Python sklearn.linear_model 模块，OrthogonalMatchingPursuit() 实例源码

def GetNeighborDims(data, paras):
    ndata, ndim=data.shape
    kND=paras["kND"]
    objOMP=OMP(n_nonzero_coefs=kND)
    idxDict=npy.ones(ndim, dtype=npy.bool)
    w=npy.zeros((ndim-1, ndim), dtype=npy.float32)
    for kk in range(ndim):
        idxDict.fill(True)
        idxDict[kk]=False
        objOMP.fit(data[:,idxDict], data[:,kk])
        w[:,kk]=objOMP.coef_.astype(npy.float32)
    return w

def omp_estimator(hparams):
    """OMP estimator"""
    omp_est = OrthogonalMatchingPursuit(n_nonzero_coefs=hparams.omp_k)
    def estimator(A_val, y_batch_val, hparams):
        x_hat_batch = []
        for i in range(hparams.batch_size):
            y_val = y_batch_val[i]
            omp_est.fit(A_val.T, y_val.reshape(hparams.num_measurements))
            x_hat = omp_est.coef_
            x_hat = np.reshape(x_hat, [-1])
            x_hat = np.maximum(np.minimum(x_hat, 1), 0)
            x_hat_batch.append(x_hat)
        x_hat_batch = np.asarray(x_hat_batch)
        return x_hat_batch
    return estimator