在python脚本中key = b'\11\11\22\22'和key = b'\x11\x11\x22\x22'有区别吗
时间: 2023-11-11 12:07:31 浏览: 53
在Python中,单引号和双引号都可以用来表示字符串,但是在表示二进制数据时,需要使用反斜杠(\)来转义特殊字符。在这个问题中,key = b'\11\11\22\22'和key = b'\x11\x11\x22\x22'都是表示16进制数0x11112222,但是前者使用了八进制转义,而后者使用了十六进制转义。因此,这两种表示方式是等价的,没有区别。
相关问题
对X11 X12 X13 X14 X15 X21 X22 X23 X24 X25 X31 X32 X33 X41 X42 X43 X51进行归一化的stata代码
以下是对X11 X12 X13 X14 X15 X21 X22 X23 X24 X25 X31 X32 X33 X41 X42 X43 X51进行归一化的Stata代码:
```
* 定义变量的最小值和最大值
local min_x11 = r(min) if var == "X11"
local max_x11 = r(max) if var == "X11"
local min_x12 = r(min) if var == "X12"
local max_x12 = r(max) if var == "X12"
local min_x13 = r(min) if var == "X13"
local max_x13 = r(max) if var == "X13"
local min_x14 = r(min) if var == "X14"
local max_x14 = r(max) if var == "X14"
local min_x15 = r(min) if var == "X15"
local max_x15 = r(max) if var == "X15"
local min_x21 = r(min) if var == "X21"
local max_x21 = r(max) if var == "X21"
local min_x22 = r(min) if var == "X22"
local max_x22 = r(max) if var == "X22"
local min_x23 = r(min) if var == "X23"
local max_x23 = r(max) if var == "X23"
local min_x24 = r(min) if var == "X24"
local max_x24 = r(max) if var == "X24"
local min_x25 = r(min) if var == "X25"
local max_x25 = r(max) if var == "X25"
local min_x31 = r(min) if var == "X31"
local max_x31 = r(max) if var == "X31"
local min_x32 = r(min) if var == "X32"
local max_x32 = r(max) if var == "X32"
local min_x33 = r(min) if var == "X33"
local max_x33 = r(max) if var == "X33"
local min_x41 = r(min) if var == "X41"
local max_x41 = r(max) if var == "X41"
local min_x42 = r(min) if var == "X42"
local max_x42 = r(max) if var == "X42"
local min_x43 = r(min) if var == "X43"
local max_x43 = r(max) if var == "X43"
local min_x51 = r(min) if var == "X51"
local max_x51 = r(max) if var == "X51"
* 进行归一化
replace X11 = (X11 - `min_x11') / (`max_x11' - `min_x11') if var == "X11"
replace X12 = (X12 - `min_x12') / (`max_x12' - `min_x12') if var == "X12"
replace X13 = (X13 - `min_x13') / (`max_x13' - `min_x13') if var == "X13"
replace X14 = (X14 - `min_x14') / (`max_x14' - `min_x14') if var == "X14"
replace X15 = (X15 - `min_x15') / (`max_x15' - `min_x15') if var == "X15"
replace X21 = (X21 - `min_x21') / (`max_x21' - `min_x21') if var == "X21"
replace X22 = (X22 - `min_x22') / (`max_x22' - `min_x22') if var == "X22"
replace X23 = (X23 - `min_x23') / (`max_x23' - `min_x23') if var == "X23"
replace X24 = (X24 - `min_x24') / (`max_x24' - `min_x24') if var == "X24"
replace X25 = (X25 - `min_x25') / (`max_x25' - `min_x25') if var == "X25"
replace X31 = (X31 - `min_x31') / (`max_x31' - `min_x31') if var == "X31"
replace X32 = (X32 - `min_x32') / (`max_x32' - `min_x32') if var == "X32"
replace X33 = (X33 - `min_x33') / (`max_x33' - `min_x33') if var == "X33"
replace X41 = (X41 - `min_x41') / (`max_x41' - `min_x41') if var == "X41"
replace X42 = (X42 - `min_x42') / (`max_x42' - `min_x42') if var == "X42"
replace X43 = (X43 - `min_x43') / (`max_x43' - `min_x43') if var == "X43"
replace X51 = (X51 - `min_x51') / (`max_x51' - `min_x51') if var == "X51"
```
在这个代码中,我们首先定义了每个变量的最小值和最大值,然后使用Stata的replace命令,将每个变量的值归一化到0和1之间。请注意,这个代码假定你的变量名是X11、X12、X13、X14、X15、X21、X22、X23、X24、X25、X31、X32、X33、X41、X42、X43和X51。如果你的变量名不同,你需要相应地修改代码。
3根K线A有两个特征方向和长度如下 , 另外3根K线B, 用python实现计算他们的向量近似度,长度有意义,不用归一化。 (x11,x12) (x21,x22) (x31,x32) K线 B (y11,y12) (y21,y22) (y31,y32)
可以使用numpy库来计算向量的近似度,具体实现步骤如下:
1. 将每组K线数据转换为向量,向量的长度为两点之间的距离,即:
```
vector_A_1 = [x12 - x11, x22 - x21]
vector_A_2 = [x22 - x21, x32 - x31]
vector_A_3 = [x32 - x31, x22 - x21]
vector_B_1 = [y12 - y11, y22 - y21]
vector_B_2 = [y22 - y21, y32 - y31]
vector_B_3 = [y32 - y31, y22 - y21]
```
2. 计算每组向量的夹角余弦值,即:
```
cosine_similarity_1 = np.dot(vector_A_1, vector_B_1) / (np.linalg.norm(vector_A_1) * np.linalg.norm(vector_B_1))
cosine_similarity_2 = np.dot(vector_A_2, vector_B_2) / (np.linalg.norm(vector_A_2) * np.linalg.norm(vector_B_2))
cosine_similarity_3 = np.dot(vector_A_3, vector_B_3) / (np.linalg.norm(vector_A_3) * np.linalg.norm(vector_B_3))
```
3. 计算向量的近似度,可以取每个夹角余弦值的平均值,即:
```
vector_similarity = (cosine_similarity_1 + cosine_similarity_2 + cosine_similarity_3) / 3
```
这样就可以得到三组K线向量的近似度了。
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