def ssgplvm_simulation_linear():
    import numpy as np
    import GPy
    N, D, Q = 1000, 20, 5
    pi = 0.2

    def sample_X(Q, pi):
        x = np.empty(Q)
        dies = np.random.rand(Q)
        for q in range(Q):
            if dies[q] >= pi:
                x[q] = np.random.randn()
            else:
                x[q] = 0.
        return x

    Y = np.empty((N, D))
    X = np.empty((N, Q))
    # Generate data from random sampled weight matrices
    for n in range(N):
        X[n] = sample_X(Q, pi)
        w = np.random.randn(D, Q)
        Y[n] = np.dot(w, X[n])
dataset/ETHPy150Open SheffieldML/GPy/GPy/examples/dimensionality_reduction.py/ssgplvm_simulation_linear/operator `<` -> `>=`