privacyLDA.py 文件源码

def selftest1():

    # Generate data
    D0 = 5
    K1 = 2
    K2 = 2
    NperClass = 500
    N = NperClass*K1*K2
    #l = 1.0e-3
    X = np.zeros((D0,NperClass,K1,K2))
    y1 = np.zeros((NperClass,K1,K2),dtype=int)
    y2 = np.zeros((NperClass,K1,K2),dtype=int)
    bias1 = np.random.normal(scale=1.0,size=(D0,K1))
    bias2 = np.random.normal(scale=1.0,size=(D0,K2))    
    for k1 in range(K1):
        for k2 in range(K2):
            X[:,:,k1,k2] = \
                np.random.normal(scale=0.25, size=(D0,NperClass)) \
                + np.kron(np.ones((1,NperClass)),bias1[:,k1].reshape((D0,1))) \
                + np.kron(np.ones((1,NperClass)),bias2[:,k2].reshape((D0,1)))
            y1[:,k1,k2] = k1*np.ones((NperClass,))
            y2[:,k1,k2] = k2*np.ones((NperClass,))

    X = X.reshape((D0,N))
    y1 = y1.reshape((N,))
    y2 = y2.reshape((N,))

    E,dd = run(X,y1,y2)
    print dd

    plt.figure(1)
    plt.clf()
    plt.subplot(1,2,1)
    plt.plot(dd)
    plt.subplot(1,2,2)
    plt.imshow(E, aspect='auto', interpolation='none')
    plt.colorbar()
    plt.show()