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    [X=left| egin{matrix} x_{11} & x_{12} & cdots & x_{1d}\ x_{21} & x_{22} & cdots & x_{2d}\ vdots & vdots & ddots & vdots \ x_{11} & x_{12} & cdots & x_{1d}\ end{matrix} ight| ]

    [egin{matrix} 1 & x & x^2\ 1 & y & y^2\ 1 & z & z^2\ end{matrix} ]

    [left{ egin{array}{c} a_1x+b_1y+c_1z=d_1\ a_2x+b_2y+c_2z=d_2\ a_3x+b_3y+c_3z=d_3 end{array} ight} ]

    [X=egin{pmatrix} 0&1&1\ 1&1&0\ 1&0&1\ end{pmatrix} ]

    1. 希腊字母表

    Sigma: (Sigma)

    2. 上下标、根号、省略号

    • 下标:_ x^2 (Longrightarrow) $ x^2$

    • 上标:^ x_i(Longrightarrow) (x_i)

    • 根号:sqrt | ysqrt{x}(Longrightarrow) (ysqrt{x})

    • 省略号:

      dots (Longrightarrowdots)

      cdots (Longrightarrowcdots)

      ddots (Longrightarrowddots)

    • 括号

    3. 运算符

    • 求和: sum_1^n(Longrightarrow) (sum_1^n)
    • 积分:int_1^n (Longrightarrow) (int_1^n)
    • 极限:lim_{x o infty}(Longrightarrow) (lim_{x o infty})
    • 分数:frac{2}{3} (Longrightarrow) $frac{2}{3} $

    4. 箭头

    leftarrow对应 (leftarrow)

    5. 分段函数

    f(n)=
    	egin{cases}
    		n/2, & 	ext{if $n$ is even}\
    		3n+1,& 	ext{if $n$ is odd}
    	end{cases}
    

    [f(n)= egin{cases} n/2, & ext{if $n$ is even}\ 3n+1,& ext{if $n$ is odd} end{cases} ]

    6. 方程组

    left.
      left{
        egin{array}{c}
          a_1x+b_1y+c_1z=d_1\
          a_2x+b_2y+c_2z=d_2\
          a_3x+b_3y+c_3z=d_3
        end{array}
      
    ight.
      
    ight>
    

    [left. left{ egin{array}{c} a_1x+b_1y+c_1z=d_1\ a_2x+b_2y+c_2z=d_2\ a_3x+b_3y+c_3z=d_3 end{array} ight. ight> ]

    7.矩阵

    7.1 基本语法

    • 起始标记 egin{matrix},结束标记 end{matrix}
    • 每一行末尾标记 \
    • 行间元素之间用 & 分隔。
    egin{matrix}
    0&1&1\
    1&1&0\
    1&0&1\
    end{matrix}
    

    [egin{matrix} 0&1&1\ 1&1&0\ 1&0&1\ end{matrix} ]

    7.2 矩阵边框

    • 在起始、结束标记用下列词替换 matrix
    • pmatrix:小括号边框
    • bmatrix:中括号边框
    • Bmatrix:大括号边框
    • vmatrix:单竖线边框
    • Vmatrix:双竖线边框
    egin{vmatrix}
    0&1&1\
    1&1&0\
    1&0&1\
    end{vmatrix}
    

    [egin{vmatrix} 0&1&1\ 1&1&0\ 1&0&1\ end{vmatrix} ]

    7.3 省略元素

    • 横省略号:cdots
    • 竖省略号:vdots
    • 斜省略号:ddots
    egin{bmatrix}
    {a_{11}}&{a_{12}}&{cdots}&{a_{1n}}\
    {a_{21}}&{a_{22}}&{cdots}&{a_{2n}}\
    {vdots}&{vdots}&{ddots}&{vdots}\
    {a_{m1}}&{a_{m2}}&{cdots}&{a_{mn}}\
    end{bmatrix}
    

    [egin{bmatrix} {a_{11}}&{a_{12}}&{cdots}&{a_{1n}}\ {a_{21}}&{a_{22}}&{cdots}&{a_{2n}}\ {vdots}&{vdots}&{ddots}&{vdots}\ {a_{m1}}&{a_{m2}}&{cdots}&{a_{mn}}\ end{bmatrix} ]

    7.4 阵列

    • 需要array环境:起始、结束处以{array}声明
    • 对齐方式:在{array}后以{}逐行统一声明
    • 左对齐:l 居中:c 右对齐:r
    • 竖直线:在声明对齐方式时,插入 | 建立竖直线
    • 插入水平线:hline
    egin{array}{c|lll}
    {↓}&{a}&{b}&{c}\
    hline
    {R_1}&{c}&{b}&{a}\
    {R_2}&{b}&{c}&{c}\
    end{array}
    

    [egin{array}{c|lll} {↓}&{a}&{b}&{c}\ hline {R_1}&{c}&{b}&{a}\ {R_2}&{b}&{c}&{c}\ end{array} ]

    • 需要array环境:起始、结束处以{array}声明

    7.5 等号上下文字

    arrowname[sub-script]{super-script}
    
    • arrowname具体见下面,等号名称
    • sub-script 代表等号下面内容
    • super-script 代表等号上面内容

    8.常用公式

    8.1 线性模型

    h(	heta) = sum_{j=0} ^n 	heta_j x_j
    

    [h( heta) = sum_{j=0} ^n heta_j x_j ]

    8.2 均方误差

    J(	heta) = frac{1}{2m}sum_{i=0}^m(y^i - h_	heta(x^i))^2
    

    [J( heta) = frac{1}{2m}sum_{i=0}^m(y^i - h_ heta(x^i))^2 ]

    8.3 求积

    H_c=sum_{l_1+dots +l_p}prod^p_{i=1} inom{n_i}{l_i}
    

    [H_c=sum_{l_1+dots +l_p}prod^p_{i=1} inom{n_i}{l_i} ]

    8.4 批梯度下降

    egin{align}
    	frac{partial J(	heta)}{partial	heta_j}
    	& = -frac1msum_{i=0}^m(y^i - h_	heta(x^i)) frac{partial}{partial	heta_j}(y^i-h_	heta(x^i))\
    	& = -frac1msum_{i=0}^m(y^i-h_	heta(x^i)) frac{partial}{partial	heta_j}(sum_{j=0}^n	heta_j x^i_j-y^i)\
    	&=-frac1msum_{i=0}^m(y^i -h_	heta(x^i)) x^i_j
    end{align}
    

    [frac{partial J( heta)}{partial heta_j} = -frac1msum_{i=0}^m(y^i - h_ heta(x^i))x^i_j ]

    [egin{align} frac{partial J( heta)}{partial heta_j} & = -frac1msum_{i=0}^m(y^i - h_ heta(x^i)) frac{partial}{partial heta_j}(y^i-h_ heta(x^i))\ & = -frac1msum_{i=0}^m(y^i-h_ heta(x^i)) frac{partial}{partial heta_j}(sum_{j=0}^n heta_j x^i_j-y^i)\ &=-frac1msum_{i=0}^m(y^i -h_ heta(x^i)) x^i_j end{align} ]


    引用

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  • 原文地址:https://www.cnblogs.com/geoffreyone/p/14992044.html
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