add doc

probreg/bcpd.py

@@ -16,6 +16,15 @@
 
 EstepResult = namedtuple("EstepResult", ["nu_d", "nu", "n_p", "px", "x_hat"])
 MstepResult = namedtuple("MstepResult", ["transformation", "u_hat", "sigma_mat", "alpha", "sigma2"])
+MstepResult.__doc__ = """Result of Maximization step.
+
+    Attributes:
+        transformation (tf.Transformation): Transformation from source to target.
+        u_hat (numpy.ndarray): A parameter used in next Estep.
+        sigma_mat (numpy.ndarray): A parameter used in next Estep.
+        alpha (float): A parameter used in next Estep.
+        sigma2 (float): Variance of Gaussian distribution.
+"""
 
 
 @six.add_metaclass(abc.ABCMeta)
@@ -132,7 +141,7 @@ def _maximization_step(source, target, rigid_trans, estep_res, gmat_inv, lmd, k,
         u_hm = u_hat - u_m
         s_xu = np.matmul(np.multiply(nu, (x_hat - x_m).T), u_hm) / n_p
         s_uu = np.matmul(np.multiply(nu, u_hm.T), u_hm) / n_p + sigma2_m * np.identity(dim)
-        phi, s_xu_d, psih = np.linalg.svd(s_xu, full_matrices=True)
+        phi, _, psih = np.linalg.svd(s_xu, full_matrices=True)
         c = np.ones(dim)
         c[-1] = np.linalg.det(np.dot(phi, psih))
         rot = np.matmul(phi * c, psih)

probreg/cpd.py

@@ -15,6 +15,13 @@
 
 EstepResult = namedtuple("EstepResult", ["pt1", "p1", "px", "n_p"])
 MstepResult = namedtuple("MstepResult", ["transformation", "sigma2", "q"])
+MstepResult.__doc__ = """Result of Maximization step.
+
+    Attributes:
+        transformation (tf.Transformation): Transformation from source to target.
+        sigma2 (float): Variance of Gaussian distribution.
+        q (float): Result of likelihood.
+"""
 
 
 @six.add_metaclass(abc.ABCMeta)

probreg/filterreg.py

@@ -26,6 +26,13 @@
 
 EstepResult = namedtuple("EstepResult", ["m0", "m1", "m2", "nx"])
 MstepResult = namedtuple("MstepResult", ["transformation", "sigma2", "q"])
+MstepResult.__doc__ = """Result of Maximization step.
+
+    Attributes:
+        transformation (tf.Transformation): Transformation from source to target.
+        sigma2 (float): Variance of Gaussian distribution.
+        q (float): Result of likelihood.
+"""
 
 
 def dualquat_from_twist(tw):

probreg/gmmtree.py

@@ -13,6 +13,12 @@
 
 EstepResult = namedtuple("EstepResult", ["moments"])
 MstepResult = namedtuple("MstepResult", ["transformation", "q"])
+MstepResult.__doc__ = """Result of Maximization step.
+
+    Attributes:
+        transformation (tf.Transformation): Transformation from source to target.
+        q (float): Result of likelihood.
+"""
 
 
 class GMMTree: