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Fixed deg free gradient

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This commit is contained in:
Alan Saul 2015-04-28 10:51:13 +01:00
parent 75ebe4bf10
commit 1acbcd2593
2 changed files with 29 additions and 29 deletions
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10
GPy/likelihoods/student_t.py
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@ -36,9 +36,6 @@ class StudentT(Likelihood):
self.log_concave = False self.log_concave = False
#def parameters_changed(self):
#self.variance = (self.v / float(self.v - 2)) * self.sigma2
def update_gradients(self, grads): def update_gradients(self, grads):
""" """
Pull out the gradients, be careful as the order must match the order Pull out the gradients, be careful as the order must match the order
@ -231,8 +228,8 @@ class StudentT(Likelihood):
df = float(self.v[:]) df = float(self.v[:])
s2 = float(self.sigma2[:]) s2 = float(self.sigma2[:])
dlogpdf_dv = 0.5*digamma(0.5*(df+1)) - 0.5*digamma(0.5*df) - 1.0/(2*df) dlogpdf_dv = 0.5*digamma(0.5*(df+1)) - 0.5*digamma(0.5*df) - 1.0/(2*df)
dlogpdf_dv += (1.0/(2*df))*(df+1)*e/(e2 + s2*df) dlogpdf_dv += 0.5*(df+1)*e2/(df*(e2 + s2*df))
dlogpdf_dv -= np.log(1 + e2/(s2*df)) dlogpdf_dv -= 0.5*np.log1p(e2/(s2*df))
return dlogpdf_dv return dlogpdf_dv
def dlogpdf_dlink_dv(self, inv_link_f, y, Y_metadata=None): def dlogpdf_dlink_dv(self, inv_link_f, y, Y_metadata=None):
@ -248,9 +245,8 @@ class StudentT(Likelihood):
e2 = np.square(e) e2 = np.square(e)
df = float(self.v[:]) df = float(self.v[:])
s2 = float(self.sigma2[:]) s2 = float(self.sigma2[:])
#derivative of hess = ((self.v + 1)*(e**2 - self.v*self.sigma2)) / ((self.sigma2*self.v + e**2)**2)
e2_s2v = e**2 + s2*df e2_s2v = e**2 + s2*df
d2logpdf_df2_dv = (e2 - s2*df - s2*(df + 1))/e2_s2v**2 - 2*s2*(df+1)*(e2 - s2*df)/e2_s2v d2logpdf_df2_dv = (-s2*(df+1) + e2 - s2*df)/e2_s2v**2 - 2*s2*(df+1)*(e2 - s2*df)/e2_s2v**3
return d2logpdf_df2_dv return d2logpdf_df2_dv
def dlogpdf_link_dtheta(self, f, y, Y_metadata=None): def dlogpdf_link_dtheta(self, f, y, Y_metadata=None):

48
GPy/testing/likelihood_tests.py
View file

@ -93,6 +93,9 @@ def dparam_checkgrad(func, dfunc, params, params_names, args, constraints=None,
if not grad.checkgrad(verbose=True): if not grad.checkgrad(verbose=True):
gradchecking = False gradchecking = False
if not grad.checkgrad(verbose=True):
gradchecking = False
return gradchecking return gradchecking
@ -116,6 +119,7 @@ class TestNoiseModels(object):
self.integer_Y = np.where(tmp > 0, tmp, 0) self.integer_Y = np.where(tmp > 0, tmp, 0)
self.var = 0.2 self.var = 0.2
self.deg_free = 4.0
#Make a bigger step as lower bound can be quite curved #Make a bigger step as lower bound can be quite curved
self.step = 1e-4 self.step = 1e-4
@ -135,56 +139,56 @@ class TestNoiseModels(object):
} }
""" """
self.noise_models = {"Student_t_default": { self.noise_models = {"Student_t_default": {
"model": GPy.likelihoods.StudentT(deg_free=5, sigma2=self.var), "model": GPy.likelihoods.StudentT(deg_free=self.deg_free, sigma2=self.var),
"grad_params": { "grad_params": {
"names": [".*t_scale2"], "names": [".*t_scale2", ".*deg_free"],
"vals": [self.var], "vals": [self.var, self.deg_free],
"constraints": [(".*t_scale2", self.constrain_positive), (".*deg_free", self.constrain_fixed)] "constraints": [(".*t_scale2", self.constrain_positive), (".*deg_free", self.constrain_positive)]
}, },
"laplace": True "laplace": True
}, },
"Student_t_1_var": { "Student_t_1_var": {
"model": GPy.likelihoods.StudentT(deg_free=5, sigma2=self.var), "model": GPy.likelihoods.StudentT(deg_free=self.deg_free, sigma2=self.var),
"grad_params": { "grad_params": {
"names": [".*t_scale2"], "names": [".*t_scale2", ".*deg_free"],
"vals": [1.0], "vals": [1.0, 8.0],
"constraints": [(".*t_scale2", self.constrain_positive), (".*deg_free", self.constrain_fixed)] "constraints": [(".*t_scale2", self.constrain_positive), (".*deg_free", self.constrain_positive)]
}, },
"laplace": True "laplace": True
}, },
"Student_t_small_deg_free": { "Student_t_small_deg_free": {
"model": GPy.likelihoods.StudentT(deg_free=1.5, sigma2=self.var), "model": GPy.likelihoods.StudentT(deg_free=1.5, sigma2=self.var),
"grad_params": { "grad_params": {
"names": [".*t_scale2"], "names": [".*t_scale2", ".*deg_free"],
"vals": [self.var], "vals": [self.var, 1.5],
"constraints": [(".*t_scale2", self.constrain_positive), (".*deg_free", self.constrain_fixed)] "constraints": [(".*t_scale2", self.constrain_positive), (".*deg_free", self.constrain_positive)]
}, },
"laplace": True "laplace": True
}, },
"Student_t_small_var": { "Student_t_small_var": {
"model": GPy.likelihoods.StudentT(deg_free=5, sigma2=self.var), "model": GPy.likelihoods.StudentT(deg_free=self.deg_free, sigma2=self.var),
"grad_params": { "grad_params": {
"names": [".*t_scale2"], "names": [".*t_scale2", ".*deg_free"],
"vals": [0.001], "vals": [0.001, self.deg_free],
"constraints": [(".*t_scale2", self.constrain_positive), (".*deg_free", self.constrain_fixed)] "constraints": [(".*t_scale2", self.constrain_positive), (".*deg_free", self.constrain_positive)]
}, },
"laplace": True "laplace": True
}, },
"Student_t_large_var": { "Student_t_large_var": {
"model": GPy.likelihoods.StudentT(deg_free=5, sigma2=self.var), "model": GPy.likelihoods.StudentT(deg_free=self.deg_free, sigma2=self.var),
"grad_params": { "grad_params": {
"names": [".*t_scale2"], "names": [".*t_scale2", ".*deg_free"],
"vals": [10.0], "vals": [10.0, self.deg_free],
"constraints": [(".*t_scale2", self.constrain_positive), (".*deg_free", self.constrain_fixed)] "constraints": [(".*t_scale2", self.constrain_positive), (".*deg_free", self.constrain_positive)]
}, },
"laplace": True "laplace": True
}, },
"Student_t_approx_gauss": { "Student_t_approx_gauss": {
"model": GPy.likelihoods.StudentT(deg_free=1000, sigma2=self.var), "model": GPy.likelihoods.StudentT(deg_free=1000, sigma2=self.var),
"grad_params": { "grad_params": {
"names": [".*t_scale2"], "names": [".*t_scale2", ".*deg_free"],
"vals": [self.var], "vals": [self.var, 1000],
"constraints": [(".*t_scale2", self.constrain_positive), (".*deg_free", self.constrain_fixed)] "constraints": [(".*t_scale2", self.constrain_positive), (".*deg_free", self.constrain_positive)]
}, },
"laplace": True "laplace": True
}, },