分析二元关系集合性质及生成反例的C++程序实现

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资源摘要信息:"确定二元关系集合的性质 在数学的集合论和离散数学中,二元关系是定义在两个集合之间的一种特殊关系。对于任意的二元关系集合,我们可以通过一系列的判定准则来分析它是否具备以下几种基本性质:自反性、对称性、传递性、反对称性以及反自反性。这些性质对于理解和描述关系的特性至关重要。 自反性:如果集合中的每一个元素都与自身在该关系中,则称该关系具有自反性。例如,在小于等于关系'≤'中,对于任意自然数a,都有a≤a,所以'≤'关系是自反的。 对称性:如果对于任意两个元素a和b,当a与b在该关系中时,b与a也在该关系中,则称该关系具有对称性。在等于关系'='中,对于任意元素a和b,如果a=b,则b=a,因此'='是具有对称性的。 传递性:如果对于任意三个元素a、b和c,当a与b以及b与c都在该关系中时,a与c也必须在该关系中,则称该关系具有传递性。在小于关系'<'中,如果a<b且b<c,则必然有a<c,因此'<'是传递的。 反对称性:如果对于任意两个不同的元素a和b,当a与b以及b与a都在该关系中时,那么a必须等于b,则称该关系具有反对称性。在小于等于关系'≤'中,如果a≤b且b≤a,则必然有a=b,因此'≤'具有反对称性。 反自反性:如果集合中的每一个元素都不与自身在该关系中,则称该关系具有反自反性。例如,在严格大于关系'>'中,没有任何自然数a满足a>a,因此'>'是反自反的。 为了自动化判定一个二元关系是否具有上述性质,我们通常需要使用计算机编程。在本资源中,我们将通过C++编程语言来实现这一判定过程。具体来说,我们会编写一个C++程序,该程序接收一组二元关系数据作为输入,然后逐一检验这些数据是否符合上述五种性质。对于每一个性质,如果该关系不具备,程序将会输出至少一个反例来证明该性质不成立。 在C++中,我们可以使用结构体或类来表示一个二元关系,并使用二维数组、向量或映射(map)来存储关系中元素对的集合。然后通过一系列的函数或方法来判断关系的性质。例如,要判断一个关系是否是自反的,我们可以遍历集合中的所有元素,检查每个元素与自身的对应关系是否存在于二元关系集合中。类似的方法也可以用于检验对称性、传递性、反对称性和反自反性。 由于二元关系的性质判定是离散数学中的一个基本问题,因此,本资源所涉及的程序不仅在理论上具有重要性,而且在实际应用中也非常有用,如在数据库理论、人工智能和计算机网络等领域都有广泛的应用。 通过编写和运行这样的程序,我们不仅能够加深对二元关系性质的理解,而且还能够掌握如何使用C++解决实际的数学问题。这对于计算机科学专业的学生或者对算法感兴趣的程序员来说都是非常宝贵的实践机会。"

翻译Device configuration register The device has various configuration settings that are global in nature. The configuration settings are as follows: • When the 33978 is in the overvoltage region, a Logic [0] on the VBATP OV bit limits the wetting current on all input channels to 2 mA and the 33978 will not be able to enter into the Low-power mode. A Logic [1] allows the device to operate normally even in the overvoltage region. The OV flag will be set when the device enters in the OV region, regardless the value of the VBATP OV bit. • WAKE_B can be used to enable an external power supply regulator to supply the VDDQ voltage rail. When the WAKE_B VDDQ check bit is a Logic [0], the WAKE_B pin is expected to be pulled-up internally or externally to VDDQ and VDDQ is expected to go low, therefore the 33978 does not wake-up on the falling edge of WAKE_B. A Logic [1], assumes the user is using an external pull-up to VBATP or VDDQ (when VDDQ is not expected to be off) and the IC wakes up on a falling edge of WAKE_B. • INT_B out is used to select how the INT_B pin operates when an interrupt occurs. The IC is able to pulse low [1] or latch low [0]. • Aconfig[1-0] is used to determine the method of selecting the AMUX output, either a SPI command or using a hardwired setup using SG[3-1]. • Inputs SP0-7 may be programmable for switch-to-battery or switch-to-ground. These inputs types are defined using the settings command. To set a SPn input for switch-to-battery, a logic [1] for the appropriate bit must be set. To set a SPn input for switch-toground, a logic [0] for the appropriate bit must be set. The MCU may change or update the programmable switch register via software at any time in Normal mode. Regardless of the setting, when the SPn input switch is closed a logic [1] is placed in the serial output response register.

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import numpy as np def sigmoid(x): # the sigmoid function return 1/(1+np.exp(-x)) class LogisticReg(object): def __init__(self, indim=1): # initialize the parameters with all zeros # w: shape of [d+1, 1] self.w = np.zeros((indim + 1, 1)) def set_param(self, weights, bias): # helper function to set the parameters # NOTE: you need to implement this to pass the autograde. # weights: vector of shape [d, ] # bias: scaler def get_param(self): # helper function to return the parameters # NOTE: you need to implement this to pass the autograde. # returns: # weights: vector of shape [d, ] # bias: scaler def compute_loss(self, X, t): # compute the loss # X: feature matrix of shape [N, d] # t: input label of shape [N, ] # NOTE: return the average of the log-likelihood, NOT the sum. # extend the input matrix # compute the loss and return the loss X_ext = np.concatenate((X, np.ones((X.shape[0], 1))), axis=1) # compute the log-likelihood def compute_grad(self, X, t): # X: feature matrix of shape [N, d] # grad: shape of [d, 1] # NOTE: return the average gradient, NOT the sum. def update(self, grad, lr=0.001): # update the weights # by the gradient descent rule def fit(self, X, t, lr=0.001, max_iters=1000, eps=1e-7): # implement the .fit() using the gradient descent method. # args: # X: input feature matrix of shape [N, d] # t: input label of shape [N, ] # lr: learning rate # max_iters: maximum number of iterations # eps: tolerance of the loss difference # TO NOTE: # extend the input features before fitting to it. # return the weight matrix of shape [indim+1, 1] def predict_prob(self, X): # implement the .predict_prob() using the parameters learned by .fit() # X: input feature matrix of shape [N, d] # NOTE: make sure you extend the feature matrix first, # the same way as what you did in .fit() method. # returns the prediction (likelihood) of shape [N, ] def predict(self, X, threshold=0.5): # implement the .predict() using the .predict_prob() method # X: input feature matrix of shape [N, d] # returns the prediction of shape [N, ], where each element is -1 or 1. # if the probability p>threshold, we determine t=1, otherwise t=-1

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