7709518362

Committed 30 Jan 2024 10:11AM UTC coverage: 77.332% (-0.2%) from 77.518%

Build # 7709518362

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push

github

Committed by

thouska

Commit Message

Merge branch 'master' of https://github.com/thouska/spotpy

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100.0

/src/spotpy/algorithms/mc.py

# -*- coding: utf-8 -*-
"""
Copyright (c) 2018 by Tobias Houska
This file is part of Statistical Parameter Optimization Tool for Python(SPOTPY).
:author: Tobias Houska
"""

from . import _algorithm


class mc(_algorithm):
    """
    The Monte Carlo algorithm generates random parameters from their respective
    distribution functions.
    """

    _unaccepted_parameter_types = ()

    def __init__(self, *args, **kwargs):
        """
        Input
        ----------
        spot_setup: class
            model: function
                Should be callable with a parameter combination of the parameter-function
                and return an list of simulation results (as long as evaluation list)
            parameter: function
                When called, it should return a random parameter combination. Which can
                be e.g. uniform or Gaussian
            objectivefunction: function
                Should return the objectivefunction for a given list of a model simulation and
                observation.
            evaluation: function
                Should return the true values as return by the model.

        dbname: str
            * Name of the database where parameter, objectivefunction value and simulation results will be saved.

        dbformat: str
            * ram: fast suited for short sampling time. no file will be created and results are saved in an array.
            * csv: A csv file will be created, which you can import afterwards.

        parallel: str
            * seq: Sequentiel sampling (default): Normal iterations on one core of your cpu.
            * mpi: Message Passing Interface: Parallel computing on cluster pcs (recommended for unix os).

        save_sim: boolean
            * True:  Simulation results will be saved
            * False: Simulation results will not be saved
        """
        kwargs["algorithm_name"] = "Monte Carlo (MC) sampler"
        super(mc, self).__init__(*args, **kwargs)

    def sample(self, repetitions):
        """
        Samples from the MonteCarlo algorithm.

        Input
        ----------
        repetitions: int
            Maximum number of runs.
        """
        self.set_repetiton(repetitions)
        print("Starting the MC algorithm with {} repetitions...".format(repetitions))
        # A generator that produces parametersets if called
        param_generator = (
            (rep, self.parameter()["random"]) for rep in range(int(repetitions))
        )
        for rep, randompar, simulations in self.repeat(param_generator):
            # A function that calculates the fitness of the run and the manages the database
            self.postprocessing(rep, randompar, simulations)
        self.final_call()

1	# -- coding: utf-8 --
2	"""	6✔
3	Copyright (c) 2018 by Tobias Houska
4	This file is part of Statistical Parameter Optimization Tool for Python(SPOTPY).
5	:author: Tobias Houska
6	"""
7
8	from . import _algorithm	6✔
9
10
11	class mc(_algorithm):	6✔
12	"""
13	The Monte Carlo algorithm generates random parameters from their respective
14	distribution functions.
15	"""
16
17	_unaccepted_parameter_types = ()	6✔
18
19	def __init__(self, args, *kwargs):	6✔
20	"""
21	Input
22	----------
23	spot_setup: class
24	model: function
25	Should be callable with a parameter combination of the parameter-function
26	and return an list of simulation results (as long as evaluation list)
27	parameter: function
28	When called, it should return a random parameter combination. Which can
29	be e.g. uniform or Gaussian
30	objectivefunction: function
31	Should return the objectivefunction for a given list of a model simulation and
32	observation.
33	evaluation: function
34	Should return the true values as return by the model.
35
36	dbname: str
37	* Name of the database where parameter, objectivefunction value and simulation results will be saved.
38
39	dbformat: str
40	* ram: fast suited for short sampling time. no file will be created and results are saved in an array.
41	* csv: A csv file will be created, which you can import afterwards.
42
43	parallel: str
44	* seq: Sequentiel sampling (default): Normal iterations on one core of your cpu.
45	* mpi: Message Passing Interface: Parallel computing on cluster pcs (recommended for unix os).
46
47	save_sim: boolean
48	* True: Simulation results will be saved
49	* False: Simulation results will not be saved
50	"""
51	kwargs["algorithm_name"] = "Monte Carlo (MC) sampler"	4✔
52	super(mc, self).__init__(args, *kwargs)	4✔
53
54	def sample(self, repetitions):	6✔
55	"""
56	Samples from the MonteCarlo algorithm.
57
58	Input
59	----------
60	repetitions: int
61	Maximum number of runs.
62	"""
63	self.set_repetiton(repetitions)	4✔
64	print("Starting the MC algorithm with {} repetitions...".format(repetitions))	4✔
65	# A generator that produces parametersets if called
66	param_generator = (	4✔
67	(rep, self.parameter()["random"]) for rep in range(int(repetitions))
68	)
69	for rep, randompar, simulations in self.repeat(param_generator):	4✔
70	# A function that calculates the fitness of the run and the manages the database
71	self.postprocessing(rep, randompar, simulations)	4✔
72	self.final_call()	4✔

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