Kovrinic

# one or the other, NOT both

[url "https://github"]
  insteadOf = git://github

# or

[url "[email protected]:"]
  insteadOf = git://github

andresbott

taken from: http://danielsokolowski.blogspot.com.es/2013_04_01_archive.html

This due to the way MPPE Microsoft point-to-point Encryption encodes data which results in the packet size being bigger then what was agreed in the VPN handshake - is my guess. There is a reported bug from 2005 which sadly hast not yet been addressed.
Fixing the issue by increasing the MTU
You can't fix this issue by modifying the MTU/MRU settings in '/etc/ppp/options' directly, you have to adjust the MTU after the PPP connection is up and this can be accomplished by adding a custom 'ip-up' script.

Below is my workaround script, place it into file '/etc/ppp/ip-up.d/mppefixmtu' and ensure that it is executable ('chmod +x mppefixmtu'):

#!/bin/sh
CURRENT_MTU="`ifconfig $1 | grep -Po '(?<=MTU:)([0-9]+)'`"

laispace

git config --global https.proxy http://127.0.0.1:1080

git config --global https.proxy https://127.0.0.1:1080

git config --global --unset http.proxy

git config --global --unset https.proxy


npm config delete proxy

chrisgillis

package main

import (
    "fmt"
	  "log"
    "net"
    "net/mail"
	  "net/smtp"
    "crypto/tls"
)

huowa222

Apache Mahout 是 ApacheSoftware Foundation (ASF) 旗下的一个开源项目，提供一些可扩展的机器学习领域经典算法的实现，旨在帮助开发人员更加方便快捷地创建智能应用程序，并且，在 Mahout 的最近版本中还加入了对Apache Hadoop 的支持，使这些算法可以更高效的运行在云计算环境中。

在现实中广泛使用的推荐系统一般都是基于协同过滤算法的，一般分为基于用户(user)的协同过滤算法和基于项目（item）的协同过滤算法，这类算法通常都需要计算user和user，item和item之间的相似度，对于不同的数据量和不同类型的数据源，都需要不同的相似度计算方法来提高推荐性能和提高推荐的准确度，幸好在mahout中提供了大量的计算相似度的组件，这些组件实现了一些不同的相似度计算方法：
如图1：基于user的相似度计算


1.Pearson correlation-based similarity（皮尔森用户协同推荐算法）（一般不建议使用）
(PearsonCorrelationSimilarity) 根据用户对于物品的喜好分数进行用户的打分进行推荐，用户之间的相似度从-1~1

fatih

package main

import (
	"container/ring"
	"fmt"
	"time"
)

func main() {
	coffee := []string{"kenya", "guatemala", "ethiopia"}

luw2007

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ICTPOS3.0词性标记集

n 名词

nr 人名

davidnunez

pm list packages -f