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Merge remote-tracking branch 'Falkor/newGP' into newGP
This commit is contained in:
Ricardo Andrade
commit
81917ceba5
4 changed files with 47 additions and 85 deletions
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@ -25,16 +25,14 @@ seed=default_seed
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"""
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X = np.arange(0,100,5)[:,None]
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F = np.round(np.sin(X/18.) + .1*X)
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E = np.random.randint(-3,3,20)[:,None]
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F = np.round(np.sin(X/18.) + .1*X) + np.arange(5,25)[:,None]
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E = np.random.randint(-5,5,20)[:,None]
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Y = F + E
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pb.plot(X,F,'k-')
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pb.plot(X,Y,'ro')
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pb.figure()
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likelihood = GPy.inference.likelihoods.poisson(Y,scale=6.)
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likelihood = GPy.inference.likelihoods.poisson(Y,scale=1.)
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m = GPy.models.GP(X,likelihood=likelihood)
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#m = GPy.models.GP(data['X'],Y=likelihood.Y)
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#m = GPy.models.GP(X,Y=likelihood.Y)
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m.constrain_positive('var')
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m.constrain_positive('len')
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@ -48,13 +48,13 @@ class EP:
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self.tau_tilde = np.zeros(self.N)
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self.v_tilde = np.zeros(self.N)
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def restart_EP(self):
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"""
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Set the EP approximation to initial state
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"""
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self.tau_tilde = np.zeros(self.N)
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self.v_tilde = np.zeros(self.N)
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self.mu = np.zeros(self.N)
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def _compute_GP_variables(self):
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#Variables to be called from GP
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mu_tilde = self.v_tilde/self.tau_tilde #When calling EP, this variable is used instead of Y in the GP model
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sigma_sum = 1./self.tau_ + 1./self.tau_tilde
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mu_diff_2 = (self.v_/self.tau_ - mu_tilde)**2
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Z_ep = np.sum(np.log(self.Z_hat)) + 0.5*np.sum(np.log(sigma_sum)) + 0.5*np.sum(mu_diff_2/sigma_sum) #Normalization constant
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return self.tau_tilde[:,None], mu_tilde[:,None], Z_ep
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class Full(EP):
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def fit_EP(self):
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@ -122,12 +122,7 @@ class Full(EP):
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self.np1.append(self.tau_tilde.copy())
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self.np2.append(self.v_tilde.copy())
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#Variables to be called from GP
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mu_tilde = self.v_tilde/self.tau_tilde #When calling EP, this variable is used instead of Y in the GP model
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sigma_sum = 1./self.tau_ + 1./self.tau_tilde
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mu_diff_2 = (self.v_/self.tau_ - mu_tilde)**2
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Z_ep = np.sum(np.log(self.Z_hat)) + 0.5*np.sum(np.log(sigma_sum)) + 0.5*np.sum(mu_diff_2/sigma_sum) #Normalization constant
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return self.tau_tilde[:,None], mu_tilde[:,None], Z_ep
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return self._compute_GP_variables()
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class DTC(EP):
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def fit_EP(self):
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@ -212,7 +207,8 @@ class DTC(EP):
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epsilon_np2 = sum((self.v_tilde-self.np2[-1])**2)/self.N
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self.np1.append(self.tau_tilde.copy())
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self.np2.append(self.v_tilde.copy())
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return self.tau_tilde[:,None], self.v_tilde[:,None], self.Z_hat[:,None], self.tau_[:,None], self.v_[:,None]
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return self._compute_GP_variables()
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class FITC(EP):
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def fit_EP(self):
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@ -313,4 +309,5 @@ class FITC(EP):
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epsilon_np2 = sum((self.v_tilde-self.np2[-1])**2)/self.N
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self.np1.append(self.tau_tilde.copy())
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self.np2.append(self.v_tilde.copy())
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return self.tau_tilde[:,None], self.v_tilde[:,None], self.Z_hat[:,None], self.tau_[:,None], self.v_[:,None]
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return self._compute_GP_variables()
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@ -9,65 +9,18 @@ import pylab as pb
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from ..util.plot import gpplot
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class likelihood:
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def __init__(self,Y,location=0,scale=1):
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"""
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Likelihood class for doing Expectation propagation
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"""
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Likelihood class for doing Expectation propagation
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:param Y: observed output (Nx1 numpy.darray)
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..Note:: Y values allowed depend on the likelihood used
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"""
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:param Y: observed output (Nx1 numpy.darray)
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..Note:: Y values allowed depend on the likelihood used
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"""
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def __init__(self,Y,location=0,scale=1):
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self.Y = Y
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self.N = self.Y.shape[0]
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self.location = location
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self.scale = scale
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def plot1D(self,X,mean,var,Z=None,mean_Z=None,var_Z=None,samples=0):
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"""
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Plot the predictive distribution of the GP model for 1-dimensional inputs
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:param X: The points at which to make a prediction
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:param Mean: mean values at X
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:param Var: variance values at X
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:param Z: Set of points to be highlighted in the plot, i.e. inducing points
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:param mean_Z: mean values at Z
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:param var_Z: variance values at Z
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:samples: Number of samples to plot
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"""
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assert X.shape[1] == 1, 'Number of dimensions must be 1'
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gpplot(X,mean,var.flatten())
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if samples: #NOTE why don't we put samples as a parameter of gpplot
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s = np.random.multivariate_normal(mean.flatten(),np.diag(var),samples)
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pb.plot(X.flatten(),s.T, alpha = 0.4, c='#3465a4', linewidth = 0.8)
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#pb.subplot(211)
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#self.plot1Da(X,mean,var,Z,mean_Z,var_Z)
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def plot1Da(self,X,mean,var,Z=None,mean_Z=None,var_Z=None):
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"""
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Plot the predictive distribution of the GP model for 1-dimensional inputs
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:param X_new: The points at which to make a prediction
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:param Mean_new: mean values at X_new
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:param Var_new: variance values at X_new
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:param X_u: input (inducing) points used to train the model
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:param Mean_u: mean values at X_u
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:param Var_new: variance values at X_u
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"""
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assert X.shape[1] == 1, 'Number of dimensions must be 1'
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gpplot(X,mean,var.flatten())
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pb.errorbar(Z.flatten(),mean_Z.flatten(),2*np.sqrt(var_Z.flatten()),fmt='r+')
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pb.plot(Z,mean_Z,'ro')
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"""
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def plot1Db(self,X_obs,X,phi,Z=None):
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assert X_obs.shape[1] == 1, 'Number of dimensions must be 1'
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gpplot(X,phi,np.zeros(X.shape[0]))
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pb.plot(X_obs,(self.Y+1)/2,'kx',mew=1.5)
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pb.ylim(-0.2,1.2)
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if Z is not None:
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pb.plot(Z,Z*0+.5,'r|',mew=1.5,markersize=12)
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"""
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def plot2D(self,X,X_new,F_new,U=None):
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"""
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Predictive distribution of the fitted GP model for 2-dimensional inputs
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@ -106,6 +59,10 @@ class probit(likelihood):
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L(x) = \\Phi (Y_i*f_i)
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$$
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"""
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def __init__(self,Y,location=0,scale=1):
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assert np.sum(np.abs(Y)-1) == 0, "Output values must be either -1 or 1"
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likelihood.__init__(self,Y,location,scale)
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def moments_match(self,i,tau_i,v_i):
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"""
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Moments match of the marginal approximation in EP algorithm
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@ -146,6 +103,10 @@ class poisson(likelihood):
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L(x) = \exp(\lambda) * \lambda**Y_i / Y_i!
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$$
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"""
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def __init__(self,Y,location=0,scale=1):
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assert len(Y[Y<0]) == 0, "Output cannot have negative values"
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likelihood.__init__(self,Y,location,scale)
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def moments_match(self,i,tau_i,v_i):
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"""
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Moments match of the marginal approximation in EP algorithm
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@ -203,20 +164,19 @@ class poisson(likelihood):
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sigma2_hat = m2 - mu_hat**2 # Second central moment
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return float(Z_hat), float(mu_hat), float(sigma2_hat)
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def plot1Db(self,X,X_new,F_new,F2_new=None,U=None):
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pb.subplot(212)
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#gpplot(X_new,F_new,np.sqrt(F2_new))
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pb.plot(X_new,F_new)#,np.sqrt(F2_new)) #FIXME
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pb.plot(X,self.Y,'kx',mew=1.5)
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if U is not None:
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pb.plot(U,np.ones(U.shape[0])*self.Y.min()*.8,'r|',mew=1.5,markersize=12)
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def predictive_mean(self,mu,variance):
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return np.exp(mu*self.scale + self.location)
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def predictive_variance(self,mu,variance):
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return mu
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def _log_likelihood_gradients():
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raise NotImplementedError
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def plot(self,X,phi,X_obs,Z=None):
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assert X_obs.shape[1] == 1, 'Number of dimensions must be 1'
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gpplot(X,phi,np.zeros(X.shape[0]))
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pb.plot(X_obs,self.Y,'kx',mew=1.5)
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if Z is not None:
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pb.plot(Z,Z*0+pb.ylim()[0],'k|',mew=1.5,markersize=12)
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class gaussian(likelihood):
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"""
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Gaussian likelihood
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@ -35,9 +35,13 @@ class sparse_GP(GP):
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:type beta: float
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:param normalize_(X|Y) : whether to normalize the data before computing (predictions will be in original scales)
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:type normalize_(X|Y): bool
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:parm likelihood: a GPy likelihood, defaults to gaussian
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:param epsilon_ep: convergence criterion for the Expectation Propagation algorithm, defaults to 0.1
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:param powerep: power-EP parameters [$\eta$,$\delta$], defaults to [1.,1.]
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:type powerep: list
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"""
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def __init__(self,X,Y=None,kernel=None, X_uncertainty=None, beta=100., Z=None,Zslices=None,M=10,normalize_X=False,normalize_Y=False,likelihood=None,method_ep='DTC',epsilon_ep=1e-3,epsilon_em=.1,power_ep=[1.,1.]):
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def __init__(self,X,Y=None,kernel=None,X_uncertainty=None,beta=100.,Z=None,Zslices=None,M=10,normalize_X=False,normalize_Y=False,likelihood=None,method_ep='DTC',epsilon_ep=1e-3,power_ep=[1.,1.]):
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if Z is None:
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self.Z = np.random.permutation(X.copy())[:M]
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@ -53,7 +57,7 @@ class sparse_GP(GP):
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self.has_uncertain_inputs=True
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self.X_uncertainty = X_uncertainty
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GP.__init__(self, X=X, Y=Y, kernel=kernel, normalize_X=normalize_X, normalize_Y=normalize_Y,likelihood=likelihood,epsilon_ep=epsilon_ep,epsilon_em=epsilon_em,power_ep=power_ep)
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GP.__init__(self, X=X, Y=Y, kernel=kernel, normalize_X=normalize_X, normalize_Y=normalize_Y,likelihood=likelihood,epsilon_ep=epsilon_ep,power_ep=power_ep)
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self.trYYT = np.sum(np.square(self.Y)) if not self.EP else None
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@ -91,6 +95,9 @@ class sparse_GP(GP):
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else:
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if self.hetero_noise:
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print "rick's stuff here"
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else:
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self.psi0 = self.kern.Kdiag(self.X,slices=self.Xslices).sum()
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self.psi1 = self.kern.K(self.Z,self.X)
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