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removed fitc_classification modle

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James Hensman 2014-01-24 10:18:13 +00:00
parent 6701741c94
commit 0c51239bdd
2 changed files with 0 additions and 48 deletions
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1
GPy/models/__init__.py
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@ -6,7 +6,6 @@ from gp_classification import GPClassification
from sparse_gp_regression import SparseGPRegression from sparse_gp_regression import SparseGPRegression
from svigp_regression import SVIGPRegression from svigp_regression import SVIGPRegression
from sparse_gp_classification import SparseGPClassification from sparse_gp_classification import SparseGPClassification
from fitc_classification import FITCClassification
from gplvm import GPLVM from gplvm import GPLVM
from bcgplvm import BCGPLVM from bcgplvm import BCGPLVM
from sparse_gplvm import SparseGPLVM from sparse_gplvm import SparseGPLVM

47
GPy/models/fitc_classification.py
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@ -1,47 +0,0 @@
# Copyright (c) 2013, Ricardo Andrade
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
from ..core import FITC
from .. import likelihoods
from .. import kern
from ..likelihoods import likelihood
class FITCClassification(FITC):
"""
FITC approximation for classification
This is a thin wrapper around the FITC class, with a set of sensible defaults
:param X: input observations
:param Y: observed values
:param likelihood: a GPy likelihood, defaults to Binomial with probit link function
:param kernel: a GPy kernel, defaults to rbf+white
:param normalize_X: whether to normalize the input data before computing (predictions will be in original scales)
:type normalize_X: False|True
:param normalize_Y: whether to normalize the input data before computing (predictions will be in original scales)
:type normalize_Y: False|True
:rtype: model object
"""
def __init__(self, X, Y=None, likelihood=None, kernel=None, normalize_X=False, normalize_Y=False, Z=None, num_inducing=10):
if kernel is None:
kernel = kern.rbf(X.shape[1]) + kern.white(X.shape[1],1e-3)
if likelihood is None:
noise_model = likelihoods.binomial()
likelihood = likelihoods.EP(Y, noise_model)
elif Y is not None:
if not all(Y.flatten() == likelihood.data.flatten()):
raise Warning, 'likelihood.data and Y are different.'
if Z is None:
i = np.random.permutation(X.shape[0])[:num_inducing]
Z = X[i].copy()
else:
assert Z.shape[1]==X.shape[1]
FITC.__init__(self, X, likelihood, kernel, Z=Z, normalize_X=normalize_X)
self.ensure_default_constraints()