def fit(self,X,y,**param):
self.neural_shape = param.get("neural_shape")
self.n_input = self.neural_shape[0]
self.n_output = self.neural_shape[-1]
self.n_hidden = self.neural_shape[1]
self.number_of_weights = self.n_hidden*(self.n_input+1)+self.n_output*(self.n_hidden+1)
self.score_fn = FeedFlow(self.neural_shape)
self.X = X
self.y = y
#setting params
self.weights = G1DList.G1DList(self.number_of_weights)
lim = np.sqrt(6)/np.sqrt((self.n_input+self.n_output))
#Khoi tao trong so
self.weights.setParams(rangemin=-lim,rangemax=lim)
# cai dat ham khoi tao
self.weights.initializator.set(Initializators.G1DListInitializatorReal)
#cai dat ham dot bien
self.weights.mutator.set(Mutators.G1DListMutatorRealGaussian)
#cai dat ham do thich nghi
self.weights.evaluator.set(self.eval_score)
# cai dat ham lai ghep
self.weights.crossover.set(Crossovers.G1DListCrossoverUniform)
#code genetic
# thiet lap he so lai ghep
self.ga = GSimpleGA.GSimpleGA(self.weights)
self.ga.selector.set(Selectors.GRouletteWheel)
self.ga.setMutationRate(self.mutation_rate)
self.ga.setCrossoverRate(self.cross_rate)
self.ga.setPopulationSize(self.pop_size)
self.ga.setGenerations(self.pop_size)
self.ga.terminationCriteria.set(GSimpleGA.ConvergenceCriteria)
self.ga.evolve(freq_stats=self.freq_stats)
self.best_archive = self.getParam()
return self
def fit(self, X, y, **param):
self.X = X
self.y = y
self.neural_shape = param.get('neural_shape')
self.archive = param.get("archive")
if param.has_key("top_k"):
self.top_k = param.get("top_k")
self.score_fn = FeedFlow(self.neural_shape)
self.score_fn.set_weights(self.optimize(X, y))
return self
def fit(self, X, y,**param):
self.X = X
self.y = y
if (param.has_key('neural_shape')):
self.neural_shape = param.get("neural_shape")
self.n_output = self.neural_shape[-1]
self.n_hidden = self.neural_shape[1:-1]
self.number_of_layers = len(self.neural_shape)
else:
self.n_input = len(X[0])
self.n_output = len(y[0])
self.neural_shape = self.hidden_nodes.tolist()
self.neural_shape.insert(0, self.n_input)
self.neural_shape.append(self.n_output)
self.n_hidden = self.hidden_nodes
self.archive = param.get("archive")
if param.has_key("top_k"):
self.top_k = param.get("top_k")
self.score_fn = FeedFlow(self.neural_shape)
self.score_fn.set_weights(self.optimize(X,y))
return self
