From 217c356a1a16166f3b7b9d876b4049d77626e833 Mon Sep 17 00:00:00 2001
From: Bas Nijholt <basnijholt@gmail.com>
Date: Wed, 13 Sep 2017 18:20:31 +0200
Subject: [PATCH] 2D: fix loss_improvements
---
adaptive/learner.py | 19 ++++++++++---------
1 file changed, 10 insertions(+), 9 deletions(-)
diff --git a/adaptive/learner.py b/adaptive/learner.py
index 40554ea1..657ccea4 100644
--- a/adaptive/learner.py
+++ b/adaptive/learner.py
@@ -5,7 +5,7 @@ from copy import deepcopy as copy
import functools
import heapq
import itertools
-from math import sqrt, isinf
+from math import sqrt, isinf, hypot
from operator import itemgetter
import holoviews as hv
@@ -622,6 +622,7 @@ class Learner2D(BaseLearner):
# XXX: Remove this once we correctly implemented the loss_improvements
self._loss_improvements = []
self.tri_combined = None
+ self._loss = np.inf
# Keeps track till which index _points and _values are filled
self.n = 0
@@ -736,7 +737,7 @@ class Learner2D(BaseLearner):
return dev * vol
def tri_radius(self, points):
- center = points.mean(axis=-2) / (self.ndim + 1)
+ center = points.mean(axis=-2)
return np.linalg.norm(points - center, axis=1).max()
def _fill_stack(self, stack_till=None):
@@ -791,12 +792,10 @@ class Learner2D(BaseLearner):
point_new = _max_disagreement_location_in_simplex(
p, v, g, transform)
- # XXX: scale dev[jsimplex] by max(z) - min(z) and tri_radius by bounds diagonal
- z_scale = self.values_combined.max() - self.values_combined.min()
x, y = self.bounds
- xy_scale = (x[1] - x[0])**2 + (y[1] - y[0])**2
- loss_improvement = sqrt((dev[jsimplex] / z_scale)**2 +
- self.tri_radius(p)**2 / xy_scale)
+ xy_scale = hypot(x[1]-x[0], y[1]-y[0])
+ loss_improvement = hypot(dev[jsimplex] / (v.max() - v.min()),
+ self.tri_radius(p) / xy_scale)
# Reduce to bounds
point_new = np.clip(point_new, *zip(*self.bounds))
@@ -842,8 +841,10 @@ class Learner2D(BaseLearner):
loss_improvements += new_loss_improvements
self.add_data(new_points, itertools.repeat(None))
n_left -= len(new_points)
+
# XXX: Remove this once we correctly implemented the loss_improvements
self._loss_improvements += loss_improvements
+ self._loss = min(self._loss, max(loss_improvements))
return points, loss_improvements
def choose_points(self, n, add_data=True):
@@ -854,8 +855,8 @@ class Learner2D(BaseLearner):
return self._choose_and_add_points(n)
def loss(self, real=True):
- # XXX: we need a smarter way of determining the loss
- return self.n_real
+ # XXX: currently the loss is set before the result of the point is known.
+ return self._loss
def remove_unfinished(self):
n_real = self.n_real
--
GitLab