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Step transformation added

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This commit is contained in:
Ricardo 2013-09-11 16:00:20 +01:00
parent 671591fa96
commit 3608290350
3 changed files with 36 additions and 6 deletions
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9
GPy/likelihoods/noise_model_constructors.py
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@ -17,9 +17,16 @@ def binomial(gp_link=None):
if isinstance(gp_link,noise_models.gp_transformations.Probit): if isinstance(gp_link,noise_models.gp_transformations.Probit):
analytical_mean = True analytical_mean = True
analytical_variance = False
elif isinstance(gp_link,noise_models.gp_transformations.Step):
analytical_mean = True
analytical_variance = True
else: else:
analytical_mean = False analytical_mean = False
analytical_variance = False analytical_variance = False
return noise_models.binomial_noise.Binomial(gp_link,analytical_mean,analytical_variance) return noise_models.binomial_noise.Binomial(gp_link,analytical_mean,analytical_variance)
def exponential(gp_link=None): def exponential(gp_link=None):

17
GPy/likelihoods/noise_models/binomial_noise.py
View file

@ -42,11 +42,18 @@ class Binomial(NoiseDistribution):
:param tau_i: precision of the cavity distribution (float) :param tau_i: precision of the cavity distribution (float)
:param v_i: mean/variance of the cavity distribution (float) :param v_i: mean/variance of the cavity distribution (float)
""" """
z = data_i*v_i/np.sqrt(tau_i**2 + tau_i) if isinstance(self.gp_link,gp_transformations.Probit):
Z_hat = std_norm_cdf(z) z = data_i*v_i/np.sqrt(tau_i**2 + tau_i)
phi = std_norm_pdf(z) Z_hat = std_norm_cdf(z)
mu_hat = v_i/tau_i + data_i*phi/(Z_hat*np.sqrt(tau_i**2 + tau_i)) phi = std_norm_pdf(z)
sigma2_hat = 1./tau_i - (phi/((tau_i**2+tau_i)*Z_hat))*(z+phi/Z_hat) mu_hat = v_i/tau_i + data_i*phi/(Z_hat*np.sqrt(tau_i**2 + tau_i))
sigma2_hat = 1./tau_i - (phi/((tau_i**2+tau_i)*Z_hat))*(z+phi/Z_hat)
elif isinstance(self.gp_link,gp_transformations.Step):
Z_hat = None
mu_hat = None
sigma2_hat = None
return Z_hat, mu_hat, sigma2_hat return Z_hat, mu_hat, sigma2_hat
def _predictive_mean_analytical(self,mu,sigma): def _predictive_mean_analytical(self,mu,sigma):

16
GPy/likelihoods/noise_models/gp_transformations.py
View file

@ -55,6 +55,8 @@ class Probit(GPTransformation):
def d2transf_df2(self,f): def d2transf_df2(self,f):
return -f * std_norm_pdf(f) return -f * std_norm_pdf(f)
def
class Log(GPTransformation): class Log(GPTransformation):
""" """
$$ $$
@ -108,4 +110,18 @@ class Reciprocal(GPTransformation):
def d2transf_df2(self,f): def d2transf_df2(self,f):
return 2./f**3 return 2./f**3
class Step(GPTransformation):
"""
$$
g(f) = I_{x \in A}
$$
"""
def transf(self,f):
#transformation goes here
return np.where(f>0, 1, 0)
def dtransf_df(self,f):
pass
def d2transf_df2(self,f):
pass