Source code for sklearn.gaussian_process.gaussian_process

# -*- coding: utf-8 -*-

# Author: Vincent Dubourg <>
#         (mostly translation, see implementation details)
# License: BSD 3 clause

from __future__ import print_function

import numpy as np
from scipy import linalg, optimize

from ..base import BaseEstimator, RegressorMixin
from ..metrics.pairwise import manhattan_distances
from ..utils import check_random_state, check_array, check_X_y
from ..utils.validation import check_is_fitted
from . import regression_models as regression
from . import correlation_models as correlation
from ..utils import deprecated

MACHINE_EPSILON = np.finfo(np.double).eps

@deprecated("l1_cross_distances was deprecated in version 0.18 "
            "and will be removed in 0.20.")
def l1_cross_distances(X):
    Computes the nonzero componentwise L1 cross-distances between the vectors
    in X.


    X : array_like
        An array with shape (n_samples, n_features)


    D : array with shape (n_samples * (n_samples - 1) / 2, n_features)
        The array of componentwise L1 cross-distances.

    ij : arrays with shape (n_samples * (n_samples - 1) / 2, 2)
        The indices i and j of the vectors in X associated to the cross-
        distances in D: D[k] = np.abs(X[ij[k, 0]] - Y[ij[k, 1]]).
    X = check_array(X)
    n_samples, n_features = X.shape
    n_nonzero_cross_dist = n_samples * (n_samples - 1) // 2
    ij = np.zeros((n_nonzero_cross_dist, 2),
    D = np.zeros((n_nonzero_cross_dist, n_features))
    ll_1 = 0
    for k in range(n_samples - 1):
        ll_0 = ll_1
        ll_1 = ll_0 + n_samples - k - 1
        ij[ll_0:ll_1, 0] = k
        ij[ll_0:ll_1, 1] = np.arange(k + 1, n_samples)
        D[ll_0:ll_1] = np.abs(X[k] - X[(k + 1):n_samples])

    return D, ij

@deprecated("GaussianProcess was deprecated in version 0.18 and will be "
            "removed in 0.20. Use the GaussianProcessRegressor instead.")
class GaussianProcess(BaseEstimator, RegressorMixin):
    """The legacy Gaussian Process model class.

    .. deprecated:: 0.18
        This class will be removed in 0.20.
        Use the :class:`GaussianProcessRegressor` instead.

    Read more in the :ref:`User Guide <gaussian_process>`.

    regr : string or callable, optional
        A regression function returning an array of outputs of the linear
        regression functional basis. The number of observations n_samples
        should be greater than the size p of this basis.
        Default assumes a simple constant regression trend.
        Available built-in regression models are::

            'constant', 'linear', 'quadratic'

    corr : string or callable, optional
        A stationary autocorrelation function returning the autocorrelation
        between two points x and x'.
        Default assumes a squared-exponential autocorrelation model.
        Built-in correlation models are::

            'absolute_exponential', 'squared_exponential',
            'generalized_exponential', 'cubic', 'linear'

    beta0 : double array_like, optional
        The regression weight vector to perform Ordinary Kriging (OK).
        Default assumes Universal Kriging (UK) so that the vector beta of
        regression weights is estimated using the maximum likelihood

    storage_mode : string, optional
        A string specifying whether the Cholesky decomposition of the
        correlation matrix should be stored in the class (storage_mode =
        'full') or not (storage_mode = 'light').
        Default assumes storage_mode = 'full', so that the
        Cholesky decomposition of the correlation matrix is stored.
        This might be a useful parameter when one is not interested in the
        MSE and only plan to estimate the BLUP, for which the correlation
        matrix is not required.

    verbose : boolean, optional
        A boolean specifying the verbose level.
        Default is verbose = False.

    theta0 : double array_like, optional
        An array with shape (n_features, ) or (1, ).
        The parameters in the autocorrelation model.
        If thetaL and thetaU are also specified, theta0 is considered as
        the starting point for the maximum likelihood estimation of the
        best set of parameters.
        Default assumes isotropic autocorrelation model with theta0 = 1e-1.

    thetaL : double array_like, optional
        An array with shape matching theta0's.
        Lower bound on the autocorrelation parameters for maximum
        likelihood estimation.
        Default is None, so that it skips maximum likelihood estimation and
        it uses theta0.

    thetaU : double array_like, optional
        An array with shape matching theta0's.
        Upper bound on the autocorrelation parameters for maximum
        likelihood estimation.
        Default is None, so that it skips maximum likelihood estimation and
        it uses theta0.

    normalize : boolean, optional
        Input X and observations y are centered and reduced wrt
        means and standard deviations estimated from the n_samples
        observations provided.
        Default is normalize = True so that data is normalized to ease
        maximum likelihood estimation.

    nugget : double or ndarray, optional
        Introduce a nugget effect to allow smooth predictions from noisy
        data.  If nugget is an ndarray, it must be the same length as the
        number of data points used for the fit.
        The nugget is added to the diagonal of the assumed training covariance;
        in this way it acts as a Tikhonov regularization in the problem.  In
        the special case of the squared exponential correlation function, the
        nugget mathematically represents the variance of the input values.
        Default assumes a nugget close to machine precision for the sake of
        robustness (nugget = 10. * MACHINE_EPSILON).

    optimizer : string, optional
        A string specifying the optimization algorithm to be used.
        Default uses 'fmin_cobyla' algorithm from scipy.optimize.
        Available optimizers are::

            'fmin_cobyla', 'Welch'

        'Welch' optimizer is dued to Welch et al., see reference [WBSWM1992]_.
        It consists in iterating over several one-dimensional optimizations
        instead of running one single multi-dimensional optimization.

    random_start : int, optional
        The number of times the Maximum Likelihood Estimation should be
        performed from a random starting point.
        The first MLE always uses the specified starting point (theta0),
        the next starting points are picked at random according to an
        exponential distribution (log-uniform on [thetaL, thetaU]).
        Default does not use random starting point (random_start = 1).

    random_state : int, RandomState instance or None, optional (default=None)
        The generator used to shuffle the sequence of coordinates of theta in
        the Welch optimizer. If int, random_state is the seed used by the
        random number generator; If RandomState instance, random_state is the
        random number generator; If None, the random number generator is the
        RandomState instance used by `np.random`.

    theta_ : array
        Specified theta OR the best set of autocorrelation parameters (the \
        sought maximizer of the reduced likelihood function).

    reduced_likelihood_function_value_ : array
        The optimal reduced likelihood function value.

    >>> import numpy as np
    >>> from sklearn.gaussian_process import GaussianProcess
    >>> X = np.array([[1., 3., 5., 6., 7., 8.]]).T
    >>> y = (X * np.sin(X)).ravel()
    >>> gp = GaussianProcess(theta0=0.1, thetaL=.001, thetaU=1.)
    >>>, y)                                      # doctest: +ELLIPSIS

    The presentation implementation is based on a translation of the DACE
    Matlab toolbox, see reference [NLNS2002]_.


    .. [NLNS2002] `H.B. Nielsen, S.N. Lophaven, H. B. Nielsen and J.
        Sondergaard.  DACE - A MATLAB Kriging Toolbox.` (2002)

    .. [WBSWM1992] `W.J. Welch, R.J. Buck, J. Sacks, H.P. Wynn, T.J. Mitchell,
        and M.D.  Morris (1992). Screening, predicting, and computer
        experiments.  Technometrics, 34(1) 15--25.`

    _regression_types = {
        'constant': regression.constant,
        'linear': regression.linear,
        'quadratic': regression.quadratic}

    _correlation_types = {
        'absolute_exponential': correlation.absolute_exponential,
        'squared_exponential': correlation.squared_exponential,
        'generalized_exponential': correlation.generalized_exponential,
        'cubic': correlation.cubic,
        'linear': correlation.linear}

    _optimizer_types = [

    def __init__(self, regr='constant', corr='squared_exponential', beta0=None,
                 storage_mode='full', verbose=False, theta0=1e-1,
                 thetaL=None, thetaU=None, optimizer='fmin_cobyla',
                 random_start=1, normalize=True,
                 nugget=10. * MACHINE_EPSILON, random_state=None):

        self.regr = regr
        self.corr = corr
        self.beta0 = beta0
        self.storage_mode = storage_mode
        self.verbose = verbose
        self.theta0 = theta0
        self.thetaL = thetaL
        self.thetaU = thetaU
        self.normalize = normalize
        self.nugget = nugget
        self.optimizer = optimizer
        self.random_start = random_start
        self.random_state = random_state

[docs] def fit(self, X, y): """ The Gaussian Process model fitting method. Parameters ---------- X : double array_like An array with shape (n_samples, n_features) with the input at which observations were made. y : double array_like An array with shape (n_samples, ) or shape (n_samples, n_targets) with the observations of the output to be predicted. Returns ------- gp : self A fitted Gaussian Process model object awaiting data to perform predictions. """ # Run input checks self._check_params() self.random_state = check_random_state(self.random_state) # Force data to 2D numpy.array X, y = check_X_y(X, y, multi_output=True, y_numeric=True) self.y_ndim_ = y.ndim if y.ndim == 1: y = y[:, np.newaxis] # Check shapes of DOE & observations n_samples, n_features = X.shape _, n_targets = y.shape # Run input checks self._check_params(n_samples) # Normalize data or don't if self.normalize: X_mean = np.mean(X, axis=0) X_std = np.std(X, axis=0) y_mean = np.mean(y, axis=0) y_std = np.std(y, axis=0) X_std[X_std == 0.] = 1. y_std[y_std == 0.] = 1. # center and scale X if necessary X = (X - X_mean) / X_std y = (y - y_mean) / y_std else: X_mean = np.zeros(1) X_std = np.ones(1) y_mean = np.zeros(1) y_std = np.ones(1) # Calculate matrix of distances D between samples D, ij = l1_cross_distances(X) if (np.min(np.sum(D, axis=1)) == 0. and self.corr != correlation.pure_nugget): raise Exception("Multiple input features cannot have the same" " target value.") # Regression matrix and parameters F = self.regr(X) n_samples_F = F.shape[0] if F.ndim > 1: p = F.shape[1] else: p = 1 if n_samples_F != n_samples: raise Exception("Number of rows in F and X do not match. Most " "likely something is going wrong with the " "regression model.") if p > n_samples_F: raise Exception(("Ordinary least squares problem is undetermined " "n_samples=%d must be greater than the " "regression model size p=%d.") % (n_samples, p)) if self.beta0 is not None: if self.beta0.shape[0] != p: raise Exception("Shapes of beta0 and F do not match.") # Set attributes self.X = X self.y = y self.D = D self.ij = ij self.F = F self.X_mean, self.X_std = X_mean, X_std self.y_mean, self.y_std = y_mean, y_std # Determine Gaussian Process model parameters if self.thetaL is not None and self.thetaU is not None: # Maximum Likelihood Estimation of the parameters if self.verbose: print("Performing Maximum Likelihood Estimation of the " "autocorrelation parameters...") self.theta_, self.reduced_likelihood_function_value_, par = \ self._arg_max_reduced_likelihood_function() if np.isinf(self.reduced_likelihood_function_value_): raise Exception("Bad parameter region. " "Try increasing upper bound") else: # Given parameters if self.verbose: print("Given autocorrelation parameters. " "Computing Gaussian Process model parameters...") self.theta_ = self.theta0 self.reduced_likelihood_function_value_, par = \ self.reduced_likelihood_function() if np.isinf(self.reduced_likelihood_function_value_): raise Exception("Bad point. Try increasing theta0.") self.beta = par['beta'] self.gamma = par['gamma'] self.sigma2 = par['sigma2'] self.C = par['C'] self.Ft = par['Ft'] self.G = par['G'] if self.storage_mode == 'light': # Delete heavy data (it will be computed again if required) # (it is required only when MSE is wanted in self.predict) if self.verbose: print("Light storage mode specified. " "Flushing autocorrelation matrix...") self.D = None self.ij = None self.F = None self.C = None self.Ft = None self.G = None return self
[docs] def predict(self, X, eval_MSE=False, batch_size=None): """ This function evaluates the Gaussian Process model at x. Parameters ---------- X : array_like An array with shape (n_eval, n_features) giving the point(s) at which the prediction(s) should be made. eval_MSE : boolean, optional A boolean specifying whether the Mean Squared Error should be evaluated or not. Default assumes evalMSE = False and evaluates only the BLUP (mean prediction). batch_size : integer, optional An integer giving the maximum number of points that can be evaluated simultaneously (depending on the available memory). Default is None so that all given points are evaluated at the same time. Returns ------- y : array_like, shape (n_samples, ) or (n_samples, n_targets) An array with shape (n_eval, ) if the Gaussian Process was trained on an array of shape (n_samples, ) or an array with shape (n_eval, n_targets) if the Gaussian Process was trained on an array of shape (n_samples, n_targets) with the Best Linear Unbiased Prediction at x. MSE : array_like, optional (if eval_MSE == True) An array with shape (n_eval, ) or (n_eval, n_targets) as with y, with the Mean Squared Error at x. """ check_is_fitted(self, "X") # Check input shapes X = check_array(X) n_eval, _ = X.shape n_samples, n_features = self.X.shape n_samples_y, n_targets = self.y.shape # Run input checks self._check_params(n_samples) if X.shape[1] != n_features: raise ValueError(("The number of features in X (X.shape[1] = %d) " "should match the number of features used " "for fit() " "which is %d.") % (X.shape[1], n_features)) if batch_size is None: # No memory management # (evaluates all given points in a single batch run) # Normalize input X = (X - self.X_mean) / self.X_std # Initialize output y = np.zeros(n_eval) if eval_MSE: MSE = np.zeros(n_eval) # Get pairwise componentwise L1-distances to the input training set dx = manhattan_distances(X, Y=self.X, sum_over_features=False) # Get regression function and correlation f = self.regr(X) r = self.corr(self.theta_, dx).reshape(n_eval, n_samples) # Scaled predictor y_ =, self.beta) +, self.gamma) # Predictor y = (self.y_mean + self.y_std * y_).reshape(n_eval, n_targets) if self.y_ndim_ == 1: y = y.ravel() # Mean Squared Error if eval_MSE: C = self.C if C is None: # Light storage mode (need to recompute C, F, Ft and G) if self.verbose: print("This GaussianProcess used 'light' storage mode " "at instantiation. Need to recompute " "autocorrelation matrix...") reduced_likelihood_function_value, par = \ self.reduced_likelihood_function() self.C = par['C'] self.Ft = par['Ft'] self.G = par['G'] rt = linalg.solve_triangular(self.C, r.T, lower=True) if self.beta0 is None: # Universal Kriging u = linalg.solve_triangular(self.G.T,, rt) - f.T, lower=True) else: # Ordinary Kriging u = np.zeros((n_targets, n_eval)) MSE =, 1), (1. - (rt ** 2.).sum(axis=0) + (u ** 2.).sum(axis=0))[np.newaxis, :]) MSE = np.sqrt((MSE ** 2.).sum(axis=0) / n_targets) # Mean Squared Error might be slightly negative depending on # machine precision: force to zero! MSE[MSE < 0.] = 0. if self.y_ndim_ == 1: MSE = MSE.ravel() return y, MSE else: return y else: # Memory management if type(batch_size) is not int or batch_size <= 0: raise Exception("batch_size must be a positive integer") if eval_MSE: y, MSE = np.zeros(n_eval), np.zeros(n_eval) for k in range(max(1, int(n_eval / batch_size))): batch_from = k * batch_size batch_to = min([(k + 1) * batch_size + 1, n_eval + 1]) y[batch_from:batch_to], MSE[batch_from:batch_to] = \ self.predict(X[batch_from:batch_to], eval_MSE=eval_MSE, batch_size=None) return y, MSE else: y = np.zeros(n_eval) for k in range(max(1, int(n_eval / batch_size))): batch_from = k * batch_size batch_to = min([(k + 1) * batch_size + 1, n_eval + 1]) y[batch_from:batch_to] = \ self.predict(X[batch_from:batch_to], eval_MSE=eval_MSE, batch_size=None) return y
def reduced_likelihood_function(self, theta=None): """ This function determines the BLUP parameters and evaluates the reduced likelihood function for the given autocorrelation parameters theta. Maximizing this function wrt the autocorrelation parameters theta is equivalent to maximizing the likelihood of the assumed joint Gaussian distribution of the observations y evaluated onto the design of experiments X. Parameters ---------- theta : array_like, optional An array containing the autocorrelation parameters at which the Gaussian Process model parameters should be determined. Default uses the built-in autocorrelation parameters (ie ``theta = self.theta_``). Returns ------- reduced_likelihood_function_value : double The value of the reduced likelihood function associated to the given autocorrelation parameters theta. par : dict A dictionary containing the requested Gaussian Process model parameters: - ``sigma2`` is the Gaussian Process variance. - ``beta`` is the generalized least-squares regression weights for Universal Kriging or given beta0 for Ordinary Kriging. - ``gamma`` is the Gaussian Process weights. - ``C`` is the Cholesky decomposition of the correlation matrix [R]. - ``Ft`` is the solution of the linear equation system [R] x Ft = F - ``G`` is the QR decomposition of the matrix Ft. """ check_is_fitted(self, "X") if theta is None: # Use built-in autocorrelation parameters theta = self.theta_ # Initialize output reduced_likelihood_function_value = - np.inf par = {} # Retrieve data n_samples = self.X.shape[0] D = self.D ij = self.ij F = self.F if D is None: # Light storage mode (need to recompute D, ij and F) D, ij = l1_cross_distances(self.X) if (np.min(np.sum(D, axis=1)) == 0. and self.corr != correlation.pure_nugget): raise Exception("Multiple X are not allowed") F = self.regr(self.X) # Set up R r = self.corr(theta, D) R = np.eye(n_samples) * (1. + self.nugget) R[ij[:, 0], ij[:, 1]] = r R[ij[:, 1], ij[:, 0]] = r # Cholesky decomposition of R try: C = linalg.cholesky(R, lower=True) except linalg.LinAlgError: return reduced_likelihood_function_value, par # Get generalized least squares solution Ft = linalg.solve_triangular(C, F, lower=True) Q, G = linalg.qr(Ft, mode='economic') sv = linalg.svd(G, compute_uv=False) rcondG = sv[-1] / sv[0] if rcondG < 1e-10: # Check F sv = linalg.svd(F, compute_uv=False) condF = sv[0] / sv[-1] if condF > 1e15: raise Exception("F is too ill conditioned. Poor combination " "of regression model and observations.") else: # Ft is too ill conditioned, get out (try different theta) return reduced_likelihood_function_value, par Yt = linalg.solve_triangular(C, self.y, lower=True) if self.beta0 is None: # Universal Kriging beta = linalg.solve_triangular(G,, Yt)) else: # Ordinary Kriging beta = np.array(self.beta0) rho = Yt -, beta) sigma2 = (rho ** 2.).sum(axis=0) / n_samples # The determinant of R is equal to the squared product of the diagonal # elements of its Cholesky decomposition C detR = (np.diag(C) ** (2. / n_samples)).prod() # Compute/Organize output reduced_likelihood_function_value = - sigma2.sum() * detR par['sigma2'] = sigma2 * self.y_std ** 2. par['beta'] = beta par['gamma'] = linalg.solve_triangular(C.T, rho) par['C'] = C par['Ft'] = Ft par['G'] = G return reduced_likelihood_function_value, par def _arg_max_reduced_likelihood_function(self): """ This function estimates the autocorrelation parameters theta as the maximizer of the reduced likelihood function. (Minimization of the opposite reduced likelihood function is used for convenience) Parameters ---------- self : All parameters are stored in the Gaussian Process model object. Returns ------- optimal_theta : array_like The best set of autocorrelation parameters (the sought maximizer of the reduced likelihood function). optimal_reduced_likelihood_function_value : double The optimal reduced likelihood function value. optimal_par : dict The BLUP parameters associated to thetaOpt. """ # Initialize output best_optimal_theta = [] best_optimal_rlf_value = [] best_optimal_par = [] if self.verbose: print("The chosen optimizer is: " + str(self.optimizer)) if self.random_start > 1: print(str(self.random_start) + " random starts are required.") percent_completed = 0. # Force optimizer to fmin_cobyla if the model is meant to be isotropic if self.optimizer == 'Welch' and self.theta0.size == 1: self.optimizer = 'fmin_cobyla' if self.optimizer == 'fmin_cobyla': def minus_reduced_likelihood_function(log10t): return - self.reduced_likelihood_function( theta=10. ** log10t)[0] constraints = [] for i in range(self.theta0.size): constraints.append(lambda log10t, i=i: log10t[i] - np.log10(self.thetaL[0, i])) constraints.append(lambda log10t, i=i: np.log10(self.thetaU[0, i]) - log10t[i]) for k in range(self.random_start): if k == 0: # Use specified starting point as first guess theta0 = self.theta0 else: # Generate a random starting point log10-uniformly # distributed between bounds log10theta0 = (np.log10(self.thetaL) + self.random_state.rand(*self.theta0.shape) * np.log10(self.thetaU / self.thetaL)) theta0 = 10. ** log10theta0 # Run Cobyla try: log10_optimal_theta = \ optimize.fmin_cobyla(minus_reduced_likelihood_function, np.log10(theta0).ravel(), constraints, disp=0) except ValueError as ve: print("Optimization failed. Try increasing the ``nugget``") raise ve optimal_theta = 10. ** log10_optimal_theta optimal_rlf_value, optimal_par = \ self.reduced_likelihood_function(theta=optimal_theta) # Compare the new optimizer to the best previous one if k > 0: if optimal_rlf_value > best_optimal_rlf_value: best_optimal_rlf_value = optimal_rlf_value best_optimal_par = optimal_par best_optimal_theta = optimal_theta else: best_optimal_rlf_value = optimal_rlf_value best_optimal_par = optimal_par best_optimal_theta = optimal_theta if self.verbose and self.random_start > 1: if (20 * k) / self.random_start > percent_completed: percent_completed = (20 * k) / self.random_start print("%s completed" % (5 * percent_completed)) optimal_rlf_value = best_optimal_rlf_value optimal_par = best_optimal_par optimal_theta = best_optimal_theta elif self.optimizer == 'Welch': # Backup of the given attributes theta0, thetaL, thetaU = self.theta0, self.thetaL, self.thetaU corr = self.corr verbose = self.verbose # This will iterate over fmin_cobyla optimizer self.optimizer = 'fmin_cobyla' self.verbose = False # Initialize under isotropy assumption if verbose: print("Initialize under isotropy assumption...") self.theta0 = check_array(self.theta0.min()) self.thetaL = check_array(self.thetaL.min()) self.thetaU = check_array(self.thetaU.max()) theta_iso, optimal_rlf_value_iso, par_iso = \ self._arg_max_reduced_likelihood_function() optimal_theta = theta_iso + np.zeros(theta0.shape) # Iterate over all dimensions of theta allowing for anisotropy if verbose: print("Now improving allowing for anisotropy...") for i in self.random_state.permutation(theta0.size): if verbose: print("Proceeding along dimension %d..." % (i + 1)) self.theta0 = check_array(theta_iso) self.thetaL = check_array(thetaL[0, i]) self.thetaU = check_array(thetaU[0, i]) def corr_cut(t, d): return corr(check_array(np.hstack([optimal_theta[0][0:i], t[0], optimal_theta[0][(i + 1)::]])), d) self.corr = corr_cut optimal_theta[0, i], optimal_rlf_value, optimal_par = \ self._arg_max_reduced_likelihood_function() # Restore the given attributes self.theta0, self.thetaL, self.thetaU = theta0, thetaL, thetaU self.corr = corr self.optimizer = 'Welch' self.verbose = verbose else: raise NotImplementedError("This optimizer ('%s') is not " "implemented yet. Please contribute!" % self.optimizer) return optimal_theta, optimal_rlf_value, optimal_par def _check_params(self, n_samples=None): # Check regression model if not callable(self.regr): if self.regr in self._regression_types: self.regr = self._regression_types[self.regr] else: raise ValueError("regr should be one of %s or callable, " "%s was given." % (self._regression_types.keys(), self.regr)) # Check regression weights if given (Ordinary Kriging) if self.beta0 is not None: self.beta0 = np.atleast_2d(self.beta0) if self.beta0.shape[1] != 1: # Force to column vector self.beta0 = self.beta0.T # Check correlation model if not callable(self.corr): if self.corr in self._correlation_types: self.corr = self._correlation_types[self.corr] else: raise ValueError("corr should be one of %s or callable, " "%s was given." % (self._correlation_types.keys(), self.corr)) # Check storage mode if self.storage_mode != 'full' and self.storage_mode != 'light': raise ValueError("Storage mode should either be 'full' or " "'light', %s was given." % self.storage_mode) # Check correlation parameters self.theta0 = np.atleast_2d(self.theta0) lth = self.theta0.size if self.thetaL is not None and self.thetaU is not None: self.thetaL = np.atleast_2d(self.thetaL) self.thetaU = np.atleast_2d(self.thetaU) if self.thetaL.size != lth or self.thetaU.size != lth: raise ValueError("theta0, thetaL and thetaU must have the " "same length.") if np.any(self.thetaL <= 0) or np.any(self.thetaU < self.thetaL): raise ValueError("The bounds must satisfy O < thetaL <= " "thetaU.") elif self.thetaL is None and self.thetaU is None: if np.any(self.theta0 <= 0): raise ValueError("theta0 must be strictly positive.") elif self.thetaL is None or self.thetaU is None: raise ValueError("thetaL and thetaU should either be both or " "neither specified.") # Force verbose type to bool self.verbose = bool(self.verbose) # Force normalize type to bool self.normalize = bool(self.normalize) # Check nugget value self.nugget = np.asarray(self.nugget) if np.any(self.nugget) < 0.: raise ValueError("nugget must be positive or zero.") if (n_samples is not None and self.nugget.shape not in [(), (n_samples,)]): raise ValueError("nugget must be either a scalar " "or array of length n_samples.") # Check optimizer if self.optimizer not in self._optimizer_types: raise ValueError("optimizer should be one of %s" % self._optimizer_types) # Force random_start type to int self.random_start = int(self.random_start)