60 lines
2.3 KiB
Python
60 lines
2.3 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# IMPORTS
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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from src.thirdparty.code import *;
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from models.generated.commands import *;
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from src.core.calls import *;
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from src.setup import config;
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from src.models.random_walk import *;
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from src.algorithms.random_walk import *;
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# EXPORTS
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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__all__ = [
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'endpoint_random_walk',
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];
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# ENDPOINT
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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@run_safely()
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def endpoint_random_walk(command: CommandRandomWalk) -> Result[CallResult, CallError]:
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landscape = Landscape(
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values = command.landscape.values,
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metric = command.landscape.neighbourhoods.metric,
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);
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match command.algorithm:
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case EnumWalkMode.adaptive:
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result = adaptive_walk_algorithm(
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landscape = landscape,
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r = command.landscape.neighbourhoods.radius,
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optimise = command.optimise,
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verbose = config.OPTIONS.random_walk.verbose
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);
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case EnumWalkMode.gradient:
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result = gradient_walk_algorithm(
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landscape = landscape,
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r = command.landscape.neighbourhoods.radius,
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optimise = command.optimise,
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verbose = config.OPTIONS.random_walk.verbose
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);
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case EnumWalkMode.metropolis:
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result = metropolis_walk_algorithm(
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landscape = landscape,
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r = command.landscape.neighbourhoods.radius,
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annealing = command.annealing,
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optimise = command.optimise,
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verbose = config.OPTIONS.random_walk.verbose
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);
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case _ as alg:
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raise Exception(f'No algorithm implemented for {alg.value}.');
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return Ok(CallResult(action_taken=True, message=result));
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