Intuitively, a canonical cover of a set of dependencies F is the “minimal” set of FDs equivalent to F. • No redundant dependencies or extraneous attributes in any one of the dependencies. • Formally, A canonical cover for F is a set of dependencies Fc such that • F logically implies all dependencies in Fc, and • Fc logically implies all dependencies in F, and • No functional dependency in Fc contains an extraneous attribute, and • Each left side of functional dependency in Fc is unique. 用中文详细讲解以上内容及相关知识点
时间: 2024-02-29 13:54:02 浏览: 34
"规范覆盖"是指一组等价于给定依赖关系集合 F 的最小函数依赖集合。规范覆盖需要满足以下条件:
1. 覆盖所有 F 中的函数依赖;
2. 没有冗余的函数依赖;
3. 没有任何一个函数依赖包含多余的属性;
4. Fc 中任何一个函数依赖的左侧都是唯一的。
在数据库中,函数依赖是指关系模式中一个属性或属性集合的取值决定了其他属性或属性集合的取值。规范覆盖的作用是简化关系模式,避免数据冗余和不一致性。通过找到规范覆盖,我们可以得到一个最小的关系模式,其中每个属性都是必需的。这有助于提高数据库的性能和可维护性。
相关问题
xf = (x[0] - b/a)*np.exp(-a*np.arange(len(x))) + b / a
is the initial value of the exponential decay.
To see why, let's first consider the case where b=0, so we have:
xf = x[0]*np.exp(-a*np.arange(len(x)))
In this case, we start with x[0] and each subsequent value is a fraction of the previous value, given by the exponential decay factor np.exp(-a). So the decay rate a determines how quickly the values decay to zero.
Now let's consider the more general case where b is nonzero. We can rewrite the equation as:
xf = (x[0] - b/a)*np.exp(-a*np.arange(len(x))) + b/a*np.exp(-a*np.arange(len(x)))
The second term is just like the previous equation, but with a constant offset of b/a. So we can interpret b/a as the initial value of the exponential decay, which is then shifted up by b/a.
This makes sense intuitively, since b represents a constant baseline that we want to subtract from the original signal x. So the exponential decay starts from x[0]-b/a, which is the initial value after the baseline subtraction.
用英语回答,Fisher信息量是什么?如何理解?
The Fisher information is a measure of how much information a random variable X carries about an unknown parameter θ of a probability distribution. It is defined as the expected value of the square of the derivative of the log-likelihood function with respect to the parameter θ.
In other words, the Fisher information quantifies the sensitivity of the log-likelihood function to changes in the parameter θ. A higher Fisher information indicates that small changes in θ will have a larger impact on the log-likelihood function, and therefore provide more information about the parameter.
Intuitively, we can think of the Fisher information as a measure of the precision with which we can estimate a parameter based on a given amount of data. A higher Fisher information means that we can estimate the parameter more precisely, while a lower Fisher information means that our estimates will be more uncertain.
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