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remove _inline_js=False in adaptive.notebook_extension call

Bas Nijholt authored on 17/09/2020 22:19:56
Showing 1 changed files
docs/source/tutorial/tutorial.AverageLearner.rst
History View file @ 3a3a33a
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@@ -14,7 +14,7 @@ Tutorial `~adaptive.AverageLearner`
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     :hide-code:
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     import adaptive
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-    adaptive.notebook_extension(_inline_js=False)
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+    adaptive.notebook_extension()
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 The next type of learner averages a function until the uncertainty in
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 the average meets some condition.
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remove thebelab buttons

Bas Nijholt authored on 01/08/2019 16:10:21
Showing 1 changed files
docs/source/tutorial/tutorial.AverageLearner.rst
History View file @ 5990c15
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@@ -10,8 +10,6 @@ Tutorial `~adaptive.AverageLearner`
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     The complete source code of this tutorial can be found in
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     :jupyter-download:notebook:`tutorial.AverageLearner`
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-.. thebe-button:: Run the code live inside the documentation!
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-
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 .. jupyter-execute::
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     :hide-code:
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add thebelab activation buttons

Bas Nijholt authored on 10/07/2019 19:30:06
Showing 1 changed files
docs/source/tutorial/tutorial.AverageLearner.rst
History View file @ b2db3d8
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@@ -10,6 +10,8 @@ Tutorial `~adaptive.AverageLearner`
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     The complete source code of this tutorial can be found in
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     :jupyter-download:notebook:`tutorial.AverageLearner`
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+.. thebe-button:: Run the code live inside the documentation!
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+
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 .. jupyter-execute::
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     :hide-code:
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do not inline the HoloViews JS

Bas Nijholt authored on 26/03/2019 12:44:31
Showing 1 changed files
docs/source/tutorial/tutorial.AverageLearner.rst
History View file @ 6b17023
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@@ -14,7 +14,7 @@ Tutorial `~adaptive.AverageLearner`
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     :hide-code:
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     import adaptive
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-    adaptive.notebook_extension()
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+    adaptive.notebook_extension(_inline_js=False)
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 The next type of learner averages a function until the uncertainty in
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 the average meets some condition.
Browse code

fix the incorrect loss requirement for the AverageLearner in the docs

Bas Nijholt authored on 21/10/2018 14:28:17
Showing 1 changed files
docs/source/tutorial/tutorial.AverageLearner.rst
History View file @ a9f6349
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@@ -40,7 +40,8 @@ implementation the seed parameter can be ignored by the function).
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 .. jupyter-execute::
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     learner = adaptive.AverageLearner(g, atol=None, rtol=0.01)
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-    runner = adaptive.Runner(learner, goal=lambda l: l.loss() < 2)
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+    # `loss < 1` means that we reached the `rtol` or `atol`
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+    runner = adaptive.Runner(learner, goal=lambda l: l.loss() < 1)
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 .. jupyter-execute::
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     :hide-code:
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change "execute" into "jupyter-execute"

Bas Nijholt authored on 18/10/2018 18:21:31
Showing 1 changed files
docs/source/tutorial/tutorial.AverageLearner.rst
History View file @ d9aaa49
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@@ -8,11 +8,10 @@ Tutorial `~adaptive.AverageLearner`
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 .. seealso::
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     The complete source code of this tutorial can be found in
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-    :jupyter-download:notebook:`AverageLearner`
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+    :jupyter-download:notebook:`tutorial.AverageLearner`
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-.. execute::
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+.. jupyter-execute::
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     :hide-code:
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-    :new-notebook: AverageLearner
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     import adaptive
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     adaptive.notebook_extension()
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@@ -25,7 +24,7 @@ the learner must formally take a single parameter, which should be used
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 like a “seed” for the (pseudo-) random variable (although in the current
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 implementation the seed parameter can be ignored by the function).
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-.. execute::
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+.. jupyter-execute::
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     def g(n):
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         import random
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@@ -38,20 +37,20 @@ implementation the seed parameter can be ignored by the function).
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         random.setstate(state)
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         return val
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-.. execute::
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+.. jupyter-execute::
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     learner = adaptive.AverageLearner(g, atol=None, rtol=0.01)
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     runner = adaptive.Runner(learner, goal=lambda l: l.loss() < 2)
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-.. execute::
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+.. jupyter-execute::
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     :hide-code:
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     await runner.task  # This is not needed in a notebook environment!
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-.. execute::
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+.. jupyter-execute::
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     runner.live_info()
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-.. execute::
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+.. jupyter-execute::
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     runner.live_plot(update_interval=0.1)
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add tutorials

Bas Nijholt authored on 17/10/2018 13:30:10
Showing 1 changed files
docs/source/tutorial/tutorial.AverageLearner.rst
History View file @ 6fca822
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new file mode 100644
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@@ -0,0 +1,57 @@
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+Tutorial `~adaptive.AverageLearner`
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+-----------------------------------
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+
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+.. note::
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+   Because this documentation consists of static html, the ``live_plot``
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+   and ``live_info`` widget is not live. Download the notebook
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+   in order to see the real behaviour.
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+
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+.. seealso::
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+    The complete source code of this tutorial can be found in
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+    :jupyter-download:notebook:`AverageLearner`
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+
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+.. execute::
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+    :hide-code:
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+    :new-notebook: AverageLearner
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+
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+    import adaptive
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+    adaptive.notebook_extension()
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+
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+The next type of learner averages a function until the uncertainty in
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+the average meets some condition.
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+
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+This is useful for sampling a random variable. The function passed to
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+the learner must formally take a single parameter, which should be used
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+like a “seed” for the (pseudo-) random variable (although in the current
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+implementation the seed parameter can be ignored by the function).
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+
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+.. execute::
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+
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+    def g(n):
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+        import random
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+        from time import sleep
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+        sleep(random.random() / 1000)
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+        # Properly save and restore the RNG state
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+        state = random.getstate()
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+        random.seed(n)
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+        val = random.gauss(0.5, 1)
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+        random.setstate(state)
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+        return val
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+
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+.. execute::
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+
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+    learner = adaptive.AverageLearner(g, atol=None, rtol=0.01)
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+    runner = adaptive.Runner(learner, goal=lambda l: l.loss() < 2)
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+
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+.. execute::
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+    :hide-code:
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+
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+    await runner.task  # This is not needed in a notebook environment!
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+
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+.. execute::
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+
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+    runner.live_info()
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+
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+.. execute::
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+
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+    runner.live_plot(update_interval=0.1)