Python method to complete logistic regression

This article mainly introduces an example of how to implement logistic regression in Python. This is an experiment in the machine learning course. It is compiled and shared with everyone. Friends who need it can refer to it. Let’s take a look together.

The principle implemented in this article is very simple. The optimization method is gradient descent. There will be test results later.

Let’s take a look at the implemented sample code first:

# coding=utf-8
from math import exp

import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets.samples_generator import make_blobs


def sigmoid(num):
 '''

 :param num: 待计算的x
 :return: sigmoid之后的数值
 '''
 if type(num) == int or type(num) == float:
  return 1.0 / (1 + exp(-1 * num))
 else:
  raise ValueError, 'only int or float data can compute sigmoid'


class logistic():
 def init(self, x, y): 
  if type(x) == type(y) == list:
   self.x = np.array(x)
   self.y = np.array(y)
  elif type(x) == type(y) == np.ndarray:
   self.x = x
   self.y = y
  else:
   raise ValueError, 'input data error'

 def sigmoid(self, x):
  '''

  :param x: 输入向量
  :return: 对输入向量整体进行simgoid计算后的向量结果
  '''
  s = np.frompyfunc(lambda x: sigmoid(x), 1, 1)
  return s(x)

 def train_with_punish(self, alpha, errors, punish=0.0001):
  '''

  :param alpha: alpha为学习速率
  :param errors: 误差小于多少时停止迭代的阈值
  :param punish: 惩罚系数
  :param times: 最大迭代次数
  :return:
  '''
  self.punish = punish
  dimension = self.x.shape[1]
  self.theta = np.random.random(dimension)
  compute_error = 100000000
  times = 0
  while compute_error > errors:
   res = np.dot(self.x, self.theta)
   delta = self.sigmoid(res) - self.y
   self.theta = self.theta - alpha * np.dot(self.x.T, delta) - punish * self.theta # 带惩罚的梯度下降方法
   compute_error = np.sum(delta)
   times += 1

 def predict(self, x):
  '''

  :param x: 给入新的未标注的向量
  :return: 按照计算出的参数返回判定的类别
  '''
  x = np.array(x)
  if self.sigmoid(np.dot(x, self.theta)) > 0.5:
   return 1
  else:
   return 0


def test1():
 '''
 用来进行测试和画图，展现效果
 :return:
 '''
 x, y = make_blobs(n_samples=200, centers=2, n_features=2, random_state=0, center_box=(10, 20))
 x1 = []
 y1 = []
 x2 = []
 y2 = []
 for i in range(len(y)):
  if y[i] == 0:
   x1.append(x[i][0])
   y1.append(x[i][1])
  elif y[i] == 1:
   x2.append(x[i][0])
   y2.append(x[i][1])
 # 以上均为处理数据，生成出两类数据
 p = logistic(x, y)
 p.train_with_punish(alpha=0.00001, errors=0.005, punish=0.01) # 步长是0.00001，最大允许误差是0.005，惩罚系数是0.01
 x_test = np.arange(10, 20, 0.01)
 y_test = (-1 * p.theta[0] / p.theta[1]) * x_test
 plt.plot(x_test, y_test, c='g', label='logistic_line')
 plt.scatter(x1, y1, c='r', label='positive')
 plt.scatter(x2, y2, c='b', label='negative')
 plt.legend(loc=2)
 plt.title('punish value = ' + p.punish.str())
 plt.show()


if name == 'main':
 test1()

The running result is as shown below

Summary

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