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src/c/o/cogent-1.5.3/cogent/seqsim/analysis.py   cogent(Download)
from cogent.util.array import without_diag
from cogent.maths.svd import three_item_combos, two_item_combos
from cogent.maths.stats.test import std
 
__author__ = "Rob Knight"
            samples = list(tree_stats(n, make_tree, result_f))
            means = average(samples)
            stdevs = std(samples)
            for i in zip(means, stdevs):
                result.extend(i)

src/c/o/cogent-1.5.3/cogent/maths/svd.py   cogent(Download)
#!/usr/bin/env python
"""Performs singular-value decomposition on a set of Q-matrices."""
 
from __future__ import division
from cogent.maths.stats.test import std # numpy.std is biased
def var(x):
    return std(x)**2
 
def ratio_two_best(eigenvalues):
    """Returns ratio of best to second best eigenvalue (from vector)."""

src/p/y/pycogent-HEAD/cogent/seqsim/analysis.py   pycogent(Download)
from cogent.util.array import without_diag
from cogent.maths.svd import three_item_combos, two_item_combos
from cogent.maths.stats.test import std
 
__author__ = "Rob Knight"
            samples = list(tree_stats(n, make_tree, result_f))
            means = average(samples)
            stdevs = std(samples)
            for i in zip(means, stdevs):
                result.extend(i)

src/p/y/pycogent-HEAD/cogent/maths/svd.py   pycogent(Download)
#!/usr/bin/env python
"""Performs singular-value decomposition on a set of Q-matrices."""
 
from __future__ import division
from cogent.maths.stats.test import std # numpy.std is biased
def var(x):
    return std(x)**2
 
def ratio_two_best(eigenvalues):
    """Returns ratio of best to second best eigenvalue (from vector)."""