StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics
https://doi.org/10.1109/TVCG.2020.3030352
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58 lines
1.4 KiB
58 lines
1.4 KiB
ndarray-gradient
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================
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Computes the gradient of an ndarray using a 2-point central finite difference template.
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# Example
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```javascript
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var pack = require('ndarray-pack')
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var pool = require('ndarray-scratch')
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var grad = require('ndarray-gradient')
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var show = require('ndarray-show')
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var X = pack([[0, 0, 0],
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[0, 1, 0],
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[0, 0, 0]])
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//Compute gradient of X
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var dX = grad(pool.zero([3,3,2]), X)
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console.log('grad(X) = \n', show(dX))
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```
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Output:
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```
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grad(X) =
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0.000 0.000 0.000
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-0.500 0.000 0.500
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0.000 0.000 0.000
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0.000 -0.500 0.000
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0.000 0.000 0.000
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0.000 0.500 0.000
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```
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# Install
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```
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npm install ndarray-gradient
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```
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# API
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### `require('ndarray-gradient')(dst, src[, bc])`
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Computes the gradient of `src` storing the result into `dst`.
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* `dst` is an array of gradient values. The shape of `dst` must be the shape of `src` with one additional dimension for the components of the gradient
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* `src` is the array to differentiate
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* `bc` is an array of boundary conditions. The boundary conditions are encoded as string values and must be one of the following values:
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+ `'clamp'` (Default) clamp boundary edges to boundary
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+ `'mirror'` mirror values across the boundary
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+ `'wrap'` wrap values across boundary
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**Returns** `dst`
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# Credits
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(c) 2014 Mikola Lysenko. MIT License |