def MAX_U(X,
y,
k_for_meaning=100,
c=0.005,
begin=None,
end=None,
num=None,
progress_bar=None,
multiprocess=False):
if progress_bar is None:
progress_bar = list
if end is None:
end = X.shape[0] - 1
if begin is None:
begin = 2 * X.shape[1]
if num is None:
num = 5
subset_sizes = np.arange(begin, end, num, dtype=np.int64)
m_size = end
X, y = shuffle(X, y)
list_of_E, list_of_S = ut.calculater(X, y, subset_sizes, k_for_meaning,
ut.uFunction, multiprocess,
progress_bar, c)
return {
'm*': subset_sizes[np.argmax(np.array(list_of_E))],
'E': np.array(list_of_E),
'S': np.array(list_of_S),
'm': np.array(subset_sizes),
}
def ALC(X,
y,
k_for_meaning=100,
l=0.25,
alpha=0.05,
begin=None,
end=None,
num=None,
progress_bar=None,
multiprocess=False):
if progress_bar is None:
progress_bar = list
if end is None:
end = X.shape[0] - 1
if begin is None:
begin = 2 * X.shape[1]
if num is None:
num = 5
subset_sizes = np.arange(begin, end, num, dtype=np.int64)
m_size = end
X, y = shuffle(X, y)
list_of_E, list_of_S = ut.calculater(X, y, subset_sizes, k_for_meaning,
ut.aDistribution, multiprocess,
progress_bar, alpha)
for m, mean, std in zip(reversed(subset_sizes), reversed(list_of_E),
reversed(list_of_S)):
if mean < l:
m_size = m
return {
'm*': m_size,
'E': np.array(list_of_E),
'S': np.array(list_of_S),
'm': np.array(subset_sizes),
}
def KL_method(X,
y,
k_for_meaning=100,
epsilon=0.01,
begin=None,
end=None,
num=None,
progress_bar=None,
multiprocess=False):
if progress_bar is None:
progress_bar = list
if end is None:
end = X.shape[0] - 1
if begin is None:
begin = 2 * X.shape[1]
if num is None:
num = 5
subset_sizes = np.arange(begin, end, num, dtype=np.int64)
m_size = end
X, y = shuffle(X, y)
list_of_E, list_of_S = ut.calculater(X, y, subset_sizes, k_for_meaning,
ut.klFunction, multiprocess,
progress_bar)
for m, mean in zip(reversed(subset_sizes), reversed(list_of_E)):
if mean < epsilon:
m_size = m
return {
'm*': m_size,
'E': np.array(list_of_E),
'S': np.array(list_of_S),
'm': np.array(subset_sizes),
}