this is to be embeded. //0216 vim of clang - https://github.com/JBakamovic/yavide # Usage overview Category | Shortcut | Description --------------------------------- | --------------------------------- | --------------------------------- **Project management** | | | `n` | Create new project | `i` | Import project with already existing code base | `o` | Open project | `c` | Close project | `s` | Save project | `d` | Delete project **Buffer management** | | | `` | Close current buffer | `` | Save current buffer | `` | Go to next buffer | `` | Go to previous buffer | `` | Scroll buffer by one line (down) | `` | Scroll buffer by one line (up) **Buffer modes** | | | `` | Enter the `normal` mode | `` | Enter the `insert` mode (append after cursor) | `` | Enter the `insert` mode (insert before cursor) | `` | Enter the `visual` mode (line mode) | `` | Enter the `visual` mode (character mode) **Buffer editing** | | | `` | Select all | `` | Cut | `` | Copy | `` | Paste | `` | Undo | `` | Redo | `` | Delete the whole line ===========多维随机变量!!!!!!!!!=========== 1.协方差矩阵: 对角相乘,然后相除,得到的比值就是相关系数的平方。 所以协方差矩阵用于查看各个随机变量之间的(线性)相关关系。 2. ===========参数估计 及 假设检验!!!!!!!!!=========== 1.经典统计和贝叶斯统计的区别: p(x;theta),p(x|theta) 2.page 286 - 我们的卡方检测呼之欲出。。。 χ2 3.“显著性”这一说法的来历 4. 5. http://zh.wikipedia.org/wiki/%E7%9A%AE%E7%88%BE%E6%A3%AE%E5%8D%A1%E6%96%B9%E6%AA%A2%E5%AE%9A “皮尔森卡方检定”可用于两种情境的变项比较:适配度检定,和独立性检定。 “适配度检定”验证一组观察值的次数分配是否异于理论上的分配。 “独立性检定”验证从两个变量抽出的配对观察值组是否互相独立(例如:每次都从A国和B国各抽一个人,看他们的反应是否与国籍无关)。 不管哪个检定都包含三个步骤: 计算卡方检定的统计值“ \chi^2 ”:把每一个观察值和理论值的差做平方后、除以理论值、再加总。 计算 \chi^2 统计值的自由度“df”。 依据研究者设定的置信水平,查出自由度为 df 的卡方分配临界值,比较它与第1步骤得出的 \chi^2 统计值,推论能否拒绝虚无假设。 //0317 http://python.jobbole.com/81131/ # initialize the list of image URLs to download urls = [ "http://www.pyimagesearch.com/wp-content/uploads/2015/01/opencv_logo.png", "http://www.pyimagesearch.com/wp-content/uploads/2015/01/google_logo.png", "http://www.pyimagesearch.com/wp-content/uploads/2014/12/adrian_face_detection_sidebar.png", ] # loop over the image URLs for url in urls: # download the image URL and display it print "downloading %s" % (url) image = url_to_image(url) cv2.imshow("Image", image) cv2.waitKey(0) # METHOD #2: scikit-image from skimage import io # loop over the image URLs for url in urls: # download the image using scikit-image print "downloading %s" % (url) image = io.imread(url) cv2.imshow("Incorrect", image) cv2.imshow("Correct", cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) cv2.waitKey(0) 判别式模型(Discriminative Model)是直接对条件概率p(y|x;θ)建模。常见的判别式模型有 线性回归模型、线性判别分析、支持向量机SVM、神经网络等。 即得到的模型是一个分类器 生成式模型(Generative Model)则会对x和y的联合分布p(x,y)建模,然后通过贝叶斯公式来求得p(yi|x),然后选取使得p(yi|x)最大的yi,即: 即得到的模型是一个生成器