线性回归的原理及Python实现

ml20170502 2023-06-11 15:43:39

1.5 求近似解

1. 使用MSE作为损失函数L L = \large\frac{1}{m}\normalsize\sum_{1}^{m}(Y_{i} - \hat Y_{i})^2 2. 已知 \hat Y=WX + b

3. 对w求偏导，得 \large\frac{\mathrm{d}L}{\mathrm{d}W}\normalsize= -\large\frac{2}{m}\normalsize\sum_{1}^{m}(Y_{i} - WX_{i} - b)X_{i}

4. 对b求偏导，得 \large\frac{\mathrm{d}L}{\mathrm{d}b}\normalsize= -\large\frac{2}{m}\normalsize\sum_{1}^{m}(Y_{i} - WX_{i} - b)

2.1 创建RegressionBase类

class RegressionBase(object):    def __init__(self):        self.bias = None        self.weights = None

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h1,h2,h3,h4,h5,h6{font-size:100%}
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small{font-size:12px}
ol,ul{list-style:none}
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a:hover{text-decoration:underline}
legend{color:#000}
fieldset,img{border:0}
button,input,select,textarea{font-size:100%}
table{border-collapse:collapse;border-spacing:0}
img{-ms-interpolation-mode:bicubic}
textarea{resize:vertical}
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.error{color:red;font-size:12px}
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.clearfix:after{content:'\20';display:block;height:0;clear:both}
.clearfix{zoom:1}
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.wordwrap{word-break:break-all;word-wrap:break-word}
.s-yahei{font-family:arial,'Microsoft Yahei','微软雅黑'}
pre.wordwrap{white-space:pre-wrap}
body{text-align:center;background:#fff;width:100%}
body,form{position:relative;z-index:0}
td{text-align:left}
img{border:0}
#s_wrap{position:relative;z-index:0;min-width:1000px}
#wrapper{height:100%}
.s-ps-sug .sug_ala{border-bottom:1px solid #e6e6e6}

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