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# -*- coding: utf-8 -*-
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import itertools
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+ import warnings
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from collections import OrderedDict
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from copy import copy
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from math import sqrt
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- import warnings
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import numpy as np
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from scipy import interpolate
@@ -95,9 +95,11 @@ def uniform_loss(ip):
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... x, y = xy
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... return x**2 + y**2
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>>>
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- >>> learner = adaptive.Learner2D(f,
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- ... bounds=[(-1, -1), (1, 1)],
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- ... loss_per_triangle=uniform_loss)
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+ >>> learner = adaptive.Learner2D(
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+ ... f,
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+ ... bounds=[(-1, -1), (1, 1)],
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+ ... loss_per_triangle=uniform_loss,
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+ ... )
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>>>
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"""
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return np .sqrt (areas (ip ))
@@ -123,10 +125,7 @@ def resolution_loss_function(min_distance=0, max_distance=1):
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... return x**2 + y**2
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>>>
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>>> loss = resolution_loss_function(min_distance=0.01, max_distance=1)
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- >>> learner = adaptive.Learner2D(f,
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- ... bounds=[(-1, -1), (1, 1)],
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- ... loss_per_triangle=loss)
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- >>>
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+ >>> learner = adaptive.Learner2D(f, bounds=[(-1, -1), (1, 1)], loss_per_triangle=loss)
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"""
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def resolution_loss (ip ):
@@ -192,7 +191,7 @@ def _get_vectors(points):
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def default_loss (ip ):
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- """Loss function that combines
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+ """Loss function that combines `deviations` and `areas` of the triangles.
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Works with `~adaptive.Learner2D` only.
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@@ -222,15 +221,15 @@ def choose_point_in_triangle(triangle, max_badness):
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Parameters
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----------
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- triangle : numpy array
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- The coordinates of a triangle with shape (3, 2)
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+ triangle : numpy.ndarray
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+ The coordinates of a triangle with shape (3, 2).
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max_badness : int
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The badness at which the point is either chosen on a edge or
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in the middle.
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Returns
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-------
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- point : numpy array
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+ point : numpy.ndarray
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The x and y coordinate of the suggested new point.
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"""
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a , b , c = triangle
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