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pcikling?

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Max Zwiessele 2013-06-25 17:43:42 +01:00
parent d3a4f99b89
commit e869fcaf65
9 changed files with 149 additions and 370 deletions
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24
GPy/models/gplvm.py
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@ -1,4 +1,4 @@
### Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# ## Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
@ -26,11 +26,11 @@ class GPLVM(GP):
:type init: 'PCA'|'random'
"""
def __init__(self, Y, input_dim, init='PCA', X = None, kernel=None, normalize_Y=False):
def __init__(self, Y, input_dim, init='PCA', X=None, kernel=None, normalize_Y=False):
if X is None:
X = self.initialise_latent(init, input_dim, Y)
if kernel is None:
kernel = kern.rbf(input_dim, ARD=input_dim>1) + kern.bias(input_dim, np.exp(-2)) + kern.white(input_dim, np.exp(-2))
kernel = kern.rbf(input_dim, ARD=input_dim > 1) + kern.bias(input_dim, np.exp(-2)) + kern.white(input_dim, np.exp(-2))
likelihood = Gaussian(Y, normalize=normalize_Y)
GP.__init__(self, X, likelihood, kernel, normalize_X=False)
self._set_params(self._get_params())
@ -42,26 +42,26 @@ class GPLVM(GP):
return np.random.randn(Y.shape[0], input_dim)
def _get_param_names(self):
return sum([['X_%i_%i'%(n,q) for q in range(self.input_dim)] for n in range(self.num_data)],[]) + GP._get_param_names(self)
return sum([['X_%i_%i' % (n, q) for q in range(self.input_dim)] for n in range(self.num_data)], []) + GP._get_param_names(self)
def _get_params(self):
return np.hstack((self.X.flatten(), GP._get_params(self)))
def _set_params(self,x):
self.X = x[:self.num_data*self.input_dim].reshape(self.num_data,self.input_dim).copy()
def _set_params(self, x):
self.X = x[:self.num_data * self.input_dim].reshape(self.num_data, self.input_dim).copy()
GP._set_params(self, x[self.X.size:])
def _log_likelihood_gradients(self):
dL_dX = 2.*self.kern.dK_dX(self.dL_dK,self.X)
dL_dX = 2.*self.kern.dK_dX(self.dL_dK, self.X)
return np.hstack((dL_dX.flatten(),GP._log_likelihood_gradients(self)))
return np.hstack((dL_dX.flatten(), GP._log_likelihood_gradients(self)))
def plot(self):
assert self.likelihood.Y.shape[1]==2
pb.scatter(self.likelihood.Y[:,0],self.likelihood.Y[:,1],40,self.X[:,0].copy(),linewidth=0,cmap=pb.cm.jet)
Xnew = np.linspace(self.X.min(),self.X.max(),200)[:,None]
assert self.likelihood.Y.shape[1] == 2
pb.scatter(self.likelihood.Y[:, 0], self.likelihood.Y[:, 1], 40, self.X[:, 0].copy(), linewidth=0, cmap=pb.cm.jet)
Xnew = np.linspace(self.X.min(), self.X.max(), 200)[:, None]
mu, var, upper, lower = self.predict(Xnew)
pb.plot(mu[:,0], mu[:,1],'k',linewidth=1.5)
pb.plot(mu[:, 0], mu[:, 1], 'k', linewidth=1.5)
def plot_latent(self, *args, **kwargs):
return util.plot_latent.plot_latent(self, *args, **kwargs)