Datasets:
thisisanshgupta
/
Pycode

Dataset card Data Studio Files
xet
 Community
text
stringlengths
0
828
model_cdf = self.cdf(np.arange(l, b + 1), *args)
unif_rands = np.random.random(size)
model_rands = np.array([np.where(tx <= model_cdf)[0][0] + l
for tx in unif_rands])
return model_rands"
3888,"def fit_mle(self, data, b=None):
""""""%(super)s
In addition to data, requires ``b``, the upper limit of the
distribution.
""""""
# Take mean of data as MLE of distribution mean, then calculate p
mu = np.mean(data)
if not b:
b = np.sum(data)
p = _geom_solve_p_from_mu_vect(mu, b)
# Just return float, not len 1 array
if len(np.atleast_1d(p)) == 1:
return float(p), b
else:
return p, b"
3889,"def fit_mle(self, data, init_vals=(80, 80)):
""""""%(super)s
In addition to data, can take init_vals which allows the user to
specify initial values for (alpha, theta) during the optimization.
""""""
if len(data) > 1:
mu = np.mean(data)
var = np.var(data)
theta0 = var / mu
alpha0 = mu / theta0
else:
alpha0 = init_vals[0]
theta0 = init_vals[1]
def mle(params):
return -np.sum(np.log(self.pmf(data, params[0], params[1])))
# Bounded fmin?
alpha, theta = optim.fmin(mle, x0=[alpha0, theta0], disp=0)
return alpha, theta"
3890,"def fit_mle(self, data, k_array=np.arange(0.1, 100, 0.1)):
""""""%(super)s
In addition to data, gives an optional keyword argument k_array
containing the values to search for k_agg. A brute force search is then
used to find the parameter k_agg.
""""""
# todo: check and mention in docstring biases of mle for k_agg
data = np.array(data)
mu = np.mean(data)
return mu, _solve_k_from_mu(data, k_array, nbinom_nll, mu)"
3891,"def translate_args(self, mu, k_agg, return_p=False):
""""""%(super)s
The keyword argument return_p computes the p values used to define the
the truncated negative binomial
""""""
if return_p:
return nbinom_ztrunc_p(mu, k_agg), k_agg
else:
return mu, k_agg"
3892,"def fit_mle(self, data, k_agg0=0.5):
""""""%(super)s
In addition to data, gives an optional keyword argument k_agg0 that
specifies the initial value of k_agg used in the optimization.
""""""
mu = np.mean(data)
def mle(k):
return -np.sum(np.log(self.pmf(data, mu, k)))
k = optim.fmin(mle, x0=k_agg0, disp=0)
return mu, k[0]"
3893,"def fit_mle(self, data, b=None):
""""""%(super)s
b : float
The upper bound of the distribution. If None, fixed at sum(data)
""""""
data = np.array(data)
length = len(data)
if not b:
b = np.sum(data)
return _trunc_logser_solver(length, b), b"
3894,"def rank(self, n, mu, sigma, crit=.5, upper=10000, xtol=1):
""""""%(super)s