本文整理匯總了Python中mne.decoding.GeneralizationAcrossTime.score_mode方法的典型用法代碼示例。如果您正苦於以下問題：Python GeneralizationAcrossTime.score_mode方法的具體用法？Python GeneralizationAcrossTime.score_mode怎麽用？Python GeneralizationAcrossTime.score_mode使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類mne.decoding.GeneralizationAcrossTime的用法示例。
在下文中一共展示了GeneralizationAcrossTime.score_mode方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_generalization_across_time
# 需要導入模塊: from mne.decoding import GeneralizationAcrossTime [as 別名]
# 或者: from mne.decoding.GeneralizationAcrossTime import score_mode [as 別名]
#.........這裏部分代碼省略.........
# check _DecodingTime class
assert_equal("<DecodingTime | start: -0.200 (s), stop: 0.499 (s), step: "
"0.050 (s), length: 0.050 (s), n_time_windows: 15>",
"%s" % gat.train_times_)
assert_equal("<DecodingTime | start: -0.200 (s), stop: 0.499 (s), step: "
"0.050 (s), length: 0.050 (s), n_time_windows: 15 x 15>",
"%s" % gat.test_times_)
# the y-check
gat.predict_mode = 'mean-prediction'
epochs2.events[:, 2] += 10
gat_ = copy.deepcopy(gat)
with use_log_level('error'):
assert_raises(ValueError, gat_.score, epochs2)
gat.predict_mode = 'cross-validation'
# Test basics
# --- number of trials
assert_true(gat.y_train_.shape[0] ==
gat.y_true_.shape[0] ==
len(gat.y_pred_[0][0]) == 14)
# --- number of folds
assert_true(np.shape(gat.estimators_)[1] == gat.cv)
# --- length training size
assert_true(len(gat.train_times_['slices']) == 15 ==
np.shape(gat.estimators_)[0])
# --- length testing sizes
assert_true(len(gat.test_times_['slices']) == 15 ==
np.shape(gat.scores_)[0])
assert_true(len(gat.test_times_['slices'][0]) == 15 ==
np.shape(gat.scores_)[1])
# Test score_mode
gat.score_mode = 'foo'
assert_raises(ValueError, gat.score, epochs)
gat.score_mode = 'fold-wise'
scores = gat.score(epochs)
assert_array_equal(np.shape(scores), [15, 15, 5])
gat.score_mode = 'mean-sample-wise'
scores = gat.score(epochs)
assert_array_equal(np.shape(scores), [15, 15])
gat.score_mode = 'mean-fold-wise'
scores = gat.score(epochs)
assert_array_equal(np.shape(scores), [15, 15])
gat.predict_mode = 'mean-prediction'
with warnings.catch_warnings(record=True) as w:
gat.score(epochs)
assert_true(any("score_mode changed from " in str(ww.message)
for ww in w))
# Test longer time window
gat = GeneralizationAcrossTime(train_times={'length': .100})
with warnings.catch_warnings(record=True):
gat2 = gat.fit(epochs)
assert_true(gat is gat2) # return self
assert_true(hasattr(gat2, 'cv_'))
assert_true(gat2.cv_ != gat.cv)
with warnings.catch_warnings(record=True): # not vectorizing
scores = gat.score(epochs)
assert_true(isinstance(scores, np.ndarray)) # type check
assert_equal(len(scores[0]), len(scores)) # shape check
assert_equal(len(gat.test_times_['slices'][0][0]), 2)
# Decim training steps
gat = GeneralizationAcrossTime(train_times={'step': .100})
with warnings.catch_warnings(record=True):
gat.fit(epochs)