python 逻辑回归 python 牛顿法实现逻辑回归（Logistic Regression）

import numpy as np

class LogisticRegression(object):
 """
 Logistic Regression Classifier training by Newton Method
 """

 def __init__(self, error: float = 0.7, max_epoch: int = 100):
  """
  :param error: float, if the distance between new weight and 
      old weight is less than error, the process 
      of traing will break.
  :param max_epoch: if training epoch >= max_epoch the process 
       of traing will break.
  """
  self.error = error
  self.max_epoch = max_epoch
  self.weight = None
  self.sign = np.vectorize(lambda x: 1 if x >= 0.5 else 0)

 def p_func(self, X_):
  """Get P(y=1 | x)
  :param X_: shape = (n_samples + 1, n_features)
  :return: shape = (n_samples)
  """
  tmp = np.exp(self.weight @ X_.T)
  return tmp / (1 + tmp)

 def diff(self, X_, y, p):
  """Get derivative
  :param X_: shape = (n_samples, n_features + 1) 
  :param y: shape = (n_samples)
  :param p: shape = (n_samples) P(y=1 | x)
  :return: shape = (n_features + 1) first derivative
  """
  return -(y - p) @ X_

 def hess_mat(self, X_, p):
  """Get Hessian Matrix
  :param p: shape = (n_samples) P(y=1 | x)
  :return: shape = (n_features + 1, n_features + 1) second derivative
  """
  hess = np.zeros((X_.shape[1], X_.shape[1]))
  for i in range(X_.shape[0]):
   hess += self.X_XT[i] * p[i] * (1 - p[i])
  return hess

 def newton_method(self, X_, y):
  """Newton Method to calculate weight
  :param X_: shape = (n_samples + 1, n_features)
  :param y: shape = (n_samples)
  :return: None
  """
  self.weight = np.ones(X_.shape[1])
  self.X_XT = []
  for i in range(X_.shape[0]):
   t = X_[i, :].reshape((-1, 1))
   self.X_XT.append(t @ t.T)

  for _ in range(self.max_epoch):
   p = self.p_func(X_)
   diff = self.diff(X_, y, p)
   hess = self.hess_mat(X_, p)
   new_weight = self.weight - (np.linalg.inv(hess) @ diff.reshape((-1, 1))).flatten()

   if np.linalg.norm(new_weight - self.weight) <= self.error:
    break
   self.weight = new_weight

 def fit(self, X, y):
  """
  :param X_: shape = (n_samples, n_features)
  :param y: shape = (n_samples)
  :return: self
  """
  X_ = np.c_[np.ones(X.shape[0]), X]
  self.newton_method(X_, y)
  return self

 def predict(self, X) -> np.array:
  """
  :param X: shape = (n_samples, n_features] 
  :return: shape = (n_samples]
  """
  X_ = np.c_[np.ones(X.shape[0]), X]
  return self.sign(self.p_func(X_))

import matplotlib.pyplot as plt
import sklearn.datasets

def plot_decision_boundary(pred_func, X, y, title=None):
 """分类器画图函数，可画出样本点和决策边界
 :param pred_func: predict函数
 :param X: 训练集X
 :param y: 训练集Y
 :return: None
 """

 # Set min and max values and give it some padding
 x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
 y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
 h = 0.01
 # Generate a grid of points with distance h between them
 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
 # Predict the function value for the whole gid
 Z = pred_func(np.c_[xx.ravel(), yy.ravel()])
 Z = Z.reshape(xx.shape)
 # Plot the contour and training examples
 plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)
 plt.scatter(X[:, 0], X[:, 1], s=40, c=y, cmap=plt.cm.Spectral)
 if title:
  plt.title(title)
 plt.show()