metric

Could anybody tell me how could I compute Equal Error Rate(EER) from ROC Curve in python? In scikit-learn there is method to compute roc curve and auc but could not find the method to compute EER. from sklearn.metrics import roc_curve, auc ANSRWER: I think I implemented myself. The idea of ROC EER is the intersection point between a stright line joining (1,0) and (0,1) and the roc Curve. It is a only point where it intersects. For a straight line with a=1 and b=1, the equation would be x+y =1 (x/a +y/b =1.0) . So the intersection point would be the values of true positive rate (tpr) and false

I'm using the facebook api to pull some metrics, I want to get the "talking about this" metric, I reviewd the "Stories and People talking about this" from the facebook api and I'm pulling the "page_storytellers" metric, but I see that the number from this metric does not match with the "talking about this" metric from the facebook original page. My question here is How does the facebook api calculate this value for (page_storytellers metric)? The "page_storytellers" metric is the correct one to get the "talking about this metric"? Thanks for your help http://developers.facebook.com/docs

1 实验目的 理解RIP路由表的建立与更新 感受RIP坏消息传得慢 2 实验内容 使用Packet Tracer，正确配置网络参数，使用命令查看和分析RIP路由信息。 建立网络拓扑结构 配置参数 分析RIP路由信息 3. 实验报告 姓名 .林雅静　 学号 201821121068 班级 计算1813 3.1 建立网络拓扑结构 网络拓扑图如下图所示： 3.2 配置参数 客户端1的IP地址为192.168.1.68 客户端2的IP地址为 路由器配置 （1）配置并激活端口 （2）配路由算法 （3）验证参数配置是否正确 3.3 测试网络连通性 在PC1，PING PC2，测试整条链路的连通性，给出截图。 如果不能正常连通，尝试使用 show ip route 和 show ip interface brief 等命令找到原因。 3.4 理解RIP路由表建立和更新 查看路由过程的信息 show ip protocols Sending updates every 30 seconds, next due in 29 seconds ：每30秒广播一次路由表，下一次更新在29秒之后 Invalid after 180 seconds ,hold down 180,flushed after 240 ：180秒后无效，限制180到240秒刷新 FastEthernet0/0 2 2 ：F0

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: In macOS Sierra, installation for xgboost with openmp enabled always fails. From https://xgboost.readthedocs.io/en/latest/build.html , I've tried: cp make/config.mk ./config.mk; make -j4 With, export CC=/usr/local/Cellar/llvm/4.0.0_1/bin/clang export CXX=/usr/local/Cellar/llvm/4.0.0_1/bin/clang++ export CXX1X=/usr/local/Cellar/llvm/4.0.0_1/bin/clang++ It fails with, clang-4.0clang-4.0: : warning: argument unused during compilation: '-pthread' [-Wunused-command-line-argument]warning: argument unused during compilation: '-pthread' [-Wunused

---恢复内容开始--- 一.个人信息 　　姓名：李宗政 　　学号：201821121029 　　班级：计算1811 二.实验内容 （1） 建立网络拓扑结构 （2）配置参数 PC0: IP:192.168.1.29 Dafault Gateway:192.168.1.30 PC1: IP:192.168.3.30 Dafault Gateway:192.168.2.29 路由器参数配置以及协议设定 R0: Router>enable Router # config Configuring from terminal, memory, or network [terminal] ? Enter configuration commands, one per line. End with CNTL/Z. Router (config) #exit Router#config t Enter configuration commands, one per line. End with CNTL/Z. Router (config) #interface Fa0/0 Router (config-if)#ip adress 192.168.1.30 255.255.255.0 Router (config-if) #no shutdown Router (config-if) #exit

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I need to creeate an new Spark DF MapType Column based on the existing columns where column name is the key and the value is the value. As Example - i've this DF: rdd = sc.parallelize([('123k', 1.3, 6.3, 7.6), ('d23d', 1.5, 2.0, 2.2), ('as3d', 2.2, 4.3, 9.0) ]) schema = StructType([StructField('key', StringType(), True), StructField('metric1', FloatType(), True), StructField('metric2', FloatType(), True), StructField('metric3', FloatType(), True)]) df = sqlContext.createDataFrame(rdd, schema) +----+-------+-------+-------+ | key|metric1

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 由 翻译 强力驱动 问题: UPDATED: In the end, the solution I opted to use for clustering my large dataset was one suggested by Anony-Mousse below. That is, using ELKI's DBSCAN implimentation to do my clustering rather than scikit-learn's. It can be run from the command line and with proper indexing, performs this task within a few hours. Use the GUI and small sample datasets to work out the options you want to use and then go to town. Worth looking into. Anywho, read on for a description of my original problem and some interesting discussion. I have a

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I was trying to use DBSCAN algorithm from scikit-learn library with cosine metric but was stuck with the error. The line of code is db = DBSCAN(eps=1, min_samples=2, metric='cosine').fit(X) where X is a csr_matrix . The error is the following: Metric 'cosine' not valid for algorithm 'auto', though the documentation says that it is possible to use this metric. I tried to use option algorithm='kd_tree' and 'ball_tree' but got the same. However, there is no error if I use euclidean or, say, l1 metric. The matrix X is large, so I can't use a

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I've created a simple data frame ( dput below): date ticker value ------------------------------ 2016-06-30 A2M.ASX 0.0686 2016-07-29 A2M.ASX -0.0134 2016-08-31 A2M.ASX -0.0650 2016-09-30 A2M.ASX 0.0145 2016-10-31 A2M.ASX 0.3600 2016-11-30 A2M.ASX -0.1429 I want to change the value column's name to whatever is in my metric variable name, and I want to do it in a dplyr way. My sample data: df = structure(list(date = c("2016-06-30", "2016-07-29", "2016-08-31", "2016-09-30", "2016-10-31", "2016-11-30"), ticker = c("A2M.ASX", "A2M.ASX", "A2M.ASX

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: Currently I'm doing a project which may require using a kNN algorithm to find the top k nearest neighbors for a given point, say P. im using python, sklearn package to do the job, but our predefined metric is not one of those default metrics. so I have to use the user defined metric, from the documents of sklearn, which can be find here and here . It seems that the latest version of sklearn kNN support the user defined metric, but i cant find how to use it: import sklearn from sklearn.neighbors import NearestNeighbors import numpy as np from