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Codecov Report
@@ Coverage Diff @@
## master #436 +/- ##
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- Coverage 91.05% 85.97% -5.08%
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Files 23 24 +1
Lines 1543 1669 +126
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+ Hits 1405 1435 +30
- Misses 138 234 +96
📣 We’re building smart automated test selection to slash your CI/CD build times. Learn more |
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You'll need to also add this to the docs, and resolve any doctest failures. I think the simplest would be to edit https://github.com/dwavesystems/dwave-system/blob/master/docs/reference/utilities.rst to something like .. _utilities_system:
=========
Utilities
=========
Graph Utilities
--------------
.. automodule:: dwave.system.utilities
.. currentmodule:: dwave.system.utilities
.. autosummary::
:toctree: generated/
common_working_graph
Temperature Utilities
---------------------
.. automodule:: dwave.system.temperatures
.. currentmodule:: dwave.system.temperatures
.. autosummary::
:toctree: generated/
effective_field
maximum_pseudolikelihood_temperature
freezeout_effective_temperature
fast_effective_temperature |
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As part of getting doctest to work had to overflow (beyond 80 characters) some lines, and # doctest: +SKIP to bypass this error: |
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@jackraymond, looks like this website detects & reject robots from accessing https://www.jstor.org/stable: if you try with We can update code line |
JoelPasvolsky
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Here are comments on the first two functions, taking a break now for other stuff
JoelPasvolsky
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Comments on third function
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Comments on function 4
Great new addition, @jackraymond , thank you!
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❤️ |
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❤️ |
randomir
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Looks good, just a few minor issues to fix.
…idden url warning)
Co-authored-by: Joel <34041130+JoelPasvolsky@users.noreply.github.com>
Co-authored-by: Joel <34041130+JoelPasvolsky@users.noreply.github.com>
Co-authored-by: Joel <34041130+JoelPasvolsky@users.noreply.github.com>
Co-authored-by: Joel <34041130+JoelPasvolsky@users.noreply.github.com>
… up efforts including type hints
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Routines are provided to efficiently establish the temperature of a Classical Boltzmann sampler from a set of samples for an arbitary Hamiltonian.
This can be used to provide an estimate to the QPU 'effective temperature' (T), where it is assumed the QPU returns Boltzmann samples P(x) = exp(-H(x)/T)/Z where H(x) is the problem Hamiltonian and Z is the partition function.
This is a common support question. Uses of the QPU as a Boltzmann sampler include machine learning applications.