Python GPLVM示例

示例#1

def test_gplvm(model_class, X, y, kernel, likelihood):
    if model_class is SparseGPRegression or model_class is VariationalSparseGP:
        gp = model_class(X, y, kernel, X, likelihood)
    else:
        gp = model_class(X, y, kernel, likelihood)

    gplvm = GPLVM(gp)
    # test inference
    gplvm.optimize(num_steps=1)
    # test forward
    gplvm(Xnew=X)

示例#2

文件： test_models.py 项目： lewisKit/pyro

def test_gplvm(model_class, X, y, kernel, likelihood):
    if model_class is SparseGPRegression or model_class is VariationalSparseGP:
        gp = model_class(X, y, kernel, X, likelihood)
    else:
        gp = model_class(X, y, kernel, likelihood)

    gplvm = GPLVM(gp)
    # test inference
    gplvm.optimize(num_steps=1)
    # test forward
    gplvm(Xnew=X)