import numpyfrom sklearn import linear_model# 准备特征数据和标签：X = numpy.array([3.78, 2.44, 2.09, 0.14, 1.72, 1.65, 4.92, 4.37, 4.96, 4.52, 3.69, 5.88]).reshape(-1,1)y = numpy.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1])# 创建并训练逻辑回归模型：logr = linear_model.LogisticRegression()logr.fit(X, y)# 定义将逻辑回归结果转换为概率的函数：def logit2prob(logr, X): # 计算对数几率 log_odds = logr.coef_ * X + logr.intercept_ # 计算几率 odds = numpy.exp(log_odds) # 计算概率 probability = odds / (1 + odds) return probability# 输出样本预测概率：print(logit2prob(logr, X))