removed spectral mixture stuff

GPy/core/parameterization/priors.py

@@ -1309,3 +1309,52 @@ class Exponential(Prior):
 
     def rvs(self, n):
         return np.random.exponential(scale=self.l, size=n)
+
+class StudentT(Prior):
+    """
+    Implementation of the student t probability function, coupled with random variables.
+
+    :param mu: mean
+    :param sigma: standard deviation
+    :param nu: degrees of freedom
+
+    .. Note:: Bishop 2006 notation is used throughout the code
+
+    """
+    domain = _REAL
+    _instances = []
+
+    def __new__(cls, mu=0, sigma=1, nu=4):  # Singleton:
+        if cls._instances:
+            cls._instances[:] = [instance for instance in cls._instances if instance()]
+            for instance in cls._instances:
+                if instance().mu == mu and instance().sigma == sigma and instance().nu == nu:
+                    return instance()
+        newfunc = super(Prior, cls).__new__
+        if newfunc is object.__new__:
+            o = newfunc(cls)
+        else:
+            o = newfunc(cls, mu, sigma, nu)
+        cls._instances.append(weakref.ref(o))
+        return cls._instances[-1]()
+
+    def __init__(self, mu, sigma, nu):
+        self.mu = float(mu)
+        self.sigma = float(sigma)
+        self.sigma2 = np.square(self.sigma)
+        self.nu = float(nu)
+
+    def __str__(self):
+        return "St({:.2g}, {:.2g}, {:.2g})".format(self.mu, self.sigma, self.nu)
+
+    def lnpdf(self, x):
+        from scipy.stats import t
+        return t.logpdf(x,self.nu,self.mu,self.sigma)
+
+    def lnpdf_grad(self, x):
+        return -(self.nu + 1.)*(x - self.mu)/( self.nu*self.sigma2 + np.square(x - self.mu) )
+
+    def rvs(self, n):
+        from scipy.stats import t
+        ret = t.rvs(self.nu, loc=self.mu, scale=self.sigma, size=n)
+        return ret    

GPy/testing/prior_tests.py

@@ -6,6 +6,19 @@ import numpy as np
 import GPy
 
 class PriorTests(unittest.TestCase):
+    def test_studentT(self):
+        xmin, xmax = 1, 2.5*np.pi
+        b, C, SNR = 1, 0, 0.1
+        X = np.linspace(xmin, xmax, 500)
+        y  = b*X + C + 1*np.sin(X)
+        y += 0.05*np.random.randn(len(X))
+        X, y = X[:, None], y[:, None]
+        m = GPy.models.GPRegression(X, y)
+        studentT = GPy.priors.StudentT(1, 2, 4)
+        # setting a StudentT prior on non-negative parameters
+        # should raise an assertionerror.
+        self.assertRaises(AssertionError, m.rbf.set_prior, studentT)
+    
     def test_lognormal(self):
         xmin, xmax = 1, 2.5*np.pi
         b, C, SNR = 1, 0, 0.1
@@ -108,7 +121,6 @@ class PriorTests(unittest.TestCase):
         self.assertRaises(AssertionError, m.rbf.set_prior, gaussian)
 
 
-
 if __name__ == "__main__":
     print("Running unit tests, please be (very) patient...")
     unittest.main()