text: import numpy as np
  
  class logHingeLoss():
      #setting initial parameters
      def __init__(self, mu=0.05, C=1, max_iter=1000, tol=0.001):
          assert mu > 0
          self.mu=mu
          assert C > 0
          self.C = C
          assert max_iter > 0 and isinstance(max_iter, int)
          self.max_iter=max_iter
          assert tol > 0
          self.tol=tol
      
      def fit(self, x, t):
          # checks for labels
          self.classes_ = np.unique(t)
          t[t==0] = -1
          
          # initail variables k, stop, w_0
          k = 0
          update = True
          w = np.random.rand(len(x[0])+1)
          
          # set up matrices
          X = np.ones((len(x),len(x[0])+1))
          X[:,:-1] = x.copy()
          
          while k < (self.max_iter) and update:
              # compute gradient
              _, p, grad = self.gradient(w, X, t)
              if np.isnan(np.sum(p)):
                  raise ValueError("Some component in approximately loss functions values (p) is not a number.")
              
              # check stopping criterion
              if np.linalg.norm(grad)<=self.tol:
                  update = False
              else:
                  # update step
                  self.eta = 1/(k+1)
                  w -= self.eta*grad
              
              k += 1
          
          self.final_iter = k
          self._coef = w[:-1]
          self._intercept = w[-1]
          
          return self
      
      def gradient(self, w, X, t):
          u = (1-t*np.matmul(X,w))
          p = np.exp(u/self.mu)/(1+np.exp(u/self.mu))
          grad = w - self.C*(np.mean(p*t*X.T,axis=1))
          return u,p,grad
  
      def predict(self, x):
          p = np.sign(np.matmul(x,self._coef)+self._intercept)
          p[p==0] = 1
          return p.astype(int)