Example #1
0
    def __init__(self, distribution, kernel, Z, threshold, spread):
        if not isinstance(distribution, Distribution):
            raise TypeError("Target must be a Distribution object")

        if not isinstance(kernel, Kernel):
            raise TypeError("Kernel must be a Kernel object")

        if not type(Z) is numpy.ndarray:
            raise TypeError("History must be a numpy array")

        if not len(Z.shape) == 2:
            raise ValueError("History must be a 2D numpy array")

        if not Z.shape[1] == distribution.dimension:
            raise ValueError(
                "History dimension does not match target dimension")

        if not Z.shape[0] > 0:
            raise ValueError("History must contain at least one point")

        if not type(threshold) is float:
            raise TypeError("Threshold must be a float")

        if not type(spread) is float:
            raise TypeError("Spread must be a float")

        if not (spread > 0. and spread < 1.):
            raise ValueError("Spread must be a probability")

        MCMCSampler.__init__(self, distribution)

        self.kernel = kernel
        self.Z = Z
        self.threshold = threshold
        self.spread = spread
 def __init__(self, distribution, kernel, Z, threshold, spread):
     if not isinstance(distribution, Distribution):
         raise TypeError("Target must be a Distribution object")
     
     if not isinstance(kernel, Kernel):
         raise TypeError("Kernel must be a Kernel object")
     
     if not type(Z) is numpy.ndarray:
         raise TypeError("History must be a numpy array")
     
     if not len(Z.shape) == 2:
         raise ValueError("History must be a 2D numpy array")
     
     if not Z.shape[1] == distribution.dimension:
         raise ValueError("History dimension does not match target dimension")
     
     if not Z.shape[0] > 0:
         raise ValueError("History must contain at least one point")
     
     if not type(threshold) is float:
         raise TypeError("Threshold must be a float")
     
     if not type(spread) is float:
         raise TypeError("Spread must be a float")
     
     if not (spread > 0. and spread < 1.):
         raise ValueError("Spread must be a probability")
     
     MCMCSampler.__init__(self, distribution)
     
     self.kernel = kernel
     self.Z = Z
     self.threshold = threshold
     self.spread = spread
Example #3
0
 def __init__(self, full_conditionals):
     if not isinstance(full_conditionals, FullConditionals):
         raise TypeError("Gibbs require full conditional distribution instance")
     
     MCMCSampler.__init__(self, full_conditionals)
     
     # pdf is constant, and therefore symmetric
     self.is_symmetric = True
Example #4
0
 def __init__(self, distribution, kernel, Z, nu2=0.1, gamma=None):
     MCMCSampler.__init__(self, distribution)
     
     self.kernel = kernel
     self.nu2 = nu2
     if gamma is not None:
         self.gamma = gamma
     else:
         self.gamma=0.2
     self.Z = Z
 def __init__(self, distribution, scale=None, cov=None):
     MCMCSampler.__init__(self, distribution)
     if scale is None:
         self.scale = (2.38 ** 2) / distribution.dimension
     else:
         self.scale = scale
     if cov is None:
         self.cov = eye(distribution.dimension)
     else:
         self.cov = cov
 def __init__(self, distribution, scale=None, cov=None):
     MCMCSampler.__init__(self, distribution)
     if scale is None:
         self.scale = (2.38**2) / distribution.dimension
     else:
         self.scale = scale
     if cov is None:
         self.cov = eye(distribution.dimension)
     else:
         self.cov = cov
Example #7
0
    def __init__(self, distribution, spread, flip_at_least_one=True):
        if not isinstance(distribution, Distribution):
            raise TypeError("Target must be a Distribution object")

        if not type(spread) is float:
            raise TypeError("Spread must be a float")

        if not (spread > 0. and spread < 1.):
            raise ValueError("Spread must be a probability")

        MCMCSampler.__init__(self, distribution)

        self.spread = spread
        self.flip_at_least_one = flip_at_least_one
 def __init__(self, distribution, spread, flip_at_least_one=True):
     if not isinstance(distribution, Distribution):
         raise TypeError("Target must be a Distribution object")
     
     if not type(spread) is float:
         raise TypeError("Spread must be a float")
     
     if not (spread > 0. and spread < 1.):
         raise ValueError("Spread must be a probability")
     
     MCMCSampler.__init__(self, distribution)
     
     self.spread = spread
     self.flip_at_least_one = flip_at_least_one
Example #9
0
 def __str__(self):
     s=self.__class__.__name__+ "=["
     s += "kernel="+ str(self.kernel)
     s += ", nu2="+ str(self.nu2)
     s += ", gamma="+ str(self.gamma)
     s += ", " + MCMCSampler.__str__(self)
     s += "]"
     return s
 def __str__(self):
     s = self.__class__.__name__ + "=["
     s += "kernel=" + str(self.kernel)
     s += ", threshold=" + str(self.threshold)
     s += ", spread=" + str(self.spread)
     s += ", " + MCMCSampler.__str__(self)
     s += "]"
     return s
Example #11
0
 def __str__(self):
     s = self.__class__.__name__ + "=["
     s += "kernel=" + str(self.kernel)
     s += ", threshold=" + str(self.threshold)
     s += ", spread=" + str(self.spread)
     s += ", " + MCMCSampler.__str__(self)
     s += "]"
     return s
 def __str__(self):
     s=self.__class__.__name__+ "=["
     s += "globalscale="+ str(self.globalscale)
     s += ", sample_discard="+ str(self.sample_discard)
     s += ", sample_lag="+ str(self.sample_lag)
     s += ", accstar="+ str(self.accstar)
     s += ", " + MCMCSampler.__str__(self)
     s += "]"
     return s
 def __init__(self, distribution, \
              mean_est=None, cov_est=None, \
              sample_discard=500, sample_lag=20, accstar=0.234):
     MCMCSampler.__init__(self, distribution)
     self.globalscale = (2.38 ** 2) / distribution.dimension
     
     if mean_est is None:
         mean_est=2*ones(distribution.dimension)
         
     if cov_est is None:
         cov_est=0.05 * eye(distribution.dimension)
         
     assert (len(mean_est) == distribution.dimension)
     assert (len(cov_est) == distribution.dimension)
         
     self.mean_est = mean_est
     self.cov_est = cov_est
     self.sample_discard = sample_discard
     self.sample_lag = sample_lag
     self.accstar = accstar
Example #14
0
 def __str__(self):
     s = self.__class__.__name__ + "=["
     s += "spread=" + str(self.spread)
     s += ", " + MCMCSampler.__str__(self)
     s += "]"
     return s
 def __str__(self):
     s = self.__class__.__name__ + "=["
     s += "scale=" + str(self.scale)
     s += ", " + MCMCSampler.__str__(self)
     s += "]"
     return s
Example #16
0
 def __str__(self):
     s = self.__class__.__name__ + "=["
     s += MCMCSampler.__str__(self)
     s += "]"
     return s