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# cogent.align.weights.util.distance_matrix

All Samples(8)  |  Call(4)  |  Derive(0)  |  Import(4)

```from cogent.parse.tree import DndParser
from cogent.util.array import hamming_distance
from cogent.align.weights.util import Weights, number_of_pseudo_seqs,\
pseudo_seqs_exact, pseudo_seqs_monte_carlo, row_to_vote, distance_matrix,\
eigenvector_for_largest_eigenvalue, DNA_ORDER,RNA_ORDER,PROTEIN_ORDER,\
```
```    so: weight(ABBA) = 0.333, weight(ABCA)=0.25, etc.
"""
distances = distance_matrix(alignment, distance_method)
sum_dist = sum(distances)
#total weights are the normalized sum of distances (sum over each column,
```
```    """

distances = distance_matrix(alignment)
v = eigenvector_for_largest_eigenvalue(distances)
return Weights(dict(zip(alignment.Names,v)))
```

```from cogent.parse.tree import DndParser
from cogent.util.array import hamming_distance
from cogent.align.weights.util import Weights, number_of_pseudo_seqs,\
pseudo_seqs_exact, pseudo_seqs_monte_carlo, row_to_vote, distance_matrix,\
eigenvector_for_largest_eigenvalue, DNA_ORDER,RNA_ORDER,PROTEIN_ORDER,\
```
```    so: weight(ABBA) = 0.333, weight(ABCA)=0.25, etc.
"""
distances = distance_matrix(alignment, distance_method)
sum_dist = sum(distances)
#total weights are the normalized sum of distances (sum over each column,
```
```    """

distances = distance_matrix(alignment)
v = eigenvector_for_largest_eigenvalue(distances)
return Weights(dict(zip(alignment.Names,v)))
```

```from cogent.core.sequence import DnaSequence, RnaSequence
from cogent.core.moltype import DNA, RNA
from cogent.align.weights.util import Weights, number_of_pseudo_seqs,\
pseudo_seqs_exact, pseudo_seqs_monte_carlo, row_to_vote, distance_matrix,\
eigenvector_for_largest_eigenvalue, DNA_ORDER,RNA_ORDER,PROTEIN_ORDER,\
```

```from cogent.core.sequence import DnaSequence, RnaSequence