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Sparse ilr #250
Sparse ilr #250
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@@ -52,7 +54,7 @@ def _balance_basis(tree_node): | |||
basis[i, :] = np.array([0]*k[i] + [a[i]]*r[i] + [b[i]]*s[i] + [0]*t[i]) | |||
# Make sure that the basis is in level order | |||
basis = basis[:, ::-1] | |||
nds = list(nds) | |||
nds = [n.name for n in nds] |
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Note that we are only keep track of names instead of entire TreeNode objects. This is to ensure consistency between the methods and immutability.
scipy.sparse.coo_matrix | ||
The ilr basis required to perform the ilr_inv transform. | ||
This is also known as the sequential binary partition. | ||
Note that this matrix is represented in clr coordinates. |
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Note that this is in clr coordinates, which is the same as the _balance_basis
function.
We have quite a few outstanding PRs, so let's merge this in once travis completes. |
This creates a sparse representation of an ilr basis. This will ultimately enable scalability of very high dimensional datasets.
This will replace the efforts in #8