随机梯度下降的逻辑回归代码
import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
ld = load_iris()
x_data = ld.data
y_target = ld.target
y_train = y_target[y_target != 2]
a = []
for i, j in enumerate(y_train):
a.append(x_data[i])
x_train = pd.DataFrame(a)
class logistic_regression():
def fit(self, x_train, y_train, k=200):
self.x_train = x_train
self.y_train = y_train
self.k = k
def sigmoid(self, inx):
return 1 / (1 + np.exp(-inx))
def logistic_regression_model(self):
x_train_1 = np.array(self.x_train)
y_train_1 = np.array(self.y_train)
weights = np.ones(np.shape(x_train_1)[1])
alpha = 0.0001
for j in range(self.k):
for i in range(np.shape(x_train_1)[0]):
h = self.sigmoid(np.sum(x_train_1[i] * weights))
error = h - y_train_1[i]
weights = weights - alpha * error * x_train_1[i]
return weights
def predict(self, x_test):
x_test = np.array(x_test)
weights_ = self.logistic_regression_model()
y_predict = []
for k in range(np.shape(x_test)[0]):
result = self.sigmoid(np.sum(x_test[k] * weights_))
if result > 0.5:
result_1 = 1
y_predict.append(result_1)
else:
result_2 = 0
y_predict.append(result_2)
return y_predict
a = logistic_regression()
a.fit(x_train, y_train)
print(a.predict(x_train))
sigmord 函数求导
逻辑回归到底属于分类还是回归？
分类。只是很多时候，对于逻辑回归的某些应用场合，最终要的结果可能不是分类的结果，而是计算出的概率。这里可能会混淆认知。
