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fixed a bug in constructor of periodic_matern52

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James Hensman 2013-07-08 13:06:02 +01:00
parent 70189a387b
commit db7485b906
3 changed files with 24 additions and 12 deletions
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24
GPy/core/gp_base.py
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@ -91,12 +91,14 @@ class GPBase(Model):
else: else:
raise NotImplementedError, "Cannot define a frame with more than two input dimensions" raise NotImplementedError, "Cannot define a frame with more than two input dimensions"
def plot(self, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20, samples=0, fignum=None, ax=None): def plot(self, plot_limits=None, which_data='all', which_parts='all', resolution=None, levels=20, samples=0, fignum=None, ax=None, fixed_inputs=[], linecol=Tango.colorsHex['darkBlue'],fillcol=Tango.colorsHex['lightBlue']):
""" """
TODO: Docstrings! TODO: Docstrings!
:param levels: for 2D plotting, the number of contour levels to use :param levels: for 2D plotting, the number of contour levels to use
is ax is None, create a new figure is ax is None, create a new figure
fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input index i should be set to value v.
""" """
# TODO include samples # TODO include samples
if which_data == 'all': if which_data == 'all':
@ -106,15 +108,25 @@ class GPBase(Model):
fig = pb.figure(num=fignum) fig = pb.figure(num=fignum)
ax = fig.add_subplot(111) ax = fig.add_subplot(111)
if self.X.shape[1] == 1: plotdims = self.input_dim - len(fixed_inputs)
if plotdims == 1:
Xu = self.X * self._Xscale + self._Xoffset # NOTE self.X are the normalized values now Xu = self.X * self._Xscale + self._Xoffset # NOTE self.X are the normalized values now
Xnew, xmin, xmax = x_frame1D(Xu, plot_limits=plot_limits) fixed_dims = np.array([i for i,v in fixed_inputs])
m, _, lower, upper = self.predict(Xnew, which_parts=which_parts) freedim = np.setdiff1d(np.arange(self.input_dim),fixed_dims)
Xnew, xmin, xmax = x_frame1D(Xu[:,freedim], plot_limits=plot_limits)
Xgrid = np.empty((Xnew.shape[0],self.input_dim))
Xgrid[:,freedim] = Xnew
for i,v in fixed_inputs:
Xgrid[:,i] = v
m, _, lower, upper = self.predict(Xgrid, which_parts=which_parts)
for d in range(m.shape[1]): for d in range(m.shape[1]):
gpplot(Xnew, m[:, d], lower[:, d], upper[:, d], axes=ax) gpplot(Xnew, m[:, d], lower[:, d], upper[:, d], axes=ax, edgecol=linecol, fillcol=fillcol)
ax.plot(Xu[which_data], self.likelihood.data[which_data, d], 'kx', mew=1.5) ax.plot(Xu[which_data,freedim], self.likelihood.data[which_data, d], 'kx', mew=1.5)
ymin, ymax = min(np.append(self.likelihood.data, lower)), max(np.append(self.likelihood.data, upper)) ymin, ymax = min(np.append(self.likelihood.data, lower)), max(np.append(self.likelihood.data, upper))
ymin, ymax = ymin - 0.1 * (ymax - ymin), ymax + 0.1 * (ymax - ymin) ymin, ymax = ymin - 0.1 * (ymax - ymin), ymax + 0.1 * (ymax - ymin)
ax.set_xlim(xmin, xmax) ax.set_xlim(xmin, xmax)

6
GPy/examples/regression.py
View file

@ -83,7 +83,7 @@ def coregionalisation_toy2(optim_iters=100):
Y = np.vstack((Y1,Y2)) Y = np.vstack((Y1,Y2))
k1 = GPy.kern.rbf(1) + GPy.kern.bias(1) k1 = GPy.kern.rbf(1) + GPy.kern.bias(1)
k2 = GPy.kern.Coregionalise(2,1) k2 = GPy.kern.coregionalise(2,1)
k = k1.prod(k2,tensor=True) k = k1.prod(k2,tensor=True)
m = GPy.models.GPRegression(X,Y,kernel=k) m = GPy.models.GPRegression(X,Y,kernel=k)
m.constrain_fixed('.*rbf_var',1.) m.constrain_fixed('.*rbf_var',1.)
@ -114,7 +114,7 @@ def coregionalisation_toy(optim_iters=100):
Y = np.vstack((Y1,Y2)) Y = np.vstack((Y1,Y2))
k1 = GPy.kern.rbf(1) k1 = GPy.kern.rbf(1)
k2 = GPy.kern.Coregionalise(2,2) k2 = GPy.kern.coregionalise(2,2)
k = k1.prod(k2,tensor=True) k = k1.prod(k2,tensor=True)
m = GPy.models.GPRegression(X,Y,kernel=k) m = GPy.models.GPRegression(X,Y,kernel=k)
m.constrain_fixed('.*rbf_var',1.) m.constrain_fixed('.*rbf_var',1.)
@ -149,7 +149,7 @@ def coregionalisation_sparse(optim_iters=100):
Z = np.hstack((np.random.rand(num_inducing,1)*8,np.random.randint(0,2,num_inducing)[:,None])) Z = np.hstack((np.random.rand(num_inducing,1)*8,np.random.randint(0,2,num_inducing)[:,None]))
k1 = GPy.kern.rbf(1) k1 = GPy.kern.rbf(1)
k2 = GPy.kern.Coregionalise(2,2) k2 = GPy.kern.coregionalise(2,2)
k = k1.prod(k2,tensor=True) + GPy.kern.white(2,0.001) k = k1.prod(k2,tensor=True) + GPy.kern.white(2,0.001)
m = GPy.models.SparseGPRegression(X,Y,kernel=k,Z=Z) m = GPy.models.SparseGPRegression(X,Y,kernel=k,Z=Z)

6
GPy/kern/constructors.py
View file

@ -227,7 +227,7 @@ def periodic_Matern52(input_dim, variance=1., lengthscale=None, period=2 * np.pi
:param n_freq: the number of frequencies considered for the periodic subspace :param n_freq: the number of frequencies considered for the periodic subspace
:type n_freq: int :type n_freq: int
""" """
part = parts.periodic_Matern52part(input_dim, variance, lengthscale, period, n_freq, lower, upper) part = parts.periodic_Matern52.PeriodicMatern52(input_dim, variance, lengthscale, period, n_freq, lower, upper)
return kern(input_dim, [part]) return kern(input_dim, [part])
def prod(k1,k2,tensor=False): def prod(k1,k2,tensor=False):
@ -296,5 +296,5 @@ def independent_outputs(k):
""" """
for sl in k.input_slices: for sl in k.input_slices:
assert (sl.start is None) and (sl.stop is None), "cannot adjust input slices! (TODO)" assert (sl.start is None) and (sl.stop is None), "cannot adjust input slices! (TODO)"
parts = [independent_outputs.IndependentOutputs(p) for p in k.parts] _parts = [parts.independent_outputs.IndependentOutputs(p) for p in k.parts]
return kern(k.input_dim+1,parts) return kern(k.input_dim+1,_parts)