示例#1
0
    def test_missing_data(self):
        from GPy import kern
        from GPy.models.bayesian_gplvm_minibatch import BayesianGPLVMMiniBatch
        from GPy.examples.dimensionality_reduction import _simulate_matern

        D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 400, 3, 4
        _, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, False)
        Y = Ylist[0]

        inan = np.random.binomial(1, .9, size=Y.shape).astype(bool) # 80% missing data
        Ymissing = Y.copy()
        Ymissing[inan] = np.nan

        k = kern.Linear(Q, ARD=True) + kern.White(Q, np.exp(-2)) # + kern.bias(Q)
        m = BayesianGPLVMMiniBatch(Ymissing, Q, init="random", num_inducing=num_inducing,
                          kernel=k, missing_data=True)
        assert(m.checkgrad())
        mul, varl = m.predict(m.X)

        k = kern.RBF(Q, ARD=True) + kern.White(Q, np.exp(-2)) # + kern.bias(Q)
        m2 = BayesianGPLVMMiniBatch(Ymissing, Q, init="random", num_inducing=num_inducing,
                          kernel=k, missing_data=True)
        assert(m.checkgrad())
        m2.kern.rbf.lengthscale[:] = 1e6
        m2.X[:] = m.X.param_array
        m2.likelihood[:] = m.likelihood[:]
        m2.kern.white[:] = m.kern.white[:]
        mu, var = m.predict(m.X)
        np.testing.assert_allclose(mul, mu)
        np.testing.assert_allclose(varl, var)

        q50 = m.predict_quantiles(m.X, (50,))
        np.testing.assert_allclose(mul, q50[0])
示例#2
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    def test_missing_data(self):
        from GPy import kern
        from GPy.models.bayesian_gplvm_minibatch import BayesianGPLVMMiniBatch
        from GPy.examples.dimensionality_reduction import _simulate_matern

        D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 400, 3, 4
        _, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, False)
        Y = Ylist[0]

        inan = np.random.binomial(1, .9, size=Y.shape).astype(
            bool)  # 80% missing data
        Ymissing = Y.copy()
        Ymissing[inan] = np.nan

        k = kern.Linear(Q, ARD=True) + kern.White(Q,
                                                  np.exp(-2))  # + kern.bias(Q)
        m = BayesianGPLVMMiniBatch(Ymissing,
                                   Q,
                                   init="random",
                                   num_inducing=num_inducing,
                                   kernel=k,
                                   missing_data=True)
        assert (m.checkgrad())

        k = kern.RBF(Q, ARD=True) + kern.White(Q, np.exp(-2))  # + kern.bias(Q)
        m = BayesianGPLVMMiniBatch(Ymissing,
                                   Q,
                                   init="random",
                                   num_inducing=num_inducing,
                                   kernel=k,
                                   missing_data=True)
        assert (m.checkgrad())
示例#3
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    def test_missing_data(self):
        from GPy import kern
        from GPy.models.bayesian_gplvm_minibatch import BayesianGPLVMMiniBatch
        from GPy.examples.dimensionality_reduction import _simulate_matern

        D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 400, 3, 4
        _, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, False)
        Y = Ylist[0]

        inan = np.random.binomial(1, .9, size=Y.shape).astype(bool) # 80% missing data
        Ymissing = Y.copy()
        Ymissing[inan] = np.nan

        k = kern.Linear(Q, ARD=True) + kern.White(Q, np.exp(-2)) # + kern.bias(Q)
        m = BayesianGPLVMMiniBatch(Ymissing, Q, init="random", num_inducing=num_inducing,
                          kernel=k, missing_data=True)
        assert(m.checkgrad())
        mul, varl = m.predict(m.X)

        k = kern.RBF(Q, ARD=True) + kern.White(Q, np.exp(-2)) # + kern.bias(Q)
        m2 = BayesianGPLVMMiniBatch(Ymissing, Q, init="random", num_inducing=num_inducing,
                          kernel=k, missing_data=True)
        assert(m.checkgrad())
        m2.kern.rbf.lengthscale[:] = 1e6
        m2.X[:] = m.X.param_array
        m2.likelihood[:] = m.likelihood[:]
        m2.kern.white[:] = m.kern.white[:]
        mu, var = m.predict(m.X)
        np.testing.assert_allclose(mul, mu)
        np.testing.assert_allclose(varl, var)

        q50 = m.predict_quantiles(m.X, (50,))
        np.testing.assert_allclose(mul, q50[0])
示例#4
0
    def test_missing_data(self):
        from GPy import kern
        from GPy.models.bayesian_gplvm_minibatch import BayesianGPLVMMiniBatch
        from GPy.examples.dimensionality_reduction import _simulate_matern

        D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 400, 3, 4
        _, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, False)
        Y = Ylist[0]

        inan = np.random.binomial(1, .9, size=Y.shape).astype(bool) # 80% missing data
        Ymissing = Y.copy()
        Ymissing[inan] = np.nan

        k = kern.Linear(Q, ARD=True) + kern.White(Q, np.exp(-2)) # + kern.bias(Q)
        m = BayesianGPLVMMiniBatch(Ymissing, Q, init="random", num_inducing=num_inducing,
                          kernel=k, missing_data=True)
        assert(m.checkgrad())

        k = kern.RBF(Q, ARD=True) + kern.White(Q, np.exp(-2)) # + kern.bias(Q)
        m = BayesianGPLVMMiniBatch(Ymissing, Q, init="random", num_inducing=num_inducing,
                          kernel=k, missing_data=True)
        assert(m.checkgrad())