用熟悉的编程语言编写一个2*2的混淆矩阵计算器,并计算f1

混淆矩阵是一种统计工具，常用于评估分类模型的性能，特别是在二分类任务中。它展示了实际类别（True Positives, True Negatives, False Positives, False Negatives）与预测结果之间的对应情况。下面是一个简单的Python示例，使用字典来模拟2x2混淆矩阵，并计算F1分数：

def confusion_matrix(actual, predicted):
    matrix = {
        "TP": 0,
        "TN": 0,
        "FP": 0,
        "FN": 0
    }
    
    for i in range(len(actual)):
        if actual[i] == predicted[i]:
            if actual[i] == 1:  # 如果真实值为正
                matrix["TP"] += 1
            else:
                matrix["TN"] += 1
        else:
            if actual[i] == 1:  # 真实值为正，预测值为负
                matrix["FN"] += 1
            else:  # 预测值为正，真实值为负
                matrix["FP"] += 1
    
    return matrix

# 示例数据
actual = [0, 1, 1, 0]
predicted = [0, 0, 1, 1]

matrix = confusion_matrix(actual, predicted)
print("Confusion Matrix:")
for category, count in matrix.items():
    print(f"{category}: {count}")

def f1_score(precision, recall):
    if precision + recall == 0:
        return 0
    else:
        return (2 * precision * recall) / (precision + recall)

# 计算精度（Precision）、召回率（Recall）
precision = matrix["TP"] / (matrix["TP"] + matrix["FP"])
recall = matrix["TP"] / (matrix["TP"] + matrix["FN"])

f1 = f1_score(precision, recall)
print("\nF1 Score:", f1)