如何学会打麻将如何快速学会打麻将

如何学会打麻将如何快速学会打麻将 ————点击图片进入游戏—————— 有真的也有假的


假的就是对你需求不关心，直接给你说个时间，然后就跟你要钱


真的是具体的问你很多细节，包括时间都是没办法给你个固定的，只能给你估个大概的时间


做这个东西是需要技术实力的


之前给一个客户做过，他是需要透视


当时的过程简单还原一下吧


用Fiddler进行数据截取








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 os
import sys
import urllib.request


import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.datasets import make_blobs, make_circles
from sklearn.preprocessing import StandardScaler


CANCER_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!")
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