python实现逻辑回归的示例
代码
importnumpyasnp
importmatplotlib.pyplotasplt
fromsklearn.datasets.samples_generatorimportmake_classification
definitialize_params(dims):
w=np.zeros((dims,1))
b=0
returnw,b
defsigmoid(x):
z=1/(1+np.exp(-x))
returnz
deflogistic(X,y,w,b):
num_train=X.shape[0]
y_hat=sigmoid(np.dot(X,w)+b)
loss=-1/num_train*np.sum(y*np.log(y_hat)+(1-y)*np.log(1-y_hat))
cost=-1/num_train*np.sum(y*np.log(y_hat)+(1-y)*np.log(1-y_hat))
dw=np.dot(X.T,(y_hat-y))/num_train
db=np.sum(y_hat-y)/num_train
returny_hat,cost,dw,db
deflinear_train(X,y,learning_rate,epochs):
#参数初始化
w,b=initialize_params(X.shape[1])
loss_list=[]
foriinrange(epochs):
#计算当前的预测值、损失和梯度
y_hat,loss,dw,db=logistic(X,y,w,b)
loss_list.append(loss)
#基于梯度下降的参数更新
w+=-learning_rate*dw
b+=-learning_rate*db
#打印迭代次数和损失
ifi%10000==0:
print("epoch%dloss%f"%(i,loss))
#保存参数
params={
'w':w,
'b':b
}
#保存梯度
grads={
'dw':dw,
'db':db
}
returnloss_list,loss,params,grads
defpredict(X,params):
w=params['w']
b=params['b']
y_pred=sigmoid(np.dot(X,w)+b)
returny_pred
if__name__=="__main__":
#生成数据
X,labels=make_classification(n_samples=100,
n_features=2,
n_informative=2,
n_redundant=0,
random_state=1,
n_clusters_per_class=2)
print(X.shape)
print(labels.shape)
#生成伪随机数
rng=np.random.RandomState(2)
X+=2*rng.uniform(size=X.shape)
#划分训练集和测试集
offset=int(X.shape[0]*0.9)
X_train,y_train=X[:offset],labels[:offset]
X_test,y_test=X[offset:],labels[offset:]
y_train=y_train.reshape((-1,1))
y_test=y_test.reshape((-1,1))
print('X_train=',X_train.shape)
print('y_train=',y_train.shape)
print('X_test=',X_test.shape)
print('y_test=',y_test.shape)
#训练
loss_list,loss,params,grads=linear_train(X_train,y_train,0.01,100000)
print(params)
#预测
y_pred=predict(X_test,params)
print(y_pred[:10])
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