需要从代码层次入手，因为平台采用了加密通讯
这里面找到CreateRawCall 函数，这个就是收发数据的地方，然后再进入这个函数去看看 内部的一些关键代码
很快就找到了 协议所在的地方，只需要破解了游戏的正常协议通讯规则，就可以模拟游戏APP发送数据包，比如某个牌还没有开出来，这个时候可以通过程序，模拟发送一次开牌 ，提前开牌，这就是这个东西的原理了 有需要参考下图找我交流
""" Implementation of sequential minimal optimization (SMO) for support vector machines (SVM). Sequential minimal optimization (SMO) is an algorithm for solving the quadratic programming (QP) problem that arises during the training of support vector machines. It was invented by John Platt in 1998.Input: 0: type: numpy.ndarray. 1: first column of ndarray must be tags of samples, must be 1 or -1. 2: rows of ndarray represent samples.Usage: Command: python3 sequential_minimum_optimization.py Code: from sequential_minimum_optimization import SmoSVM, Kernel kernel = Kernel(kernel='poly', degree=3., coef0=1., gamma=0.5) init_alphas = np.zeros(train.shape[0]) SVM = SmoSVM(train=train, alpha_list=init_alphas, kernel_func=kernel, cost=0.4, b=0.0, tolerance=0.001) SVM.fit() predict = SVM.predict(test_samples)Reference: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/smo-book.pdf https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/tr-98-14.pdf http://web.cs.iastate.edu/~honavar/smo-svm.pdf"""import osimport sysimport urllib.requestimport matplotlib.pyplot as pltimport numpy as npimport pandas as pdfrom sklearn.datasets import make_blobs, make_circlesfrom sklearn.preprocessing import StandardScalerCANCER_DATASET_URL = "http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data"class SmoSVM: def __init__( self, train, kernel_func, alpha_list=None, cost=0.4, b=0.0, tolerance=0.001, auto_norm=True, ): self._init = True self._auto_norm = auto_norm self._c = np.float64(cost) self._b = np.float64(b) self._tol = np.float64(tolerance) if tolerance > 0.0001 else np.float64(0.001) self.tags = train[:, 0] self.samples = self._norm(train[:, 1:]) if self._auto_norm else train[:, 1:] self.alphas = alpha_list if alpha_list is not None else np.zeros(train.shape[0]) self.Kernel = kernel_func self._eps = 0.001 self._all_samples = list(range(self.length)) self._K_matrix = self._calculate_k_matrix() self._error = np.zeros(self.length) self._unbound = [] self.choose_alpha = self._choose_alphas() # Calculate alphas using SMO algorithm def fit(self): K = self._k state = None while True: # 1: Find alpha1, alpha2 try: i1, i2 = self.choose_alpha.send(state) state = None except StopIteration: print("Optimization done!\nEvery sample satisfy the KKT condition!") break
又如：在你坐庄的十四张牌里真人棋牌游戏，除了两乘一坎外，剩下五张为一对三饼做将，还有五、六和一张三条，无疑是打出三条听四、七条。
