コード例 #1
0
ファイル: demo_NodeKernel.py プロジェクト: zdn123/pyGPs
    y_train = y[index_train, :]
    x_test = x[index_test, :]
    y_test = y[index_test, :]

    # gp
    model.optimize(x_train, y_train)
    model.predict(x_test)

    # evaluation
    predictive_class_rbf = np.sign(model.ym)
    ACC_rbf = valid.ACC(predictive_class_rbf, y_test)

    ## DIFFUSION Kernel
    # compute kernel matrix and initalize GP with precomputed kernel
    model = pyGPs.GPC()
    M1, M2 = graphUtil.formKernelMatrix(Matrix, index_train, index_test)
    k = pyGPs.cov.Pre(M1, M2)
    model.setPrior(kernel=k)

    # if you only use precomputed kernel matrix, there is no training data needed,
    # but you still need to specify x_train (due to general structure of pyGPs)
    # e.g. you can use the following:
    n = len(index_train)
    x_train = np.zeros((n, 1))

    # gp
    model.optimize(x_train, y_train)
    model.predict(x_test)

    # evaluation
    predictive_class_diff = np.sign(model.ym)
コード例 #2
0
ファイル: demo_GraphKernel.py プロジェクト: Homesh01/pyGPs
    print('number of kernel iterations =', t)
    Matrix = K[:,:,t]
    # normalize kernel matrix (not useful for MUTAG)
    # Matrix = graphUtil.normalizeKernel(Matrix)
            
    # start cross-validation for this t
    for index_train, index_test in valid.k_fold_index(N, K=10):
        
        y_train = graph_label[index_train,:]
        y_test  = graph_label[index_test,:]

        n1 = len(index_train)
        n2 = len(index_test)        
        
        model = pyGPs.GPC()
        M1,M2 = graphUtil.formKernelMatrix(Matrix, index_train, index_test)
        k = pyGPs.cov.Pre(M1,M2)
        model.setPrior(kernel=k)

        # gp
        x_train = np.zeros((n1,1)) 
        x_test = np.zeros((n2,1))       
        model.getPosterior(x_train, y_train)
        model.predict(x_test)
        predictive_class = np.sign(model.ym)

        # evaluation 
        acc = valid.ACC(predictive_class, y_test)   
        ACC.append(acc)