Core Image provides two filters, CILightenBlendMode and CIMaximumCompositing, which both appear to use the formula outputColorComponent = max(backgroundColorComponent, foregroundColorComponent). And from their respective descriptions in the Core Image Filter Reference, it sounds like these two filters do exactly the same thing.
What's the difference between these two filters? Why isn't there just a single filter?
(The same question applies also to the CIDarkenBlendMode and CIMinimumCompositing pair of filters.)
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I am trying to crawl different websites (e-commerce websites) and extract specific information from the pages of each website (i.e. product price, quantity, date of publication, etc.).
My question is: how to configure the parsing since each website has a different HTML layout which means I need different Xpaths for the same item depending on the website? Can we add multiple parser bolts in the topology for each website? If yes, how can we assign different parsefilters.json files to each parser bolt?
You need #586. At the moment there is no way to do it but to put all your XPATH expressions regardless of the site you want to use them on in the parsefilters.json.
You can't assign different parsefilters.json to the various instances of a bolt.
UPDATE however you could have multiple XpathFilters sections within the parseFilters.json. Each could cover a specific source, however, there is currently no way of constraining which source a parse filter gets applied to. You could extend XPathFilter so that it takes some extra config e.g. regular expression a URL must match in order to be applied. That would work quite nicely I think.
I've recently added JsoupFilters which will be in the next release. These should be useful for your use case but that still doesn't solve the issue that you need an implementation of the filter that organizes the resources per host. It shouldn't be too hard to implement taking the URL filter one as a example and would also make a very nice contribution to the project.
When training a doc2vec model using a corpus in the TaggedDocument class, you can provide a list of tags. When the doc2vec model is trained it learns a vector representation for the tags. For example you could have one tag representing the document, and another representing some classification that can be shared between documents.
How would one provide additional tags when streaming a corpus using TaggedLineDocument?
The TaggedLineDocument class only considers documents to be one per line, with a single tag that is their line-number.
If you want more tags, you'll have to provide your own iterable which does that. It should only be a few lines of code, depending on where your other tags come from. You can use the source for TaggedLineDocument – which is itself only 9 lines of Python code –as a model to build on:
https://github.com/RaRe-Technologies/gensim/blob/e4199cb4e9a90df44ca59c1d0505b138caa21951/gensim/models/doc2vec.py#L1126
Note: while supplying ore than one tag per document is a natural extension of the original 'Paragraph Vectors' approach, and often can provide benefits, sometimes it also 'dilutes' the salience of each tag's vector – which will be a special concern as the average number of tags per document grows, or the model acquires many more tags than unique documents. So be sure to comparatively evaluate whether any multiple-tag strategy is helping or hurting, in different modes, and whether things like pre-known categories work better as extra tags or known-labels for some later steps.
I got a problem that I have some measurement data ( like Echo measurements), that can potentially have multiple values associated with it. In other words, you have a single measurement, but multiple values associated.
Is there a standard way to represent multiple values for a single measurement as a Observation?If so, what is the best way?
I notice that under observation, you can have multiple components, should I put my LOINC code for my measurement just at observation level and put each value at component level? Or I have to use extensions?
Thanks!
I am not sure exactly what your data looks like but here are a couple of patterns:
There is sampledData Datatype that can be used for datastreams like an EKG
example
If you have discrete values that are all interpreted together with an observation ( they can't stand alone as independent observations) the using components with an Observation.code= code, Observation.value[x] is empty , Observation.component.code= code, Observation.component.value[x]= result value. here is an example of this pattern.
In some cases you will have an Observation.value[x] as well.
Note Observation.component.code is required for each component.
For grouping indpendent observations together using component is not appropriate. This grouping is done using DiagnosticReport.result or Observation.related. The DiagnosticReport resource which typically used for reporting diagnostics in responce to an order.
I'm new to OSM querying, but would like to query vector data for a large area. Thus I need to limit the results I would like to get by tagging the request.
http://www.informationfreeway.org/api/0.6/way[tag=value][bbox=x,y,z,j]
I'd like to filter for specific tag/values when querying for a way. Though I don't know which tags/values exist. Is there a list listing the most common of them?
You are approaching your problem from the wrong direction. The number of different tags is almost unlimited. According to taginfo there are currently 75 380 856 different tags. I'm pretty sure you are not interested in most of them. Likewise you are probably not even interested in many of the most common tags.
What data do you want to query?
The OSM wiki should be your starting point for generating a list of tags you are interested in. For a generic overview take a look at the map features. Are you interested in streets? Then visit at the highway key. Routing? Then take a look at the routing wiki page.
Always remember that these lists aren't complete. People can use any tag they like (but should use well-established tags whenever possible of course).
Also consider using Overpass API instead of XAPI. Overpass API is much more powerful.
I'm looking for a way to specify that the images returned by the Google Custom Search API have a square format.
I've tried tbs=iar:s (because I've read Using the Custom Search API (REST JSON) to search for square images), but it doesn't work.
Have you an idea please ?
The problem is that tbs query parameter only applies to a regular image search on Google. For example, if you wanted to search for cat pictures with a square aspect ratio, you could do a search like this:
http://images.google.com/?q=cat&tbs=iar:s
But the Custom Search API uses a completely different set of parameters. The full list of supported parameters is shown in the REST documentation.
Some of the tbs queries do have equivalents. For example:
tbs=ic:gray translates to imgColorType=gray
tbs=isz:m translates to imgSize=medium
tbs=itp:clipart translates to imgType=clipart
But sadly there appears to be no equivalent for the iar aspect ratio filter. I tried guessing a few queries (things like imgAspectRatio=square) in case there was an undocumented parameter, but didn't have any luck with that.
The best alternative I could suggest is using imgSize=icon. This tends to return images that have a square aspect ratio, but with the unfortunate side effect that the images also tend to be rather small (the largest size I've seen returned is 256x256). Depending on your needs though, this may be good enough.
I apologise if this isn't particularly useful to you. I'm not just trying to grab the bounty on this question, so feel free not to vote this answer up. I just wanted to let you know what I had found in case it was of some help.
You can simply use both tbs=isz:l,iar:s that way it will return only large images with same aspect ration.