Documented scheduler module

docs/source/scheduler.rst

@@ -1,4 +1,6 @@
 Hyperparameter Scheduling
 =========================
-.. automodule:: rltorch.scheduler
+.. autoclass:: rltorch.scheduler.LinearScheduler
+   :members:
+.. autoclass:: rltorch.scheduler.ExponentialScheduler
    :members:

rltorch/scheduler/ExponentialScheduler.py

@@ -1,5 +1,30 @@
 from .Scheduler import Scheduler
 class ExponentialScheduler(Scheduler):
+    r"""
+    A exponential scheduler that given a certain number
+    of iterations, spaces the values between
+    a start and an end point in an exponential order.
+
+    Notes
+    -----
+    The forumula used to produce the value :math:`y` is based on the number of
+    times you call `next`. (denoted as :math:`i`)
+
+    :math:`y(1) = initial\_value`
+    :math:`y(i) = y(1) \cdot base^{i - 1}`
+    :math:`base = \sqrt[iterations]{\frac{end\_value}{initial\_value}}`.
+
+    Another property is that :math:`y(iterations) = end\_value`.
+
+    Parameters
+    ----------
+    initial_value : number
+      The first value returned in the schedule.
+    end_value: number
+      The value returned when the maximum number of iterations are reached
+    iterations: int
+      The total number of iterations
+    """
     def __init__(self, initial_value, end_value, iterations):
         super(ExponentialScheduler, self).__init__(initial_value, end_value, iterations)
         self.base = (end_value / initial_value) ** (1.0 / iterations)

rltorch/scheduler/LinearScheduler.py

@@ -1,5 +1,27 @@
 from .Scheduler import Scheduler
 class LinearScheduler(Scheduler):
+    r"""
+    A linear scheduler that given a certain number
+    of iterations, equally spaces the values between
+    a start and an end point.
+
+    Notes
+    -----
+    The forumula used to produce the value :math:`y` is based on the number of
+    times you call `next`. (denoted as :math:`i`)
+
+    :math:`y(i) = slope \cdot (i - 1) + initial\_value`
+    where :math:`slope = \frac{end\_value - initial\_value)}{iterations}`.
+
+    Parameters
+    ----------
+    initial_value : number
+      The first value returned in the schedule.
+    end_value: number
+      The value returned when the maximum number of iterations are reached
+    iterations: int
+      The total number of iterations
+    """
     def __init__(self, initial_value, end_value, iterations):
         super(LinearScheduler, self).__init__(initial_value, end_value, iterations)
         self.slope = (end_value - initial_value) / iterations