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works well now

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Ricardo 2013-09-13 12:33:16 +01:00
parent 6299b219b5
commit 86b5b3aa05
1 changed files with 31 additions and 24 deletions
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55
GPy/likelihoods/gaussian_mixed_noise.py
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@ -10,33 +10,39 @@ from . import Gaussian
class Gaussian_Mixed_Noise(likelihood): class Gaussian_Mixed_Noise(likelihood):
"""
Gaussian Likelihood for multiple outputs
This is a wrapper around likelihood.Gaussian class
:param data_list: data observations
:type data_list: list of numpy arrays (num_data_output_i x 1), one array per output
:param noise_params: noise parameters of each output
:type noise_params: list of floats, one per output
:param normalize: whether to normalize the data before computing (predictions will be in original scales)
:type normalize: False|True
"""
def __init__(self, data_list, noise_params=None, normalize=True): def __init__(self, data_list, noise_params=None, normalize=True):
self.Nparams = len(data_list)
self.n_list = [data.size for data in data_list]
self.index = np.vstack([np.repeat(i,n)[:,None] for i,n in zip(range(self.Nparams),self.n_list)])
if noise_params is None: if noise_params is None:
noise_params = [1.] * len(data_list) noise_params = [1.] * self.Nparams
else:
assert len(data_list) == len(noise_params) assert self.Nparams == len(noise_params), 'Number of noise parameters does not match the number of noise models.'
self.noise_model_list = [Gaussian(Y,variance=v,normalize = normalize) for Y,v in zip(data_list,noise_params)] self.noise_model_list = [Gaussian(Y,variance=v,normalize = normalize) for Y,v in zip(data_list,noise_params)]
self.n_list = [data.size for data in data_list]
n_models = len(data_list)
self.n_params = [noise_model._get_params().size for noise_model in self.noise_model_list] self.n_params = [noise_model._get_params().size for noise_model in self.noise_model_list]
self.index = np.vstack([np.repeat(i,n)[:,None] for i,n in zip(range(n_models),self.n_list)])
self.data = np.vstack(data_list) self.data = np.vstack(data_list)
self.N, self.output_dim = self.data.shape self.N, self.output_dim = self.data.shape
self._offset = np.zeros((1, self.output_dim)) self._offset = np.zeros((1, self.output_dim))
self._scale = np.ones((1, self.output_dim)) self._scale = np.ones((1, self.output_dim))
self.is_heteroscedastic = True #TODO check how to deal with this self.is_heteroscedastic = True
self.Z = 0. # a correction factor which accounts for the approximation made self.Z = 0. # a correction factor which accounts for the approximation made
self.set_data(data_list) self.set_data(data_list)
#self._variance = np.asarray(variance) + 1.
self._set_params(np.asarray(noise_params)) self._set_params(np.asarray(noise_params))
def set_data(self, data_list): def set_data(self, data_list):
@ -45,9 +51,10 @@ class Gaussian_Mixed_Noise(likelihood):
assert D == self.output_dim assert D == self.output_dim
self.Y = (self.data - self._offset) / self._scale self.Y = (self.data - self._offset) / self._scale
if D > self.N: if D > self.N:
self.YYT = np.dot(self.Y, self.Y.T) raise NotImplementedError
self.trYYT = np.trace(self.YYT) #self.YYT = np.dot(self.Y, self.Y.T)
self.YYT_factor = jitchol(self.YYT) #self.trYYT = np.trace(self.YYT)
#self.YYT_factor = jitchol(self.YYT)
else: else:
self.YYT = None self.YYT = None
self.trYYT = np.sum(np.square(self.Y)) self.trYYT = np.sum(np.square(self.Y))
@ -82,18 +89,18 @@ class Gaussian_Mixed_Noise(likelihood):
def _set_params(self,p): def _set_params(self,p):
cs_params = np.cumsum([0]+self.n_params) cs_params = np.cumsum([0]+self.n_params)
for i in range(len(self.n_params)): for i in range(len(self.n_params)):
self.noise_model_list[i]._set_params(p[cs_params[i]:cs_params[i+1]]) self.noise_model_list[i]._set_params(p[cs_params[i]:cs_params[i+1]])
self.precision = np.hstack([np.repeat(noise_model.precision,n) for noise_model,n in zip(self.noise_model_list,self.n_list)])[:,None] self.precision = np.hstack([np.repeat(noise_model.precision,n) for noise_model,n in zip(self.noise_model_list,self.n_list)])[:,None]
self.V = (self.precision) * self.Y
self.V = self.precision * self.Y
self.VVT_factor = self.precision * self.YYT_factor self.VVT_factor = self.precision * self.YYT_factor
self.covariance_matrix = np.eye(self.N) * 1./self.precision self.covariance_matrix = np.eye(self.N) * 1./self.precision
#self._variance = x
def _gradients(self,partial): def _gradients(self,partial):
#NOTE this is not tested gradients = []
return np.hstack([noise_model._gradients(partial) for noise_model in self.noise_model_list]) aux = np.cumsum([0]+self.n_list)
for ai,af,noise_model in zip(aux[:-1],aux[1:],self.noise_model_list):
gradients += [noise_model._gradients(partial[ai:af])]
return np.hstack(gradients)