problems.py 文件源码

def bernoulli_gaussian_trial(M=250,N=500,L=1000,pnz=.1,kappa=None,SNR=40):

    A = np.random.normal(size=(M, N), scale=1.0 / math.sqrt(M)).astype(np.float32)
    if kappa >= 1:
        # create a random operator with a specific condition number
        U,_,V = la.svd(A,full_matrices=False)
        s = np.logspace( 0, np.log10( 1/kappa),M)
        A = np.dot( U*(s*np.sqrt(N)/la.norm(s)),V).astype(np.float32)
    A_ = tf.constant(A,name='A')
    prob = TFGenerator(A=A,A_=A_,pnz=pnz,kappa=kappa,SNR=SNR)
    prob.name = 'Bernoulli-Gaussian, random A'

    bernoulli_ = tf.to_float( tf.random_uniform( (N,L) ) < pnz)
    xgen_ = bernoulli_ * tf.random_normal( (N,L) )
    noise_var = pnz*N/M * math.pow(10., -SNR / 10.)
    ygen_ = tf.matmul( A_,xgen_) + tf.random_normal( (M,L),stddev=math.sqrt( noise_var ) )

    prob.xval = ((np.random.uniform( 0,1,(N,L))<pnz) * np.random.normal(0,1,(N,L))).astype(np.float32)
    prob.yval = np.matmul(A,prob.xval) + np.random.normal(0,math.sqrt( noise_var ),(M,L))
    prob.xinit = ((np.random.uniform( 0,1,(N,L))<pnz) * np.random.normal(0,1,(N,L))).astype(np.float32)
    prob.yinit = np.matmul(A,prob.xinit) + np.random.normal(0,math.sqrt( noise_var ),(M,L))
    prob.xgen_ = xgen_
    prob.ygen_ = ygen_
    prob.noise_var = noise_var

    return prob