def _evoked_from_epoch_data(self, data, info, picks, n_events, kind):
"""Helper to create an evoked object from epoch data"""
info = deepcopy(info)
evoked = EvokedArray(data, info, tmin=self.times[0],
comment=self.name, nave=n_events, kind=kind,
verbose=self.verbose)
# XXX: above constructor doesn't recreate the times object precisely
evoked.times = self.times.copy()
# pick channels
if picks is None:
picks = _pick_data_channels(evoked.info, exclude=[])
ch_names = [evoked.ch_names[p] for p in picks]
evoked.pick_channels(ch_names)
if len(evoked.info['ch_names']) == 0:
raise ValueError('No data channel found when averaging.')
if evoked.nave < 1:
warn('evoked object is empty (based on less than 1 epoch)')
return evoked
python类less()的实例源码
def calculate_accuracy(threshold, dist, actual_issame):
predict_issame = np.less(dist, threshold)
tp = np.sum(np.logical_and(predict_issame, actual_issame))
fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))
tn = np.sum(np.logical_and(np.logical_not(predict_issame), np.logical_not(actual_issame)))
fn = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame))
tpr = 0 if (tp+fn==0) else float(tp) / float(tp+fn)
fpr = 0 if (fp+tn==0) else float(fp) / float(fp+tn)
acc = float(tp+tn)/dist.size
return tpr, fpr, acc
def calculate_val_far(threshold, dist, actual_issame):
predict_issame = np.less(dist, threshold)
true_accept = np.sum(np.logical_and(predict_issame, actual_issame))
false_accept = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))
n_same = np.sum(actual_issame)
n_diff = np.sum(np.logical_not(actual_issame))
val = float(true_accept) / float(n_same)
far = float(false_accept) / float(n_diff)
return val, far
def build_feature_files(base_directory,
new_directory,
data_loader,
n=None,
negative_example_keep_prob=1.0):
os.makedirs(new_directory, exist_ok=False)
episode_paths = frame.episode_paths(base_directory)
label_counts = [0, 0]
if n is not None:
np.random.shuffle(episode_paths)
episode_paths = episode_paths[:n]
for episode_path in tqdm.tqdm(episode_paths):
try:
features, labels = data_loader.load_features_and_labels([episode_path])
except:
traceback.print_exc()
else:
keep = np.logical_or(labels, (np.less(
np.random.rand(len(labels)), negative_example_keep_prob)))
labels = labels[keep]
for i in range(len(label_counts)):
label_counts[i] += np.count_nonzero(labels == i)
features = {k: v[keep] for k, v in features.items()}
new_path = path_relative_to_new_directory(base_directory, new_directory, episode_path,
".features")
os.makedirs(os.path.dirname(new_path), exist_ok=True)
with open(new_path, 'wb') as f:
pickle.dump((features, labels), f)
return label_counts
def unk_filter(data):
if config['voc_size'] == -1:
return copy.copy(data)
else:
mask = (np.less(data, config['voc_size'])).astype(dtype='int32')
data = copy.copy(data * mask + (1 - mask))
return data
def unk_filter(data):
if config['voc_size'] == -1:
return copy.copy(data)
else:
mask = (np.less(data, config['voc_size'])).astype(dtype='int32')
data = copy.copy(data * mask + (1 - mask))
return data
# training
def unk_filter(data):
if config['voc_size'] == -1:
return copy.copy(data)
else:
mask = (np.less(data, config['voc_size'])).astype(dtype='int32')
data = copy.copy(data * mask + (1 - mask))
return data
# training
def unk_filter(data):
if config['voc_size'] == -1:
return copy.copy(data)
else:
mask = (np.less(data, config['voc_size'])).astype(dtype='int32')
data = copy.copy(data * mask + (1 - mask))
return data
# training
def unk_filter(data):
if config['voc_size'] == -1:
return copy.copy(data)
else:
mask = (np.less(data, config['voc_size'])).astype(dtype='int32')
data = copy.copy(data * mask + (1 - mask))
return data
def unk_filter(data):
if config['voc_size'] == -1:
return copy.copy(data)
else:
mask = (np.less(data, config['voc_size'])).astype(dtype='int32')
data = copy.copy(data * mask + (1 - mask))
return data
# training
def __init__(self, custom_model, filepath, monitor='val_loss', verbose=0,
save_best_only=False, save_weights_only=False,
mode='auto', period=1):
super(CustomModelCheckpoint, self).__init__()
self.custom_model = custom_model
self.monitor = monitor
self.verbose = verbose
self.filepath = filepath
self.save_best_only = save_best_only
self.save_weights_only = save_weights_only
self.period = period
self.epochs_since_last_save = 0
if mode not in ['auto', 'min', 'max']:
warnings.warn('CustomModelCheckpoint mode %s is unknown, '
'fallback to auto mode.' % (mode),
RuntimeWarning)
mode = 'auto'
if mode == 'min':
self.monitor_op = np.less
self.best = np.Inf
elif mode == 'max':
self.monitor_op = np.greater
self.best = -np.Inf
else:
if 'acc' in self.monitor or self.monitor.startswith('fmeasure'):
self.monitor_op = np.greater
self.best = -np.Inf
else:
self.monitor_op = np.less
self.best = np.Inf
def clip(self, a, m, M, out=None):
# use slow-clip
selector = np.less(a, m) + 2*np.greater(a, M)
return selector.choose((a, m, M), out=out)
# Handy functions
def test_roundtrip_str(self):
x = self.x.real.ravel()
s = "@".join(map(str, x))
y = np.fromstring(s, sep="@")
# NB. str imbues less precision
nan_mask = ~np.isfinite(x)
assert_array_equal(x[nan_mask], y[nan_mask])
assert_array_almost_equal(x[~nan_mask], y[~nan_mask], decimal=5)
def test_datetime_compare(self):
# Test all the comparison operators
a = np.datetime64('2000-03-12T18:00:00.000000')
b = np.array(['2000-03-12T18:00:00.000000',
'2000-03-12T17:59:59.999999',
'2000-03-12T18:00:00.000001',
'1970-01-11T12:00:00.909090',
'2016-01-11T12:00:00.909090'],
dtype='datetime64[us]')
assert_equal(np.equal(a, b), [1, 0, 0, 0, 0])
assert_equal(np.not_equal(a, b), [0, 1, 1, 1, 1])
assert_equal(np.less(a, b), [0, 0, 1, 0, 1])
assert_equal(np.less_equal(a, b), [1, 0, 1, 0, 1])
assert_equal(np.greater(a, b), [0, 1, 0, 1, 0])
assert_equal(np.greater_equal(a, b), [1, 1, 0, 1, 0])
def test_datetime_compare_nat(self):
dt_nat = np.datetime64('NaT', 'D')
dt_other = np.datetime64('2000-01-01')
td_nat = np.timedelta64('NaT', 'h')
td_other = np.timedelta64(1, 'h')
for op in [np.equal, np.less, np.less_equal,
np.greater, np.greater_equal]:
if op(dt_nat, dt_nat):
assert_warns(FutureWarning, op, dt_nat, dt_nat)
if op(dt_nat, dt_other):
assert_warns(FutureWarning, op, dt_nat, dt_other)
if op(dt_other, dt_nat):
assert_warns(FutureWarning, op, dt_other, dt_nat)
if op(td_nat, td_nat):
assert_warns(FutureWarning, op, td_nat, td_nat)
if op(td_nat, td_other):
assert_warns(FutureWarning, op, td_nat, td_other)
if op(td_other, td_nat):
assert_warns(FutureWarning, op, td_other, td_nat)
assert_warns(FutureWarning, np.not_equal, dt_nat, dt_nat)
assert_(np.not_equal(dt_nat, dt_other))
assert_(np.not_equal(dt_other, dt_nat))
assert_warns(FutureWarning, np.not_equal, td_nat, td_nat)
assert_(np.not_equal(td_nat, td_other))
assert_(np.not_equal(td_other, td_nat))
def test_result_values(self):
for f, fcmp in zip(self.nanfuncs, [np.greater, np.less]):
for row in _ndat:
with warnings.catch_warnings(record=True):
warnings.simplefilter('always')
ind = f(row)
val = row[ind]
# comparing with NaN is tricky as the result
# is always false except for NaN != NaN
assert_(not np.isnan(val))
assert_(not fcmp(val, row).any())
assert_(not np.equal(val, row[:ind]).any())
def test_basic_ufuncs(self):
# Test various functions such as sin, cos.
(x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
assert_equal(np.cos(x), cos(xm))
assert_equal(np.cosh(x), cosh(xm))
assert_equal(np.sin(x), sin(xm))
assert_equal(np.sinh(x), sinh(xm))
assert_equal(np.tan(x), tan(xm))
assert_equal(np.tanh(x), tanh(xm))
assert_equal(np.sqrt(abs(x)), sqrt(xm))
assert_equal(np.log(abs(x)), log(xm))
assert_equal(np.log10(abs(x)), log10(xm))
assert_equal(np.exp(x), exp(xm))
assert_equal(np.arcsin(z), arcsin(zm))
assert_equal(np.arccos(z), arccos(zm))
assert_equal(np.arctan(z), arctan(zm))
assert_equal(np.arctan2(x, y), arctan2(xm, ym))
assert_equal(np.absolute(x), absolute(xm))
assert_equal(np.angle(x + 1j*y), angle(xm + 1j*ym))
assert_equal(np.angle(x + 1j*y, deg=True), angle(xm + 1j*ym, deg=True))
assert_equal(np.equal(x, y), equal(xm, ym))
assert_equal(np.not_equal(x, y), not_equal(xm, ym))
assert_equal(np.less(x, y), less(xm, ym))
assert_equal(np.greater(x, y), greater(xm, ym))
assert_equal(np.less_equal(x, y), less_equal(xm, ym))
assert_equal(np.greater_equal(x, y), greater_equal(xm, ym))
assert_equal(np.conjugate(x), conjugate(xm))
def test_masked_where_condition(self):
# Tests masking functions.
x = array([1., 2., 3., 4., 5.])
x[2] = masked
assert_equal(masked_where(greater(x, 2), x), masked_greater(x, 2))
assert_equal(masked_where(greater_equal(x, 2), x),
masked_greater_equal(x, 2))
assert_equal(masked_where(less(x, 2), x), masked_less(x, 2))
assert_equal(masked_where(less_equal(x, 2), x),
masked_less_equal(x, 2))
assert_equal(masked_where(not_equal(x, 2), x), masked_not_equal(x, 2))
assert_equal(masked_where(equal(x, 2), x), masked_equal(x, 2))
assert_equal(masked_where(not_equal(x, 2), x), masked_not_equal(x, 2))
assert_equal(masked_where([1, 1, 0, 0, 0], [1, 2, 3, 4, 5]),
[99, 99, 3, 4, 5])
def test_testUfuncs1(self):
# Test various functions such as sin, cos.
(x, y, a10, m1, m2, xm, ym, z, zm, xf, s) = self.d
self.assertTrue(eq(np.cos(x), cos(xm)))
self.assertTrue(eq(np.cosh(x), cosh(xm)))
self.assertTrue(eq(np.sin(x), sin(xm)))
self.assertTrue(eq(np.sinh(x), sinh(xm)))
self.assertTrue(eq(np.tan(x), tan(xm)))
self.assertTrue(eq(np.tanh(x), tanh(xm)))
with np.errstate(divide='ignore', invalid='ignore'):
self.assertTrue(eq(np.sqrt(abs(x)), sqrt(xm)))
self.assertTrue(eq(np.log(abs(x)), log(xm)))
self.assertTrue(eq(np.log10(abs(x)), log10(xm)))
self.assertTrue(eq(np.exp(x), exp(xm)))
self.assertTrue(eq(np.arcsin(z), arcsin(zm)))
self.assertTrue(eq(np.arccos(z), arccos(zm)))
self.assertTrue(eq(np.arctan(z), arctan(zm)))
self.assertTrue(eq(np.arctan2(x, y), arctan2(xm, ym)))
self.assertTrue(eq(np.absolute(x), absolute(xm)))
self.assertTrue(eq(np.equal(x, y), equal(xm, ym)))
self.assertTrue(eq(np.not_equal(x, y), not_equal(xm, ym)))
self.assertTrue(eq(np.less(x, y), less(xm, ym)))
self.assertTrue(eq(np.greater(x, y), greater(xm, ym)))
self.assertTrue(eq(np.less_equal(x, y), less_equal(xm, ym)))
self.assertTrue(eq(np.greater_equal(x, y), greater_equal(xm, ym)))
self.assertTrue(eq(np.conjugate(x), conjugate(xm)))
self.assertTrue(eq(np.concatenate((x, y)), concatenate((xm, ym))))
self.assertTrue(eq(np.concatenate((x, y)), concatenate((x, y))))
self.assertTrue(eq(np.concatenate((x, y)), concatenate((xm, y))))
self.assertTrue(eq(np.concatenate((x, y, x)), concatenate((x, ym, x))))
def test_testMinMax2(self):
# Test of minumum, maximum.
assert_(eq(minimum([1, 2, 3], [4, 0, 9]), [1, 0, 3]))
assert_(eq(maximum([1, 2, 3], [4, 0, 9]), [4, 2, 9]))
x = arange(5)
y = arange(5) - 2
x[3] = masked
y[0] = masked
assert_(eq(minimum(x, y), where(less(x, y), x, y)))
assert_(eq(maximum(x, y), where(greater(x, y), x, y)))
assert_(minimum(x) == 0)
assert_(maximum(x) == 4)
