dwave.embedding.chain_break_frequency¶
- chain_break_frequency(samples_like, embedding)[source]¶
Determine the frequency of chain breaks in the given samples.
- Parameters
samples_like (samples_like/
dimod.SampleSet
) – A collection of raw samples. ‘samples_like’ is an extension of NumPy’s array_like. Seedimod.as_samples()
.embedding (dict) – Mapping from source graph to target graph as a dict of form {s: {t, …}, …}, where s is a source-model variable and t is a target-model variable.
- Returns
Frequency of chain breaks as a dict in the form {s: f, …}, where s is a variable in the source graph and float f the fraction of broken chains.
- Return type
Examples
This example embeds a single source node, ‘a’, as a chain of two target nodes (0, 1) and uses
chain_break_frequency()
to show that out of two synthetic samples, one ([-1, +1]) represents a broken chain.>>> import numpy as np ... >>> samples = np.array([[-1, +1], [+1, +1]]) >>> embedding = {'a': {0, 1}} >>> print(dwave.embedding.chain_break_frequency(samples, embedding)['a']) 0.5