expect value of fuzzy variable and fuzzy expected value model

Expected valueThis article is about the term used in probability theory and statistics. For other uses, see Expected value (disambiguation).In probability theory, the expected value (or expectation, or mathematical expectation, or mean, or the first moment) of a random variable is the weighted average of all possible values that this random variable can take on. The weights used in computing this average correspond to the probabilities in case of a discrete random variable, or densities in case of a continuous random variable. From a rigorous theoretical standpoint, the expected value is the integral of the random variable with respect to its probability measure.[1][2]The expected value may be intuitively understood by the law of large numbers: The expected value, when it exists, is almost surely the limit of the sample mean as sample size grows to infinity. More informally, it can be interpreted as the long-run average of the results of many independent repetitions of an experiment (e.g. a dice roll). The value may not be expected in the ordinary sense—the "expected value" itself may be unlikely or even impossible (such as having 2.5 children), just like the sample mean.The expected value does not exist for some distributions with large "tails", such as the Cauchy distribution.[3]It is possible to construct an expected value equal to the probability of an event by taking the expectation of an indicator function that is one if the event has occurred and zero otherwise. This relationship can be used to translate properties of expected values into properties of probabilities, e.g. using the law of large numbers to justify estimating probabilities by frequencies.[edit] Definition[edit] Discrete random variable, finite caseSuppose random variable X can take value x1 with probability p1, value x2 with probability p2, and so on, up to value x k with probability p k. Then the expectation of this random variable X is defined asSince all probabilities p i add up to one: p1 + p2 + ... + p k = 1, the expected value can be viewed as the weighted average, with p i’s being the weights:If all outcomes x i are equally likely (that is, p1 = p2 = ... = p k), then the weighted average turns into the simple average. This is intuitive: the expected value of a random variable is the average of all values it can take; thus the expected value is what you expect to happen on average. If the outcomes x i are not equiprobable, then the simple average ought to be replaced with the weighted average, which takes into account the fact that some outcomes are more likely than the others. The intuition however remains the same: the expected value of X is what you expect to happen on average.An illustration of the convergence of sequence averages of rolls of a die to the expected value of 3.5 as the number of rolls (trials) grows.Example 1. Let X represent the outcome of a roll of a six-sided die. More specifically, X will be the number of pips showing on the top face of the die after the toss. The possible values for X are1, 2, 3, 4, 5, 6, all equally likely (each having the probability of 16). The expectation of X isIf you roll the die n times and compute the average (mean) of the results, then as n grows, the average will almost surelyconverge to the expected value, a fact known as the strong law of large numbers. One example sequence of ten rolls of the die is 2, 3, 1, 2, 5, 6, 2, 2, 2, 6, which has the average of 3.1, with the distance of 0.4 from the expected value of 3.5. The convergence is relatively slow: the probability that the average falls within the range 3.5 ± 0.1 is 21.6% for ten rolls, 46.1% for a hundred rolls and 93.7% for a thousand rolls. See the figure for an illustration of the averages of longer sequences of rolls of the die and how they converge to the expected value of 3.5. More generally, the rate of convergence can be roughly quantified by e.g. Chebyshev's inequality and the Berry-Esseen theorem.Example 2. The roulette game consists of a small ball and a wheel with 38 numbered pockets around the edge. As the wheel is spun, the ball bounces around randomly until it settles down in one of the pockets. Suppose random variable X represents the (monetary) outcome of a $1 bet on a single number ("straight up" bet). If the bet wins (which happens with probability 138), the payoff is $35; otherwise the player loses the bet. The expected profit from such a bet will be[edit] Discrete random variable, countable caseLet X be a discrete random variable taking values x1, x2, ... with probabilities p1, p2, ... respectively. Then the expected value of this random variable is the infinite sumprovided that this series converges absolutely (that is, the sum must remain finite if we were to replace all xi's with their absolute values). If this series does not converge absolutely, we say that the expected value of X does not exist.For example, suppose random variable X takes values 1, −2, 3, −4, ..., with respective probabilities c12, c22, c32, c42, ..., where c = 6π2 is a normalizing constant that ensures the probabilities sum up to one. Then the infinite sumconverges and its sum is equal to ln(2) ≃ 0.69315. However it would be incorrect to claim that the expected value of X is equal to this number—in fact E[X] does not exist, as this series does not converge absolutely (see harmonic series).[edit] Univariate continuous random variableIf the probability distribution of X admits a probability density function f(x), then the expected value can be computed as[edit] General definitionIn general, if X is a random variable defined on a probability space(Ω, Σ, P), then the expected value of X, denoted by E[X], ⟨X⟩, X or E[X], is defined as Lebesgue integralWhen this integral exists, it is defined as the expectation of X. Note that not all random variables have a finite expected value, since the integral may not converge absolutely; furthermore, for some it is not defined at all (e.g., Cauchy distribution). Two variables with the same probability distribution will have the same expected value, if it is defined.It follows directly from the discrete case definition that if X is a constant random variable, i.e. X = b for some fixed real number b, then the expected value of X is also b.The expected value of an arbitrary function of X, g(X), with respect to the probability density function ƒ(x) is given by the inner product of ƒ and g:This is sometimes called the law of the unconscious statistician. Using representations as Riemann–Stieltjes integral and integration by parts the formula can be restated as∙if ,∙if .As a special case let α denote a positive real number, thenIn particular, for α = 1, this reduces to:ifPr[X≥ 0] = 1, where F is the cumulative distribution function of X.[edit] Conventional terminology∙When one speaks of the "expected price", "expected height", etc. one means the expected value of a random variable that is a price, a height, etc.∙When one speaks of the "expected number of attempts needed to get one successful attempt", one might conservatively approximate it as the reciprocal of the probability of success for such an attempt. Cf. expected value of the geometric distribution.[edit] Properties[edit] ConstantsThe expected value of a constant is equal to the constant itself; i.e., if c is a constant, then E[c] = c. [edit] MonotonicityIf X and Y are random variables such that X≤ Y almost surely, then E[X] ≤ E[Y].[edit] LinearityThe expected value operator (or expectation operator) E is linear in the sense thatNote that the second result is valid even if X is not statistically independent of Y. Combining the results from previous three equations, we can see thatfor any two random variables X and Y (which need to be defined on the same probability space) and any real numbers a and b.[edit] Iterated expectation[edit] Iterated expectation for discrete random variablesFor any two discrete random variables X, Y one may define the conditional expectation:[4]which means that E[X|Y](y) is a function of y.Then the expectation of X satisfiesHence, the following equation holds:[5]that is,The right hand side of this equation is referred to as the iterated expectation and is also sometimes called the tower rule or the tower property. This proposition is treated in law of total expectation. [edit] Iterated expectation for continuous random variablesIn the continuous case, the results are completely analogous. The definition of conditional expectation would use inequalities, density functions, and integrals to replace equalities, mass functions, and summations, respectively. However, the main result still holds:[edit] InequalityIf a random variable X is always less than or equal to another random variable Y, the expectation of X is less than or equal to that of Y:If X≤ Y, then E[X] ≤ E[Y].In particular, if we set Y to |X| we know X≤ Y and −X≤ Y. Therefore we know E[X] ≤ E[Y] and E[-X] ≤ E[Y]. From the linearity of expectation we know -E[X] ≤ E[Y].Therefore the absolute value of expectation of a random variable is less than or equal to the expectation of its absolute value:[edit] Non-multiplicativityIf one considers the joint probability density function of X and Y, say j(x,y), then the expectation of XY isIn general, the expected value operator is not multiplicative, i.e. E[XY] is not necessarily equal toE[X]·E[Y]. In fact, the amount by which multiplicativity fails is called the covariance:Thus multiplicativity holds precisely when Cov(X, Y) = 0, in which case X and Y are said to be uncorrelated (independent variables are a notable case of uncorrelated variables).Now if X and Y are independent, then by definition j(x,y) = ƒ(x)g(y) where ƒand g are the marginal PDFs for X and Y. ThenandCov(X, Y) = 0.Observe that independence of X and Y is required only to write j(x,y) = ƒ(x)g(y), and this is required to establish the second equality above. The third equality follows from a basic application of the Fubini-Tonelli theorem.[edit] Functional non-invarianceIn general, the expectation operator and functions of random variables do not commute; that isA notable inequality concerning this topic is Jensen's inequality, involving expected values of convex (or concave) functions.[edit] Uses and applicationsThe expected values of the powers of X are called the moments of X; the moments about the mean of X are expected values of powers of X− E[X]. The moments of some random variables can be used to specify their distributions, via their moment generating functions.To empirically estimate the expected value of a random variable, one repeatedly measures observations of the variable and computes the arithmetic mean of the results. If the expected value exists, this procedure estimates the true expected value in an unbiased manner and has the property of minimizing the sum of the squares of the residuals (the sum of the squared differences between the observations and the estimate). The law of large numbers demonstrates (under fairly mild conditions) that, as the size of the sample gets larger, the variance of this estimate gets smaller.This property is often exploited in a wide variety of applications, including general problems of statistical estimation and machine learning, to estimate (probabilistic) quantities of interest via Monte Carlo methods, since most quantities of interest can be written in terms of expectation, e.g.where is the indicator function for set , i.e..In classical mechanics, the center of mass is an analogous concept to expectation. For example, suppose X is a discrete random variable with values x i and corresponding probabilities p i. Now consider a weightless rod on which are placed weights, at locations x i along the rod and havingmasses p i (whose sum is one). The point at which the rod balances is E[X].Expected values can also be used to compute the variance, by means of the computational formula for the varianceA very important application of the expectation value is in the field of quantum mechanics. Theexpectation value of a quantum mechanical operator operating on a quantum state vectoris written as . The uncertainty in can be calculated using the formula.[edit] Expectation of matricesIf X is an matrix, then the expected value of the matrix is defined as the matrix of expected values:This is utilized in covariance matrices.[edit] Formulas for special cases[edit] Discrete distribution taking only non-negative integer valuesWhen a random variable takes only values in {0,1,2,3,...} we can use the following formula for computing its expectation (even when the expectation is infinite):Proof:interchanging the order of summation, we haveas claimed. This result can be a useful computational shortcut. For example, suppose we toss a coin where the probability of heads is p. How many tosses can we expect until the first heads (not including the heads itself)? Let X be this number. Note that we are counting only the tails and notthe heads which ends the experiment; in particular, we can have X = 0. The expectation of X maybe computed by . This is because the number of tosses is at least i exactly when the first i tosses yielded tails. This matches the expectation of a random variable with anExponential distribution. We used the formula for Geometric progression:[edit] Continuous distribution taking non-negative valuesAnalogously with the discrete case above, when a continuous random variable X takes only non-negative values, we can use the following formula for computing its expectation (even when the expectation is infinite):Proof: It is first assumed that X has a density f X(x). We present two techniques:∙Using integration by parts (a special case of Section 1.4 above):and the bracket vanishes because[6]1 − F(x) = o(1 / x) as .∙Using an interchange in order of integration:In case no density exists, it is seen that[edit] HistoryThe idea of the expected value originated in the middle of the 17th century from the study of the so-called problem of points. This problem is: how to divide the stakes in a fair way between two players who have to end their game before it's properly finished? This problem had been debated for centuries, and many conflicting proposals and solutions had been suggested over the years, when it was posed in 1654 to Blaise Pascal by a French nobleman chevalier de Méré. deMéréclaimed that this problem couldn't be solved and that it showed just how flawed mathematics was when it came to its application to the real world. Pascal, being a mathematician, got provoked and determined to solve the problem once and for all. He began to discuss the problem in a now famous series of letters to Pierre de Fermat. Soon enough they both independently came up with a solution. They solved the problem in different computational ways but their results were identical because their computations were based on the same fundamental principle. The principle is that the value of a future gain should be directly proportional to the chance of getting it. This principle seemed to have come absolutely natural to both of them. They were very pleased by the fact that they had found essentially the same solution and this in turn made them absolutely convinced they had solved the problem conclusively. However, they did not publish their findings. They onlyinformed a small circle of mutual scientific friends in Paris about it.[7]Three years later, in 1657, a Dutch mathematician Christiaan Huygens, who had just visited Paris, published a treatise (see Huygens (1657)) "De ratiociniis in ludoaleæ" on probability theory. In this book he considered the problem of points and presented a solution based on the same principle as the solutions of Pascal and Fermat. Huygens also extended the concept of expectation by adding rules for how to calculate expectations in more complicated situations than the original problem (e.g., for three or more players). In this sense this book can be seen as the first successful attempt of laying down the foundations of the theory of probability.In the foreword to his book, Huygens wrote: "It should be said, also, that for some time some of the best mathematicians of France have occupied themselves with this kind of calculus so that no one should attribute to me the honour of the first invention. This does not belong to me. But these savants, although they put each other to the test by proposing to each other many questions difficult to solve, have hidden their methods. I have had therefore to examine and go deeply for myself into this matter by beginning with the elements, and it is impossible for me for this reason to affirm that I have even started from the same principle. But finally I have found that my answers in many cases do not differ from theirs." (cited by Edwards (2002)). Thus, Huygens learned about de Méré's problem in 1655 during his visit to France; later on in 1656 from his correspondence with Carcavi he learned that his method was essentially the same as Pascal's; so that before his book went to press in 1657 he knew about Pascal's priority in this subject.Neither Pascal nor Huygens used the term "expectation" in its modern sense. In particular, Huygens writes: "That my Chance or Expectation to win any thing is worth just such a Sum, as wou'd procure me in the same Chance and Expectation at a fair Lay. ... If I expect a or b, and have an equal Chance of gaining them, my Expectation is worth a+b2." More than a hundred years later, in 1814, Pierre-Simon Laplace published his tract "Théorieanalytique des probabilités", where the concept of expected value was defined explicitly:... this advantage in the theory of chance is the product of the sum hoped for by the probability of obtaining it; it is the partial sum which ought to result when we do not wish to run the risks of the event in supposing that the division is made proportional to the probabilities. This division is the only equitable one when all strange circumstances are eliminated; because an equal degree of probability gives an equal right for the sum hoped for. We will call this advantage mathematical hope.The use of letter E to denote expected value goes back to W.A. Whitworth (1901) "Choice and chance". The symbol has become popular since for English writers it meant "Expectation", for Germans "Erwartungswert", and for French "Espérancemathématique".[8]。

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python未知异常处理方法以Python未知异常处理方法为标题，我将为大家详细介绍Python 中处理未知异常的方法。

一、什么是异常在编程过程中，异常是指由于代码错误或其他问题导致程序无法正常执行而产生的错误。

当出现异常时，程序将会中断执行并抛出异常信息。

二、异常处理的重要性异常处理是编写高质量程序的关键之一。

如果程序没有适当的异常处理机制，当出现异常时，程序将会崩溃并给用户带来不良体验。

通过合理的异常处理，可以使程序更加健壮和稳定，提高用户体验。

三、常见的异常类型在Python中，常见的异常类型包括但不限于：1. SyntaxError：语法错误，通常是由于程序代码书写错误导致的。

2. NameError：变量名错误，通常是由于使用了未定义的变量或函数导致的。

3. TypeError：类型错误，通常是由于对不支持的数据类型进行操作导致的。

4. ValueError：数值错误，通常是由于传入的参数超出范围或不合法导致的。

5. FileNotFoundError：文件未找到错误，通常是由于打开不存在的文件导致的。

四、异常处理的方法1. try-except语句try-except语句是Python中最基本的异常处理方法。

通过使用try 语句块来尝试执行可能引发异常的代码，如果发生异常，则由except语句块来处理异常。

具体语法如下：```pythontry:# 可能引发异常的代码except 异常类型:# 异常处理代码```在except语句块中，可以根据需要捕获特定的异常类型，并编写相应的处理代码。

如果不指定异常类型，则会捕获所有类型的异常。

2. try-except-else语句try-except-else语句是在try-except语句的基础上增加了else语句块。

当try语句块中的代码顺利执行完毕且没有发生异常时，将会执行else语句块中的代码。

具体语法如下：```pythontry:# 可能引发异常的代码except 异常类型:# 异常处理代码else:# 未发生异常时执行的代码```通过使用try-except-else语句，我们可以将正常的代码和异常处理代码分离，使程序更加清晰易读。

Ambiguous operators need parentheses -----------不明确的运算需要用括号括起Ambiguous symbol ''xxx'' ----------------不明确的符号Argument list syntax error ----------------参数表语法错误Array bounds missing ------------------丢失数组界限符Array size toolarge -----------------数组尺寸太大Bad character in paramenters ------------------参数中有不适当的字符Bad file name format in include directive --------------------包含命令中文件名格式不正确Bad ifdef directive synatax ------------------------------编译预处理ifdef有语法错Bad undef directive syntax ---------------------------编译预处理undef有语法错Bit field too large ----------------位字段太长Call of non-function -----------------调用未定义的函数Call to function with no prototype ---------------调用函数时没有函数的说明Cannot modify a const object ---------------不允许修改常量对象Case outside of switch ----------------漏掉了case 语句Case syntax error ------------------ Case 语法错误Code has no effect -----------------代码不可述不可能执行到Compound statement missing{ --------------------分程序漏掉"{"Conflicting type modifiers ------------------不明确的类型说明符Constant expression required ----------------要求常量表达式Constant out of range in comparison -----------------在比较中常量超出范围Conversion may lose significant digits -----------------转换时会丢失意义的数字Conversion of near pointer not allowed -----------------不允许转换近指针Could not find file ''xxx'' -----------------------找不到XXX 文件Declaration missing ; ----------------说明缺少"；" Declaration syntax error -----------------说明中出现语法错误Default outside of switch ------------------ Default 出现在switch语句之外Define directive needs an identifier ------------------定义编译预处理需要标识符Division by zero ------------------用零作除数Do statement must have while ------------------ Do-while语句中缺少while部分Enum syntax error ---------------------枚举类型语法错误Enumeration constant syntax error -----------------枚举常数语法错误Error directive :xxx ------------------------错误的编译预处理命令Error writing output file ---------------------写输出文件错误Expression syntax error -----------------------表达式语法错误Extra parameter in call ------------------------调用时出现多余错误File name too long ----------------文件名太长Function call missing -----------------函数调用缺少右括号Fuction definition out of place ------------------函数定义位置错误Fuction should return a value ------------------函数必需返回一个值Goto statement missing label ------------------ Goto语句没有标号Hexadecimal or octal constant too large ------------------16进制或8进制常数太大Illegal character ''x'' ------------------非法字符x Illegal initialization ------------------非法的初始化Illegal octal digit ------------------非法的8进制数字houjiumingIllegal pointer subtraction ------------------非法的指针相减Illegal structure operation ------------------非法的结构体操作Illegal use of floating point -----------------非法的浮点运算Illegal use of pointer --------------------指针使用非法Improper use of a typedefsymbol ----------------类型定义符号使用不恰当In-line assembly not allowed -----------------不允许使用行间汇编Incompatible storage class -----------------存储类别不相容Incompatible type conversion --------------------不相容的类型转换Incorrect number format -----------------------错误的数据格式Incorrect use of default --------------------- Default使用不当Invalid indirection ---------------------无效的间接运算Invalid pointer addition ------------------指针相加无效Irreducible expression tree -----------------------无法执行的表达式运算Lvalue required ---------------------------需要逻辑值0或非0值Macro argument syntax error -------------------宏参数语法错误Macro expansion too long ----------------------宏的扩展以后太长Mismatched number of parameters in definition---------------------定义中参数个数不匹配Misplaced break ---------------------此处不应出现break语句Misplaced continue ------------------------此处不应出现continue语句Misplaced decimal point --------------------此处不应出现小数点Misplaced elif directive --------------------不应编译预处理elifMisplaced else ----------------------此处不应出现else Misplaced else directive ------------------此处不应出现编译预处理elseMisplaced endif directive -------------------此处不应出现编译预处理endifMust be addressable ----------------------必须是可以编址的Must take address of memory location ------------------必须存储定位的地址No declaration for function ''xxx'' -------------------没有函数xxx的说明No stack ---------------缺少堆栈No type information ------------------没有类型信息Non-portable pointer assignment --------------------不可移动的指针（地址常数）赋值Non-portable pointer comparison --------------------不可移动的指针（地址常数）比较Non-portable pointer conversion ----------------------不可移动的指针（地址常数）转换Not a valid expression format type ---------------------不合法的表达式格式Not an allowed type ---------------------不允许使用的类型Numeric constant too large -------------------数值常太大Out of memory -------------------内存不够用Parameter ''xxx'' is never used ------------------能数xxx没有用到Pointer required on left side of -> -----------------------符号->的左边必须是指针Possible use of ''xxx'' before definition -------------------在定义之前就使用了xxx（警告）Possibly incorrect assignment ----------------赋值可能不正确Redeclaration of ''xxx'' -------------------重复定义了xxx Redefinition of ''xxx'' is not identical ------------------- xxx的两次定义不一致Register allocation failure ------------------寄存器定址失败Repeat count needs an lvalue ------------------重复计数需要逻辑值Size of structure or array not known ------------------结构体或数给大小不确定Statement missing ; ------------------语句后缺少"；" Structure or union syntax error --------------结构体或联合体语法错误Structure size too large ----------------结构体尺寸太大Sub scripting missing ] ----------------下标缺少右方括号Superfluous & with function or array ------------------函数或数组中有多余的"&"Suspicious pointer conversion ---------------------可疑的指针转换Symbol limit exceeded ---------------符号超限Too few parameters in call -----------------函数调用时的实参少于函数的参数不Too many default cases ------------------- Default太多(switch 语句中一个)Too many error or warning messages --------------------错误或警告信息太多Too many type in declaration -----------------说明中类型太多Too much auto memory in function -----------------函数用到的局部存储太多Too much global data defined in file ------------------文件中全局数据太多Two consecutive dots -----------------两个连续的句点Type mismatch in parameter xxx ----------------参数xxx类型不匹配Type mismatch in redeclaration of ''xxx'' ---------------- xxx 重定义的类型不匹配Unable to create output file ''xxx'' ----------------无法建立输出文件xxxUnable to open include file ''xxx'' ---------------无法打开被包含的文件xxxUnable to open input file ''xxx'' ----------------无法打开输入文件xxxUndefined label ''xxx'' -------------------没有定义的标号xxx Undefined structure ''xxx'' -----------------没有定义的结构xxxUndefined symbol ''xxx'' -----------------没有定义的符号xxx Unexpected end of file in comment started on line xxx----------从xxx行开始的注解尚未结束文件不能结束Unexpected end of file in conditional started on line xxx ----从xxx 开始的条件语句尚未结束文件不能结束Unknown assemble instruction ----------------未知的汇编结构Unknown option ---------------未知的操作Unknown preprocessor directive: ''xxx'' -----------------不认识的预处理命令xxxUnreachable code ------------------无路可达的代码Unterminated string or character constant -----------------字符串缺少引号User break ----------------用户强行中断了程序Void functions may not return a value ----------------- Void类型的函数不应有返回值Wrong number of arguments -----------------调用函数的参数数目错''xxx'' not an argument ----------------- xxx不是参数''xxx'' not part of structure -------------------- xxx不是结构体的一部分xxx statement missing ( -------------------- xxx语句缺少左括号xxx statement missing ) ------------------ xxx语句缺少右括号xxx statement missing ; -------------------- xxx缺少分号xxx'' declared but never used -------------------说明了xxx 但没有使用xxx'' is assigned a value which is never used----------------------给xxx赋了值但未用过Zero length structure ------------------结构体的长度为零。

编程中常见的变量错误类型及调试方法在编程过程中，变量是不可或缺的元素之一。

然而，由于编程语言的复杂性和人为的疏忽，我们常常会遇到各种变量错误。

本文将介绍一些常见的变量错误类型，并提供相应的调试方法，帮助编程工程师更好地解决问题。

1. 变量未声明错误这是最常见的变量错误之一。

当我们在使用一个变量之前未对其进行声明时，编译器或解释器会报错。

这种错误通常是由于拼写错误、大小写错误或者变量作用域错误引起的。

调试方法：- 检查变量名的拼写和大小写，确保与声明时保持一致。

- 确认变量的作用域是否正确，如果需要，在合适的位置进行声明。

2. 变量类型错误在编程中，每个变量都有一个特定的类型，例如整数、浮点数、字符串等。

当我们将一个变量赋值给不兼容的类型时，会导致变量类型错误。

调试方法：- 检查变量的赋值语句，确认赋值的类型与变量声明的类型是否一致。

- 使用类型转换函数将变量转换为正确的类型。

3. 变量命名错误变量命名错误可能是由于拼写错误、使用了保留字或者使用了无效的字符导致的。

这种错误可能会导致编译器或解释器无法识别变量。

调试方法：- 检查变量名的拼写和使用的字符，确保符合编程语言的命名规则。

- 避免使用保留字作为变量名。

4. 变量作用域错误变量作用域错误是指变量在不正确的作用域中被引用或赋值。

例如，在一个函数内部引用一个在函数外部声明的变量，或者在一个循环内部定义一个在循环外部已经定义的变量。

调试方法：- 确认变量的作用域是否正确，如果需要，在合适的位置进行声明。

- 检查变量的引用或赋值语句，确保在正确的作用域内。

5. 变量未初始化错误当我们在使用一个未初始化的变量时，会导致变量未初始化错误。

这种错误可能会导致程序的不可预测行为。

调试方法：- 确保在使用变量之前对其进行初始化，赋予一个合适的初始值。

- 检查变量的初始化语句，确认是否存在遗漏。

总结：在编程过程中，变量错误是常见的问题之一。

通过仔细检查变量的声明、赋值、命名和作用域，我们可以有效地解决这些问题。

Package‘lsm’October13,2022Type PackageTitle Estimation of the log Likelihood of the Saturated ModelVersion0.2.1.2Date2022-02-03Author Humberto Llinas[aut](<https:///0000-0002-2976-5109>),Omar Fabregas[aut](<https:///0000-0001-6853-6280>),Jorge Villalba[aut,cre](<https:///0000-0002-2888-9660>)Maintainer Jorge Villalba<*******************>Description When the values of the outcome variable Y are either0or1,the function lsm()calculates the estimation of the log likelihoodin the saturated model.This model is characterized by Llinas(2006,ISSN:2389-8976)in section2.3through the assumptions1and2.The function LogLik()works(almost perfectly)when the number of independentvariables K is high,but for small K it calculates wrong values in some cases.For this reason,when Y is dichotomous and the data are grouped in J populations,it is recommended to use the function lsm()because it works very well for all K.Depends R(>=3.5.0)Imports stats,dplyr(>=1.0.0),ggplot2(>=1.0.0)Encoding UTF-8License MIT+file LICENSELazyData TRUERoxygenNote7.1.2NeedsCompilation yesRepository CRANDate/Publication2022-02-0403:10:02UTCR topics documented:chdage (2)confint.lsm (3)12chdage icu (4)lowbwt (5)lsm (6)predict.lsm (9)pros (10)summary.lsm (11)survey (12)uis (15)Index16 chdage Coronary Heart Disease StudyDescriptionCoronary Heart Disease StudyUsagechdageFormatA data frame with100observations on the following3variables.ID identification codeAGE age in yearsCHD presence(1)or absence(0)of evidence of significant coronary heart diseaseReferencesHosmer,D.(2013).Wiley Series in Probability and Statistics Ser.:Applied Logistic Regression(3).New York:John Wiley&Sons,Incorporated.Examples#data(chdage)#maybe str(chdage);plot(chdage)...confint.lsm3 confint.lsm Confidence Intervals for lsm ObjectsDescriptionProvides a confint method for lsm objects.Usage##S3method for class lsmconfint(object,parm,level=0.95,...)Argumentsobject The type of prediction required.The default is on the scale of the linear predic-tors.The alternative response gives the predicted probabilities.parm further arguments passed to or from other methods.level The type of prediction required.The default is on the scale of the linear predic-tors.The alternative response gives the predicted probabilities....further arguments passed to or from other methods.Detailsconfint Method for lsmThe saturated model is characterized by the assumptions1and2presented in section2.3by Llinas (2006,ISSN:2389-8976).Valuelsm returns an object of class"lsm".An object of class"lsm"is a list containing at least the following components:object a lsm objectparm parameterlevel confidence levels...additional parametersAuthor(s)Jorge Villalba Acevedo[cre,aut],Cartagena-Colombia.4icuReferences[1]Humberto Jesus Llinas.(2006).Accuracies in the theory of the logistic models.Revista Colom-biana De Estadistica,29(2),242-244.[2]Hosmer,D.(2013).Wiley Series in Probability and Statistics Ser.:Applied Logistic Regression(3).New York:John Wiley&Sons,Incorporated.[3]Chambers,J.M.and Hastie,T.J.(1992)Statistical Models in S.Wadsworth&Brooks/Cole.Examples#Hosmer,D.(2013)page3:Age and coranary Heart Disease(CHD)Status of20subjects: #AGE<-c(20,23,24,25,25,26,26,28,28,29,30,30,30,30,30,30,30,32,33,33) #CHD<-c(0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0)#data<-data.frame(CHD,AGE)#Ela<-lsm(CHD~AGE,family=binomial,data)#summary(Ela)icu icuDescriptionicuUsageicuFormatA data frame with200observations on the following21variables.ID a numeric vectorSTA a numeric vectorAGE a numeric vectorGENDER a numeric vectorRACE a numeric vectorSER a numeric vectorCAN a numeric vectorCRN a numeric vectorINF a numeric vectorCPR a numeric vectorSYS a numeric vectorHRA a numeric vectorlowbwt5 PRE a numeric vectorTYP a numeric vectorFRA a numeric vectorPO2a numeric vectorPH a numeric vectorPCO a numeric vectorBIC a numeric vectorCRE a numeric vectorLOC a numeric vectorReferencesHosmer,D.(2013).Wiley Series in Probability and Statistics Ser.:Applied Logistic Regression(3).New York:John Wiley&Sons,Incorporated.Examples#data(icu)#maybe str(icu);plot(icu)...lowbwt lowbwtDescriptionlowbwtUsagelowbwtFormatA data frame with189observations on the following11variables.ID a numeric vectorSMOKE a numeric vectorRACE a numeric vectorAGE a numeric vectorLWT a numeric vectorBWT a numeric vectorLOW a numeric vectorPTL a numeric vectorHT a numeric vectorUI a numeric vectorFTV a numeric vector6lsm ReferencesHosmer,D.(2013).Wiley Series in Probability and Statistics Ser.:Applied Logistic Regression(3).New York:John Wiley&Sons,Incorporated.Examples#data(lowbwt)#maybe str(lowbwt);plot(lowbwt)...lsm Estimation of the log Likelihood of the Saturated ModelDescriptionWhen the values of the outcome variable Y are either0or1,the function lsm()calculates the estimation of the log likelihood in the saturated model.This model is characterized by Llinas (2006,ISSN:2389-8976)in section2.3through the assumptions1and2.If Y is dichotomous and the data are grouped in J populations,it is recommended to use the function lsm()because it works very well for all K.Usagelsm(formula,family=binomial,data=environment(formula))Argumentsformula An expression of the form y~model,where y is the outcome variable(binary or dichotomous:its values are0or1).family an optional funtion for example binomial.data an optional data frame,list or environment(or object coercible by as.data.frame to a data frame)containing the variables in the model.If not found in data,thevariables are taken from environment(formula),typically the environment fromwhich lsm()is called.DetailsEstimation of the log Likelihood of the Saturated ModelThe saturated model is characterized by the assumptions1and2presented in section2.3by Llinas (2006,ISSN:2389-8976).lsm7Valuelsm returns an object of class"lsm".An object of class"lsm"is a list containing at least the following components:coefficients Vector of coefficients estimations.Std.Error Vector of the coefficients’s standard error.ExpB Vector with the exponential of the coefficients.Wald Value of the Wald statistic.DF Degree of freedom for the Chi-squared distribution.P.value P-value with the Chi-squared distribution.Log_Lik_CompleteEstimation of the log likelihood in the complete model.Log_Lik_Null Estimation of the log likelihood in the null model.Log_Lik_Logit Estimation of the log likelihood in the logistic model.Log_Lik_SaturateEstimation of the log likelihood in the saturate model.Populations Number of populations in the saturated model.Dev_Null_vs_LogitValue of the test statistic(Hypothesis:null vs logistic models).Dev_Logit_vs_CompleteValue of the test statistic(Hypothesis:logistic vs complete models).Dev_Logit_vs_SaturateValue of the test statistic(Hypothesis:logistic vs saturated models).Df_Null_vs_LogitDegree of freedom for the test statistic’s distribution(Hypothesis:null vs logis-tic models).Df_Logit_vs_CompleteDegree of freedom for the test statistic’s distribution(Hypothesis:logistic vssaturated models).Df_Logit_vs_SaturateDegree of freedom for the test statistic’s distribution(Hypothesis:Logistic vssaturated models)P.v_Null_vs_Logitp-values for the hypothesis test:null vs logistic models.P.v_Logit_vs_Completep-values for the hypothesis test:logistic vs complete models.P.v_Logit_vs_Saturatep-values for the hypothesis test:logistic vs saturated models.Logit Vector with the log-odds.p_hat Vector with the probabilities that the outcome variable takes the value1,given the jth population.odd Vector with the values of the odd in each jth population.8lsmOR Vector with the values of the odd ratio for each coefficient of the variables.z_j Vector with the values of each Zj(the sum of the observations in the jth popu-lation).n_j Vector with the nj(the number of the observations in each jth population).p_j Vector with the estimation of each pj(the probability of success in the jth population).v_j Vector with the variance of the Bernoulli variables in the jth population.m_j Vector with the expected values of Zj in the jth population.V_j Vector with the variances of Zj in the jth population.V Variance and covariance matrix of Z,the vector that contains all the Zj.S_p Score vector in the saturated model.I_p Information matrix in the saturated model.Zast_j Vector with the values of the standardized variable of Zj.mcov Variance and covariance matrix for coefficient estimates.mcor Correlation matrix for coefficient estimates.Esm Estimates in the saturated model.Elm Estimates in the logistic model.Author(s)Humberto Llinas Solano[aut],Universidad del Norte,Barranquilla-Colombia\Omar Fabregas Cera [aut],Universidad del Norte,Barranquilla-Colombia\Jorge Villalba Acevedo[cre,aut],Universi-dad Tecnológica de Bolívar,Cartagena-Colombia.References[1]Humberto Jesus Llinas.(2006).Accuracies in the theory of the logistic models.Revista Colom-biana De Estadistica,29(2),242-244.[2]Hosmer,D.(2013).Wiley Series in Probability and Statistics Ser.:Applied Logistic Regression(3).New York:John Wiley&Sons,Incorporated.[3]Chambers,J.M.and Hastie,T.J.(1992)Statistical Models in S.Wadsworth&Brooks/Cole.Examples#Hosmer,D.(2013)page3:Age and coranary Heart Disease(CHD)Status of20subjects: #library(lsm)#AGE<-c(20,23,24,25,25,26,26,28,28,29,30,30,30,30,30,30,30,32,33,33)#CHD<-c(0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0)#data<-data.frame(CHD,AGE)#lsm(CHD~AGE,family=binomial,data)##For more ease,use the following notation.predict.lsm9#lsm(y~.,data)#Other case.#y<-c(1,0,1,0,1,1,1,1,0,0,1,1)#x1<-c(2,2,2,5,5,5,5,8,8,11,11,11)#data<-data.frame(y,x1)#ELAINYS<-lsm(y~x1,family=binomial,data)#summary(ELAINYS)#Other case.#y<-as.factor(c(1,0,1,0,1,1,1,1,0,0,1,1))#x1<-as.factor(c(2,2,2,5,5,5,5,8,8,11,11,11))#data<-data.frame(y,x1)#ELAINYS1<-lsm(y~x1,family=binomial,data)#confint(ELAINYS1)predict.lsm Predict Method for lsm ObjectsDescriptionObtains predictions from afitted lsm object.Usage##S3method for class lsmpredict(object,newdata,type=c("link","response","odd"),interval=c("none","confidence","prediction","odd"),level=0.95,...)Argumentsobject Afitted object of class lsm.newdata Optionally,a data frame in which to look for variables with which to predict.If omitted,thefitted linear predictors are used.type The type of prediction required.The default is on the scale of the linear predic-tors.The alternative response gives the predicted probabilities.10pros interval gives thelevel gives the...further arguments passed to or from other methods.DetailsPredict Method for lsm FitsValueA vector or matrix of predictions.following components:pros prosDescriptionprosUsageprosFormatA data frame with380observations on the following9variables.ID a numeric vectorCAPSULE a numeric vectorAGE a numeric vectorRACE a numeric vectorDPROS a numeric vectorDCAPS a numeric vectorPSA a numeric vectorVOL a numeric vectorGLEASON a numeric vectorReferencesHosmer,D.(2013).Wiley Series in Probability and Statistics Ser.:Applied Logistic Regression(3).New York:John Wiley&Sons,Incorporated.Examples#data(pros)#maybe str(pros);plot(pros)...summary.lsm11 summary.lsm Summarizing Method for lsm ObjectsDescriptionProvides a summary method for lsm objects.Usage##S3method for class lsmsummary(object,...)Argumentsobject An expression of the form y~model,where y is the outcome variable(binary or dichotomous:its values are0or1)....further arguments passed to or from other methods.Detailssummary Method for lsmThe saturated model is characterized by the assumptions1and2presented in section2.3by Llinas (2006,ISSN:2389-8976).ValueAn object of class"lsm"is a list containing at least the following components:object a lsm object...additional parametersAuthor(s)Jorge Villalba Acevedo[cre,aut],Cartagena-Colombia.References[1]Humberto Jesus Llinas.(2006).Accuracies in the theory of the logistic models.Revista Colom-biana De Estadistica,29(2),242-244.[2]Hosmer,D.(2013).Wiley Series in Probability and Statistics Ser.:Applied Logistic Regression(3).New York:John Wiley&Sons,Incorporated.[3]Chambers,J.M.and Hastie,T.J.(1992)Statistical Models in S.Wadsworth&Brooks/Cole.Examples#Hosmer,D.(2013)page3:Age and coranary Heart Disease(CHD)Status of20subjects: #AGE<-c(20,23,24,25,25,26,26,28,28,29,30,30,30,30,30,30,30,32,33,33) #CHD<-c(0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0)#data<-data.frame(CHD,AGE)#Ela<-lsm(CHD~AGE,family=binomial,data)#summary(Ela)survey surveyDescriptionThe data was collected by applying a survey to a sample of university students.UsagesurveyFormatA data frame(tibble)with800observations and66variables,which are described below:Observation Student.ID Identification code.Gender Gender of the student,1=Female;2=Male.Like What do you do most often in your free time?1=Network(Check social networks);2=TV (Watch TV).Age Age of the student(in years),Numeric vector from12.0to30.0Smoke Do you smoke?0=No;1=Yes.Height Height of the student(in meters),Numeric vector from1.50to1.90.Weight Weight of the student(in kilograms),numeric vector from49to120.BMI Body mass index of the student(kg/m^2),numeric vector from14to54.School Type of school students come from,1=Private;2=Public.SES Socio-economic stratus of the student,1=Low;2=Medium;3=High.Enrollment What was your type funding to study at the university?1=Credit;2=Scholarship;3 =Savings.Score Percentage of success in a certain test,numeric vector from0to100%MotherHeight Height of the mother of the student(in meters),numeric vector1=Short;2= Normal;3=Tall.MotherAge Age of the mother of the student(in years),numeric vector from39to89.MotherCHD Has your mother had coronary heart disease?0=No;1=Yes.FatherHeight Height of the father of the student(in meters),numeric vector1=Short;2=Nor-mal;3=Tall.FatherAge Age of the father of the student(in years),numeric vector from39to89 FatherCHD Has your fatner had coronary heart diseasea,1=No;2=Yes.Status Student’s academic status at the end of the previous semester,1=Distinguished;2= Normal;3=Regular.SemAcum Average of allfinal grades in the previous semester,numeric vector from0.0to5.0 Exam1First exam taken last semester,numeric vector from0.0to5.0Exam2Second exam taken last semester,numeric vector from0.0to5.0Exam3Third exam taken last semester,numeric vector from0.0to5.0Exam4Last exam taken last semester,numeric vector from0.0to5.0ExamAcum Sum of the four exams mentioned above,numeric vector from0.0to5.0Definitive Average of the four exams mentioned above,numeric vector from0.0to5.0 Expense Average of your monthly expenses(in10thousand Colombian pesos),numeric vector from23.0to90.0Income Father’s monthly income(in millions of Colombian pesos),numeric vector from1.0to3.0 Gas Value paid for gas service in the last month(in thousands of Colombian pesos),numeric vector from15.0to28.0Course What type of virtual classes do you prefer?1=Virtual;2=Face-to-face.Law Opinion on a law,1=In disagreement;2=AgreeEconomic How was your family’s economy during the pandemic?1=Bad;2=Regular;3=Good. Race Does the student belong to an ethnic group?1=None;2=EthnicRegion Region of the country where the student comes from,1=North;2=Center;3=South. EMO1During this period of preventative isolation,you frequently become nervous or restless for no reason,1=Never,2=Rarely;3=Almost always;4=Always.EMO2During this period of preventative isolation,you are often irritable,1=Never,2=Rarely;3 =Almost always;4=Always.EMO3During this period of preventive isolation,you are often sad or despondent,1=Never,2= Rarely;3=Almost always;4=AlwaysEMO4During this period of preventive isolation,you are often easily frightened,1=Never,2= Rarely;3=Almost always;4=AlwaysEMO5During this period of preventative isolation,you often have trouble thinking clearly,1= Never,2=Rarely;3=Almost always;4=AlwaysGOAL1I am concerned that I may not be able to understand the contents of my subjects this semester as thoroughly as I would like,1=Strongly agree;2=Disagree;3=Undecided;4=Agree;5 =Strongly agree.GOAL2It is important for me to do better than other students in my subjects this semester,1= Strongly agree;2=Disagree;3=Undecided;4=Agree;5=Strongly agree.GOAL3I am concerned that I may not learn all that I can learn in my subjects this semester,1= Strongly agree;2=Disagree;3=Undecided;4=Agree;5=Strongly agree.Pre_STAT1I like statistics,1=Strongly agree;2=Disagree;3=Undecided;4=Agree;5= Strongly agree.Pre_STAT2I don’t focus when I make problems statistics,1=Strongly agree;2=Disagree;3= Undecided;4=Agree;5=Strongly agree.Pre_STAT3I don’t understand statistics much because of my way of thinking,1=Strongly agree;2=Disagree;3=Undecided;4=Agree;5=Strongly agree.Pre_STAT4I use statistics in everyday life,1=Strongly agree;2=Disagree;3=Undecided;4= Agree;5=Strongly agree.Post_STAT1I like statistics,1=Strongly agree;2=Disagree;3=Undecided;4=Agree;5= Strongly agree.Post_STAT2I don’t focus when I make problems statistics,1=Strongly agree;2=Disagree;3= Undecided;4=Agree;5=Strongly agree.Post_STAT3I don’t understand statistics much because of my way of thinking,1=Strongly agree;2=Disagree;3=Undecided;4=Agree;5=Strongly agree.Post_STAT4I use statistics in everyday life,1=Strongly agree;2=Disagree;3=Undecided;4= Agree;5=Strongly agree.Pre_IDARE1I feel calm,1=Nothing;2=Little;3=Quite a bit;4=A lot.Pre_IDARE2I feel safe,1=Nothing;2=Little;3=Quite a bit;4=A lot.Pre_IDARE3I feel nervous,1=Nothing;2=Little;3=Quite a bit;4=A lot.Pre_IDARE4I’m stressed,1=Nothing;2=Little;3=Quite a bit;4=A lot.Pre_IDARE5I am comfortable,1=Nothing;2=Little;3=Quite a bit;4=A lot.Post_IDARE1I feel calm,1=Nothing;2=Little;3=Quite a bit;4=A lot.Post_IDARE2I feel safe,1=Nothing;2=Little;3=Quite a bit;4=A lot.Post_IDARE3I feel nervous,1=Nothing;2=Little;3=Quite a bit;4=A lot.Post_IDARE4I’m stressed,1=Nothing;2=Little;3=Quite a bit;4=A lot.Post_IDARE5I am comfortable,1=Nothing;2=Little;3=Quite a bit;4=A lot.PSICO1I feel good,1=Almost never;2=Sometimes;3=Frequently;4=Almost always.PSICO2I get tired quickly,1=Almost never;2=Sometimes;3=Frequently;4=Almost always.PSICO3I feel like crying,1=Almost never;2=Sometimes;3=Frequently;4=Almost always.PSICO4I would like to be as happy as others seem to be,1=Almost never;2=Sometimes;3= Frequently;4=Almost always.PSICO5I lose opportunities for not being able to decide quickly,1=Almost never;2=Sometimes;3=Frequently;4=Almost always.DetailssurveyExamples#data(survey)#maybe str(survey);plot(survey)...uis15 uis uisDescriptionuisUsageuisFormatA data frame with575observations on the following9variables.ID a numeric vectorAGE a numeric vectorBECK a numeric vectorIVHX a numeric vectorNDRUGTX a numeric vectorRACE a numeric vectorTREAT a numeric vectorSITE a numeric vectorDFREE a numeric vectorReferencesHosmer,D.(2013).Wiley Series in Probability and Statistics Ser.:Applied Logistic Regression(3).New York:John Wiley&Sons,Incorporated.Examples#data(uis)#maybe str(uis);plot(uis)...Index∗cedagechdage,2∗dataicu,4pros,10survey,12uis,15∗lowbwtlowbwt,5chdage,2confint.lsm,3icu,4lowbwt,5lsm,6predict.lsm,9pros,10summary.lsm,11survey,12uis,1516。

火力发电厂热控专工岗位职责第1篇：热控专工岗位职责热控专工岗位职责岗位关系本岗位对发电部长负责。

职责1 在发电部长的领导下，负责本专业的技术管理工作，接受生产副总经理的技术领导，并对其负责。

2 贯彻执行国家和行业的有关技术政策和上级颁发的有关规章制度。

3 对本专业管理工作提供技术支持。

4 审核本专业设备点检定修各项管理标准、工作标准和技术标准，并负责监督、指导、管理、考核。

5 审核本专业点检定修计划及零购、科技、反措的项目及费用计划等，并负责监督、指导、管理。

并与每月25号前对发电部上交月度专业备件申购计划和资金申报计划。

6 负责技改项目的立项、并组织实施，对费用进行控制，组织验收和后评估工作。

在发电部与上级安生部专业主管领导下做好月度检修计划和监督实施。

7 审核本专业设备检修作业文件包、试验标准、备品配件定额、检修工时定额，监督设备台帐及技术档案执行情况；每月30号之前做好备品备件登记。

8 审核本专业检修项目及相应的组织措施、技术措施。

审核或组织编制本专业特殊、重大检修项目的技术方案并监督执行。

9 负责本专业设备缺陷管理考核工作，严格执行公司缺陷管理规章制度；每月25号前组织专业开缺陷分析会。

10 指导本专业设备异常情况分析及采取相应防范措施；制定专业事故防范措施，并协助其它专业制定事故预案，参加本专业有关的事故分析。

11 对本专业设备的“四保持”工作进行检查与考核；每次停机后做好电子间卫生和专业设备清洁卫生。

12 负责检查、督促、指导本专业的设备检修及维护的技术工作，对本专业维护工作进行业务指导；完成每星期五组织一次专业安全和技能培训。

13 负责热控设备检修质量的厂级验收，并检查督促有关部门执行检修质量验收制度。

14跟踪设备的健康状况，组织检查、指导设备的状态诊断及设备劣化倾向分析，并提出处理意见。

15 完成每星期上交安生部和公司发电部的周工作总结和每月2号前专业月度总结；负责本专业与其他专业或部门的业务协调工作。

gcc 常见的编译警告与错误（按字母顺序排列）C语言初学者遇到的最大问题往往是看不懂编译错误，进而不知如何修改程序。

有鉴于此，本附录罗列了用gcc编译程序时经常出现的编译警告与错误。

需要提醒读者的是，出现警告（warning）并不影响目标程序的生成，但出现错误（error）则无法生成目标程序。

为便于读者查阅，下面列出了经常遇到的警告与错误，给出了中英文对照（英文按字典顺序排列），并对部分错误与警告做了必要的解释。

#%s expects \FILENAME\ or …#%s 需要 \FILENAME\ 或…#%s is a deprecated GCC extension#%s 是一个已过时的 GCC 扩展#%s is a GCC extension#%s 是一个 GCC 扩展#~ error:#~ 错误：#~ In file included from %s:%u#~ 在包含自 %s：%u 的文件中#~ internal error:#~ 内部错误：#~ no newline at end of file#~ 文件未以空白行结束#~ warning:#~ 警告：#elif after #else#elif 出现在 #else 后#elif without #if#elif 没有匹配的 #if#else after #else#else 出现在 #else 后#else without #if#else 没有匹配的 #if#endif without #if#endif 没有匹配的 #if#include nested too deeply#include 嵌套过深#include_next in primary source file#include_next 出现在主源文件中#pragma %s %s is already registered#pragma %s %s 已经被注册#pragma %s is already registered#pragma %s 已经被注册#pragma once in main file#pragma once 出现在主文件中#pragma system_header ignored outside include file#pragma system_heade 在包含文件外被忽略%.*s is not a valid universal character%.*s 不是一个有效的 Unicode 字符%s in preprocessing directive预处理指示中出现 %s%s is a block device%s 是一个块设备%s is shorter than expected%s 短于预期%s is too large%s 过大%s with no expression%s 后没有表达式%s: not used because `%.*s’ defined as `%s’ not `%.*s’ %s：未使用因为‘%.*s’被定义为‘%s’而非‘%*.s’%s: not used because `%.*s’ is poisoned%s：未使用因为‘%.*s’已被投毒%s: not used because `%.*s’ not def ined%s：未使用因为‘%.*s’未定义%s: not used because `%s’ is defined%s：未使用因为‘%s’已定义%s: not used because `__COUNTER__’ is invalid%s：未使用因为‘__COUNTER__’无效(\%s\ is an alternative token for \%s\ in C++)(在 C++ 中“%s”会是“%s”的替代标识符)(this will be reported only once per input file)(此警告为每个输入文件只报告一次)\%s\ after # is not a positive integer# 后的“%s”不是一个正整数\%s\ after #line is not a positive integer#line 后的“%s”不是一个正整数\%s\ cannot be used as a macro name as it is an operator in C++ “%s”不能被用作宏名，因为它是 C++ 中的一个操作符\%s\ is not a valid filename“%s”不是一个有效的文件名\%s\ is not defined“%s”未定义\%s\ may not appear in macro parameter list“%s不能出现在宏参数列表中\%s\ re-asserted重断言“%s”\%s\ redefined“%s重定义\/*\ within comment“/*出现在注释中\\x used with no following hex digits\\x 后没有 16 进制数字\defined\ cannot be used as a macro name“defined不能被用作宏名__COUNTER__ expanded inside directive with -fdirectives-only带 -fdirectives-only 时 __COUNTER__ 在指示中扩展__VA_ARGS__ can only appear in the expansion of a C99 variadic macro __VA_ARGS__ 只能出现在 C99 可变参数宏的展开中_Pragma takes a parenthesized string literal_Pragma 需要一个括起的字符串字面常量‘%.*s’ is not in NFC‘%.*s’不在 NFC 中‘%.*s’ is not in NFKC‘%.*s’不在 NFKC 中‘##’ cannot appear at either end of a macro expansion‘##’不能出现在宏展开的两端‘#’ is not followed by a macro parameter‘#’后没有宏参数‘$’ in identifier or number‘$’出现在标识符或数字中‘:’ without preceding ‘?’‘:’前没有‘?’‘?’ without following ‘:’‘?’后没有‘:’'return' with a value, in function returning void在void返回类型的函数中，return返回值。