Browse code
move cquad tests and rewrite
Bas Nijholt authored on 01/11/2017 16:43:19
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+# -*- coding: utf-8 -*- |
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+ |
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+import numpy as np |
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+import pytest |
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+from ..learner import IntegratorLearner |
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+from .algorithm_4 import algorithm_4, f0, f7, f21, f24, f63, fdiv |
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+ |
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+ |
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+def run_integrator_learner(f, a, b, tol, nr_points): |
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+ learner = IntegratorLearner(f, bounds=(a, b), tol=tol) |
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+ for _ in range(nr_points): |
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+ points, _ = learner.choose_points(1) |
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+ learner.add_data(points, map(learner.function, points)) |
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+ return learner |
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+ |
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+ |
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+def same_ivals(f, a, b, tol, verbose=True): |
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+ igral, err, nr_points, ivals = algorithm_4(f, a, b, tol) |
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+ |
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+ learner = run_integrator_learner(f, a, b, tol, nr_points) |
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+ |
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+ if verbose: |
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+ print('igral difference', learner.igral-igral,
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+ 'err difference', learner.err - err) |
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+ |
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+ return learner.equal(ivals, verbose=verbose) |
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+ |
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+ |
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+def test_cquad(): |
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+ for i, args in enumerate([[f0, 0, 3, 1e-5], |
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+ [f7, 0, 1, 1e-6], |
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+ [f21, 0, 1, 1e-3], |
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+ [f24, 0, 3, 1e-3]]): |
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+ assert same_ivals(*args, verbose=True), 'Function {}'.format(i)
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+ |
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+ |
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+@pytest.mark.xfail |
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+def test_machine_precision(verbose=True): |
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+ f, a, b, tol = [f63, 0, 1, 1e-10] |
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+ igral, err, nr_points, ivals = algorithm_4(f, a, b, tol) |
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+ |
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+ learner = run_integrator_learner(f, a, b, tol, nr_points) |
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+ |
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+ if verbose: |
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+ print('igral difference', learner.igral-igral,
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+ 'err difference', learner.err - err) |
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+ |
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+ assert learner.equal(ivals, verbose=verbose) |
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+ |
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+ |
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+def test_machine_precision2(): |
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+ f, a, b, tol = [f63, 0, 1, 1e-10] |
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+ igral, err, nr_points, ivals = algorithm_4(f, a, b, tol) |
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+ |
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+ learner = run_integrator_learner(f, a, b, tol, nr_points) |
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+ |
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+ np.testing.assert_almost_equal(igral, learner.igral) |
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+ np.testing.assert_almost_equal(err, learner.err) |
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+ |
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+ |
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+def test_divergence(): |
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+ """This function should raise a DivergentIntegralError.""" |
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+ f, a, b, tol = fdiv, 0, 1, 1e-6 |
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+ try: |
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+ igral, err, nr_points, ivals = algorithm_4(f, a, b, tol) |
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+ except Exception: |
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+ print('The integral is diverging.')
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+ |
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+ try: |
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+ learner = run_integrator_learner(f, a, b, tol, nr_points) |
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+ except Exception: |
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+ print('The integral is diverging.')
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@@ -1,66 +0,0 @@ |
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-import numpy as np |
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-from adaptive.learner import IntegratorLearner |
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-from algorithm_4 import algorithm_4 |
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- |
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-def same_ivals(f, a, b, tol, verbose): |
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- igral, err, nr_points, ivals = algorithm_4(f, a, b, tol) |
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- |
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- learner = IntegratorLearner(f, bounds=(a, b), tol=tol) |
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- for i in range(nr_points): |
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- points, loss_improvement = learner.choose_points(1) |
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- learner.add_data(points, map(learner.function, points)) |
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- if verbose: |
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- print('igral diff, ', learner.igral-igral, 'err diff', learner.err - err)
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- return learner.equal(ivals, verbose=verbose) |
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- |
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- |
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-def same_ivals_up_to(f, a, b, tol): |
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- igral, err, nr_points, ivals = algorithm_4(f, a, b, tol) |
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- |
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- learner = IntegratorLearner(f, bounds=(a, b), tol=tol) |
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- j = 0 |
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- equal_till = 0 |
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- for i in range(nr_points): |
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- points, loss_improvement = learner.choose_points(1) |
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- learner.add_data(points, map(learner.function, points)) |
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- if not learner._stack: |
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- try: |
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- j += 1 |
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- if learner.equal(ivals): |
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- equal_till = i + 1 |
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- except: |
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- all_equal = False |
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- |
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- return 'equal_till nr_points={} of {}'.format(equal_till, nr_points)
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- |
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-if __name__ == '__main__': |
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- old_settings = np.seterr(all='ignore') |
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- from algorithm_4 import f0, f7, f24, f21, f63, fdiv |
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- for i, args in enumerate([[f0, 0, 3, 1e-5], |
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- [f7, 0, 1, 1e-6], |
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- [f21, 0, 1, 1e-3], |
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- [f24, 0, 3, 1e-3], |
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- [f63, 0, 1, 1e-10]]): |
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- print('\nFunction {}'.format(i))
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- if same_ivals(*args, verbose=True): |
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- print(True) |
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- else: |
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- print(same_ivals_up_to(*args)) |
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- |
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- # This function should raise a DivergentIntegralError. |
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- print('Function ', i+1)
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- f, a, b, tol = [fdiv, 0, 1, 1e-6] |
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- try: |
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- igral, err, nr_points, ivals = algorithm_4(f, a, b, tol) |
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- except Exception: |
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- print('The integral is diverging.')
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- |
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- try: |
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- learner = IntegratorLearner(f, bounds=(a, b), tol=tol) |
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- for i in range(nr_points): |
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- points, loss_improvement = learner.choose_points(1) |
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- learner.add_data(points, map(learner.function, points)) |
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- except Exception: |
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- print('The integral is diverging.')
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- |
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- np.seterr(**old_settings) |