From 0d7a9aa8c699b58b7bd7a38832f01a2f1245f7bc Mon Sep 17 00:00:00 2001
From: Bas Nijholt <basnijholt@gmail.com>
Date: Wed, 25 Sep 2019 13:32:10 +0200
Subject: [PATCH] use np instead of numpy
---
paper.md | 6 +++---
1 file changed, 3 insertions(+), 3 deletions(-)
diff --git a/paper.md b/paper.md
index 8dd5c35..03025ef 100755
--- a/paper.md
+++ b/paper.md
@@ -294,7 +294,7 @@ To change the loss function for the `Learner1D` we pass a loss function, like
def distance_loss(xs, ys): # used by default
dx = xs[1] - xs[0]
dy = ys[1] - ys[0]
- return numpy.hypot(dx, dy)
+ return np.hypot(dx, dy)
learner = Learner1D(peak, bounds=(-1, 1), loss_per_interval=distance_loss)
```
@@ -314,7 +314,7 @@ from adaptive import LearnerND
def ring(xy): # pretend this is a slow function
x, y = xy
a = 0.2
- return x + numpy.exp(-(x**2 + y**2 - 0.75**2)**2/a**4)
+ return x + np.exp(-(x**2 + y**2 - 0.75**2)**2/a**4)
learner = adaptive.LearnerND(ring, bounds=[(-1, 1), (-1, 1)])
runner = Runner(learner, goal)
@@ -326,7 +326,7 @@ For example, the loss function used to find the iso-line in Fig. @fig:isoline (b
from adaptive.learner.learnerND import default_loss
def gaussian(x, mu, sigma):
- return np.exp(-(x - mu) ** 2 / sigma ** 2 / 2)
+ return np.exp(-(x - mu)**2 / sigma**2 / 2)
def isoline_loss_function(level, sigma, priority):
def loss(simplex, values, value_scale):
--
GitLab