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else:
backend = PlaybackBackend(
recording_filename=filename
)
gui = GUI(backend)
return wrapped(gui, *args, **kwargs)"
3877,"def reverse_cyk_transforms(root):
# type: (Nonterminal) -> Nonterminal
""""""
Reverse transformation made to grammar before CYK.
Performs following steps:
- transform from chomsky normal form
- restore unit rules
- restore epsilon rules
:param root: Root node of the parsed tree.
:return: Restored parsed tree.
""""""
root = InverseContextFree.transform_from_chomsky_normal_form(root)
root = InverseContextFree.unit_rules_restore(root)
root = InverseContextFree.epsilon_rules_restore(root)
return root"
3878,"def _geom_solve_p_from_mu(mu, b):
""""""
For the geom_uptrunc, given mu and b, return p.
Ref: Harte 2011, Oxford U Press. Eq. 7.50.
""""""
def p_eq(x, mu, b):
x, mu, b = Decimal(x), Decimal(mu), Decimal(b)
return ( (x / (1 - x)) - ((b + 1) / (x**-b - 1)) - mu )
# x here is the param raised to the k_agg power, or 1 - p
return 1 - optim.brentq(p_eq, 1e-16, 100, args=(mu, b), disp=True)"
3879,"def _nbinom_ztrunc_p(mu, k_agg):
"""""" Calculates p parameter for truncated negative binomial
Function given in Sampford 1955, equation 4
Note that omega = 1 / 1 + p in Sampford
""""""
p_eq = lambda p, mu, k_agg: (k_agg * p) / (1 - (1 + p)**-k_agg) - mu
# The upper bound needs to be large. p will increase with increasing mu
# and decreasing k_agg
p = optim.brentq(p_eq, 1e-10, 1e10, args=(mu, k_agg))
return p"
3880,"def _ln_choose(n, k_agg):
'''
log binomial coefficient with extended gamma factorials. n and k_agg may be
int or array - if both array, must be the same length.
'''
gammaln = special.gammaln
return gammaln(n + 1) - (gammaln(k_agg + 1) + gammaln(n - k_agg + 1))"
3881,"def _solve_k_from_mu(data, k_array, nll, *args):
""""""
For given args, return k_agg from searching some k_range.
Parameters
----------
data : array
k_range : array
nll : function
args :
Returns
--------
:float
Minimum k_agg
""""""
# TODO: See if a root finder like fminbound would work with Decimal used in
# logpmf method (will this work with arrays?)
nll_array = np.zeros(len(k_array))
for i in range(len(k_array)):
nll_array[i] = nll(data, k_array[i], *args)
min_nll_idx = np.argmin(nll_array)
return k_array[min_nll_idx]"
3882,"def _trunc_logser_solver(bins, b):
""""""
Given bins (S) and b (N) solve for MLE of truncated logseries
parameter p
Parameters
-----------
bins : float
Number of bins. Considered S in an ecological context
b : float
Upper truncation of distribution. Considered N in an ecological context
Returns
-------
: float
MLE estimate of p