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65 lines
2.5 KiB
Python
65 lines
2.5 KiB
Python
# Copyright (c) 2013, the GPy Authors (see AUTHORS.txt)
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# Licensed under the BSD 3-clause license (see LICENSE.txt)
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from ..core import GP
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from .. import likelihoods
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from .. import kern
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import numpy as np
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from ..inference.latent_function_inference.expectation_propagation import EP
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class GPClassification(GP):
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"""
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Gaussian Process classification
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This is a thin wrapper around the models.GP class, with a set of sensible defaults
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:param X: input observations
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:param Y: observed values, can be None if likelihood is not None
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:param kernel: a GPy kernel, defaults to rbf
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:param likelihood: a GPy likelihood, defaults to Bernoulli
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:param inference_method: Latent function inference to use, defaults to EP
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:type inference_method: :class:`GPy.inference.latent_function_inference.LatentFunctionInference`
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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, Y, kernel=None,Y_metadata=None, mean_function=None, inference_method=None,
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likelihood=None, normalizer=False):
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if kernel is None:
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kernel = kern.RBF(X.shape[1])
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if likelihood is None:
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likelihood = likelihoods.Bernoulli()
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if inference_method is None:
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inference_method = EP()
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super(GPClassification, self).__init__(X=X, Y=Y, kernel=kernel, likelihood=likelihood, inference_method=inference_method,
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mean_function=mean_function, name='gp_classification', normalizer=normalizer)
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@staticmethod
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def from_gp(gp):
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from copy import deepcopy
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gp = deepcopy(gp)
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GPClassification(gp.X, gp.Y, gp.kern, gp.likelihood, gp.inference_method, gp.mean_function, name='gp_classification')
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def to_dict(self, save_data=True):
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model_dict = super(GPClassification,self).to_dict(save_data)
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model_dict["class"] = "GPy.models.GPClassification"
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return model_dict
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@staticmethod
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def from_dict(input_dict, data=None):
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import GPy
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m = GPy.core.model.Model.from_dict(input_dict, data)
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return GPClassification.from_gp(m)
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def save_model(self, output_filename, compress=True, save_data=True):
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self._save_model(output_filename, compress=True, save_data=True)
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@staticmethod
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def _build_from_input_dict(input_dict, data=None):
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input_dict = GPClassification._format_input_dict(input_dict, data)
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input_dict.pop('name', None) # Name parameter not required by GPClassification
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return GPClassification(**input_dict)
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