| 85 |
provided that X*Y, X/Y, X+Y, X-Y are already supported. |
provided that X*Y, X/Y, X+Y, X-Y are already supported. |
| 86 |
Nearly every Diderot program would be simplified by this. |
Nearly every Diderot program would be simplified by this. |
| 87 |
|
|
| 88 |
Want: non-trivial field expressions & functions: |
[GLK:5] Want: non-trivial field expressions & functions: |
| 89 |
image(2)[2] Vimg = load(...); |
image(2)[2] Vimg = load(...); |
| 90 |
field#0(2)[] Vlen = |Vimg ⊛ bspln3|; |
field#0(2)[] Vlen = |Vimg ⊛ bspln3|; |
| 91 |
to get a scalar field of vector length, or |
to get a scalar field of vector length, or |
| 151 |
|
|
| 152 |
Python/ctypes interface to run-time |
Python/ctypes interface to run-time |
| 153 |
|
|
| 154 |
|
Allow the convolution to be specified either as a single 1D kernel |
| 155 |
|
(as we have it now): |
| 156 |
|
field#2(3)[] F = bspln3 ⊛ img; |
| 157 |
|
or, as a tensor product of kernels, one for each axis, e.g. |
| 158 |
|
field#0(3)[] F = (bspln3 ⊗ bspln3 ⊗ tent) ⊛ img; |
| 159 |
|
This is especially important for things like time-varying data, or |
| 160 |
|
other multi-dimensional fields where one axis of the domain is very |
| 161 |
|
different from the rest. What is very unclear is how, in such cases, |
| 162 |
|
we should notate the gradient, when we only want to differentiate with |
| 163 |
|
respect to some of the axes. |
| 164 |
|
|
| 165 |
============================== |
============================== |
| 166 |
LONG TERM ==================== |
LONG TERM ==================== |
| 167 |
============================== |
============================== |
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