def predict(self, x, significance=None):
ncal_ngt_neq = np.stack([p._get_stats(x) for p in self.predictors],
axis=3)
ncal_ngt_neq = ncal_ngt_neq.sum(axis=3)
p = calc_p(ncal_ngt_neq[:, :, 0],
ncal_ngt_neq[:, :, 1],
ncal_ngt_neq[:, :, 2],
smoothing=self.predictors[0].smoothing)
if significance:
return p > significance
else:
return p
def predict(self, x, significance=None):
"""Predict the output values for a set of input patterns.
Parameters
----------
x : numpy array of shape [n_samples, n_features]
Inputs of patters for which to predict output values.
significance : float or None
Significance level (maximum allowed error rate) of predictions.
Should be a float between 0 and 1. If ``None``, then the p-values
are output rather than the predictions.
Returns
-------
p : numpy array of shape [n_samples, n_classes]
If significance is ``None``, then p contains the p-values for each
sample-class pair; if significance is a float between 0 and 1, then
p is a boolean array denoting which labels are included in the
prediction sets.
"""
# TODO: if x == self.last_x ...
n_test_objects = x.shape[0]
p = np.zeros((n_test_objects, self.classes.size))
ncal_ngt_neq = self._get_stats(x)
for i in range(len(self.classes)):
for j in range(n_test_objects):
p[j, i] = calc_p(
ncal_ngt_neq[j, i, 0],
ncal_ngt_neq[j, i, 1],
ncal_ngt_neq[j, i, 2],
self.smoothing,
)
if significance is not None:
return p > significance
else:
return p