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unified framework for addition and product of kernels, with a tensor flag (boolean) instead of and
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Nicolas
parent
50b7958051
commit
652b3ce2c6
8 changed files with 132 additions and 168 deletions
115
GPy/kern/kern.py
115
GPy/kern/kern.py
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@ -7,7 +7,6 @@ import pylab as pb
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from ..core.parameterised import parameterised
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from kernpart import kernpart
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import itertools
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from prod_orthogonal import prod_orthogonal
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from prod import prod
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from ..util.linalg import symmetrify
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@ -84,96 +83,72 @@ class kern(parameterised):
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count += p.Nparam
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def __add__(self, other):
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assert self.D == other.D
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newkern = kern(self.D, self.parts + other.parts, self.input_slices + other.input_slices)
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# transfer constraints:
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newkern.constrained_indices = self.constrained_indices + [i+self.Nparam for i in other.constrained_indices]
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newkern.constraints = self.constraints + other.constraints
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newkern.fixed_indices = self.fixed_indices + [self.Nparam + x for x in other.fixed_indices]
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newkern.fixed_values = self.fixed_values + other.fixed_values
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newkern.tied_indices = self.tied_indices + [self.Nparam + x for x in other.tied_indices]
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return newkern
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"""
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Shortcut for `add`.
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"""
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return self.add(other)
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def add(self, other):
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def add(self, other,tensor=False):
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"""
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Add another kernel to this one. Both kernels are defined on the same _space_
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:param other: the other kernel to be added
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:type other: GPy.kern
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"""
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return self + other
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if tensor:
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D = self.D + other.D
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self_input_slices = [slice(*sl.indices(self.D)) for sl in self.input_slices]
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other_input_indices = [sl.indices(other.D) for sl in other.input_slices]
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other_input_slices = [slice(i[0] + self.D, i[1] + self.D, i[2]) for i in other_input_indices]
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def add_orthogonal(self, other):
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"""
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Add another kernel to this one. Both kernels are defined on separate spaces
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:param other: the other kernel to be added
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:type other: GPy.kern
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"""
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# deal with input slices
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D = self.D + other.D
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self_input_slices = [slice(*sl.indices(self.D)) for sl in self.input_slices]
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other_input_indices = [sl.indices(other.D) for sl in other.input_slices]
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other_input_slices = [slice(i[0] + self.D, i[1] + self.D, i[2]) for i in other_input_indices]
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newkern = kern(D, self.parts + other.parts, self_input_slices + other_input_slices)
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newkern = kern(D, self.parts + other.parts, self_input_slices + other_input_slices)
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# transfer constraints:
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newkern.constrained_indices = self.constrained_indices + [x+self.Nparam for x in other.constrained_indices]
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newkern.constraints = self.constraints + other.constraints
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newkern.fixed_indices = self.fixed_indices + [self.Nparam + x for x in other.fixed_indices]
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newkern.fixed_values = self.fixed_values + other.fixed_values
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newkern.constraints = self.constraints + other.constraints
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newkern.constrained_bounded_uppers = self.constrained_bounded_uppers + other.constrained_bounded_uppers
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newkern.tied_indices = self.tied_indices + [self.Nparam + x for x in other.tied_indices]
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# transfer constraints:
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newkern.constrained_indices = self.constrained_indices + [x+self.Nparam for x in other.constrained_indices]
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newkern.constraints = self.constraints + other.constraints
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newkern.fixed_indices = self.fixed_indices + [self.Nparam + x for x in other.fixed_indices]
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newkern.fixed_values = self.fixed_values + other.fixed_values
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newkern.constraints = self.constraints + other.constraints
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newkern.tied_indices = self.tied_indices + [self.Nparam + x for x in other.tied_indices]
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else:
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assert self.D == other.D
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newkern = kern(self.D, self.parts + other.parts, self.input_slices + other.input_slices)
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# transfer constraints:
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newkern.constrained_indices = self.constrained_indices + [i+self.Nparam for i in other.constrained_indices]
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newkern.constraints = self.constraints + other.constraints
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newkern.fixed_indices = self.fixed_indices + [self.Nparam + x for x in other.fixed_indices]
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newkern.fixed_values = self.fixed_values + other.fixed_values
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newkern.tied_indices = self.tied_indices + [self.Nparam + x for x in other.tied_indices]
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return newkern
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def __mul__(self, other):
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"""
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Shortcut for `prod_orthogonal`. Note that `+` assumes that we sum 2 kernels defines on the same space whereas `*` assumes that the kernels are defined on different subspaces.
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Shortcut for `prod`.
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"""
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return self.prod(other)
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def prod(self, other):
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def prod(self, other,tensor=False):
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"""
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multiply two kernels defined on the same spaces.
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multiply two kernels (either on the same space, or on the tensor product of the input space)
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:param other: the other kernel to be added
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:type other: GPy.kern
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"""
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K1 = self.copy()
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K2 = other.copy()
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newkernparts = [prod(k1, k2) for k1, k2 in itertools.product(K1.parts, K2.parts)]
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slices = []
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for sl1, sl2 in itertools.product(K1.input_slices, K2.input_slices):
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s1, s2 = [False] * K1.D, [False] * K2.D
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for sl1, sl2 in itertools.product(K1.input_slices,K2.input_slices):
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s1, s2 = [False]*K1.D, [False]*K2.D
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s1[sl1], s2[sl2] = [True], [True]
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slices += [s1 + s2]
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slices += [s1+s2]
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newkernparts = [prod(k1, k2,tensor) for k1, k2 in itertools.product(K1.parts, K2.parts)]
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if tensor:
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newkern = kern(K1.D + K2.D, newkernparts, slices)
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else:
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newkern = kern(K1.D, newkernparts, slices)
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newkern = kern(K1.D, newkernparts, slices)
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newkern._follow_constrains(K1, K2)
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return newkern
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def prod_orthogonal(self, other):
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"""
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multiply two kernels. Both kernels are defined on separate spaces.
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:param other: the other kernel to be added
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:type other: GPy.kern
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"""
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K1 = self.copy()
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K2 = other.copy()
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newkernparts = [prod_orthogonal(k1, k2) for k1, k2 in itertools.product(K1.parts, K2.parts)]
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slices = []
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for sl1, sl2 in itertools.product(K1.input_slices, K2.input_slices):
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s1, s2 = [False] * K1.D, [False] * K2.D
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s1[sl1], s2[sl2] = [True], [True]
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slices += [s1 + s2]
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newkern = kern(K1.D + K2.D, newkernparts, slices)
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newkern._follow_constrains(K1, K2)
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return newkern
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def _follow_constrains(self, K1, K2):
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@ -469,9 +444,9 @@ class kern(parameterised):
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return target_mu, target_S
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def plot(self, x=None, plot_limits=None, which_functions='all', resolution=None, *args, **kwargs):
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if which_functions == 'all':
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which_functions = [True] * self.Nparts
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def plot(self, x=None, plot_limits=None, which_parts='all', resolution=None, *args, **kwargs):
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if which_parts == 'all':
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which_parts = [True] * self.Nparts
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if self.D == 1:
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if x is None:
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x = np.zeros((1, 1))
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@ -488,7 +463,7 @@ class kern(parameterised):
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raise ValueError, "Bad limits for plotting"
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Xnew = np.linspace(xmin, xmax, resolution or 201)[:, None]
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Kx = self.K(Xnew, x, slices2=which_functions)
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Kx = self.K(Xnew, x, which_parts)
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pb.plot(Xnew, Kx, *args, **kwargs)
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pb.xlim(xmin, xmax)
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pb.xlabel("x")
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@ -514,7 +489,7 @@ class kern(parameterised):
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xg = np.linspace(xmin[0], xmax[0], resolution)
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yg = np.linspace(xmin[1], xmax[1], resolution)
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Xnew = np.vstack((xx.flatten(), yy.flatten())).T
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Kx = self.K(Xnew, x, slices2=which_functions)
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Kx = self.K(Xnew, x, which_parts)
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Kx = Kx.reshape(resolution, resolution).T
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pb.contour(xg, yg, Kx, vmin=Kx.min(), vmax=Kx.max(), cmap=pb.cm.jet, *args, **kwargs)
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pb.xlim(xmin[0], xmax[0])
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