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从回归分析中得出的数据。并产生T。
对数据进得自回归分析预测。并生成下一周期的预测值-Of data into the prediction derived from the regression analysis. And generate the next cycle of prediction value
- 2022-10-23 03:00:03下载
- 积分:1
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犯错
java1
- 2022-06-29 06:18:03下载
- 积分:1
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data access layer in three tier architecture in ASP.Net
我假设您非常了解ASP.Net. 在这个文件中有一个数据访问层的代码。使用以下方法和功能
- 2022-07-13 09:57:51下载
- 积分:1
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求解整数的最大公约数
求解整数的最大公约数-the common denominator for Solving Integer
- 2022-03-02 07:14:48下载
- 积分:1
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GEP的python版本
比较详尽的GEP的python版本,包括了GEP常用的算法实现。GEP算法的基本过程和GA的过程非常相似,GEP处理的对象可以是单基因或多基因组成的染色体(基因组)。基因由线性的、固定长度的符号串组成。
- 2023-01-12 19:55:03下载
- 积分:1
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数值型货币的大写转换1(未完待续)
数值型货币的大写转换1(未完待续)-numerical monetary capital of a conversion (to be continued)
- 2022-01-25 16:28:00下载
- 积分:1
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这是一个简单的演示如何评价文本的数学表达式
This a simple demo of how to evaluate mathematical expressions in text
format, including provision for variables and functions. The code consists of
three simple classes: 1) Calc - which does the main calculations, 2) Stack -
which is used to push and pop intermediate operators and numbers and 3)
Symbol Table. The symbol table is a collection of calc symbols, a type structure
consisting of various elements, including the expression name, function name,
and the final value of the expression The evaluation procedure is done in three
steps: 1) simplify the expression by removing all the elements within () and
storing them in subexpressions within the symbol table. 2) Each of these sub
expressions are then converted to RPN format (Reverse polar notation) and
evaluated 3) Finally, the main expression is converted to RPN and evaluated-This is a simple demo of how to evaluate mathematical expressions in text
format, including provision for variables and functi
- 2022-02-20 06:26:41下载
- 积分:1
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该程序用来解方程组的,非线性的和线性的,带有参数的方程组
该程序用来解方程组的,非线性的和线性的,带有参数的方程组-The program group for the solution of equations, non-linear and linear equations with parameters
- 2022-11-10 18:50:03下载
- 积分:1
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零树编码源码
零树编码源码-Zerotree coding source
- 2022-04-20 07:15:44下载
- 积分:1
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BP神经网络的数据分类——语音特征信号分类
BP神经网络模型是一种典型的前向型神经网络,具有良好的自学习、自适应、联想记忆、并行处理和非线形转换的能力,是目前应用最为广泛的一种神经网络模型。本文介绍了BP神经网络的实现以及其在数据挖掘分类方面的应用。
- 2022-05-24 03:50:26下载
- 积分:1