num_rows = 4
        num_cols = 3
        M = 0.3 * np.ones((num_rows,num_cols))
        U = 0.5 * np.identity(num_rows) + 0.5 * np.ones((num_rows, num_rows))
        V = 0.7 * np.identity(num_cols) + 0.3 * np.ones((num_cols, num_cols))
        N = 10

        frozen = matrix_normal(mean=M, rowcov=U, colcov=V)
        X1 = frozen.rvs(size=N, random_state=1234)
        X2 = frozen.rvs(size=N, random_state=4321)
        X = np.concatenate((X1[np.newaxis,:,:,:],X2[np.newaxis,:,:,:]), axis=0)
        assert_equal(X.shape, (2, N, num_rows, num_cols))

        array_logpdf = frozen.logpdf(X)
        assert_equal(array_logpdf.shape, (2, N))
        for i in range(2):
            for j in range(N):
                separate_logpdf = matrix_normal.logpdf(X[i,j], mean=M,
                                                       rowcov=U, colcov=V)
                assert_allclose(separate_logpdf, array_logpdf[i,j], 1E-10) 

# 需要导入模块: from scipy.stats import multivariate_normal [as 别名]
# 或者: from scipy.stats.multivariate_normal import logpdf [as 别名]
def test_frozen_dirichlet(self):
        np.random.seed(2846)

        n = np.random.randint(1, 32)
        alpha = np.random.uniform(10e-10, 100, n)

        d = dirichlet(alpha)

        assert_equal(d.var(), dirichlet.var(alpha))
        assert_equal(d.mean(), dirichlet.mean(alpha))
        assert_equal(d.entropy(), dirichlet.entropy(alpha))
        num_tests = 10
        for i in range(num_tests):
            x = np.random.uniform(10e-10, 100, n)
            x /= np.sum(x)
            assert_equal(d.pdf(x[:-1]), dirichlet.pdf(x[:-1], alpha))
            assert_equal(d.logpdf(x[:-1]), dirichlet.logpdf(x[:-1], alpha)) 

# 需要导入模块: from scipy.stats import multivariate_normal [as 别名]
# 或者: from scipy.stats.multivariate_normal import logpdf [as 别名]
def GP(seq_length=30, num_samples=28*5*100, num_signals=1, scale=0.1, kernel='rbf', **kwargs):
    # the shape of the samples is num_samples x seq_length x num_signals
    samples = np.empty(shape=(num_samples, seq_length, num_signals))
    #T = np.arange(seq_length)/seq_length    # note, between 0 and 1
    T = np.arange(seq_length)    # note, not between 0 and 1
    if kernel == 'periodic':
        cov = periodic_kernel(T)
    elif kernel =='rbf':
        cov = rbf_kernel(T.reshape(-1, 1), gamma=scale)
    else:
        raise NotImplementedError
    # scale the covariance
    cov *= 0.2
    # define the distribution
    mu = np.zeros(seq_length)
    print(np.linalg.det(cov))
    distribution = multivariate_normal(mean=np.zeros(cov.shape[0]), cov=cov)
    pdf = distribution.logpdf
    # now generate samples
    for i in range(num_signals):
        samples[:, :, i] = distribution.rvs(size=num_samples)
    return samples, pdf 

# 需要导入模块: from scipy.stats import multivariate_normal [as 别名]
# 或者: from scipy.stats.multivariate_normal import logpdf [as 别名]
def changepoint_pdf(Y, cov_ms, cov_Ms):
    """
    """
    seq_length = Y.shape[0]
    logpdf = []
    for (i, m) in enumerate(range(int(seq_length/2), seq_length-1)):
        Y_m = Y[:m, 0]
        Y_M = Y[m:, 0]
        M = seq_length - m
        # generate mean function for second part
        Ymin = np.min(Y_m)
        initial_val = Y_m[-1]
        if Ymin > 1:
            final_val = (1.0 - M/seq_length)*Ymin
        else:
            final_val = (1.0 + M/seq_length)*Ymin
        mu_M = np.linspace(initial_val, final_val, M)
        # ah yeah
        logpY_m = multivariate_normal.logpdf(Y_m, mean=np.zeros(m), cov=cov_ms[i])
        logpY_M = multivariate_normal.logpdf(Y_M, mean=mu_M, cov=cov_Ms[i])
        logpdf_m = logpY_m + logpY_M
        logpdf.append(logpdf_m)
    return logpdf 

# 需要导入模块: from scipy.stats import multivariate_normal [as 别名]
# 或者: from scipy.stats.multivariate_normal import logpdf [as 别名]
def logpdf(x, mean=None, cov=1, allow_singular=True):
    """
    Computes the log of the probability density function of the normal
    N(mean, cov) for the data x. The normal may be univariate or multivariate.

    Wrapper for older versions of scipy.multivariate_normal.logpdf which
    don't support support the allow_singular keyword prior to verion 0.15.0.

    If it is not supported, and cov is singular or not PSD you may get
    an exception.

    `x` and `mean` may be column vectors, row vectors, or lists.
    """

    if mean is not None:
        flat_mean = np.asarray(mean).flatten()
    else:
        flat_mean = None

    flat_x = np.asarray(x).flatten()

    if _support_singular:
        return multivariate_normal.logpdf(flat_x, flat_mean, cov, allow_singular)
    return multivariate_normal.logpdf(flat_x, flat_mean, cov) 

# 需要导入模块: from scipy.stats import multivariate_normal [as 别名]
# 或者: from scipy.stats.multivariate_normal import logpdf [as 别名]
def predict(self, X):
        X.interpolate(inplace=True)
        X.bfill(inplace=True)
        self.model.eval()
        input_data, target_data = self._input_and_target_data_eval(X)

        predictions = self.model(input_data)
        errors, stacked_preds = self._calc_errors(predictions, target_data, return_stacked_predictions=True)

        if self.details:
            self.prediction_details.update({'predictions_mean': np.pad(
                stacked_preds.mean(axis=3).squeeze(0).T, ((0, 0), (self.len_in + self.len_out - 1, 0)),
                'constant', constant_values=np.nan)})
            self.prediction_details.update({'errors_mean': np.pad(
                errors.mean(axis=3).reshape(-1), (self.len_in + self.len_out - 1, 0),
                'constant', constant_values=np.nan)})

        norm = errors.reshape(errors.shape[0] * errors.shape[1], X.shape[-1] * self.len_out)
        scores = -multivariate_normal.logpdf(norm, mean=self.mean, cov=self.cov, allow_singular=True)
        scores = np.pad(scores, (self.len_in + self.len_out - 1, 0), 'constant', constant_values=np.nan)
        return scores 

# 需要导入模块: from scipy.stats import multivariate_normal [as 别名]
# 或者: from scipy.stats.multivariate_normal import logpdf [as 别名]
def test_logpdf(self):
        # Check that the log of the pdf is in fact the logpdf
        np.random.seed(1234)
        x = np.random.randn(5)
        mean = np.random.randn(5)
        cov = np.abs(np.random.randn(5))
        d1 = multivariate_normal.logpdf(x, mean, cov)
        d2 = multivariate_normal.pdf(x, mean, cov)
        assert_allclose(d1, np.log(d2)) 

# 需要导入模块: from scipy.stats import multivariate_normal [as 别名]
# 或者: from scipy.stats.multivariate_normal import logpdf [as 别名]
def test_logpdf_default_values(self):
        # Check that the log of the pdf is in fact the logpdf
        # with default parameters Mean=None and cov = 1
        np.random.seed(1234)
        x = np.random.randn(5)
        d1 = multivariate_normal.logpdf(x)
        d2 = multivariate_normal.pdf(x)
        # check whether default values are being used
        d3 = multivariate_normal.logpdf(x, None, 1)
        d4 = multivariate_normal.pdf(x, None, 1)
        assert_allclose(d1, np.log(d2))
        assert_allclose(d3, np.log(d4)) 

# 需要导入模块: from scipy.stats import multivariate_normal [as 别名]
# 或者: from scipy.stats.multivariate_normal import logpdf [as 别名]
def test_frozen(self):
        # The frozen distribution should agree with the regular one
        np.random.seed(1234)
        x = np.random.randn(5)
        mean = np.random.randn(5)
        cov = np.abs(np.random.randn(5))
        norm_frozen = multivariate_normal(mean, cov)
        assert_allclose(norm_frozen.pdf(x), multivariate_normal.pdf(x, mean, cov))
        assert_allclose(norm_frozen.logpdf(x),
                        multivariate_normal.logpdf(x, mean, cov))
        assert_allclose(norm_frozen.cdf(x), multivariate_normal.cdf(x, mean, cov))
        assert_allclose(norm_frozen.logcdf(x),
                        multivariate_normal.logcdf(x, mean, cov)) 

# 需要导入模块: from scipy.stats import multivariate_normal [as 别名]
# 或者: from scipy.stats.multivariate_normal import logpdf [as 别名]
def test_exception_singular_cov(self):
        np.random.seed(1234)
        x = np.random.randn(5)
        mean = np.random.randn(5)
        cov = np.ones((5, 5))
        e = np.linalg.LinAlgError
        assert_raises(e, multivariate_normal, mean, cov)
        assert_raises(e, multivariate_normal.pdf, x, mean, cov)
        assert_raises(e, multivariate_normal.logpdf, x, mean, cov)
        assert_raises(e, multivariate_normal.cdf, x, mean, cov)
        assert_raises(e, multivariate_normal.logcdf, x, mean, cov) 

# 需要导入模块: from scipy.stats import multivariate_normal [as 别名]
# 或者: from scipy.stats.multivariate_normal import logpdf [as 别名]
def test_numpy_rvs_shape_compatibility(self):
        np.random.seed(2846)
        alpha = np.array([1.0, 2.0, 3.0])
        x = np.random.dirichlet(alpha, size=7)
        assert_equal(x.shape, (7, 3))
        assert_raises(ValueError, dirichlet.pdf, x, alpha)
        assert_raises(ValueError, dirichlet.logpdf, x, alpha)
        dirichlet.pdf(x.T, alpha)
        dirichlet.pdf(x.T[:-1], alpha)
        dirichlet.logpdf(x.T, alpha)
        dirichlet.logpdf(x.T[:-1], alpha) 

# 需要导入模块: from scipy.stats import multivariate_normal [as 别名]
# 或者: from scipy.stats.multivariate_normal import logpdf [as 别名]
def test_alpha_with_zeros(self):
        np.random.seed(2846)
        alpha = [1.0, 0.0, 3.0]
        # don't pass invalid alpha to np.random.dirichlet
        x = np.random.dirichlet(np.maximum(1e-9, alpha), size=7).T
        assert_raises(ValueError, dirichlet.pdf, x, alpha)
        assert_raises(ValueError, dirichlet.logpdf, x, alpha) 

# 需要导入模块: from scipy.stats import multivariate_normal [as 别名]
# 或者: from scipy.stats.multivariate_normal import logpdf [as 别名]
def test_data_with_zeros(self):
        alpha = np.array([1.0, 2.0, 3.0, 4.0])
        x = np.array([0.1, 0.0, 0.2, 0.7])
        dirichlet.pdf(x, alpha)
        dirichlet.logpdf(x, alpha)
        alpha = np.array([1.0, 1.0, 1.0, 1.0])
        assert_almost_equal(dirichlet.pdf(x, alpha), 6)
        assert_almost_equal(dirichlet.logpdf(x, alpha), np.log(6)) 

# 需要导入模块: from scipy.stats import multivariate_normal [as 别名]
# 或者: from scipy.stats.multivariate_normal import logpdf [as 别名]
def test_data_with_zeros_and_small_alpha(self):
        alpha = np.array([1.0, 0.5, 3.0, 4.0])
        x = np.array([0.1, 0.0, 0.2, 0.7])
        assert_raises(ValueError, dirichlet.pdf, x, alpha)
        assert_raises(ValueError, dirichlet.logpdf, x, alpha)