rework example

Author: basnijholt

adaptive/learner/average_learner1D.py

@@ -98,6 +98,10 @@ def total_samples(self):
         """Returns the total number of samples"""
         return sum(self._number_samples.values())
 
+    @property
+    def min_samples_per_point(self):
+        return min(self._number_samples.values())
+
     def ask(self, n, tell_pending=True):
         """Return 'n' points that are expected to maximally reduce the loss."""
         # If some point is undersampled, resample it

docs/source/tutorial/tutorial.AverageLearner1D.rst

@@ -28,7 +28,7 @@ First, we define the (noisy) function to be sampled. Note that the parameter
 
 .. jupyter-execute::
 
-    def f(x, sigma=0, peak_width=0.05, offset=-0.5):
+    def noisy_peak(x, sigma=0, peak_width=0.05, offset=-0.5):
         y = x ** 3 - x + 3 * peak_width ** 2 / (peak_width ** 2 + (x - offset) ** 2)
         noise = np.random.normal(0, sigma)
         return y + noise
@@ -38,14 +38,14 @@ This is how the function looks in the absence of noise:
 .. jupyter-execute::
 
     xs = np.linspace(-2, 2, 500)
-    ys = f(xs, sigma=0)
+    ys = noisy_peak(xs, sigma=0)
     hv.Path((xs, ys))
 
 And an example of a single realization of the noisy function:
 
 .. jupyter-execute::
 
-    ys = [f(x, sigma=1) for x in xs]
+    ys = [noisy_peak(x, sigma=1) for x in xs]
     hv.Path((xs, ys))
 
 To obtain an estimate of the mean value of the function at each point ``x``, we
@@ -57,7 +57,7 @@ We start by initializing a 1D average learner:
 
 .. jupyter-execute::
 
-    learner = adaptive.AverageLearner1D(partial(f, sigma=1), bounds=(-2, 2))
+    learner = adaptive.AverageLearner1D(partial(noisy_peak, sigma=1), bounds=(-2, 2))
 
 As with other types of learners, we need to initialize a runner with a certain
 goal to run our learner. In this case, we set 10000 samples as the goal (the

example-notebook.ipynb

@@ -312,15 +312,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def f(x, sigma=0, peak_width=0.05, offset=-0.5, wait=False):\n",
-    "    from time import sleep\n",
-    "    from random import random\n",
-    "\n",
-    "    if wait:\n",
-    "        sleep(random())\n",
-    "\n",
-    "    function = x ** 3 - x + 3 * peak_width ** 2 / (peak_width ** 2 + (x - offset) ** 2)\n",
-    "    return function + np.random.normal(0, sigma)"
+    "def noisy_peak(x, sigma=0, peak_width=0.05, offset=-0.5):\n",
+    "    y = x ** 3 - x + 3 * peak_width ** 2 / (peak_width ** 2 + (x - offset) ** 2)\n",
+    "    noise = np.random.normal(0, sigma)\n",
+    "    return y + noise"
    ]
   },
   {
@@ -336,9 +331,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "x = np.linspace(-2,2,500)\n",
-    "plt.plot(x, f(x, sigma=0));"
+    "xs = np.linspace(-2, 2, 500)\n",
+    "ys = noisy_peak(xs, sigma=0)\n",
+    "hv.Path((xs, ys))"
    ]
   },
   {
@@ -354,7 +349,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plt.plot(x, [f(xi, sigma=1) for xi in x]);"
+    "ys = [noisy_peak(x, sigma=1) for x in xs]\n",
+    "hv.Path((xs, ys))"
    ]
   },
   {
@@ -370,10 +366,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "learner = adaptive.AverageLearner1D(function=partial(f, sigma=1), bounds=(-2,2))\n",
+    "learner = adaptive.AverageLearner1D(partial(noisy_peak, sigma=1), bounds=(-2, 2))\n",
+    "\n",
+    "\n",
+    "def goal(l):\n",
+    "    return l.total_samples >= 10_000 and l.min_samples_per_point >= 20\n",
+    "\n",
     "\n",
-    "runner = adaptive.Runner(learner, goal=lambda l: l.total_samples >= 10000 \n",
-    "                                                 and min(l._number_samples.values()) >= 20)\n",
+    "runner = adaptive.Runner(learner, goal=goal)\n",
     "runner.live_info()"
    ]
   },