bryanyang0528

# delete local tag '12345'
git tag -d 12345
# delete remote tag '12345' (eg, GitHub version too)
git push origin :refs/tags/12345
# alternative approach
git push --delete origin tagName
git tag -d tagName

bryanyang0528

from math import (
        pi,
        cos,
        sin,
        log,
        atan,
        exp
    )

bryanyang0528

#!/bin/sh

#
# chkconfig: 35 99 99
# description: Node.js /home/nodejs/sample/app.js
#

. /etc/rc.d/init.d/functions

USER="nodejs"

bryanyang0528

Web Development with Laravel 5

目標

如何在開發的過程中加入測試。

1. Model
2. Repository
3. Controller
4. Auth

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import tensorflow as tf
import numpy as np
import time

N=10000
K=4
MAX_ITERS = 1000

start = time.time()

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1. General Background and Overview

• Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
• Models and Issues in Data Stream Systems
• Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
• Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
• [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

bryanyang0528

http://d.stavrovski.net/blog/post/how-to-install-and-setup-oracle-java-jdk-in-centos-6

# rpm
wget --no-cookies \
--no-check-certificate \
--header "Cookie: oraclelicense=accept-securebackup-cookie" \
"http://download.oracle.com/otn-pub/java/jdk/7u55-b13/jdk-7u55-linux-x64.rpm" \
-O jdk-7-linux-x64.rpm

# ubuntu

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core-site.xml
=================================
<property>
  <name>fs.defaultFS</name>
  <value>hdfs://ec2-54-148-213-237.us-west-2.compute.amazonaws.com</value>
</property>
<property>
  <name>hadoop.tmp.dir</name>
  <value>/home/hadoop/local/var/hadoop/tmp/hadoop-${user.name}</value>
</property>

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# coding=utf-8
# Goal: parse house information for each district from websites
# for each district, get 「土地區段位置或建物區門牌」,「建物型態」,「建物現況格局」,「坪數」,「屋齡」,「總價元」,「資料來源」into csv file

# Procedure:
# 1. get the number of page for each district by parsing first html content
# 2. for each district put all html page together, use htmlparser to parse content and save data into file

import sys
import math

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package thunder.streaming

import org.apache.spark.{SparkConf, Logging}
import org.apache.spark.rdd.RDD
import org.apache.spark.SparkContext._
import org.apache.spark.streaming._
import org.apache.spark.streaming.dstream.DStream
import org.apache.spark.mllib.clustering.KMeansModel

import scala.util.Random.nextDouble