opt_einsum.contract(subscripts, *operands[, …]) |
Evaluates the Einstein summation convention on the operands. |
opt_einsum.contract_path(*operands, **kwargs) |
Find a contraction order ‘path’, without performing the contraction. |
opt_einsum.contract_expression(subscripts, …) |
Generate a reusable expression for a given contraction with specific shapes, which can, for example, be cached. |
opt_einsum.contract.ContractExpression(…) |
Helper class for storing an explicit contraction_list which can then be repeatedly called solely with the array arguments. |
opt_einsum.contract.PathInfo(…) |
A printable object to contain information about a contraction path. |
opt_einsum.paths.optimal(inputs, output, …) |
Computes all possible pair contractions in a depth-first recursive manner, sieving results based on memory_limit and the best path found so far. |
opt_einsum.paths.greedy(inputs, output, …) |
Finds the path by a three stage algorithm: |
opt_einsum.paths.branch(inputs, output, …) |
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opt_einsum.parser.get_symbol(i) |
Get the symbol corresponding to int i - runs through the usual 52 letters before resorting to unicode characters, starting at chr(192). |
opt_einsum.sharing.shared_intermediates([cache]) |
Context in which contract intermediate results are shared. |
opt_einsum.paths.PathOptimizer |
Base class for different path optimizers to inherit from. |
opt_einsum.paths.BranchBound([nbranch, …]) |
Explores possible pair contractions in a depth-first recursive manner like the optimal approach, but with extra heuristic early pruning of branches as well sieving by memory_limit and the best path found so far. |
opt_einsum.path_random.RandomOptimizer([…]) |
Base class for running any random path finder that benefits from repeated calling, possibly in a parallel fashion. |
opt_einsum.path_random.RandomGreedy([…]) |
| Parameters: |
- cost_fn (callable, optional) – A function that returns a heuristic ‘cost’ of a potential contraction
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opt_einsum.paths.DynamicProgramming([…]) |
Finds the optimal path of pairwise contractions without intermediate outer products based a dynamic programming approach presented in Phys. |