Added some missing files.

GPy/likelihoods/skew_exponential.py

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+# Copyright (c) 2014 The GPy authors (see AUTHORS.txt)
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+
+import sympy as sym
+from GPy.util.symbolic import normcdfln
+import numpy as np
+from ..util.univariate_Gaussian import std_norm_pdf, std_norm_cdf
+#from GPy.util.functions import clip_exp
+import link_functions
+from symbolic import Symbolic
+from scipy import stats
+
+class Skew_exponential(Symbolic):
+    """
+    Negative binomial
+
+    .. math::
+
+    .. Note::
+        Y takes real values.
+        link function is identity
+
+    .. See also::
+        symbolic.py, for the parent class
+    """
+    def __init__(self, gp_link=None, shape=1.0, scale=1.0):
+        parameters={'scale':scale, 'shape':shape}
+        if gp_link is None:
+            gp_link = link_functions.Identity()
+
+        #func_modules = [{'exp':clip_exp}]
+
+        scale = sym.Symbol('scale', positive=True, real=True)
+        shape = sym.Symbol('shape', real=True)
+        y_0 = sym.Symbol('y_0', real=True)
+        f_0 = sym.Symbol('f_0', real=True) 
+        log_pdf=sym.log(shape)-sym.log(scale)-((y_0-f_0)/scale) + normcdfln(shape*(y_0-f_0)/scale) 
+        super(Skew_exponential, self).__init__(log_pdf=log_pdf, gp_link=gp_link, name='Skew_exponential', parameters=parameters)
+
+        # TODO: Check this.
+        self.log_concave = True
+
+
+            

GPy/likelihoods/skew_normal.py

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+# Copyright (c) 2014 The GPy authors (see AUTHORS.txt)
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+
+try:
+    import sympy as sym
+    sympy_available=True
+    from sympy.utilities.lambdify import lambdify
+    from GPy.util.symbolic import normcdfln, normcdf
+except ImportError:
+    sympy_available=False
+
+import numpy as np
+from ..util.univariate_Gaussian import std_norm_pdf, std_norm_cdf
+from GPy.util.functions import clip_exp
+import link_functions
+from symbolic import Symbolic
+from scipy import stats
+
+class Skew_normal(Symbolic):
+    """
+    Skew Normal distribution.
+
+    .. math::
+
+    .. Note::
+    Y takes real values.
+    link function is identity
+
+    .. See also::
+    symbolic.py, for the parent class
+    """
+    def __init__(self, gp_link=None, shape=1.0, scale=1.0):
+        parameters = {'scale': scale, 'shape':shape}
+        if gp_link is None:
+            gp_link = link_functions.Identity()
+
+        # # this likelihood has severe problems with likelihoods saturating exponentials, so clip_exp is used in place of the true exp as a solution for dealing with the numerics.
+        # func_modules = [{'exp':clip_exp}]
+        func_modules = []
+
+        scale = sym.Symbol('scale', positive=True, real=True)
+        shape = sym.Symbol('shape', real=True)
+        y_0 = sym.Symbol('y_0', real=True)
+        f_0 = sym.Symbol('f_0', real=True) 
+        log_pdf=-sym.log(scale)-1./2*sym.log(2*sym.pi)-1./2*((y_0-f_0)/scale)**2 + sym.log(2) + normcdfln(shape*(y_0-f_0)/scale) 
+        super(Skew_normal, self).__init__(log_pdf=log_pdf, parameters=parameters, gp_link=gp_link, name='Skew_normal', func_modules=func_modules)
+
+        self.log_concave = True
+        
+
+
+            

GPy/likelihoods/sstudent_t.py

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+# Copyright (c) 2014 The GPy authors (see AUTHORS.txt)
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+
+import sympy as sym
+from sympy.utilities.lambdify import lambdify
+from GPy.util.symbolic import gammaln
+
+import numpy as np
+import link_functions
+from symbolic import Symbolic
+from scipy import stats
+
+class SstudentT(Symbolic):
+    """
+    Symbolic variant of the Student-t distribution.
+
+    .. math::
+
+    .. Note::
+        Y takes real values.
+        link function is identity
+
+    .. See also::
+        symbolic.py, for the parent class
+    """
+    def __init__(self, gp_link=None, deg_free=5.0, t_scale2=1.0):
+        parameters = {'deg_free':5.0, 't_scale2':1.0}
+        if gp_link is None:
+            gp_link = link_functions.Identity()
+
+        # this likelihood has severe problems with likelihoods saturating ...
+        y_0 = sym.Symbol('y_0', real=True)
+        f_0 = sym.Symbol('f_0', real=True)
+        nu = sym.Symbol('nu', positive=True, real=True)
+        t_scale2 = sym.Symbol('t_scale2', positive=True, real=True)
+        log_pdf = (gammaln((nu + 1) * 0.5)
+                - gammaln(nu * 0.5)
+                - 0.5*sym.log(t_scale2 * nu * sym.pi)
+                   - 0.5*(nu + 1)*sym.log(1 + (1/nu)*(((y_0-f_0)**2)/t_scale2)))
+        super(SstudentT, self).__init__(log_pdf=log_pdf, parameters=parameters, gp_link=gp_link, name='SstudentT')
+        self.log_concave = False
+
+
+