som.py
def _init_learning_params(self):
        '''
        Learning parameters annealing scheme for the ordering phase (as proposed in Silva'15)
        '''
        time = np.arange(1,self.T)
        self.sigma_annealing = self.sigma_0*(self.sigma_f/self.sigma_0)**(time/self.T)
        self.eta_annealing = self.eta_0*(self.eta_0/self.eta_0)**(time/self.T)

        Learning parameters annealing scheme for the ordering phase (as proposed in Silva'15)
        
Learning parameters annealing scheme for the ordering phase (as proposed in Silva'15)
Learning parameters annealing scheme for the ordering phase ( as proposed in Silva ' 15 )
def _init_learning_params(self):
        time = np.arange(1,self.T)
        self.sigma_annealing = self.sigma_0*(self.sigma_f/self.sigma_0)**(time/self.T)
        self.eta_annealing = self.eta_0*(self.eta_0/self.eta_0)**(time/self.T)
def _init_learning_params ( self ) : time = np . arange ( 1 , self . T ) self . sigma_annealing = self . sigma_0 * ( self . sigma_f / self . sigma_0 ) ** ( time / self . T ) self . eta_annealing = self . eta_0 * ( self . eta_0 / self . eta_0 ) ** ( time / self . T )
Learning parameters annealing scheme for the ordering phase (as proposed in Silva'15)
Learning parameters annealing scheme for the ordering phase ( as proposed in Silva ' 15 )
'''
        Learning parameters annealing scheme for the ordering phase (as proposed in Silva'15)
        '''
self