physbo.gp.core.prior module¶
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class
physbo.gp.core.prior.prior(mean, cov)[ソース]¶ prior of gaussian process
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cat_params(mean_params, cov_params)[ソース]¶ パラメータ: - mean_params (numpy.ndarray) -- Mean values of parameters
- cov_params (numpy.ndarray) -- Covariance matrix of parameters
戻り値: 戻り値の型: numpy.ndarray
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decomp_params(params)[ソース]¶ decomposing the parameters to those of mean values and covariance matrix for priors
パラメータ: params (numpy.ndarray) -- parameters 戻り値: - mean_params (numpy.ndarray)
- cov_params (numpy.ndarray)
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get_cov(X, Z=None, params=None, diag=False)[ソース]¶ Calculating the variance-covariance matrix of priors
パラメータ: - X (numpy.ndarray) -- N x d dimensional matrix. Each row of X denotes the d-dimensional feature vector of search candidate.
- Z (numpy.ndarray) -- N x d dimensional matrix. Each row of Z denotes the d-dimensional feature vector of tests.
- params (numpy.ndarray) -- Parameters.
- diag (bool) -- If X is the diagonalization matrix, true.
戻り値: 戻り値の型: numpy.ndarray
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get_grad_cov(X, params=None)[ソース]¶ Calculating the covariance matrix priors
パラメータ: - X (numpy.ndarray) -- N x d dimensional matrix. Each row of X denotes the d-dimensional feature vector of search candidate.
- params (numpy.ndarray) -- Parameters.
戻り値: 戻り値の型: numpy.ndarray
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get_grad_mean(num_data, params=None)[ソース]¶ Calculating the gradiant of mean values of priors
パラメータ: - num_data (int) -- Total number of data
- params (numpy.ndarray) -- Parameters
戻り値: 戻り値の型: numpy.ndarray
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get_mean(num_data, params=None)[ソース]¶ Calculating the mean value of priors
パラメータ: - num_data (int) -- Total number of data
- params (numpy.ndarray) -- Parameters
戻り値: 戻り値の型: float
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sampling(X, N=1)[ソース]¶ Sampling from GP prior
パラメータ: - X (numpy.ndarray) -- N x d dimensional matrix. Each row of X denotes the d-dimensional feature vector of search candidate.
- N (int) --
戻り値: 戻り値の型: float
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set_cov_params(params)[ソース]¶ Setting parameters for covariance matrix of priors
パラメータ: params (numpy.ndarray) -- Parameters
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