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I'm working on a book for Manning and want to use Alchemy News API as part of one of the examples. I have a free license which says it allows for 1,000 transactions per day. Does that mean 1,000 queries or something else? I hit the limit today way earlier than I expected to, at significantly less than 1,000 queries.
Each type of call has different amounts of transactions it uses. The text analysis uses just 1 transaction but the image analysis uses more. I believe it used about 5 transactions per image but it's been awhile since I've used the image recognition.
The number of transactions used is given in the response from AlchemyAPI. It also gives you more details in the documentation.
Query Cost + Result Cost = Total Cost
This is something I learnt today. Therefore you'll run out of transactions easily with loads of data. Keep your queries to a minimum.
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is there a specific reason why Ryanair is missing from the QPX Express API results while it appears in a normal search on Google Flight?
Thanks
Max
I noticed that the QPX API and Ita Software Matrix (by Google) gives the same results, not including a lot of companies like Ryanair.
I asked Google Support about it.
The answer is:
"QPX Express API and Matrix only include fares that are filed with ATPCO. There are a number of small carriers and low cost carriers that do not file."
Asking Google if they consider to add these flights to their APIs the answer was:
"Airlines determine which distribution channel they want to participate in. If any of these airlines were to file with ATPCO, then we would certainly be interested."
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I'm working on a school project on product analysis which is based on sentimental analysis. I've been looking for a training dataset for quite a some time now and what I've been able to find so far is a dataset for movie reviews. My question is, can I use this dataset for training the classifier, i.e. will it have an effect on the accuracy of classification? If so, does anyone here know where I can get a free dataset for product reviews?
I am assuming you are using some textual model like the bag of words model.
From my experiments, you usually don't get good results when changing from one domain to another (even if the train data set and the test are all products, but of different categories!).
Think of it logically, an oven that gets hot quickly usually indicate a good product. Is it also the same for laptops?
When I experimented with it a few years ago I used amazon comments as both train set and also to test my algorithms.
The comments are short and informative and were enough to get ~80% accuracy. The 'ground' truth was the stars system, where 1-2 stars were 'negative', 3 stars - 'neutral', and 4-5 stars 'positive'.
I used a pearl script from esuli.it to crawl amazon's comments.
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I should to prepare myself for upcoming task which consist of a lot of graphs.
I need some data (available in free domain) to train myself.
Bigger - is better...
could you suggest some open data resource?
I'll appreciate this.
You can visit http://snap.stanford.edu/data/ . It contains many different kind of network or graph data.
Here is an answer for your could you suggest some open data resource? and not for which consist of a lot of graphs. So, plz, keep it in mind.
Here (data.gov.au) you can find a huge datasets (864!) of a different types in a different formats (txt, csv, xml, ). You will find a Finance, Industry, Geography, etc. datasets.
In other case, if you want some special (and meaningful data, for example, global population density) you can see this (a bit outdated, but usefull) source from readwriteweb.com.
And one more source: "Open Governmental Datasets" - it's worth to see it indeed.
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For the purposes of history on wikipedia, is anyone familiar with the origin of the phrase "embarrassingly parallel". I've always thought that it may have been coined by a random Google employee who first worked on map-reduce. Does anyone have any concrete info on the origin?
The first use I could find in an advanced Google book search was from an IEEE Computer Society digest published in 1978. The context and the fact that the author had "embarrassingly" in quotes indicates to me that the phrase was not coined here, but had been used before this.
It's decades old, but I first heard it used in reference to graphics rendering. Imagine you're rendering an animated movie: each frame is 2000x1000 pixels, there are 24 frames per second, 60 seconds in a minute, and 100 minutes in the movie. That's almost 300 billion pixels that can all be computed in parallel. That's so parallel that it's embarassing to compute it serially.
Try this search : http://www.google.co.nz/search?rlz=1C1GGLS_enNZ364NZ365&sourceid=chrome&ie=UTF-8&q=etymology+embarrassingly+parallel
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i would like to know what algorithm and what programming language wikipedia is using to randomly choose an article to display.
i would also like to know how does it work so fast?
Here's information on that.
Every article is assigned a random number between 0 and 1 when it is created (these are indexed in SQL, which is what makes selection fast). When you click random article it generates a target random number and then returns the article whose recorded random number is closest to this target.
If you are interested you can read the actual code here.
Something along this lines:
"SELECT cur_id,cur_title
FROM cur USE INDEX (cur_random)
WHERE cur_namespace=0 AND cur_is_redirect=0
AND cur_random>RAND()
ORDER BY cur_random
LIMIT 1"
From MediaWiki.org:
MediaWiki is a free software wiki
package written in PHP, originally
for use on Wikipedia. It is now used
by several other projects of the
non-profit Wikimedia Foundation and by
many other wikis, including this
website, the home of MediaWiki.
MediaWiki is open source, so you can download the code and inspect it, to see how they have implemented this feature.
If you look at the source, they use PHP/MySQL a sort and filter pages by pregenerated random values (page_random column) that have an index on them.