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WIP: unify Learner1D, Learner2D and LearnerND
- #220
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WIP: unify Learner1D, Learner2D and LearnerND
#220
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Commits on Oct 6, 2019
WIP: add new learner concept and example notebook
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WIP: correct implementation of unfinished point removal
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add necessary information to scale data for loss functions that want to
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start internal queue attributes with underscore
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recalculate losses when scale increases by a certain factor
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blackify
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make 1D points use floats rather than length-1 tuples
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correct point insertion logic
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Commits on Oct 7, 2019
add parallel example to proof of concept notebook
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remove superfluous method and docstrings from Interval class
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import SortedList and SortedDict and update 'insert_points'
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fixup docstrings etc.
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add first implementation of ConvexHull domain
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make everything work
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make everything work again
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update Interval class
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add 'remove' method to Domain
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implement 'ask(tell_pending=False)'
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flakify
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Commits on Oct 9, 2019
small refactors and comments
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rename bound_points to boundary_points
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do not evaluate boundary points in __init__
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put pending points in separate data structure from data
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replace '_scaled_loss' with 'priority'
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rename 'self.loss' to 'self.loss_function'
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store priorities as (priority, loss)
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docstring update
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correctly compute point priorities
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refactor comments and fix minor logic bug
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introduce a dict of losses as an optimization
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clarify the ordering of queried items and priorities
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don't store a tuple as priority, use 'self.losses' from with 'self.loss'
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comments
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make points a tuple as they should be
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correctly update pending points and data
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update example notebook
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Commits on Oct 11, 2019
implement methods 'vertices' and 'subpoints' for 'Domain'
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abstract out choosing points in an interval
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catch interface bugs
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implement '__contains__' for 'ConvexHull'
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use subdomain volume rather than constant for the loss before data is
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correct implementation of queue
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fix neighbor detection when adding points
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abstract out making new subtriangulations
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black
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add map from subpoints to subdomains to speed up triangulation splitting
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separate Queue and Domain into separate modules
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add tests for priority queue and domains
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add hypothesis to test requirements
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rename 'sub_domains' to 'sub_triangulations'
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flake
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add __all__ to domain.py
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use math module
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docstring formatting
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increase allowed time for tests to complete
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remove Queue.priorities method
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Queue.items() returns items in priority order
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make LossFunction an ABC
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allow the learner to take a ConvexHull as bounds
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add more comments to the learner
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show that the new learner works for ND output
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typo
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correct sign in simplex boundary equations
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correct abstract loss function interface
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rename need_loss_udate_factor
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correct boundary point choosing logic
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make point adding idempotent in learner
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fix bugs in 1D domain
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fix bug in tell_pending
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catch edge case in loss
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implement data saving
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explicitly set inf
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add new learner to some tests (those that it passes)
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add xfailing tests for new learner
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correctly set inf
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replace Domain.__contains__ with Domain.contains_subdomain
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correct implementation of points outside domain
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implement method 'Domain.encloses' for finding points inside a domain
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allow NewLearnerND to accept points not in the domain
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make sure we can ask for more points after adding data outside the
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add new learner to datasaving test
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correct conditional
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make 'points_inside' select points from more than 1 subdomain
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correct name of domain test
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rename point loss to point priority
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do 1 operation per line only
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correct docstring
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correct test that all internal points are reassigned when splitting
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run all domain tests over random domains, rather than hypercubes
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update priority queue docstring
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neater ordering
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Commits on Oct 14, 2019
add clarifying comment about adding points to neighboring subdomains
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add shortcuts when creating triangulations with exactly 1 simplex
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fix bug where we receive more points than we asked for
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add learner tests that simulate the runner and randomly asking/telling
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create queue on initialization rather than calling 'insert'
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suppress context when raising when an item was not in a queue
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fix bug in subdomain updating logic when telling data
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improve comment
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blackify domain.py
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check that points matrix is well conditioned
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remove stateful invariant checks
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make random domains by sampling points uniformly in the unit hypercube
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simplify utilities for generating domains and points inside/outside them
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