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I need to calculate my shipping based on dimensions. Some of my items are less than 2 pounds but are larger in size.
I googled and found a $240 extension for this but it's too expensive.
Is there any alternative where I can send dimensions to UPS, USPS and Fedex and get the shipping rates from them in my Magento store?
Shipping based on dimensions just ends up being reduced back to weight. Which is why it is called dimensional weight.
http://www.fedex.com/gb/tools/dimweight.html
Basically just enter the dimensional weight or the actual weight of the product when creating your products and shipping will be accurate.
Technically you could program this problem away, but it would be hard to justify the effort.
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How to configure a policy engine that is suitable to calculate risks based on attributes that have been collected from the user based on given access control. for instance Attribute based access control ?
is there a risk algorithm that can be helpful in this case ?
XACML itself (the spec) and its alternatives (OPA, Oso, ...) do not tell you how to compute a risk score. There are risk score engines for that. For instance take into account user attributes e.g. age, years driving, alcohol level in your bloodstream... Policy engines need to know a risk score but the calculation of that score should be devolved to risk engines.
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Given an input-image & an expression-image. I want to put the expression(smile,laugh,anger,etc) into input image. I have tried some logics using opencv & dlib. But couldnot generate any good result.
Method tried:
A. 1). Fetch landmark points of two faces. 2). Fetch the region of interest(whole face / lips / eyes /etc) 3). Use opencv shape transformation. Warp the ROI based of landmark-points-match 4). Put back the warped-ROI back into original image.
B. The ML project on git https://github.com/datitran/face2face-demo says to transfers expressions to only a specific person for which it is trained. But I want to have some generic.
Any guidance on what should be the correct/better path to take; Is there any known library ?
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I am trying to train tenserflow U-net, for multi-class segmentation of heart. I have 3 labels and the prediction has 3 probability maps (one probability map for every label). I trained with momentum optimizer which is also the default optimizer of the network. In the very beginning iterations, the probability mapping of label 1 and label 2 are different but after some iterations (or epochs) the probability map of label 1 and label 2 become exactly like each other and technically I have a binary label segmentation. I have seen other networks that have similar architecture like U-net and they have trained on the multi-class dataset. I want to find some multiclass segmentation examples with U-net but all examples are binary.
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Is there any probabilistic algorithm for calculating set cardinality taking into account that must support deleting elements from set? I've been using HyperLogLogs for calculating cardinalities of some sets and their unions but when necessity of deleting elements from set emerged my current solution became unappropriate. Maybe there are some researches or papers related to this topic you could advise.
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Anyone can link good article about ELO Rating system algorithm? How it works and why there is a constant? Also what are score and expected Score?
Wikipedia page is a good starting point.
In Elo formula you have two constants:
denominator (400),
K-factor (usually 10 to 40).
The base formula is simple:
Updated Elo points are previous points corrected by the performance, where S_A is the score (three values: 1 for win, 0.5 for draw, 0 for loss) and E_A is the expected score (that is "his probability of winning plus half his probability of drawing"),
Here's a comparison between Elo and some post-Elo rating systems.