code: def _fit_binary(self, X, y):
      p = np.asarray(self.alpha + X[y == 1].sum(axis=0)).flatten()
      q = np.asarray(self.alpha + X[y == 0].sum(axis=0)).flatten()
      r = np.log(p / np.abs(p).sum()) - np.log(q / np.abs(q).sum())
      b = np.log((y == 1).sum()) - np.log((y == 0).sum())
  
      if isinstance(X, spmatrix):
          indices = np.arange(len(r))
          r_sparse = coo_matrix(
              (r, (indices, indices)), shape=(len(r), len(r))
          )
          X_scaled = X * r_sparse
      else:
          X_scaled = X * r
  
      lsvc = LinearSVC(
          C=self.C, fit_intercept=self.fit_intercept, max_iter=10000
      ).fit(X_scaled, y)
  
      mean_mag = np.abs(lsvc.coef_).mean()
  
      coef_ = (1 - self.beta) * mean_mag * r + self.beta * (
          r * lsvc.coef_
      )
  
      intercept_ = (
          1 - self.beta
      ) * mean_mag * b + self.beta * lsvc.intercept_
  
      return coef_, intercept_
  
imports: from .None import utils
  from .imports import *
label_desc: Use image node_id 3 for calling the NBSVM obj's underlying member method code with example usage: obj._fit_binary(X, y) and returns: coef_,  intercept_