请给我具体完整的代码

为了提供完整且详细的代码，我会展示如何使用Python和sklearn库实现Logistic回归算法的全部流程，包括数据生成、模型训练、预测以及结果可视化。请注意，由于绘图和损失函数历史记录的细节会因库的不同而略有差异，以下代码示例主要关注核心算法和关键部分。

首先，我们需要导入必要的库：

import numpy as np
import pandas as pd
from sklearn.datasets import make_blobs
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, confusion_matrix
import matplotlib.pyplot as plt

接下来，我们定义数据生成和处理函数：

def generate_two_gaussians(n_samples, n_features, means, covariances):
    X = []
    y = []

    for i in range(len(means)):
        X.append(np.random.multivariate_normal(means[i], covariances[i], int(n_samples / len(means))))
        y.extend([i] * int(n_samples / len(means)))

    X = np.concatenate(X)
    y = np.array(y).reshape(-1, 1)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    return X_train, X_test, y_train, y_test

# 参数设置
n_samples = 400
n_features = 2
means = [[-5, 0], [0, 5]]
covariances = [np.eye(n_features)] * 2

接着，创建并训练模型：

def train_and_evaluate_logistic_regression(X_train, y_train):
    lr = LogisticRegression(max_iter=1000)
    lr.fit(X_train, y_train)
    
    # 训练集上的分类准确率
    y_pred_train = lr.predict(X_train)
    acc_train = accuracy_score(y_train, y_pred_train)
    
    # 测试集上的分类
    y_pred_test = lr.predict(X_test)
    acc_test = accuracy_score(y_test, y_pred_test)

    print(f"Training accuracy: {acc_train}")
    print(f"Testing accuracy: {acc_test}")

    return lr

# 运行
X_train, X_test, y_train, y_test = generate_two_gaussians(n_samples, n_features, means, covariances)
lr_model = train_and_evaluate_logistic_regression(X_train, y_train)

最后，对测试集进行预测并绘制决策边界：

def plot_decision_boundary(X, y, model):
    # 预测所有测试点的概率
    probas = model.predict_proba(X)

    # 可视化数据
    plt.figure(figsize=(8, 6))
    plt.scatter(X[:, 0], X[:, 1], c=y, cmap='viridis', edgecolor='black')

    # 添加决策边界
    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.01), np.arange(y_min, y_max, 0.01))
    grid = np.c_[xx.ravel(), yy.ravel()]
    z = model.predict(grid)
    z = z.reshape(xx.shape)

    plt.contourf(xx, yy, z, cmap='viridis', alpha=0.3)

    plt.xlabel("Feature 1")
    plt.ylabel("Feature 2")
    plt.title("Decision Boundary")

plot_decision_boundary(X_test, y_test, lr_model)

至于损失函数的变化，`sklearn`库并没有直接提供损失函数的图形化显示，你可以选择自定义记录损失并在训练过程中保存，例如使用`lr.loss_`。如果要查看损失函数的变化，你可能需要在训练过程中保存每轮迭代后的`loss_`值，然后用`plt.plot()`来画出。