LR

Python手动实现逻辑斯蒂回归

python实现LR.py

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+#!/usr/bin/env python 
+# -*- coding: utf-8 -*- 
+# @Time : 2019-08-14 10:35 
+# @Author : YJM
+# @Site :  
+# @File : python实现LR.py 
+# @Software: PyCharm
+
+import numpy as np
+
+
+class LogisticRegressionSelf:
+
+    def __init__(self):
+        """初始化Logistic regression模型"""
+        self.coef_ = None #维度
+        self.intercept_ = None #截距
+        self._theta = None
+
+    #sigmoid函数，私有化函数
+    def _sigmoid(self,x):
+        y = 1.0 / (1.0 + np.exp(-x))
+        return y
+
+    def fit(self,X_train,y_train,eta=0.01,n_iters=1e4):
+        assert X_train.shape[0] == y_train.shape[0], '训练数据集的长度需要和标签长度保持一致'
+
+        #计算损失函数
+        def J(theta,X_b,y):
+            p_predcit = self._sigmoid(X_b.dot(theta))
+            try:
+                return -np.sum(y*np.log(p_predcit) + (1-y)*np.log(1-p_predcit)) / len(y)
+            except:
+                return float('inf')
+
+        #求sigmoid梯度的导数
+        def dJ(theta,X_b,y):
+            x = self._sigmoid(X_b.dot(theta))
+            return X_b.T.dot(x-y)/len(X_b)
+
+        #模拟梯度下降
+        def gradient_descent(X_b,y,initial_theta,eta,n_iters=1e4,epsilon=1e-8):
+            theta = initial_theta
+            i_iter = 0
+            while i_iter < n_iters:
+                gradient = dJ(theta,X_b,y)
+                last_theta = theta
+                theta = theta - eta * gradient
+                i_iter += 1
+                if (abs(J(theta,X_b,y) - J(last_theta,X_b,y)) < epsilon):
+                    break
+            return theta
+
+        X_b = np.hstack([np.ones((len(X_train),1)),X_train])
+        initial_theta = np.zeros(X_b.shape[1]) #列向量
+        self._theta = gradient_descent(X_b,y_train,initial_theta,eta,n_iters)
+        self.intercept_ = self._theta[0] #截距
+        self.coef_ = self._theta[1:] #维度
+        return self
+
+    def predict_proba(self,X_predict):
+        X_b = np.hstack([np.ones((len(X_predict), 1)), X_predict])
+        return self._sigmoid(X_b.dot(self._theta))
+
+    def predict(self,X_predict):
+        proba = self.predict_proba(X_predict)
+        return np.array(proba > 0.5,dtype='int')