GPy/models/new_gp_heteroscedastic_regression.py
def train(self, X, Y):
        """
        Takes new X and Y to train.
        """
        self.X = X
        self.Y = Y
        if X.size == 0 or Y.size == 0:
            return
        self.n,self.D = X.shape
        self.Lambda0 = np.ones([self.n,1])*math.log(0.5);
        self.Lambda0 = Param('Lambda0', self.Lambda0)
        self.link_parameter(self.Lambda0,index=0)
        if self.optimize:
            self.optimize_restarts(num_restarts=self.num_restarts, robust=True)

        Takes new X and Y to train.
        
Takes new X and Y to train.
Takes new X and Y to train .
def train(self, X, Y):
        self.X = X
        self.Y = Y
        if X.size == 0 or Y.size == 0:
            return
        self.n,self.D = X.shape
        self.Lambda0 = np.ones([self.n,1])*math.log(0.5);
        self.Lambda0 = Param('Lambda0', self.Lambda0)
        self.link_parameter(self.Lambda0,index=0)
        if self.optimize:
            self.optimize_restarts(num_restarts=self.num_restarts, robust=True)
def train ( self , X , Y ) : self . X = X self . Y = Y if X . size == 0 or Y . size == 0 : return self . n , self . D = X . shape self . Lambda0 = np . ones ( [ self . n , 1 ] ) * math . log ( 0.5 ) ; self . Lambda0 = Param ( 'Lambda0' , self . Lambda0 ) self . link_parameter ( self . Lambda0 , index = 0 ) if self . optimize : self . optimize_restarts ( num_restarts = self . num_restarts , robust = True )
Takes new X and Y to train.
Takes new X and Y to train .
"""
        Takes new X and Y to train.
        """
self
X
Y