Datasets:

yyjb5

/

text_code_search_net_python_codegemma_bo

like 0

def twofilter_smoothing(self, t, info, phi, loggamma, linear_cost=False,

def _twofilter_smoothing_ON2(self, t, ti, info, phi, lwinfo):

def _twofilter_smoothing_ON(self, t, ti, info, phi, lwinfo, return_ess,

def multiSMC(nruns=10, nprocs=0, out_func=None, **args):

def logpt(self, t, xp, x):

def reset_weights(self):

def setup_auxiliary_weights(self):

def exp_and_normalise(lw):

def essl(lw):

def log_sum_exp(v):

def log_sum_exp_ab(a, b):

def log_mean_exp(v, W=None):

def wmean_and_var(W, x):

def wmean_and_var_str_array(W, x):

def wquantiles(W, x, alphas=(0.25, 0.50, 0.75)):

def wquantiles_str_array(W, x, alphas=(0.25, 0.50, 0,75)):

def resampling_scheme(func):

def inverse_cdf(su, W):

def uniform_spacings(N):

def add(self, delta):

def dequeue(self, k):

def hilbert_array(xint):

def hilbert_sort(x):

def mean_sq_jump_dist(self, discard_frac=0.1):

def update(self, v):

def cartesian_lists(d):

def cartesian_args(args, listargs, dictargs):

def worker(qin, qout, f):

def distribute_work(f, inputs, outputs=None, nprocs=1, out_key='output'):

def distinct_seeds(k):

def multiplexer(f=None, nruns=1, nprocs=1, seeding=None, **args):

def reject_sv(m, s, y):

def predmean(self, xp):

def diff_ft(self, xt, yt):

def approx_likelihood(self, yt):

def approx_post(self, xp, yt):

def simulate(self, T):

def logpt(self, t, xp, x):

def EXt(self, xp):

def posterior(self, x, sigma=1.):

def posterior(self, x):

def posterior(self, x, s=1.):

def ppf(self, u):

def posterior(self, x, Sigma=None):

def psit(t, xp, x):

def avg_n_nplusone(x):

def interpoled_resampling(W, x):

def line_to_variables(source, line, inherit_permission, parent):

def parse_type(string,capture_strings,settings):

def sort_items(self,items,args=False):

def contents_size(self):

def markdown(self,md,project):

def sort(self):

def make_links(self, project):

def iterator(self, *argv):

def prune(self):

def get_used_entities(self,use_specs):

def prune(self):

def get_name(self,item):

def initialize():

def main(proj_data,proj_docs,md):

def convertToFree(stream, length_limit=True):

def continueLine(self):

def id_mods(obj,modlist,intrinsic_mods={},submodlist=[]):

def allfiles(self):

def correlate(self):

def markdown(self,md,base_url='..'):

def make_links(self,base_url='..'):

def make_srcdir_list(self,exclude_dirs):

def sub_notes(docs):

def get_parens(line,retlevel=0,retblevel=0):

def paren_split(sep,string):

def quote_split(sep,string):

def split_path(path):

def sub_links(string,project):

def sub_macros(string,base_url):

def copytree(src, dst):

def truncate(string, width):