python 井字棋

python演示神经网络来逼近股价走势的预测，基于时间序列的预测(Python demonstrates neural network to approximate the prediction of stock price trend, based on time series prediction)

说明：  实现双目测距功能，需要python环境，安装OpenCV，自行标定(To achieve binocular ranging function, you need Python environment, install OpenCV, self-calibration)

说明：  包括分类与预测、聚类分析、关联基本挖掘建模办法，规则、时序模式以及离群点监测(It includes classification and prediction, clustering analysis, association basic mining modeling methods, rules, temporal patterns and outlier monitoring)

说明：  基于经验模态分解成多个模态和一个残余量，再利用长短神经网络预测分别训练每一个模态和残余量，最后重构结果，得到预测结果(Prediction based on empirical mode decomposition and long short neural network)

python实现决策树分类的三种经典算法(Python realizes three classical arithmetic of decision tree classification)

基于开源框架scrapy-redis开发的分布式网络爬虫，编程语言为python,数据库为mongodb,采用抓取新浪微博平台的进行测试。

说明：  利用python实现的二分法思想以及几种排序、二叉树的删除算法实现(The idea of dichotomy realized by Python and the implementation of several sorting and deletion algorithms of binary tree)

1. 以身高为例，画出男女生身高的直方图并做对比； 2. 采用最大似然估计方法，求男女生身高以及体重分布的参数； 3. 采用贝叶斯估计方法，求男女生身高以及体重分布的参数（注明自己选定的参数情况）； 4. 采用最小错误率贝叶斯决策，画出类别判定的决策面。并判断某样本的身高体重分别为(160,45)时应该属于男生还是女生？为(178,70)时呢？(1. Take the height as an example, draw the histogram of the height of boys and girls and make a comparison. 2. Using the maximum likelihood estimation method, the parameters of height and weight distribution of male and female students are obtained. 3. Using Bayesian estimation method, the parameters of height and weight distribution of male and female students were calculated (indicating the parameters selected by themselves). 4. Using Bayesian decision-making with minimum error rate, the decision-making surface of category decision-making is drawn. When the height and weight of a sample are (160,45), should it belong to boys or girls? For (178,70)?)

说明：  python core test files

pydev models example