标签:一个 oba __name__ code == utf-8 from 参数 reg
对系数做约束。
# encoding: utf-8
import numpy as np
from scipy import optimize
class nnegLR(object):
def __init__(self, lambdo=1, fit_intercept=True):
self.coef_, self.intercept_ = None, None
self.lambdo = lambdo
self.intercept = fit_intercept
def fit(self, X, y, C=1):
‘‘‘
X 样本矩阵, 一行代表一个样本, y
C is weight of each sample, 大小同y
‘‘‘
sety = set(y)
if sety == set([0, 1]):
y[y != 1] = -1
N, D = X.shape
if self.intercept is True:
X = np.concatenate((X, np.ones((N, 1))), axis=1)
def func_grad(w):
# 计算函数值
yz = y * (X @ w)
f = np.sum(self.lambdo/2*(w*w))
# 考虑精度
f += np.sum(
C * np.where(yz >= 0, np.log(1+np.exp(-yz)), -yz + np.log(1+np.exp(yz)))
)
# 计算梯度
z = 1 / (1 + np.exp(-yz))
g = self.lambdo * w + X.T @ (C*(z-1)*y)
return f, g
# 利用l_bfgs_b算法优化
d = D+1 if self.intercept is True else D
w0 = np.ones(d)
bounds = [(0, np.inf)] * D
if self.intercept is True:
bounds.append([-np.inf, np.inf])
w, f, d = optimize.fmin_l_bfgs_b(func_grad, w0, bounds=bounds)
# 输出参数
self.coef_ = w[:D]
self.intercept_ = w[-1] if self.intercept is True else None
def predict(self, X):
assert self.coef_ is not None
z = X @ self.coef_
if self.intercept is True:
z += self.intercept_
p = 1 / (1 + np.exp(-z))
return p
if __name__ == ‘__main__‘:
from pylab import r_, randn, ones, zeros
X = r_[randn(1000, 2)+1, randn(100, 2)-1]
y = r_[ones((1000)), zeros((100))]
lr = nnegLR(lambdo=0.001)
lr.fit(X, y)
print(lr.coef_)
from sklearn import linear_model
logreg = linear_model.LogisticRegression(C=1e3)
logreg.fit(X, y)
print(logreg.coef_)
# print(lr.predict(X))
# print(logreg.predict_proba(X))
标签:一个 oba __name__ code == utf-8 from 参数 reg
原文地址:https://www.cnblogs.com/bregman/p/12896082.html