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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/docs.rst
History View file @ 3a3a33a
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@@ -48,7 +48,7 @@ on the *Play* :fa:`play` button or move the sliders.
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     from adaptive.learner.learner1D import uniform_loss, default_loss
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     import holoviews as hv
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     import numpy as np
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-    adaptive.notebook_extension(_inline_js=False)
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+    adaptive.notebook_extension()
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     %output holomap='scrubber'
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54 54 
 `adaptive.Learner1D`
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float -> int

Bas Nijholt authored on 08/04/2020 21:38:52
Showing 1 changed files
docs/source/docs.rst
History View file @ 920a2c8
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@@ -99,7 +99,7 @@ on the *Play* :fa:`play` button or move the sliders.
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     def plot(learner, npoints):
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         adaptive.runner.simple(learner, lambda l: l.npoints == npoints)
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         learner2 = adaptive.Learner2D(ring, bounds=learner.bounds)
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-        xs = ys = np.linspace(*learner.bounds[0], learner.npoints**0.5)
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+        xs = ys = np.linspace(*learner.bounds[0], int(learner.npoints**0.5))
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         xys = list(itertools.product(xs, ys))
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         learner2.tell_many(xys, map(ring, xys))
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         return (learner2.plot().relabel('homogeneous grid')
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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/docs.rst
History View file @ 6b17023
... ... 
@@ -48,7 +48,7 @@ on the *Play* :fa:`play` button or move the sliders.
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     from adaptive.learner.learner1D import uniform_loss, default_loss
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     import holoviews as hv
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     import numpy as np
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-    adaptive.notebook_extension()
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+    adaptive.notebook_extension(_inline_js=False)
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     %output holomap='scrubber'
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54 54 
 `adaptive.Learner1D`
Browse code

documentation improvements

Bas Nijholt authored on 19/10/2018 14:19:42
Showing 1 changed files
docs/source/docs.rst
History View file @ 5e3edf4
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new file mode 100644
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@@ -0,0 +1,162 @@
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+Implemented algorithms
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+----------------------
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+
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+The core concept in ``adaptive`` is that of a *learner*. A *learner*
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+samples a function at the best places in its parameter space to get
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+maximum “information” about the function. As it evaluates the function
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+at more and more points in the parameter space, it gets a better idea of
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+where the best places are to sample next.
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+
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+Of course, what qualifies as the “best places” will depend on your
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+application domain! ``adaptive`` makes some reasonable default choices,
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+but the details of the adaptive sampling are completely customizable.
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+
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+The following learners are implemented:
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+
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+- `~adaptive.Learner1D`, for 1D functions ``f: ℝ → ℝ^N``,
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+- `~adaptive.Learner2D`, for 2D functions ``f: ℝ^2 → ℝ^N``,
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+- `~adaptive.LearnerND`, for ND functions ``f: ℝ^N → ℝ^M``,
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+- `~adaptive.AverageLearner`, For stochastic functions where you want to
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+  average the result over many evaluations,
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+- `~adaptive.IntegratorLearner`, for
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+  when you want to intergrate a 1D function ``f: ℝ → ℝ``.
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+
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+Meta-learners (to be used with other learners):
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+
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+- `~adaptive.BalancingLearner`, for when you want to run several learners at once,
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+  selecting the “best” one each time you get more points,
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+- `~adaptive.DataSaver`, for when your function doesn't just return a scalar or a vector.
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+
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+In addition to the learners, ``adaptive`` also provides primitives for
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+running the sampling across several cores and even several machines,
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+with built-in support for
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+`concurrent.futures <https://docs.python.org/3/library/concurrent.futures.html>`_,
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+`ipyparallel <https://ipyparallel.readthedocs.io/en/latest/>`_ and
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+`distributed <https://distributed.readthedocs.io/en/latest/>`_.
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+
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+Examples
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+--------
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+
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+Here are some examples of how Adaptive samples vs. homogeneous sampling. Click
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+on the *Play* :fa:`play` button or move the sliders.
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+
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+.. jupyter-execute::
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+    :hide-code:
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+
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+    import itertools
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+    import adaptive
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+    from adaptive.learner.learner1D import uniform_loss, default_loss
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+    import holoviews as hv
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+    import numpy as np
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+    adaptive.notebook_extension()
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+    %output holomap='scrubber'
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+
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+`adaptive.Learner1D`
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+~~~~~~~~~~~~~~~~~~~~
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+
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+.. jupyter-execute::
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+    :hide-code:
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+
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+    %%opts Layout [toolbar=None]
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+    def f(x, offset=0.07357338543088588):
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+        a = 0.01
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+        return x + a**2 / (a**2 + (x - offset)**2)
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+
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+    def plot_loss_interval(learner):
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+        if learner.npoints >= 2:
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+            x_0, x_1 = max(learner.losses, key=learner.losses.get)
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+            y_0, y_1 = learner.data[x_0], learner.data[x_1]
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+            x, y = [x_0, x_1], [y_0, y_1]
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+        else:
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+            x, y = [], []
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+        return hv.Scatter((x, y)).opts(style=dict(size=6, color='r'))
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+
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+    def plot(learner, npoints):
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+        adaptive.runner.simple(learner, lambda l: l.npoints == npoints)
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+        return (learner.plot() * plot_loss_interval(learner))[:, -1.1:1.1]
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+
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+    def get_hm(loss_per_interval, N=101):
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+        learner = adaptive.Learner1D(f, bounds=(-1, 1),
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+                                     loss_per_interval=loss_per_interval)
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+        plots = {n: plot(learner, n) for n in range(N)}
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+        return hv.HoloMap(plots, kdims=['npoints'])
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+
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+    (get_hm(uniform_loss).relabel('homogeneous samping')
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+     + get_hm(default_loss).relabel('with adaptive'))
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+
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+`adaptive.Learner2D`
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+~~~~~~~~~~~~~~~~~~~~
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+
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+.. jupyter-execute::
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+    :hide-code:
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+
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+    def ring(xy):
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+        import numpy as np
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+        x, y = xy
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+        a = 0.2
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+        return x + np.exp(-(x**2 + y**2 - 0.75**2)**2/a**4)
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+
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+    def plot(learner, npoints):
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+        adaptive.runner.simple(learner, lambda l: l.npoints == npoints)
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+        learner2 = adaptive.Learner2D(ring, bounds=learner.bounds)
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+        xs = ys = np.linspace(*learner.bounds[0], learner.npoints**0.5)
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+        xys = list(itertools.product(xs, ys))
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+        learner2.tell_many(xys, map(ring, xys))
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+        return (learner2.plot().relabel('homogeneous grid')
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+                + learner.plot().relabel('with adaptive')
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+                + learner2.plot(tri_alpha=0.5).relabel('homogeneous sampling')
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+                + learner.plot(tri_alpha=0.5).relabel('with adaptive')).cols(2)
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+
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+    learner = adaptive.Learner2D(ring, bounds=[(-1, 1), (-1, 1)])
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+    plots = {n: plot(learner, n) for n in range(4, 1010, 20)}
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+    hv.HoloMap(plots, kdims=['npoints']).collate()
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+
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+`adaptive.AverageLearner`
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+~~~~~~~~~~~~~~~~~~~~~~~~~
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+
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+.. jupyter-execute::
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+    :hide-code:
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+
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+    def g(n):
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+        import random
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+        random.seed(n)
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+        val = random.gauss(0.5, 0.5)
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+        return val
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+
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+    learner = adaptive.AverageLearner(g, atol=None, rtol=0.01)
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+
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+    def plot(learner, npoints):
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+        adaptive.runner.simple(learner, lambda l: l.npoints == npoints)
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+        return learner.plot().relabel(f'loss={learner.loss():.2f}')
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+
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+    plots = {n: plot(learner, n) for n in range(10, 10000, 200)}
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+    hv.HoloMap(plots, kdims=['npoints'])
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+
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+`adaptive.LearnerND`
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+~~~~~~~~~~~~~~~~~~~~
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+
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+.. jupyter-execute::
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+    :hide-code:
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+
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+    def sphere(xyz):
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+        import numpy as np
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+        x, y, z = xyz
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+        a = 0.4
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+        return np.exp(-(x**2 + y**2 + z**2 - 0.75**2)**2/a**4)
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+
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+    learner = adaptive.LearnerND(sphere, bounds=[(-1, 1), (-1, 1), (-1, 1)])
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+    adaptive.runner.simple(learner, lambda l: l.npoints == 3000)
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+
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+    learner.plot_3D()
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+
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+see more in the :ref:`Tutorial Adaptive`.
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+
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+.. include:: ../../README.rst
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+    :start-after: not-in-documentation-end
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+    :end-before: credits-end
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+
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+.. mdinclude:: ../../AUTHORS.md
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+
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+.. include:: ../../README.rst
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+    :start-after: credits-end
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+    :end-before: references-start