799308b5fb08e939e574c5078b546c24a0967d82,testing/test_mean_functions.py,TestModelCompositionOperations,setUp,#TestModelCompositionOperations#,65

Before Change

        zero = GPflow.mean_functions.Zero()
        one = GPflow.mean_functions.Constant(np.ones(self.output_dim))
        
        linear1 = GPflow.mean_functions.Linear(rng.randn(self.input_dim, self.output_dim), rng.randn(self.output_dim)),
        linear2 = GPflow.mean_functions.Linear(rng.randn(self.input_dim, self.output_dim), rng.randn(self.output_dim)),
        linear3 = GPflow.mean_functions.Linear(rng.randn(self.input_dim, self.output_dim), rng.randn(self.output_dim)),
        
        const1 = GPflow.mean_functions.Constant(rng.randn(self.output_dim))
        const2 = GPflow.mean_functions.Constant(rng.randn(self.output_dim))
        const3 = GPflow.mean_functions.Constant(rng.randn(self.output_dim))
        
        
        const1inv = GPflow.mean_functions.Constant(-1 * const1.c)
        linear1inv = GPflow.mean_functions.Linear(-1 * linear1.A, linear1.b * -1)
        
        //a * (b + c)
        const_set1 = GPflow.mean_functions.Product(const1,
                                                  GPflow.mean_functions.Additive(const2, const3))
        linear_set1 = GPflow.mean_functions.Product(linear1,
                                                  GPflow.mean_functions.Additive(linear2, linear3))

        //ab + ac
        const_set2 = GPflow.mean_functions.Additive(GPflow.mean_functions.Product(const1, const2),
                                                   GPflow.mean_functions.Product(const1, const3))

        linear_set2 = GPflow.mean_functions.Additive(GPflow.mean_functions.Product(linear1, linear2),
                                                     GPflow.mean_functions.Product(linear1, linear3))
        //a-a = 0, (a + b) -a = b = a + (b - a)
        
        linear1_minus_linear1 =  GPflow.mean_functions.Additive(linear1, linear1inv)   
        const1_minus_const1=  GPflow.mean_functions.Additive(const1, const1inv)

        comp_minus_constituent1 = GPflow.mean_functions.Additive(GPflow.mean_functions.Additive(linear1, linear2),
                                                                      linear1inv)
        comp_minus_constituent2 = GPflow.mean_functions.Additive(linear1,
                                                                      GPflow.mean_functions.Additive(linear2,
                                                                                                     linear1inv))  
                                                    
        k = GPflow.kernels.Bias(self.input_dim)        
        
        self.m_linear_set1 = GPflow.gpr.GPR(X, Y, mean_function=linear_set1, kern=k)
        self.m_linear_set2 = GPflow.gpr.GPR(X, Y, mean_function=linear_set2, kern=k)
        
        self.m_const_set1 = GPflow.gpr.GPR(X, Y, mean_function=const_set1, kern=k)
        self.m_const_set2 = GPflow.gpr.GPR(X, Y, mean_function=const_set2, kern=k)
        
        self.m_linear_min_linear= GPflow.gpr.GPR(X, Y, mean_function=linear1_minus_linear1, kern=k)
        self.m_const_min_const = GPflow.gpr.GPR(X, Y, mean_function=const1_minus_const1, kern=k)        
        
        self.m_constituent = GPflow.gpr.GPR(X, Y, mean_function=linear2, kern=k) 
        self.m_zero = GPflow.gpr.GPR(X, Y, mean_function=zero, kern=k)        
        
        self.m_comp_minus_constituent1 = GPflow.gpr.GPR(X, Y, mean_function=comp_minus_constituent1, kern=k)
        self.m_comp_minus_constituent2 = GPflow.gpr.GPR(X, Y, mean_function=comp_minus_constituent2, kern=k)

             
    def test_precedence(self):

After Change

        zero = GPflow.mean_functions.Zero()
        one = GPflow.mean_functions.Constant(np.ones(self.output_dim))
        
        linear1 = GPflow.mean_functions.Linear(rng.randn(self.input_dim, self.output_dim), rng.randn(self.output_dim))
        linear2 = GPflow.mean_functions.Linear(rng.randn(self.input_dim, self.output_dim), rng.randn(self.output_dim))
        linear3 = GPflow.mean_functions.Linear(rng.randn(self.input_dim, self.output_dim), rng.randn(self.output_dim))
        
        const1 = GPflow.mean_functions.Constant(rng.randn(self.output_dim))
        const2 = GPflow.mean_functions.Constant(rng.randn(self.output_dim))
        const3 = GPflow.mean_functions.Constant(rng.randn(self.output_dim))

        const1inv = GPflow.mean_functions.Constant(const1.c.get_free_state() *-1)
        linear1inv = GPflow.mean_functions.Linear(A = linear1.A.get_free_state() * -1., b = linear1.b.get_free_state() * -1.)
        
        //a * (b + c)
        const_set1 = GPflow.mean_functions.Product(const1,
                                                  GPflow.mean_functions.Additive(const2, const3))
        linear_set1 = GPflow.mean_functions.Product(linear1,
                                                  GPflow.mean_functions.Additive(linear2, linear3))

        //ab + ac
        const_set2 = GPflow.mean_functions.Additive(GPflow.mean_functions.Product(const1, const2),
                                                   GPflow.mean_functions.Product(const1, const3))

        linear_set2 = GPflow.mean_functions.Additive(GPflow.mean_functions.Product(linear1, linear2),
                                                     GPflow.mean_functions.Product(linear1, linear3))
        //a-a = 0, (a + b) -a = b = a + (b - a)
        
        linear1_minus_linear1 =  GPflow.mean_functions.Additive(linear1, linear1inv)   
        const1_minus_const1=  GPflow.mean_functions.Additive(const1, const1inv)

        comp_minus_constituent1 = GPflow.mean_functions.Additive(GPflow.mean_functions.Additive(linear1, linear2),
                                                                      linear1inv)
        comp_minus_constituent2 = GPflow.mean_functions.Additive(linear1,
                                                                      GPflow.mean_functions.Additive(linear2,
                                                                                                     linear1inv))  
                                                    
        k = GPflow.kernels.Bias(self.input_dim)        
        
        self.m_linear_set1 = GPflow.gpr.GPR(X, Y, mean_function=linear_set1, kern=k)
        self.m_linear_set2 = GPflow.gpr.GPR(X, Y, mean_function=linear_set2, kern=k)
        
        self.m_const_set1 = GPflow.gpr.GPR(X, Y, mean_function=const_set1, kern=k)
        self.m_const_set2 = GPflow.gpr.GPR(X, Y, mean_function=const_set2, kern=k)
        
        self.m_linear_min_linear= GPflow.gpr.GPR(X, Y, mean_function=linear1_minus_linear1, kern=k)
        self.m_const_min_const = GPflow.gpr.GPR(X, Y, mean_function=const1_minus_const1, kern=k)        
        
        self.m_constituent = GPflow.gpr.GPR(X, Y, mean_function=linear2, kern=k) 
        self.m_zero = GPflow.gpr.GPR(X, Y, mean_function=zero, kern=k)        
        
        self.m_comp_minus_constituent1 = GPflow.gpr.GPR(X, Y, mean_function=comp_minus_constituent1, kern=k)
        self.m_comp_minus_constituent2 = GPflow.gpr.GPR(X, Y, mean_function=comp_minus_constituent2, kern=k)

             
    def test_precedence(self):

Instances