added todo

GPy/kern/parts/periodic_Matern32.py

@@ -10,22 +10,23 @@ from GPy.util.decorators import silence_errors
 class PeriodicMatern32(Kernpart):
     """
     Kernel of the periodic subspace (up to a given frequency) of a Matern 3/2 RKHS. Only defined for input_dim=1.
-
-    :param input_dim: the number of input dimensions
-    :type input_dim: int
-    :param variance: the variance of the Matern kernel
-    :type variance: float
-    :param lengthscale: the lengthscale of the Matern kernel
-    :type lengthscale: np.ndarray of size (input_dim,)
-    :param period: the period
-    :type period: float
-    :param n_freq: the number of frequencies considered for the periodic subspace
-    :type n_freq: int
-    :rtype: kernel object
-
     """
 
     def __init__(self, input_dim=1, variance=1., lengthscale=None, period=2 * np.pi, n_freq=10, lower=0., upper=4 * np.pi):
+        """
+        :param input_dim: the number of input dimensions
+        :type input_dim: int
+        :param variance: the variance of the Matern kernel
+        :type variance: float
+        :param lengthscale: the lengthscale of the Matern kernel
+        :type lengthscale: np.ndarray of size (input_dim,)
+        :param period: the period
+        :type period: float
+        :param n_freq: the number of frequencies considered for the periodic subspace
+        :type n_freq: int
+        :rtype: kernel object
+        """
+
         assert input_dim==1, "Periodic kernels are only defined for input_dim=1"
         self.name = 'periodic_Mat32'
         self.input_dim = input_dim

KALMAN_TODO

@@ -0,0 +1,43 @@
+A todo list for the state space models
+
+1) Documentation for the functions
+
+2) implementation for 
+Matern32 - Done
+Matern52
+exponential - Done
+bias
+white
+rbf
+periodic Materns >???
+periodic rbf
+
+3) Addition and multiplication
+addition - computing the sde variables is done, needs brushing
+derivatives
+
+multiplication
+F = kron_sum(F1, F2)
+L, Qc, H P_inf: kron_prod
+derivatives!
+
+4) Examples. Just a draw from a mater32. Show that the standard method gives the same result as the SS
+
+5) Tests. Ensure that the log-likelihood is the same for the standard as the SS
+          same for the derivatives.
+          checkgradding
+          check adding and multiplication works for various combinations
+
+6) plotting
+plot the kalman fitered result, and the smoothed result.
+Copy AS's animation
+posterior samples
+
+
+
+
+
+
+
+
+