From b6a05a8a78e4a9944387c0e6d2f1486bbc7e5772 Mon Sep 17 00:00:00 2001
From: tianxuzhang <987095891@qq.com>
Date: Sun, 3 Dec 2023 23:15:45 +0800
Subject: [PATCH] =?UTF-8?q?feat:=20=E5=A4=9A=E4=BB=BB=E5=8A=A1lasso?=
 =?UTF-8?q?=E5=9B=9E=E5=BD=92?=
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---
 ...346\200\247\345\233\236\345\275\222.ipynb" | 70 +++++++++++++++++++
 ...345\222\214\350\214\203\346\225\260.ipynb" | 28 ++++++--
 2 files changed, 91 insertions(+), 7 deletions(-)

diff --git "a/docs/\345\233\236\345\275\222/\346\255\243\345\210\231\345\214\226\347\272\277\346\200\247\345\233\236\345\275\222.ipynb" "b/docs/\345\233\236\345\275\222/\346\255\243\345\210\231\345\214\226\347\272\277\346\200\247\345\233\236\345\275\222.ipynb"
index 7d08994..28d38db 100644
--- "a/docs/\345\233\236\345\275\222/\346\255\243\345\210\231\345\214\226\347\272\277\346\200\247\345\233\236\345\275\222.ipynb"
+++ "b/docs/\345\233\236\345\275\222/\346\255\243\345\210\231\345\214\226\347\272\277\346\200\247\345\233\236\345\275\222.ipynb"
@@ -147,6 +147,76 @@
     "print(best_alpha_lassolars)\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "8c102d8d",
+   "metadata": {},
+   "source": [
+    "### 多任务lasso回归\n",
+    "\n",
+    "多任务Lasso（Multi-Task Lasso）是一种用于多任务回归的正则化方法。它是对Lasso回归的扩展，可以同时处理多个相关联的目标变量。\n",
+    "\n",
+    "在多任务Lasso中，目标函数的表达式如下：\n",
+    "\n",
+    "$\\underset{W}{\\text{minimize }} \\frac{1}{2n_{samples}} ||X W - Y||F ^ 2 + \\alpha ||W||{21}$\n",
+    "\n",
+    "其中，$X$ 是输入特征矩阵，$Y$ 是多个目标变量的观测值矩阵，**$W$** 是模型的系数矩阵，(\\alpha) 是正则化参数。\n",
+    "\n",
+    "* 第一项 $\\frac{1}{2n_{samples}} ||X W - Y||F ^ 2$ 衡量了模型预测值与真实观测值之间的误差。这里使用了Frobenius范数来计算误差的平方和，同时将其除以 (2n{samples}) 进行归一化，其中 (n_{samples}) 是样本数量。\n",
+    "\n",
+    "* 第二项 $\\alpha ||W||_{21}$ 是指系数矩阵 $W$ 的 $L2,1$ 范数，也称为分组Lasso范数。$L2,1$ 范数将每个任务之间的系数向量视为一个分组，并对每个分组的L2范数进行惩罚。这鼓励模型选择相同或相似的特征子集来适应多个相关任务。\n",
+    "\n",
+    "通过调整正则化参数 $\\alpha$ 的大小，可以控制模型的拟合程度和稀疏性。\n",
+    "\n",
+    "多任务Lasso适用于在多个相关任务上进行回归建模，并且鼓励共享特征选择。它可以通过联合优化多个相关任务来提高模型的性能，并且可以识别出对所有任务都有影响的重要特征。\n",
+    "\n",
+    "在Scikit-learn中，可以使用MultiTaskLasso类来实现多任务Lasso回归，并进行参数估计和模型拟合。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "2bb9e85b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "模型系数：\n",
+      "[[ 0.02731864  0.08910204  0.03778079  0.00933277 -0.01963377 -0.00207699\n",
+      "   0.02826692  0.00827997 -0.12920136 -0.04683641]\n",
+      " [-0.06873417 -0.0625006   0.02790663 -0.00367414  0.11322009  0.00652524\n",
+      "  -0.00146783 -0.05008547 -0.02371156  0.03941871]\n",
+      " [-0.02352807 -0.08121243  0.06746598  0.02794926 -0.04672092 -0.03314402\n",
+      "   0.02533989 -0.03688576  0.08089949  0.032083  ]\n",
+      " [ 0.029961   -0.03568944  0.07128624 -0.01492458  0.0525043   0.09876974\n",
+      "   0.10219089 -0.01597094 -0.02868559  0.05300582]\n",
+      " [-0.03094184 -0.12592359 -0.17639916  0.00561484 -0.01498715 -0.06237257\n",
+      "  -0.00310203  0.01522918  0.07753082  0.02590595]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import MultiTaskLasso\n",
+    "import numpy as np\n",
+    "\n",
+    "# 生成样本数据\n",
+    "np.random.seed(0)\n",
+    "n_samples, n_features = 100, 10\n",
+    "X = np.random.randn(n_samples, n_features)\n",
+    "Y = np.random.randn(n_samples, 5)  # 生成5个相关联的目标变量\n",
+    "\n",
+    "# 创建MultiTaskLasso对象并进行拟合\n",
+    "alpha = 0.1  # 正则化参数\n",
+    "multi_task_lasso = MultiTaskLasso(alpha=alpha)\n",
+    "multi_task_lasso.fit(X, Y)\n",
+    "\n",
+    "# 输出模型系数\n",
+    "print(\"模型系数：\")\n",
+    "print(multi_task_lasso.coef_)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "12cc34bc",
diff --git "a/docs/\345\233\236\345\275\222/\350\267\235\347\246\273\345\222\214\350\214\203\346\225\260.ipynb" "b/docs/\345\233\236\345\275\222/\350\267\235\347\246\273\345\222\214\350\214\203\346\225\260.ipynb"
index 6d07335..5509ca6 100644
--- "a/docs/\345\233\236\345\275\222/\350\267\235\347\246\273\345\222\214\350\214\203\346\225\260.ipynb"
+++ "b/docs/\345\233\236\345\275\222/\350\267\235\347\246\273\345\222\214\350\214\203\346\225\260.ipynb"
@@ -250,25 +250,39 @@
     "\n",
     "以下是几种常见的范数及其公式：\n",
     "\n",
-    "L1范数（曼哈顿距离或绝对值范数）：\n",
+    "* L1范数（曼哈顿距离或绝对值范数）：\n",
+    "\n",
     "对于一个n维向量x，L1范数表示为：\n",
     "$$|x|_1 = \\sum_{i=1}^{n} |x_i|$$\n",
     "\n",
-    "L2范数（欧几里得范数）：\n",
+    "* L2范数（欧几里得范数）：\n",
+    "\n",
     "对于一个n维向量x，L2范数表示为：\n",
     "$$|x|_2 = \\sqrt{\\sum_{i=1}^{n} |x_i|^2}$$\n",
     "\n",
-    "Lp范数（p范数）：\n",
+    "* Lp范数（p范数）：\n",
+    "\n",
     "对于一个n维向量x，Lp范数表示为：\n",
     "$$|x|_p = \\left(\\sum_{i=1}^{n} |x_i|^p\\right)^{\\frac{1}{p}}$$\n",
     "\n",
-    "无穷范数（最大值范数）：\n",
+    "*  无穷范数（最大值范数）：\n",
+    "\n",
     "对于一个n维向量x，无穷范数表示为：\n",
     "$$|x|_{\\infty} = \\max(|x_1|, |x_2|, …, |x_n|)$$\n",
     "\n",
-    "矩阵Frobenius范数：\n",
+    "*  矩阵Frobenius范数：\n",
+    "\n",
+    "弗罗贝尼乌斯\n",
+    "\n",
     "对于一个m×n矩阵A，Frobenius范数表示为：\n",
-    "$$|A|_F = \\sqrt{\\sum_{i=1}^{m}\\sum_{j=1}^{n} |a_{ij}|^2}$$"
+    "$$|A|_F = \\sqrt{\\sum_{i=1}^{m}\\sum_{j=1}^{n} |a_{ij}|^2}$$\n",
+    "\n",
+    "\n",
+    "*  L2,1范数（多任务学习的正则化项）：\n",
+    "\n",
+    "对于一个m×n的系数矩阵 $A$，L2,1范数定义为各个任务之间的L2范数的和。其表达式为：\n",
+    "\n",
+    "$$ |A|{2,1} = \\sum{j=1}^{m} \\sqrt{\\sum_{i=1}^{n} w_{ij}^2} $$"
    ]
   },
   {
@@ -309,7 +323,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,