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35 lines
1.2 KiB
Python
35 lines
1.2 KiB
Python
import numpy as np
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class likelihood:
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"""
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The atom for a likelihood class
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This object interfaces the GP and the data. The most basic likelihood
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(Gaussian) inherits directly from this, as does the EP algorithm
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Some things must be defined for this to work properly:
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self.Y : the effective Gaussian target of the GP
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self.N, self.D : Y.shape
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self.covariance_matrix : the effective (noise) covariance of the GP targets
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self.Z : a factor which gets added to the likelihood (0 for a Gaussian, Z_EP for EP)
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self.is_heteroscedastic : enables significant computational savings in GP
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self.precision : a scalar or vector representation of the effective target precision
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self.YYT : (optional) = np.dot(self.Y, self.Y.T) enables computational savings for D>N
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"""
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def __init__(self,data):
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raise ValueError, "this class is not to be instantiated"
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def _get_params(self):
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raise NotImplementedError
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def _get_param_names(self):
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raise NotImplementedError
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def _set_params(self,x):
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raise NotImplementedError
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def fit(self):
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raise NotImplementedError
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def _gradients(self,partial):
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raise NotImplementedError
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