use common text from README

Author: basnijholt

README.rst

@@ -25,10 +25,6 @@ to see examples of how to use ``adaptive`` or visit the
 
 .. summary-end
 
-**WARNING: adaptive is still in a beta development stage**
-
-.. not-in-documentation-start
-
 Implemented algorithms
 ----------------------
 
@@ -44,6 +40,8 @@ but the details of the adaptive sampling are completely customizable.
 
 The following learners are implemented:
 
+.. not-in-documentation-start
+
 - ``Learner1D``, for 1D functions ``f: ℝ → ℝ^N``,
 - ``Learner2D``, for 2D functions ``f: ℝ^2 → ℝ^N``,
 - ``LearnerND``, for ND functions ``f: ℝ^N → ℝ^M``,
@@ -52,10 +50,16 @@ The following learners are implemented:
 - ``AverageLearner1D``, for stochastic 1D functions where you want to
   estimate the mean value of the function at each point,
 - ``IntegratorLearner``, for
-  when you want to intergrate a 1D function ``f: ℝ → ℝ``,
+  when you want to intergrate a 1D function ``f: ℝ → ℝ``.
 - ``BalancingLearner``, for when you want to run several learners at once,
   selecting the “best” one each time you get more points.
 
+Meta-learners (to be used with other learners):
+
+- ``BalancingLearner``, for when you want to run several learners at once,
+  selecting the “best” one each time you get more points,
+- ``DataSaver``, for when your function doesn't just return a scalar or a vector.
+
 In addition to the learners, ``adaptive`` also provides primitives for
 running the sampling across several cores and even several machines,
 with built-in support for

docs/source/algorithms_and_examples.rst

@@ -1,17 +1,6 @@
-Implemented algorithms
-----------------------
-
-The core concept in ``adaptive`` is that of a *learner*. A *learner*
-samples a function at the best places in its parameter space to get
-maximum “information” about the function. As it evaluates the function
-at more and more points in the parameter space, it gets a better idea of
-where the best places are to sample next.
-
-Of course, what qualifies as the “best places” will depend on your
-application domain! ``adaptive`` makes some reasonable default choices,
-but the details of the adaptive sampling are completely customizable.
-
-The following learners are implemented:
+.. include:: ../../README.rst
+    :start-after: summary-end
+    :end-before: not-in-documentation-start
 
 - `~adaptive.Learner1D`, for 1D functions ``f: ℝ → ℝ^N``,
 - `~adaptive.Learner2D`, for 2D functions ``f: ℝ^2 → ℝ^N``,
@@ -22,7 +11,7 @@ The following learners are implemented:
   estimate the mean value of the function at each point,
 - `~adaptive.IntegratorLearner`, for
   when you want to intergrate a 1D function ``f: ℝ → ℝ``.
-- ``~adaptive.BalancingLearner``, for when you want to run several learners at once,
+- `~adaptive.BalancingLearner`, for when you want to run several learners at once,
   selecting the “best” one each time you get more points.
 
 Meta-learners (to be used with other learners):