def score_all(kde_true, kde_hs, grid):
"""
evaluates kde_true on grid to get true probs
returns error of each kde_hs's estimate of these probs
"""
ps_true = pykde.kde_eval(kde_true, grid)
Ps = [pykde.kde_eval(kde_h, grid) for kde_h in kde_hs]
return [loss(ps_true, ps) for ps in Ps]
def score_all(kde_true, kde_hs, grid):
"""
evaluates kde_true on grid to get true probs
returns error of each kde_hs's estimate of these probs
"""
ps_true = pykde.kde_eval(kde_true, grid)
Ps = [pykde.kde_eval(kde_h, grid) for kde_h in kde_hs]
return [loss(ps_true, ps) for ps in Ps]
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
