From d057831d5915302deb5495868e87810ef562ee07 Mon Sep 17 00:00:00 2001 From: Bas Nijholt Date: Thu, 7 Mar 2019 15:13:38 +0100 Subject: [PATCH] change example in the docs --- docs/source/tutorial/tutorial.AverageLearner.rst | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/source/tutorial/tutorial.AverageLearner.rst b/docs/source/tutorial/tutorial.AverageLearner.rst index 5cb9e41d5..450cab3db 100644 --- a/docs/source/tutorial/tutorial.AverageLearner.rst +++ b/docs/source/tutorial/tutorial.AverageLearner.rst @@ -74,7 +74,7 @@ So, the ``learner`` compares the loss of __potential new intervals (or triangles The relative importance of both can be adjusted by a hyperparameter ``learner.weight``, see the doc-string for more information. -Let's again try to learn some functions but now with uniformly distributed noise. We start with 1D and then go to 2D. +Let's again try to learn some functions but now with [heteroscedastic](https://en.wikipedia.org/wiki/Heteroscedasticity) noise. We start with 1D and then go to 2D. `~adaptive.AverageLearner1D` ............................ @@ -119,13 +119,13 @@ Let's again try to learn some functions but now with uniformly distributed noise def noisy_ring(xy_seed): import numpy as np - import random + from random import uniform (x, y), seed = xy_seed - random.seed(xy_seed) # to make the random function deterministic a = 0.2 - ring = x + np.exp(-(x**2 + y**2 - 0.75**2)**2/a**4) - noise = random.uniform(-0.5, 0.5) - return ring + noise + z = (x**2 + y**2 - 0.75**2) / a**2 + plateau = np.arctan(z) + noise = uniform(-10, 10) * np.exp(-z**2) + return plateau + noise learner = adaptive.AverageLearner2D(noisy_ring, bounds=[(-1, 1), (-1, 1)]) runner = adaptive.Runner(learner, goal=lambda l: l.loss() < 0.01)