12 簡單線性回歸的實現

這一篇部落格主要想要實現一下之前推導的簡單的線性回歸演算法。

下面我們對上述過程進行封裝：

import numpy as np

class
******linearregression1
:    def __init__
(self):""
"初始化 ****** linear regression 模型"
""        self.a_ = none
self.b_ = none
def fit
(self, x_train, y_train):""
"根據訓練資料集 x_train, y_train訓練模型"
""        assert x_train.ndim ==
1, \
"****** linear regression can only solve single feature training data"
assert len
(x_train)
==len
(y_train)
, \            "the size of x_train must be equal to the size of y_train"
x_mean = np.
mean
(x_train)
y_mean = np.
mean
(y_train)
num =
0.0        d =
0.0for x, y in zip
(x_train, y_train)
:            num +
=(x - x_mean)
*(y - y_mean)
d +=(x - x_mean)**
2        self.a_ = num / d
self.b_ = y_mean - self.a_ * x_mean
return self
def predict
(self, x_predict)
: # x_predict 為乙個向量
"""給定**資料集x_predict, 返回表示x_predict的結果向量"
""        assert x_predict.ndim ==
1, \
"****** linear regression can only solve single feature training data"
assert self.a_ is not none and self.b_ is not none, \
"must fit before predict!"
return np.
array
([self.
_predict
(x)for x in x_predict]
)    def _predict
(self, x_single)
: # x_single 為乙個數
"""給定單個**資料x_single, 返回x_single的**結果值"
""return self.a_ * x_single + self.b_
def __repr__
(self)
:return
"******linearregression1()"

具體**見 12-簡單線性回歸的實現

TensorFlow 實現簡單線性回歸

import tensorflow as tf import numpy as np import random create data x data np.random.rand 100 astype np.float32 使用numpy生成100個隨機點 noise np.random.norm...

簡單線性回歸

真實值 y theta x varepsilon 值 hat theta x varepsilon 為誤差 項，服從 均值為0 方差為 為誤差項，服從均值為0，方差為 為誤差項，服 從均值為 0，方差 為 sigma 的高斯分布。已知若干樣本，可以得到若干 varepsilon 值，根 據極大似 然...

簡單線性回歸

資料預處理 data student data 1 刪除缺失值 lm data na.omit data 散點圖 plot height,weight,data data,main scatter plot col 1,pch col為顏色，pch為形狀 箱線圖 boxplot height wei...