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114 lines
4.3 KiB
Python
114 lines
4.3 KiB
Python
'''
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Created on 17 Jul 2013
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@author: maxz
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'''
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from GPy.core.model import Model
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import itertools
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import numpy
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def get_shape(x):
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if isinstance(x, numpy.ndarray):
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return x.shape
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return ()
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def at_least_one_element(x):
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if isinstance(x, (list, tuple)):
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return x
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return [x]
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def flatten_if_needed(x):
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return numpy.atleast_1d(x).flatten()
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class GradientChecker(Model):
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def __init__(self, f, df, x0, names=None, *args, **kwargs):
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"""
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:param f: Function to check gradient for
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:param df: Gradient of function to check
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:param x0:
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Initial guess for inputs x (if it has a shape (a,b) this will be reflected in the parameter names).
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Can be a list of arrays, if takes a list of arrays. This list will be passed
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to f and df in the same order as given here.
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If only one argument, make sure not to pass a list!!!
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:type x0: [array-like] | array-like | float | int
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:param names:
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Names to print, when performing gradcheck. If a list was passed to x0
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a list of names with the same length is expected.
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:param args: Arguments passed as f(x, *args, **kwargs) and df(x, *args, **kwargs)
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Examples:
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---------
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from GPy.models import GradientChecker
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N, M, Q = 10, 5, 3
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Sinusoid:
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X = numpy.random.rand(N, Q)
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grad = GradientChecker(numpy.sin,numpy.cos,X,'x')
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grad.checkgrad(verbose=1)
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Using GPy:
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X, Z = numpy.random.randn(N,Q), numpy.random.randn(M,Q)
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kern = GPy.kern.linear(Q, ARD=True) + GPy.kern.rbf(Q, ARD=True)
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grad = GradientChecker(kern.K,
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lambda x: 2*kern.dK_dX(numpy.ones((1,1)), x),
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x0 = X.copy(),
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names='X')
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grad.checkgrad(verbose=1)
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grad.randomize()
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grad.checkgrad(verbose=1)
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"""
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Model.__init__(self)
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if isinstance(x0, (list, tuple)) and names is None:
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self.shapes = [get_shape(xi) for xi in x0]
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self.names = ['X{i}'.format(i=i) for i in range(len(x0))]
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elif isinstance(x0, (list, tuple)) and names is not None:
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self.shapes = [get_shape(xi) for xi in x0]
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self.names = names
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elif names is None:
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self.names = ['X']
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self.shapes = [get_shape(x0)]
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else:
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self.names = names
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self.shapes = [get_shape(x0)]
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for name, xi in zip(self.names, at_least_one_element(x0)):
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self.__setattr__(name, numpy.float_(xi))
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# self._param_names = []
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# for name, shape in zip(self.names, self.shapes):
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# self._param_names.extend(map(lambda nameshape: ('_'.join(nameshape)).strip('_'), itertools.izip(itertools.repeat(name), itertools.imap(lambda t: '_'.join(map(str, t)), itertools.product(*map(lambda xi: range(xi), shape))))))
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self.args = args
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self.kwargs = kwargs
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self._f = f
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self._df = df
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def _get_x(self):
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if len(self.names) > 1:
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return [self.__getattribute__(name) for name in self.names] + list(self.args)
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return [self.__getattribute__(self.names[0])] + list(self.args)
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def log_likelihood(self):
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return float(numpy.sum(self._f(*self._get_x(), **self.kwargs)))
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def _log_likelihood_gradients(self):
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return numpy.atleast_1d(self._df(*self._get_x(), **self.kwargs)).flatten()
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def _get_params(self):
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return numpy.atleast_1d(numpy.hstack(map(lambda name: flatten_if_needed(self.__getattribute__(name)), self.names)))
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def _set_params(self, x):
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current_index = 0
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for name, shape in zip(self.names, self.shapes):
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current_size = numpy.prod(shape)
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self.__setattr__(name, x[current_index:current_index + current_size].reshape(shape))
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current_index += current_size
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def _get_param_names(self):
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_param_names = []
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for name, shape in zip(self.names, self.shapes):
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_param_names.extend(map(lambda nameshape: ('_'.join(nameshape)).strip('_'), itertools.izip(itertools.repeat(name), itertools.imap(lambda t: '_'.join(map(str, t)), itertools.product(*map(lambda xi: range(xi), shape))))))
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return _param_names
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