本文整理汇总了Python中mne.decoding.GeneralizationAcrossTime.predict_type方法的典型用法代码示例。如果您正苦于以下问题：Python GeneralizationAcrossTime.predict_type方法的具体用法？Python GeneralizationAcrossTime.predict_type怎么用？Python GeneralizationAcrossTime.predict_type使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类mne.decoding.GeneralizationAcrossTime的用法示例。
在下文中一共展示了GeneralizationAcrossTime.predict_type方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_generalization_across_time
# 需要导入模块: from mne.decoding import GeneralizationAcrossTime [as 别名]
# 或者: from mne.decoding.GeneralizationAcrossTime import predict_type [as 别名]
def test_generalization_across_time():
"""Test time generalization decoding
"""
from sklearn.svm import SVC
raw = io.Raw(raw_fname, preload=False)
events = read_events(event_name)
picks = pick_types(raw.info, meg='mag', stim=False, ecg=False,
eog=False, exclude='bads')
picks = picks[0:2]
decim = 30
# Test on time generalization within one condition
with warnings.catch_warnings(record=True):
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True, decim=decim)
# Test default running
gat = GeneralizationAcrossTime()
assert_equal("<GAT | no fit, no prediction, no score>", "%s" % gat)
with warnings.catch_warnings(record=True):
gat.fit(epochs)
assert_equal("<GAT | fitted, start : -0.200 (s), stop : 0.499 (s), no "
"prediction, no score>", '%s' % gat)
gat.predict(epochs)
assert_equal("<GAT | fitted, start : -0.200 (s), stop : 0.499 (s), "
"predict_type : 'predict' on 15 epochs, no score>",
"%s" % gat)
gat.score(epochs)
assert_equal("<GAT | fitted, start : -0.200 (s), stop : 0.499 (s), "
"predict_type : 'predict' on 15 epochs,\n scored "
"(accuracy_score)>", "%s" % gat)
with warnings.catch_warnings(record=True):
gat.fit(epochs, y=epochs.events[:, 2])
old_type = gat.predict_type
gat.predict_type = 'foo'
assert_raises(ValueError, gat.predict, epochs)
gat.predict_type = old_type
old_mode = gat.predict_mode
gat.predict_mode = 'super-foo-mode'
assert_raises(ValueError, gat.predict, epochs)
gat.predict_mode = old_mode
gat.score(epochs, y=epochs.events[:, 2])
assert_true("accuracy_score" in '%s' % gat.scorer_)
epochs2 = epochs.copy()
# check _DecodingTime class
assert_equal("<DecodingTime | start: -0.200 (s), stop: 0.499 (s), step: "
"0.047 (s), length: 0.047 (s), n_time_windows: 15>",
"%s" % gat.train_times)
assert_equal("<DecodingTime | start: -0.200 (s), stop: 0.499 (s), step: "
"0.047 (s), length: 0.047 (s), n_time_windows: 15 x 15>",
"%s" % gat.test_times_)
# the y-check
gat.predict_mode = 'mean-prediction'
epochs2.events[:, 2] += 10
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] ==
gat.y_pred_.shape[2] == 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 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)
scores = gat.score(epochs)
assert_true(isinstance(scores, list)) # 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)
gat.score(epochs)
assert_equal(len(gat.scores_), 8)
# Test start stop training
gat = GeneralizationAcrossTime(train_times={'start': 0.090,
#.........这里部分代码省略.........