ratquad working

GPy/kern/_src/rational_quadratic.py

@@ -1,82 +0,0 @@
-# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
-# Licensed under the BSD 3-clause license (see LICENSE.txt)
-
-
-from kernpart import Kernpart
-import numpy as np
-
-class RationalQuadratic(Kernpart):
-    """
-    rational quadratic kernel
-
-    .. math::
-
-       k(r) = \sigma^2 \\bigg( 1 + \\frac{r^2}{2 \ell^2} \\bigg)^{- \\alpha} \ \ \ \ \  \\text{ where  } r^2 = (x-y)^2
-
-    :param input_dim: the number of input dimensions
-    :type input_dim: int (input_dim=1 is the only value currently supported)
-    :param variance: the variance :math:`\sigma^2`
-    :type variance: float
-    :param lengthscale: the lengthscale :math:`\ell`
-    :type lengthscale: float
-    :param power: the power :math:`\\alpha`
-    :type power: float
-    :rtype: Kernpart object
-
-    """
-    def __init__(self,input_dim,variance=1.,lengthscale=1.,power=1.):
-        assert input_dim == 1, "For this kernel we assume input_dim=1"
-        self.input_dim = input_dim
-        self.num_params = 3
-        self.name = 'rat_quad'
-        self.variance = variance
-        self.lengthscale = lengthscale
-        self.power = power
-
-    def _get_params(self):
-        return np.hstack((self.variance,self.lengthscale,self.power))
-
-    def _set_params(self,x):
-        self.variance = x[0]
-        self.lengthscale = x[1]
-        self.power = x[2]
-
-    def _get_param_names(self):
-        return ['variance','lengthscale','power']
-
-    def K(self,X,X2,target):
-        if X2 is None: X2 = X
-        dist2 = np.square((X-X2.T)/self.lengthscale)
-        target += self.variance*(1 + dist2/2.)**(-self.power)
-
-    def Kdiag(self,X,target):
-        target += self.variance
-
-    def _param_grad_helper(self,dL_dK,X,X2,target):
-        if X2 is None: X2 = X
-        dist2 = np.square((X-X2.T)/self.lengthscale)
-
-        dvar = (1 + dist2/2.)**(-self.power)
-        dl = self.power * self.variance * dist2 / self.lengthscale * (1 + dist2/2.)**(-self.power-1)
-        dp = - self.variance * np.log(1 + dist2/2.) * (1 + dist2/2.)**(-self.power)
-
-        target[0] += np.sum(dvar*dL_dK)
-        target[1] += np.sum(dl*dL_dK)
-        target[2] += np.sum(dp*dL_dK)
-
-    def dKdiag_dtheta(self,dL_dKdiag,X,target):
-        target[0] += np.sum(dL_dKdiag)
-        # here self.lengthscale and self.power have no influence on Kdiag so target[1:] are unchanged
-
-    def gradients_X(self,dL_dK,X,X2,target):
-        """derivative of the covariance matrix with respect to X."""
-        if X2 is None:
-            dist2 = np.square((X-X.T)/self.lengthscale)
-            dX = -2.*self.variance*self.power * (X-X.T)/self.lengthscale**2 *  (1 + dist2/2./self.lengthscale)**(-self.power-1)
-        else:
-            dist2 = np.square((X-X2.T)/self.lengthscale)
-            dX = -self.variance*self.power * (X-X2.T)/self.lengthscale**2 *  (1 + dist2/2./self.lengthscale)**(-self.power-1)
-        target += np.sum(dL_dK*dX,1)[:,np.newaxis]
-
-    def dKdiag_dX(self,dL_dKdiag,X,target):
-        pass

GPy/kern/_src/stationary.py

@@ -206,9 +206,19 @@ class ExpQuad(Stationary):
         return -dist*self.K(X, X2)
 
 class RatQuad(Stationary):
+    """
+    Rational Quadratic Kernel
+
+    .. math::
+
+       k(r) = \sigma^2 \\bigg( 1 + \\frac{r^2}{2} \\bigg)^{- \\alpha}
+
+    """
+
+
     def __init__(self, input_dim, variance=1., lengthscale=None, power=2., ARD=False, name='ExpQuad'):
         super(RatQuad, self).__init__(input_dim, variance, lengthscale, ARD, name)
-        self.power = Param('power', power, Logexp)
+        self.power = Param('power', power, Logexp())
         self.add_parameters(self.power)
 
     def K(self, X, X2=None):

GPy/plotting/matplot_dep/models_plots.py

@@ -20,7 +20,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
       - In higher dimensions, use fixed_inputs to plot the GP  with some of the inputs fixed.
 
     Can plot only part of the data and part of the posterior functions
-    using which_data_rowsm which_data_ycols. 
+    using which_data_rowsm which_data_ycols.
 
     :param plot_limits: The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits
     :type plot_limits: np.array
@@ -56,10 +56,10 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
     if ax is None:
         fig = pb.figure(num=fignum)
         ax = fig.add_subplot(111)
-    
+
     X, Y = param_to_array(model.X, model.Y)
-    if model.has_uncertain_inputs(): X_variance = model.X_variance
+    if hasattr(model, 'has_uncertain_inputs') and model.has_uncertain_inputs(): X_variance = model.X_variance
-    
+
     #work out what the inputs are for plotting (1D or 2D)
     fixed_dims = np.array([i for i,v in fixed_inputs])
     free_dims = np.setdiff1d(np.arange(model.input_dim),fixed_dims)
@@ -95,7 +95,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
                 ax.plot(Xnew, yi[:,None], Tango.colorsHex['darkBlue'], linewidth=0.25)
                 #ax.plot(Xnew, yi[:,None], marker='x', linestyle='--',color=Tango.colorsHex['darkBlue']) #TODO apply this line for discrete outputs.
 
-        
+
         #add error bars for uncertain (if input uncertainty is being modelled)
         if hasattr(model,"has_uncertain_inputs") and model.has_uncertain_inputs():
             ax.errorbar(X[which_data_rows, free_dims].flatten(), Y[which_data_rows, which_data_ycols].flatten(),