逻辑回归

from math import exp
import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

定义LR回归模型

class LogisticReression:
def init(self,max_iter=200,learning_rate=0.01):
self.max_iter = max_iter
self.learing_rate = learning_rate
def sigmoid(self,x):
return 1/(1 + exp(-x) )
def data_matrix(self,X):
data_mat = []
print("X:",X)
for d in X:
data_mat.append([1.0,d])
print("data_mat:",data_mat)
return data_mat
#训练
def train(self,X,y):
#label = np.mat(y)
data_mat = self.data_matrix(X)#mn
self.weights = np.zeros((len(data_mat[0]),1),dtype=np.float32)
print("weight:",len(self.weights))
for iter_ in range(self.max_iter):
for i in range(len(X)):#这里是每个样本更新一次权重，其目的是避免样本过多造成的计算量大
result = self.sigmoid(np.dot(data_mat[i],self.weights))
error = y[i] - result
self.weights += self.learing_rate * error * np.transpose([data_mat[i]])
print("LR模型学习率={}，最大迭代次数={}".format(self.learing_rate,self.max_iter))
#准确率
def accuracy(self,X_test,y_test):
right = 0
X_test = self.data_matrix(X_test)
for x,y in zip(X_test,y_test):
result = np.dot(x,self.weights)
if (result > 0 and y == 1) or (result < 0 and y == 0):
right += 1

构建数据

def create_data():
iris = load_iris()
df = pd.DataFrame(iris.data,columns = iris.feature_names)
df['label'] = iris.target
df.columns = ['sepal length','sepal width','petal length','petal width','label']
data = np.array(df.iloc[:100,[0,1,-1]])
print("data:",data)
return data[:,:2],data[:,-1]
X,y = create_data()
X_train,X_test,y_train,y_test = train_test_split(X, y, test_size=0.3)

训练

LR = LogisticReression()
LR.train(X_train, y_train)

计算精度

LR.accuracy(X_test, y_test)

效果展示

x_ponits = np.arange(3, 9)
y_ = -(LR.weights[1]*x_ponits + LR.weights[0])/LR.weights[2]
plt.plot(x_ponits, y_)

绘制图

plt.scatter(X[:50,0],X[:50,1], label='0')
plt.scatter(X[50:,0],X[50:,1], label='1')
plt.legend()
plt.show()