/usr/share/pyshared/mvpa2/clfs/blr.py is in python-mvpa2 2.1.0-1.
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# vi: set ft=python sts=4 ts=4 sw=4 et:
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#
# Copyright (c) 2008 Emanuele Olivetti <emanuele@relativita.com>
# See COPYING file distributed along with the PyMVPA package for the
# copyright and license terms.
#
### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
"""Bayesian Linear Regression (BLR)."""
__docformat__ = 'restructuredtext'
import numpy as np
from mvpa2.base.state import ConditionalAttribute
from mvpa2.clfs.base import Classifier, accepts_dataset_as_samples
if __debug__:
from mvpa2.misc import debug
class BLR(Classifier):
"""Bayesian Linear Regression (BLR).
"""
predicted_variances = ConditionalAttribute(enabled=False,
doc="Variance per each predicted value")
log_marginal_likelihood = ConditionalAttribute(enabled=False,
doc="Log Marginal Likelihood")
__tags__ = [ 'blr', 'regression', 'linear' ]
def __init__(self, sigma_p = None, sigma_noise=1.0, **kwargs):
"""Initialize a BLR regression analysis.
Parameters
----------
sigma_noise : float
the standard deviation of the gaussian noise.
(Defaults to 0.1)
"""
# init base class first
Classifier.__init__(self, **kwargs)
# pylint happiness
self.w = None
# It does not make sense to calculate a confusion matrix for a
# BLR:
self.ca.enable('training_stats', False)
# set the prior on w: N(0,sigma_p) , specifying the covariance
# sigma_p on w:
self.sigma_p = sigma_p
# set noise level:
self.sigma_noise = sigma_noise
self.ca.predicted_variances = None
self.ca.log_marginal_likelihood = None
# Yarik: what was those about??? just for future in
# compute_log_marginal_likelihood ?
# self.targets = None
pass
def __repr__(self):
"""String summary of the object
"""
return """BLR(w=%s, sigma_p=%s, sigma_noise=%f, enable_ca=%s)""" % \
(self.w, self.sigma_p, self.sigma_noise, str(self.ca.enabled))
def compute_log_marginal_likelihood(self):
"""
Compute log marginal likelihood using self.train_fv and self.targets.
"""
# log_marginal_likelihood = None
# return log_marginal_likelihood
raise NotImplementedError
def _train(self, data):
"""Train regression using `data` (`Dataset`).
"""
# BLR relies on numerical labels
train_labels = self._attrmap.to_numeric(data.sa[self.get_space()].value)
# provide a basic (i.e. identity matrix) and correct prior
# sigma_p, if not provided before or not compliant to 'data':
if self.sigma_p == None: # case: not provided
self.sigma_p = np.eye(data.samples.shape[1]+1)
elif self.sigma_p.shape[1] != (data.samples.shape[1]+1): # case: wrong dimensions
self.sigma_p = np.eye(data.samples.shape[1]+1)
else:
# ...then everything is OK :)
pass
# add one fake column of '1.0' to model the intercept:
self.samples_train = np.hstack([data.samples,np.ones((data.samples.shape[0],1))])
if type(self.sigma_p)!=type(self.samples_train): # if sigma_p is a number...
self.sigma_p = np.eye(self.samples_train.shape[1])*self.sigma_p # convert in matrix
pass
self.A_inv = np.linalg.inv(1.0/(self.sigma_noise**2) *
np.dot(self.samples_train.T,
self.samples_train) +
np.linalg.inv(self.sigma_p))
self.w = 1.0/(self.sigma_noise**2) * np.dot(self.A_inv,
np.dot(self.samples_train.T,
train_labels))
pass
@accepts_dataset_as_samples
def _predict(self, data):
"""
Predict the output for the provided data.
"""
data = np.hstack([data,np.ones((data.shape[0],1),dtype=data.dtype)])
predictions = np.dot(data,self.w)
if self.ca.is_enabled('predicted_variances'):
# do computation only if conditional attribute was enabled
self.ca.predicted_variances = np.dot(data, np.dot(self.A_inv, data.T)).diagonal()[:,np.newaxis]
self.ca.estimates = predictions
return predictions
def set_hyperparameters(self,*args):
"""
Set hyperparameters' values.
Note that this is a list so the order of the values is
important.
"""
args=args[0]
self.sigma_noise = args[0]
if len(args)>1:
self.sigma_p = np.array(args[1:]) # XXX check if this is ok
pass
return
pass
if __name__ == "__main__":
import pylab
pylab.close("all")
pylab.ion()
from mvpa2.misc.data_generators import linear_awgn
train_size = 10
test_size = 100
F = 1 # dimensions of the dataset
# np.random.seed(1)
slope = np.random.rand(F)
intercept = np.random.rand(1)
print "True slope:",slope
print "True intercept:",intercept
dataset_train = linear_awgn(train_size, intercept=intercept, slope=slope)
dataset_test = linear_awgn(test_size, intercept=intercept, slope=slope, flat=True)
regression = True
logml = False
b = BLR(sigma_p=np.eye(F+1), sigma_noise=0.1)
b.ca.enable("predicted_variances")
b.train(dataset_train)
predictions = b.predict(dataset_test.samples)
print "Predicted slope and intercept:",b.w
if F==1:
pylab.plot(dataset_train.samples,
dataset_train.sa[b.get_space()].value,
"ro", label="train")
pylab.plot(dataset_test.samples, predictions, "b-", label="prediction")
pylab.plot(dataset_test.samples,
predictions+np.sqrt(b.ca.predicted_variances),
"b--", label="pred(+/-)std")
pylab.plot(dataset_test.samples,
predictions-np.sqrt(b.ca.predicted_variances),
"b--", label=None)
pylab.legend()
pylab.xlabel("samples")
pylab.ylabel("labels")
pylab.title("Bayesian Linear Regression on dataset 'linear_AWGN'")
pass
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