aronwc

""" Example using GenSim's LDA and sklearn. """

import numpy as np

from gensim import matutils
from gensim.models.ldamodel import LdaModel
from sklearn import linear_model
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer

rjweiss

{
 "metadata": {
  "name": "Python_classification.ipynb"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {

dstahlke

# Compute the Lovasz, Schrijver, and Szegedy numbers for graphs.
#
# A graph with 32 vertices takes under one second, so it's not the fastest.
# Probably the specialized code for Lovasz number from
#   http://dollar.biz.uiowa.edu/~sburer/pmwiki/pmwiki.php%3Fn=Main.SDPLR%3Faction=logout.html
# would be much faster (I haven't tried it).

# Copyright (c) 2013 Daniel Stahlke ([email protected])
#
# Permission is hereby granted, free of charge, to any person obtaining a copy

blmarket

// run this with sbt run

import org.apache.hadoop.hbase._
import org.apache.hadoop.hbase.client._
import org.apache.hadoop.hbase.util._
import org.apache.hadoop.hbase.client.{HBaseAdmin,Scan,Result}
import org.apache.hadoop.hbase.mapreduce.{TableMapper,TableMapReduceUtil}
import org.apache.hadoop.hbase.HBaseConfiguration
import org.apache.hadoop.hbase.io.ImmutableBytesWritable

qrtt1

由於 Google API 久久才會用一次，但它的文件對我來說實在是不好閱讀，應該花點時間把它筆記起來才是。

學習曲線分析

閱讀 Google API 使用文件，覺得難以閱讀的主要原因是它分成許多部分，每一部分不一定跟你要做的功能相關，而是在滿足你能使用 API 的基本條件。要攻略使用文件就必需清楚知道哪些是屬於「共通科目」、「必修科目」屬於任何產品的 API 使用前都必需滿足的。

內容分為下列主題：

1. API 的設計哲學：Service Discovery (描述 API 的 API)
2. API 開通與證認：API Console & OAuth 2

mages

Interactive charts and slides with R, googleVis and knitr
========================================
```{r results='asis', echo=FALSE, message=FALSE, tidy=FALSE}
library(googleVis)
G <- gvisGeoChart(Exports, "Country", "Profit", 
                  options=list(width=250, height=120), chartid="c1")

T <- gvisBarChart(Exports[,1:2], yvar="Profit", xvar="Country",                  
                  options=list(width=250, height=260, 
                               legend='none'), chartid="c2")

diogojc

import numpy as np
from scipy.sparse import csc_matrix

def pageRank(G, s = .85, maxerr = .001):
    """
    Computes the pagerank for each of the n states.

    Used in webpage ranking and text summarization using unweighted
    or weighted transitions respectively.

hdclark

// Compiling with  g++ Shogun.cpp -lshogun -o shoguntest


#include <shogun/features/Labels.h>
#include <shogun/features/SimpleFeatures.h>
#include <shogun/kernel/GaussianKernel.h>
#include <shogun/classifier/svm/LibSVM.h>
#include <shogun/base/init.h>
#include <shogun/lib/common.h>
#include <shogun/io/SGIO.h>

mblondel

# Mathieu Blondel, September 2010
# License: BSD 3 clause

import numpy as np
from numpy import linalg
import cvxopt
import cvxopt.solvers

def linear_kernel(x1, x2):
    return np.dot(x1, x2)