def kde_fit_and_score(Y, Yhs, n=None):
"""
Y is [n x p]
Yhs is list of [n x p]
fits kdes for Y and Yhs
returns scores of each kde from Yhs
based on deviation from kde fit on Y
"""
n = n if n is not None else 50 * Y.shape[0]
# fit kdes
kde_base, bandwidth = pykde.kde_fit_cv(Y)
# kdes = [pykde.kde_fit(Yh, bandwidth) for Yh in Yhs]
kdes = [pykde.kde_fit_cv(Yh) for Yh in Yhs]
# print bandwidth, [kde[1] for kde in kdes]
kdes = [kde[0] for kde in kdes]
# opt1: eval on random grid sampled within bounds of all points
# grid = sample_unif_grid(np.vstack([np.vstack(Yhs), Y]), n)
# opt2: eval on Y
# grid = Y
# opt3: eval on random sample from kde_base
grid = kde_base.sample(n)
return score_all(kde_base, kdes, grid)
def kde_fit_and_score(Y, Yhs, n=None):
"""
Y is [n x p]
Yhs is list of [n x p]
fits kdes for Y and Yhs
returns scores of each kde from Yhs
based on deviation from kde fit on Y
"""
n = n if n is not None else 50*Y.shape[0]
# fit kdes
kde_base, bandwidth = pykde.kde_fit_cv(Y)
# kdes = [pykde.kde_fit(Yh, bandwidth) for Yh in Yhs]
kdes = [pykde.kde_fit_cv(Yh) for Yh in Yhs]
# print bandwidth, [kde[1] for kde in kdes]
kdes = [kde[0] for kde in kdes]
# opt1: eval on random grid sampled within bounds of all points
# grid = sample_unif_grid(np.vstack([np.vstack(Yhs), Y]), n)
# opt2: eval on Y
# grid = Y
# opt3: eval on random sample from kde_base
grid = kde_base.sample(n)
return score_all(kde_base, kdes, grid)
def eval_all(matfile, outfile=None):
"""
fit kde to Y
then return probability of each Yh under that kde
"""
Y, Yhs = load_Ys(matfile)
kde_base, bandwidth = pykde.kde_fit_cv(Y)
fnm = matfile.replace('.mat', '.pickle')
pickle.dump(kde_base, open(fnm, "wb"))
scorefcn = lambda ps: np.sum(np.log(ps))
if len(Yhs.shape) == 1:
scores = [scorefcn(pykde.kde_eval(kde_base, y)) for y in Yhs]
else:
scores = [[scorefcn(pykde.kde_eval(kde_base, y)) for y in Yh]\
for Yh in Yhs]
write_scores(scores, outfile)
return scores
def eval_all(matfile, outfile=None):
"""
fit kde to Y
then return probability of each Yh under that kde
"""
Y, Yhs = load_Ys(matfile)
kde_base, bandwidth = pykde.kde_fit_cv(Y)
fnm = matfile.replace('.mat', '.pickle')
pickle.dump(kde_base, open(fnm, "wb"))
scorefcn = lambda ps: np.sum(np.log(ps))
if len(Yhs.shape) == 1:
scores = [scorefcn(pykde.kde_eval(kde_base, y)) for y in Yhs]
else:
scores = [[scorefcn(pykde.kde_eval(kde_base, y)) for y in Yh]\
for Yh in Yhs]
write_scores(scores, outfile)
return scores
