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src/c/o/cogent-1.5.3/cogent/seqsim/usage.py   cogent(Download)
        of the one scipy function that still needs to be removed
"""
from cogent.maths.scipy_optimize import fmin, brent
from cogent.util.array import scale_trace, norm_diff, \
    has_neg_off_diags, sum_neg_off_diags, with_diag, without_diag
                return abs(sum(diagonal(expm(q)(t)*freqs)) - similarity)
        initial_guess = array([1.0])
        result = fmin(similarity_f, initial_guess, disp=0)
        #disp=0 turns off fmin messages
        return result
            return result
        a = array(q)
        xmin = fmin(func=err_f, x0=a, disp=0)
        r = reshape(xmin, (4,3))
        new_q = with_diag(r, -sum(r, 1))

src/p/y/pycogent-HEAD/cogent/seqsim/usage.py   pycogent(Download)
        of the one scipy function that still needs to be removed
"""
from cogent.maths.scipy_optimize import fmin, brent
from cogent.util.array import scale_trace, norm_diff, \
    has_neg_off_diags, sum_neg_off_diags, with_diag, without_diag
                return abs(sum(diagonal(expm(q)(t)*freqs)) - similarity)
        initial_guess = array([1.0])
        result = fmin(similarity_f, initial_guess, disp=0)
        #disp=0 turns off fmin messages
        return result
            return result
        a = array(q)
        xmin = fmin(func=err_f, x0=a, disp=0)
        r = reshape(xmin, (4,3))
        new_q = with_diag(r, -sum(r, 1))

src/c/o/cogent-1.5.3/cogent/maths/fit_function.py   cogent(Download)
from __future__ import division
from numpy import array
from cogent.maths.scipy_optimize import fmin
 
__author__ = "Antonio Gonzalez Pena"
   param_guess = array(range(n_params))
   for i in range(iterations):
       xopt = fmin(f2min, param_guess, disp=0)
       param_guess = xopt
 

src/p/y/pycogent-HEAD/cogent/maths/fit_function.py   pycogent(Download)
from __future__ import division
from numpy import array
from cogent.maths.scipy_optimize import fmin
 
__author__ = "Antonio Gonzalez Pena"
   param_guess = array(range(n_params))
   for i in range(iterations):
       xopt = fmin(f2min, param_guess, disp=0)
       param_guess = xopt
 

src/c/o/cogent-1.5.3/cogent/maths/scipy_optimisers.py   cogent(Download)
#!/usr/bin/env python
 
from __future__ import division
import numpy, math, warnings
from cogent.maths.scipy_optimize import fmin_bfgs, fmin_powell, fmin, brent
    def _minimise(self, f, x, **kw):
        return fmin(f, x, **kw)
 
 
DefaultLocalOptimiser = Powell

src/p/y/pycogent-HEAD/cogent/maths/scipy_optimisers.py   pycogent(Download)
#!/usr/bin/env python
 
from __future__ import division
import numpy, math, warnings
from cogent.maths.scipy_optimize import fmin_bfgs, fmin_powell, fmin, brent
    def _minimise(self, f, x, **kw):
        return fmin(f, x, **kw)
 
 
DefaultLocalOptimiser = Powell