class_docstr: A basic class for Principal Component Analysis (PCA).
  
  p is the number of dimensions, while N is the number of data points
docstr: input is an n X q array, where q <= p
  
  output is p X n
docstr_tok: ["input","is","an","n","X","q","array",",","where","q","<","=","p","output","is","p","X","n"]
code: def deproject(self, A, normed=True):
      """
      input is an n X q array, where q <= p
  
      output is p X n
      """
      A = np.atleast_2d(A)
      n, q = A.shape
      p = self.A.shape[1]
      if q > p:
          raise ValueError("q > p")
  
      evinv = np.linalg.inv(np.matrix(self.getEigenvectors()).T)
  
      zs = np.zeros((n, p))
      zs[:, :q] = A
  
      proj = evinv * zs.T
  
      if normed:
          return np.array(proj.T).T
      else:
          mns = np.mean(self.A, axis=0)
          sds = np.std(self.M, axis=0)
          return (np.array(proj.T) * sds + mns).T
  
file_path: statsmodels/statsmodels/sandbox/pca.py
imports: import numpy
label_desc: Use image node_id 13 for calling the Pca obj's underlying member method code with example usage: obj.deproject(A, normed) and returns: np, unknown