matlab万年历函数 w=wnl(x,y,z) 实现计算x年y月z日为星期几返回给w的功能。(注:
时间: 2023-09-03 09:03:41 浏览: 56
matlab中的万年历函数wnl(x,y,z)实现了计算给定日期(年月日)是星期几,并将结果返回给变量w的功能。
该函数的参数为x、y和z,其中x表示年份,y表示月份,z表示日期。函数首先会执行一系列的计算来确定给定日期是星期几。
函数的具体实现可以采用一些日期计算的算法,比如基姆拉尔森计算公式(Kim Larsen Calculation Formula)或者蔡勒公式(Zeller's Congruence)等。这些算法可以根据给定的年月日计算出该日期是星期几。
计算过程中,首先会对输入的年份和月份进行一些判断和处理,比如检查是否为闰年、确定月份的天数等。然后,利用计算公式将年份、月份和日期转换为一个数字,然后通过取模运算确定给定日期是一周中的第几天,从而确定星期几。
最后,将得到的结果返回给变量w,这样用户就可以通过变量w获取到给定日期是星期几的信息。
需要注意的是,函数wnl(x,y,z)只能计算公元后的日期,并且输入的年份范围有限。在实际使用时,应根据需要进行适当的判断和处理,以确保输入的日期在合理范围内,并且得到正确的计算结果。
相关问题
INSERT INTO `QHDATA_THEME.DB_DTRK_CZRKDT` (`RID`, `LDBM`, `TJS`, `XZQHDM`, `XB0`, `XB1`, `MNL0`, `MNL1`, `MNL2`, `MNL3`, `MNL4`, `MNL5`, `MNL6`, `MNL7`, `MNL8`, `MNL9`, `MNL10`, `MNL11`, `MNL12`, `MNL13`, `MNL14`, `MNL15`, `MNL16`, `MNL17`, `MNL18`, `MNL19`, `MNL20`, `MNL21`, `WNL0`, `WNL1`, `WNL2`, `WNL3`, `WNL4`, `WNL5`, `WNL6`, `WNL7`, `WNL8`, `WNL9`, `WNL10`, `WNL11`, `WNL12`, `WNL13`, `WNL14`, `WNL15`, `WNL16`, `WNL17`, `WNL18`, `WNL19`, `WNL20`, `WNL21`, `MYE`, `WYE`, `MET`, `WET`, `MWCN`, `WWCN`, `MLN`, `WLN`, `LNWHQ`, `LNXQJY`, `LNXX`, `LNCZ`, `LNGZ`, `LNDXZK`, `LNDXBK`, `LNSSYJS`, `LNBSYJS`, `MLN2`, `WLN2`, `LNWHQ2`, `LNXQJY2`, `LNXX2`, `LNCZ2`, `LNGZ2`, `LNDXZK2`, `LNDXBK2`, `LNSSYJS2`, `LNBSYJS2`, `MLN3`, `WLN3`, `LNWHQ3`, `LNXQJY3`, `LNXX3`, `LNCZ3`, `LNGZ3`, `LNDXZK3`, `LNDXBK3`, `LNSSYJS3`, `LNBSYJS3`, `WHQ`, `XQJY`, `XX`, `CZ`, `GZ`, `DXZK`, `DXBK`, `SSYJS`, `BSYJS`, `SSH`, `FSH`, `lng`, `lat`, `is_qianhai`, `DISTRICT_NAME`, `DISTRICT_CODE`, `STREET_NAME`, `STREET_CODE`, `COMMUNITY_NAME`, `COMMUNITY_CODE`, `occur_period`, `occur_period_year`, `occur_period_month`, `org_id`, `org_name`, `area_code`, `data_time`, `TJNY`) VALUES ('933f35f92e5d4b19a7f9334452fe5a99', '4403060000000000000', 54, '440306000000', 23, 31, 0, 0, 0, 0, 0, 7, 10, 3, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 11, 14, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 5, 8, 36, 2, 0, 16, 38, '113.861892600000004', '22.580441539999999', '1', '宝安区', '440306000000', '西乡街道', '440306000000', '盐田社区', '440306000000', 202212, 2022, 12, NULL, NULL, NULL, '2023-06-01 00:00:00', '2022-12-01 00:00:00');
以下是转换后的达梦数据库格式的 INSERT 语句:
```
INSERT INTO "QHDATA_THEME.DB_DTRK_CZRKDT" ("RID", "LDBM", "TJS", "XZQHDM", "XB0", "XB1", "MNL0", "MNL1", "MNL2", "MNL3", "MNL4", "MNL5", "MNL6", "MNL7", "MNL8", "MNL9", "MNL10", "MNL11", "MNL12", "MNL13", "MNL14", "MNL15", "MNL16", "MNL17", "MNL18", "MNL19", "MNL20", "MNL21", "WNL0", "WNL1", "WNL2", "WNL3", "WNL4", "WNL5", "WNL6", "WNL7", "WNL8", "WNL9", "WNL10", "WNL11", "WNL12", "WNL13", "WNL14", "WNL15", "WNL16", "WNL17", "WNL18", "WNL19", "WNL20", "WNL21", "MYE", "WYE", "MET", "WET", "MWCN", "WWCN", "MLN", "WLN", "LNWHQ", "LNXQJY", "LNXX", "LNCZ", "LNGZ", "LNDXZK", "LNDXBK", "LNSSYJS", "LNBSYJS", "MLN2", "WLN2", "LNWHQ2", "LNXQJY2", "LNXX2", "LNCZ2", "LNGZ2", "LNDXZK2", "LNDXBK2", "LNSSYJS2", "LNBSYJS2", "MLN3", "WLN3", "LNWHQ3", "LNXQJY3", "LNXX3", "LNCZ3", "LNGZ3", "LNDXZK3", "LNDXBK3", "LNSSYJS3", "LNBSYJS3", "WHQ", "XQJY", "XX", "CZ", "GZ", "DXZK", "DXBK", "SSYJS", "BSYJS", "SSH", "FSH", "lng", "lat", "is_qianhai", "DISTRICT_NAME", "DISTRICT_CODE", "STREET_NAME", "STREET_CODE", "COMMUNITY_NAME", "COMMUNITY_CODE", "occur_period", "occur_period_year", "occur_period_month", "org_id", "org_name", "area_code", "data_time", "TJNY") SELECT '933f35f92e5d4b19a7f9334452fe5a99', '4403060000000000000', 54, '440306000000', 23, 31, 0, 0, 0, 0, 0, 7, 10, 3, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 11, 14, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 5, 8, 36, 2, 0, 16, 38, '113.861892600000004', '22.580441539999999', '1', '宝安区', '440306000000', '西乡街道', '440306000000', '盐田社区', '440306000000', 202212, 2022, 12, NULL, NULL, NULL, '2023-06-01 00:00:00', '2022-12-01 00:00:00' FROM DUAL;
```
需要注意的是,达梦数据库的表名、列名都需要使用双引号包裹起来,而且数据值需要使用单引号包裹起来。同时,达梦数据库也不支持直接使用 VALUES 插入数据,需要使用 SELECT 和 DUAL 语句。
python 词形还原
在Python中,可以使用nltk模块中的WordNetLemmatizer函数来进行词形还原。以下是一个示例的Python代码:
```
from nltk.stem import WordNetLemmatizer
wnl = WordNetLemmatizer()
# 词形还原名词
print(wnl.lemmatize('cars', 'n'))
print(wnl.lemmatize('men', 'n'))
# 词形还原动词
print(wnl.lemmatize('running', 'v'))
print(wnl.lemmatize('ate', 'v'))
# 词形还原形容词
print(wnl.lemmatize('saddest', 'a'))
print(wnl.lemmatize('fancier', 'a'))
```
输出结果为:
```
car
man
run
eat
sad
fancy
```
以上代码中,'n'代表名词,'v'代表动词,'a'代表形容词。通过指定单词的词性,WordNetLemmatizer函数可以将单词还原为字典中的原型词汇。