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gradient checker comments and import updates

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Max Zwiessele 2013-11-27 13:16:00 +00:00
parent 77a0d61bf6
commit 4be3f4482d
2 changed files with 25 additions and 25 deletions
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41
GPy/_models/gradient_checker.py
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@ -28,38 +28,37 @@ class GradientChecker(Model):
:param df: Gradient of function to check :param df: Gradient of function to check
:param x0: :param x0:
Initial guess for inputs x (if it has a shape (a,b) this will be reflected in the parameter names). Initial guess for inputs x (if it has a shape (a,b) this will be reflected in the parameter names).
Can be a list of arrays, if takes a list of arrays. This list will be passed Can be a list of arrays, if f takes a list of arrays. This list will be passed
to f and df in the same order as given here. to f and df in the same order as given here.
If only one argument, make sure not to pass a list!!! If f takes only one argument, make sure not to pass a list for x0!!!
:type x0: [array-like] | array-like | float | int :type x0: [array-like] | array-like | float | int
:param names: :param list names:
Names to print, when performing gradcheck. If a list was passed to x0 Names to print, when performing gradcheck. If a list was passed to x0
a list of names with the same length is expected. a list of names with the same length is expected.
:param args: Arguments passed as f(x, *args, **kwargs) and df(x, *args, **kwargs) :param args kwargs: Arguments passed as f(x, *args, **kwargs) and df(x, *args, **kwargs)
Examples: Examples:
--------- ---------
from GPy.models import GradientChecker from GPy.models import GradientChecker
N, M, Q = 10, 5, 3 N, M, Q = 10, 5, 3
Sinusoid: Sinusoid:
X = numpy.random.rand(N, Q) X = numpy.random.rand(N, Q)
grad = GradientChecker(numpy.sin,numpy.cos,X,'x') grad = GradientChecker(numpy.sin,numpy.cos,X,'sin_in')
grad.checkgrad(verbose=1) grad.checkgrad(verbose=1)
Using GPy: Using GPy:
X, Z = numpy.random.randn(N,Q), numpy.random.randn(M,Q) X, Z = numpy.random.randn(N,Q), numpy.random.randn(M,Q)
kern = GPy.kern.linear(Q, ARD=True) + GPy.kern.rbf(Q, ARD=True) kern = GPy.kern.linear(Q, ARD=True) + GPy.kern.rbf(Q, ARD=True)
grad = GradientChecker(kern.K, grad = GradientChecker(kern.K,
lambda x: 2*kern.dK_dX(numpy.ones((1,1)), x), lambda x: kern.dK_dX(numpy.ones((1,1)), x),
x0 = X.copy(), x0 = X.copy(),
names='X') names=['X_input'])
grad.checkgrad(verbose=1) grad.checkgrad(verbose=1)
grad.randomize() grad.randomize()
grad.checkgrad(verbose=1) grad.checkgrad(verbose=1)
""" """
Model.__init__(self) Model.__init__(self)
if isinstance(x0, (list, tuple)) and names is None: if isinstance(x0, (list, tuple)) and names is None:

9
GPy/kern/kern.py
View file

@ -737,15 +737,16 @@ class kern(Parameterized):
else: else:
raise NotImplementedError, "Cannot plot a kernel with more than two input dimensions" raise NotImplementedError, "Cannot plot a kernel with more than two input dimensions"
from GPy.core.model import Model from ..core.model import Model
class Kern_check_model(Model): class Kern_check_model(Model):
"""This is a dummy model class used as a base class for checking that the gradients of a given kernel are implemented correctly. It enables checkgradient() to be called independently on a kernel.""" """This is a dummy model class used as a base class for checking that the gradients of a given kernel are implemented correctly. It enables checkgradient() to be called independently on a kernel."""
def __init__(self, kernel=None, dL_dK=None, X=None, X2=None): def __init__(self, kernel=None, dL_dK=None, X=None, X2=None):
num_samples = 20 num_samples = 20
num_samples2 = 10 num_samples2 = 10
if kernel==None: if kernel==None:
import GPy
kernel = GPy.kern.rbf(1) kernel = GPy.kern.rbf(1)
del GPy
if X==None: if X==None:
X = np.random.normal(size=(num_samples, kernel.input_dim)) X = np.random.normal(size=(num_samples, kernel.input_dim))
if dL_dK==None: if dL_dK==None:
@ -760,7 +761,7 @@ class Kern_check_model(Model):
self.dL_dK = dL_dK self.dL_dK = dL_dK
#self.constrained_indices=[] #self.constrained_indices=[]
#self.constraints=[] #self.constraints=[]
Model.__init__(self) super(Kern_check_model, self).__init__()
def is_positive_definite(self): def is_positive_definite(self):
v = np.linalg.eig(self.kernel.K(self.X))[0] v = np.linalg.eig(self.kernel.K(self.X))[0]
@ -863,7 +864,6 @@ def kern_test(kern, X=None, X2=None, output_ind=None, verbose=False, X_positive=
if output_ind is not None: if output_ind is not None:
assert(output_ind<kern.input_dim) assert(output_ind<kern.input_dim)
X[:, output_ind] = np.random.randint(low=0,high=kern.parts[0].output_dim, size=X.shape[0]) X[:, output_ind] = np.random.randint(low=0,high=kern.parts[0].output_dim, size=X.shape[0])
import ipdb; ipdb.set_trace()
if X2==None: if X2==None:
X2 = np.random.randn(20, kern.input_dim) X2 = np.random.randn(20, kern.input_dim)
if X_positive: if X_positive:
@ -964,3 +964,4 @@ def kern_test(kern, X=None, X2=None, output_ind=None, verbose=False, X_positive=
return False return False
return pass_checks return pass_checks
del Model