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@@ -14,25 +14,24 @@ class Algorithm():
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self.validator = _validator
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self.policy = _policy
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- self.currentSolution = self.initializer()
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-
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- # evaluate current solution
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- self.currentSolution.evaluate(self.evaluator)
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-
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- # keep in memory best known solution (current solution)
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- self.bestSolution = self.currentSolution
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-
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# other parameters
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self.maxEvalutations = 0 # by default
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- self.numberOfEvaluations = 0
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self.maximise = _maximise
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+ self.initRun()
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+
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- def reinit(self):
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+ def initRun(self):
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"""
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- Reinit the whole variable
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+ Reinit the whole variables
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"""
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- self.currentSolution = self.initializer
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+
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+ self.currentSolution = self.initializer()
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+
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+ # evaluate current solution
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+ self.currentSolution.evaluate(self.evaluator)
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+
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+ # keep in memory best known solution (current solution)
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self.bestSolution = self.currentSolution
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self.numberOfEvaluations = 0
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@@ -92,9 +91,10 @@ class Algorithm():
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Run the specific algorithm following number of evaluations to find optima
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"""
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self.maxEvalutations = _evaluations
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+ self.initRun()
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logging.info("Run %s with %s evaluations" % (self.__str__(), _evaluations))
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-
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+
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def progress(self):
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logging.info("-- Evaluation n°%s of %s, %s%%" % (self.numberOfEvaluations, self.maxEvalutations, "{0:.2f}".format((self.numberOfEvaluations) / self.maxEvalutations * 100.)))
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