Comments (3)
Added a precondition check.
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Thanks! But what do you think about returning an empty/zero matrix instead of treating it as an error? That seems less surprising mathematically, since empty matrices are valid objects. That's what e.g. numpy chooses to do:
>>> M = lambda r, c: np.random.rand(r, c)
>>> np.dot(M(1,2), M(2,0))
array([], shape=(1, 0), dtype=float64)
>>> np.dot(M(0,2), M(2,3))
array([], shape=(0, 3), dtype=float64)
>>> np.dot(M(1,0), M(0,3))
array([[0., 0., 0.]])
from surge.
Interesting, what about this: https://github.com/mattt/Surge/pull/93
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Related Issues (20)
- Move Project to Separate Organization HOT 5
- How to read the results of "eigenDecompose" HOT 2
- Efficient non-allocating column/row-based Matrix iterators
- Efficient randomized initializers for Array/Vector/Matrix
- Auto-generate unit tests & benchmarks
- Migrate from `Float`/`Double` to `T: FloatingPoint` HOT 6
- Migrate API to a more idiomatic naming scheme HOT 1
- 3.0 Roadmap HOT 4
- feature request more numpy like dot
- is there some missing matrix functionality? HOT 1
- Warning: "Inout expression creates a temporary pointer, but argument 'imagp' should be a pointer that outlives the call to 'init(realp:imagp:)'" HOT 3
- missing power operator HOT 1
- Add documentation comments to public declarations
- Availability on Apple Watch? HOT 4
- randomNormal not documented HOT 1
- Not working on Watch app HOT 3
- some comfuse HOT 2
- Project Dead? HOT 5
- test_sqrt_in_place_array_float() fails on M1 Pro
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