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Merge pull request #13 from s3alfisc/get_weights-port
add get_weights port
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import numpy as np | ||
from itertools import product | ||
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class WildDrawFunctionException(Exception): | ||
pass | ||
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def rademacher(n: int) -> np.ndarray: | ||
rng = np.random.default_rng() | ||
return rng.choice([-1,1],size=n, replace=True) | ||
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def mammen(n: int) -> np.ndarray: | ||
rng = np.random.default_rng() | ||
return rng.choice( | ||
a= np.array([-1, 1]) * (np.sqrt(5) + np.array([-1, 1])) / 2, #TODO: #10 Should this divide the whole expression by 2 or just the second part | ||
size=n, | ||
replace=True, | ||
p = (np.sqrt(5) + np.array([1, -1])) / (2 * np.sqrt(5)) | ||
) | ||
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def norm(n): | ||
rng = np.random.default_rng() | ||
return rng.normal(size=n) | ||
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def webb(n): | ||
rng = np.random.default_rng() | ||
return rng.choice( | ||
a = np.concatenate([-np.sqrt(np.array([3,2,1]) / 2), np.sqrt(np.array([1,2,3]) / 2)]), | ||
replace=True, | ||
size=n | ||
) | ||
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wild_draw_fun_dict = { | ||
'rademacher' : rademacher, | ||
'mammen' : mammen, | ||
'norm' : norm, | ||
'webb' : webb | ||
} | ||
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def get_weights(t : str, full_enumeration: bool, N_G_bootcluster: int, boot_iter: int) -> np.ndarray: | ||
"""draw bootstrap weights | ||
Args: | ||
t (str): the type of the weights distribution. Either 'rademacher', 'mammen', 'norm' or 'webb' | ||
full_enumeration (bool): should deterministic full enumeration be employed | ||
N_G_bootcluster (int): the number of bootstrap clusters | ||
boot_iter (int): the number of bootstrap iterations | ||
Returns: | ||
np.ndarray: a matrix of dimension N_G_bootcluster x (boot_iter + 1) | ||
""" | ||
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#TODO: we can use the `case` feature in python, but that's only available in 3.10+ will do a 3.7 version for now | ||
# Will take out this and make separate functions for readability | ||
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wild_draw_fun = wild_draw_fun_dict.get(t) | ||
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if wild_draw_fun is None: | ||
raise WildDrawFunctionException("Function type specified is not supported or there is a typo.") | ||
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# do full enumeration for rademacher weights if bootstrap iterations | ||
# B exceed number of possible permutations else random sampling | ||
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# full_enumeration only for rademacher weights (set earlier) | ||
if full_enumeration: | ||
t = 0 # what is this needed for? | ||
# with N_G_bootcluster draws, get all combinations of [-1,1] WITH | ||
# replacement, in matrix form | ||
v0 = np.transpose(np.array(list(product([-1,1], repeat=N_G_bootcluster)))) | ||
else: | ||
# else: just draw with replacement - by chance, some permutations | ||
# might occur more than once | ||
v0 = wild_draw_fun(n = N_G_bootcluster * boot_iter) | ||
v0.reshape(N_G_bootcluster, boot_iter) # weights matrix | ||
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v = np.insert(v0, 0, 1,axis = 1) | ||
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return v | ||
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