schrodinger.application.canvas.r_group_dee module
R-Group Analysis Dead End elimination
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class schrodinger.application.canvas.r_group_dee.SimulatedAnnealing(energy_matrix, sa_seed=None, t_factor=None, tmax_mult=None)[source]
Bases: object
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static getChoiceSpectrum(choices)[source]
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__init__(energy_matrix, sa_seed=None, t_factor=None, tmax_mult=None)[source]
Initializer for simulated annealing class.
- Parameters
energy_matrix (DEE_EnergyMatrix) – precalculated pairwise matrix used by SA.
sa_seed (float) – SA random number generator seed
t_fac – Factor (<1) by which T will be multiplied at each T change
tmax_mult (float) – Factor by which no. of st. will be multiplied to get starting T
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run()[source]
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is_another_best_config(solution)[source]
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new_best(energy, solution)[source]
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report_t(t, start_E, end_E, tsteps, totalCnt, acceptCnt)[source]
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getNewSolution(solution_old)[source]
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neighbor(solution)[source]
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new_temperature(old_t)[source]
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boltzmann_probability(e_old, e_new, t)[source]
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getBestMatch()[source]
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class schrodinger.application.canvas.r_group_dee.DEE_Backtracking(energy_matrix)[source]
Bases: object
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__init__(energy_matrix)[source]
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minimize()[source]
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backtrack(solution)[source]
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reject(solution)[source]
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accept(solution)[source]
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first(solution)[source]
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next(solution)[source]
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output(solution)[source]
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getBestMatch()[source]
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class schrodinger.application.canvas.r_group_dee.DEE_EnergyMatrix(choices, uij)[source]
Bases: object
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__init__(choices, uij)[source]
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numPos()[source]
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numChoices()[source]
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calculateEnergy(solution)[source]
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calculateEnergyDifference(solution_old, e_old, istruct, solution_new)[source]
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initialSolution()[source]
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checkSolution(solution)[source]
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convertSolution(solution)[source]
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applyEnergyFilter(solution, current_energy)[source]
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eliminateSingles()[source]
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eliminatePairs()[source]
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applyGoldsteinSingles(k, a, b)[source]
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schrodinger.application.canvas.r_group_dee.main()[source]