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Inherited from |
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Inherited from Inherited from Inherited from Inherited from |
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predicted_variances = StateVariable(enabled= False, doc= "Vari
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log_marginal_likelihood = StateVariable(enabled= False, doc= "
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log_marginal_likelihood_gradient = StateVariable(enabled= Fals
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_clf_internals = ['gpr', 'regression', 'retrainable'] Describes some specifics about the classifier -- is that it is doing regression for instance.... |
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sigma_noise = Parameter(0.001, allowedtype= 'float', min= 1e-1
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lm = Parameter(0.0, min= 0.0, allowedtype= 'float', doc= """Th
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kernel = property(fget= lambda self: self.__kernel)
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Inherited from Inherited from |
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Inherited from |
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Reset some internal variables to None. To be used in constructor and untrain() |
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data (Dataset).
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Set hyperparameters' values. Note that 'hyperparameter' is a sequence so the order of its values is important. First value must be sigma_noise, then other kernel's hyperparameters values follow in the exact order the kernel expect them to be. |
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predicted_variances
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log_marginal_likelihood
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log_marginal_likelihood_gradient
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sigma_noise
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lm
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