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Support multi-dimensional output for CP regression #252
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Codecov Report
@@ Coverage Diff @@
## main #252 +/- ##
==========================================
- Coverage 86.86% 83.49% -3.37%
==========================================
Files 120 120
Lines 7390 7423 +33
==========================================
- Hits 6419 6198 -221
- Misses 971 1225 +254
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I will add new test cases for multi-dimensional outputs🙂 |
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This looks great @merajhashemi! Could you please run black formatting on the code? black tensorly
in the top directory should do it.
for i in range( | ||
1, T.ndim(X) | ||
): # The first dimension of X is the number of samples | ||
W.append(T.tensor(rng.randn(X.shape[i], self.weight_rank), **T.context(X))) |
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Check that I resolved the merge conflict correctly here.
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Indeed, I definitely think I messed up the merge here.
This pr resolves #51 (CP only).