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94 lines
3.7 KiB
Python
94 lines
3.7 KiB
Python
# Copyright (c) 2013, Ricardo Andrade
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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import numpy as np
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from ..core import SparseGP
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from .. import likelihoods
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from .. import kern
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from ..inference.latent_function_inference import EPDTC
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class SparseGPClassification(SparseGP):
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"""
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Sparse Gaussian Process model for classification
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This is a thin wrapper around the sparse_GP class, with a set of sensible defaults
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:param X: input observations
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:param Y: observed values
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:param likelihood: a GPy likelihood, defaults to Binomial with probit link_function
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:param kernel: a GPy kernel, defaults to rbf+white
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:param normalize_X: whether to normalize the input data before computing (predictions will be in original scales)
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:type normalize_X: False|True
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:param normalize_Y: whether to normalize the input data before computing (predictions will be in original scales)
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:type normalize_Y: False|True
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:rtype: model object
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"""
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def __init__(self, X, Y=None, likelihood=None, kernel=None, Z=None, num_inducing=10, Y_metadata=None):
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if kernel is None:
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kernel = kern.RBF(X.shape[1])
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likelihood = likelihoods.Bernoulli()
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if Z is None:
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i = np.random.permutation(X.shape[0])[:num_inducing]
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Z = X[i].copy()
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else:
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assert Z.shape[1] == X.shape[1]
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SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method=EPDTC(), name='SparseGPClassification',Y_metadata=Y_metadata)
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class SparseGPClassificationUncertainInput(SparseGP):
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"""
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Sparse Gaussian Process model for classification with uncertain inputs.
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This is a thin wrapper around the sparse_GP class, with a set of sensible defaults
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:param X: input observations
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:type X: np.ndarray (num_data x input_dim)
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:param X_variance: The uncertainty in the measurements of X (Gaussian variance, optional)
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:type X_variance: np.ndarray (num_data x input_dim)
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:param Y: observed values
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:param kernel: a GPy kernel, defaults to rbf+white
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:param Z: inducing inputs (optional, see note)
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:type Z: np.ndarray (num_inducing x input_dim) | None
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:param num_inducing: number of inducing points (ignored if Z is passed, see note)
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:type num_inducing: int
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:rtype: model object
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.. Note:: If no Z array is passed, num_inducing (default 10) points are selected from the data. Other wise num_inducing is ignored
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.. Note:: Multiple independent outputs are allowed using columns of Y
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"""
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def __init__(self, X, X_variance, Y, kernel=None, Z=None, num_inducing=10, Y_metadata=None, normalizer=None):
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from GPy.core.parameterization.variational import NormalPosterior
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if kernel is None:
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kernel = kern.RBF(X.shape[1])
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likelihood = likelihoods.Bernoulli()
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if Z is None:
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i = np.random.permutation(X.shape[0])[:num_inducing]
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Z = X[i].copy()
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else:
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assert Z.shape[1] == X.shape[1]
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X = NormalPosterior(X, X_variance)
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SparseGP.__init__(self, X, Y, Z, kernel, likelihood,
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inference_method=EPDTC(),
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name='SparseGPClassification', Y_metadata=Y_metadata, normalizer=normalizer)
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def parameters_changed(self):
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#Compute the psi statistics for N once, but don't sum out N in psi2
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self.psi0 = self.kern.psi0(self.Z, self.X)
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self.psi1 = self.kern.psi1(self.Z, self.X)
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self.psi2 = self.kern.psi2n(self.Z, self.X)
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self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.Z, self.likelihood, self.Y, self.Y_metadata, psi0=self.psi0, psi1=self.psi1, psi2=self.psi2)
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self._update_gradients()
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