Python sklearn.gaussian_process.kernels.ConstantKernel() Examples

The following are 6 code examples of sklearn.gaussian_process.kernels.ConstantKernel(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module sklearn.gaussian_process.kernels , or try the search function .
Example #1
Source File: test_gpc.py    From Mastering-Elasticsearch-7.0 with MIT License 6 votes vote down vote up 
def test_random_starts():
    # Test that an increasing number of random-starts of GP fitting only
    # increases the log marginal likelihood of the chosen theta.
    n_samples, n_features = 25, 2
    rng = np.random.RandomState(0)
    X = rng.randn(n_samples, n_features) * 2 - 1
    y = (np.sin(X).sum(axis=1) + np.sin(3 * X).sum(axis=1)) > 0

    kernel = C(1.0, (1e-2, 1e2)) \
        * RBF(length_scale=[1e-3] * n_features,
              length_scale_bounds=[(1e-4, 1e+2)] * n_features)
    last_lml = -np.inf
    for n_restarts_optimizer in range(5):
        gp = GaussianProcessClassifier(
            kernel=kernel, n_restarts_optimizer=n_restarts_optimizer,
            random_state=0).fit(X, y)
        lml = gp.log_marginal_likelihood(gp.kernel_.theta)
        assert_greater(lml, last_lml - np.finfo(np.float32).eps)
        last_lml = lml
Example #2
Source File: gaussianproc.py    From pyFTS with GNU General Public License v3.0 6 votes vote down vote up 
def __init__(self, **kwargs):
        super(GPR, self).__init__(**kwargs)
        self.name = "GPR"
        self.detail = "Gaussian Process Regression"
        self.is_high_order = True
        self.has_point_forecasting = True
        self.has_interval_forecasting = True
        self.has_probability_forecasting = True
        self.uod_clip = False
        self.benchmark_only = True
        self.min_order = 1
        self.alpha = kwargs.get("alpha", 0.05)
        self.data = None

        self.lscale = kwargs.get('length_scale', 1)

        self.kernel = ConstantKernel(1.0) * RBF(length_scale=self.lscale)
        self.model = GaussianProcessRegressor(kernel=self.kernel, alpha=.05,
                                      n_restarts_optimizer=10,
                                      normalize_y=False)
        #self.model_fit = None
Example #3
Source File: test_gpc.py    From twitter-stock-recommendation with MIT License 6 votes vote down vote up 
def test_random_starts():
    # Test that an increasing number of random-starts of GP fitting only
    # increases the log marginal likelihood of the chosen theta.
    n_samples, n_features = 25, 2
    rng = np.random.RandomState(0)
    X = rng.randn(n_samples, n_features) * 2 - 1
    y = (np.sin(X).sum(axis=1) + np.sin(3 * X).sum(axis=1)) > 0

    kernel = C(1.0, (1e-2, 1e2)) \
        * RBF(length_scale=[1e-3] * n_features,
              length_scale_bounds=[(1e-4, 1e+2)] * n_features)
    last_lml = -np.inf
    for n_restarts_optimizer in range(5):
        gp = GaussianProcessClassifier(
            kernel=kernel, n_restarts_optimizer=n_restarts_optimizer,
            random_state=0).fit(X, y)
        lml = gp.log_marginal_likelihood(gp.kernel_.theta)
        assert_greater(lml, last_lml - np.finfo(np.float32).eps)
        last_lml = lml
Example #4
Source File: track_lib.py    From TNT with GNU General Public License v3.0 5 votes vote down vote up 
def GP_regression(tr_x,tr_y,test_x):
    A = np.ones((len(tr_x),2))
    A[:,0] = tr_x[:,0]
    p = np.matmul(np.linalg.pinv(A),tr_y)
    mean_tr_y = np.matmul(A,p)
    A = np.ones((len(test_x),2))
    A[:,0] = test_x[:,0]
    mean_test_y = np.matmul(A,p)
    kernel = ConstantKernel(100,(1e-5, 1e5))*RBF(1, (1e-5, 1e5))+RBF(1, (1e-5, 1e5))
    gp = GaussianProcessRegressor(kernel=kernel, alpha=1, n_restarts_optimizer=9)
    gp.fit(tr_x, tr_y-mean_tr_y)
    test_y, sigma = gp.predict(test_x, return_std=True)
    test_y = test_y+mean_test_y
    #import pdb; pdb.set_trace()
    return test_y
Example #5
Source File: track_lib.py    From TNT with GNU General Public License v3.0 5 votes vote down vote up 
def GP_regression(tr_x,tr_y,test_x):
    A = np.ones((len(tr_x),2))
    A[:,0] = tr_x[:,0]
    p = np.matmul(np.linalg.pinv(A),tr_y)
    mean_tr_y = np.matmul(A,p)
    A = np.ones((len(test_x),2))
    A[:,0] = test_x[:,0]
    mean_test_y = np.matmul(A,p)
    kernel = ConstantKernel(100,(1e-5, 1e5))*RBF(1, (1e-5, 1e5))+RBF(1, (1e-5, 1e5))
    gp = GaussianProcessRegressor(kernel=kernel, alpha=1, n_restarts_optimizer=9)
    gp.fit(tr_x, tr_y-mean_tr_y)
    test_y, sigma = gp.predict(test_x, return_std=True)
    test_y = test_y+mean_test_y
    #import pdb; pdb.set_trace()
    return test_y
Example #6
Source File: kernels.py    From chocolate with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def __init__(self, space):
        self.space = space
        self.k = kernels.ConstantKernel() * kernels.RBF()