code: def euclidean_projection(V, n_features, n_classes, z, gamma):
      """
      L2 Norm regularized euclidean projection min_W  1/2 ||W- V||_2^2 + z * ||W||_2
      """
      W_projection = np.zeros((n_features, n_classes))
      for i in range(n_features):
          if LA.norm(V[i, :]) > z / gamma:
              W_projection[i, :] = (
                  1 - z / (gamma * LA.norm(V[i, :]))
              ) * V[i, :]
          else:
              W_projection[i, :] = np.zeros(n_classes)
      return W_projection
  
file_path: scikit-feature/skfeature/utility/sparse_learning.py
imports: import numpy
  from numpy import linalg
label_desc: Use image node_id 6 for calling a global function with example usage: euclidean_projection(V, n_features, n_classes, z, gamma) and returns: W_projection