From 3511b9454ebedb74d832b0a170bb15739a1eb755 Mon Sep 17 00:00:00 2001
From: Bas Nijholt <basnijholt@gmail.com>
Date: Wed, 25 Sep 2019 13:31:27 +0200
Subject: [PATCH] simplify
---
paper.md | 9 ++++-----
1 file changed, 4 insertions(+), 5 deletions(-)
diff --git a/paper.md b/paper.md
index 31c4af9..8dd5c35 100755
--- a/paper.md
+++ b/paper.md
@@ -323,19 +323,18 @@ runner = Runner(learner, goal)
Again, like the `Learner1D`, it is possible to specify a custom loss function.
For example, the loss function used to find the iso-line in Fig. @fig:isoline (b) is
```python
-def isoline_loss_function(level, sigma, priority):
- from adaptive.learner.learnerND import default_loss
+from adaptive.learner.learnerND import default_loss
- def gaussian(x, mu, sigma):
- return np.exp(-(x - mu) ** 2 / sigma ** 2 / 2)
+def gaussian(x, mu, sigma):
+ return np.exp(-(x - mu) ** 2 / sigma ** 2 / 2)
+def isoline_loss_function(level, sigma, priority):
def loss(simplex, values, value_scale):
values = np.array(values)
dist = abs(level * value_scale - values).mean()
L_default = default_loss(simplex, values, value_scale)
L_dist = priority * gaussian(dist, 0, sigma)
return L_dist + L_default
-
return loss
loss_per_simplex = isoline_loss_function(0.1, 0.4, 0.5)
--
GitLab