The following are code examples for showing how to use . They are extracted from open source Python projects. You can vote up the examples you like or vote down the exmaples you don’t like. You can also save this page to your account.
Example 1
def eliminate_overlapping_locations(f, separation):
""" Makes sure that no position is within `separation` from each other, by
deleting one of the that are to close to each other.
"""
separation = validate_tuple(separation, f.shape[1])
assert np.greater(separation, 0).all()
# Rescale positions, so that pairs are identified below a distance of 1.
f = f / separation
while True:
duplicates = cKDTree(f, 30).query_pairs(1)
if len(duplicates) == 0:
break
to_drop = []
for pair in duplicates:
to_drop.append(pair[1])
f = np.delete(f, to_drop, 0)
return f * separation Example 2
def peaks(spectra,frequency,number=3,thresh=0.01):
""" Return the peaks from the Fourier transform
Variables:
number: integer. number of peaks to print.
thresh: float. Threshhold intensity for printing.
Returns: Energy (eV), Intensity (depends on type of spectra)
"""
from scipy.signal import argrelextrema as pks
# find all peak indices [idx], and remove those below thresh [jdx]
idx = pks(np.abs(spectra),np.greater,order=3)
jdx = np.where((np.abs(spectra[idx]) >= thresh))
kdx = idx[0][jdx[0]] # indices of peaks matching criteria
if number > len(kdx):
number = len(kdx)
print("First "+str(number)+" peaks (eV) found: ")
for i in xrange(number):
print("{0:.4f}".format(frequency[kdx][i]*27.2114),
"{0:.4f}".format(spectra[kdx][i])) Example 3
def equal(x1, x2):
"""
Return (x1 == x2) element-wise.
Unlike `numpy.equal`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray or bool
Output array of bools, or a single bool if x1 and x2 are scalars.
See Also
--------
not_equal, greater_equal, less_equal, greater, less
"""
return compare_chararrays(x1, x2, '==', True) Example 4
def not_equal(x1, x2):
"""
Return (x1 != x2) element-wise.
Unlike `numpy.not_equal`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray or bool
Output array of bools, or a single bool if x1 and x2 are scalars.
See Also
--------
equal, greater_equal, less_equal, greater, less
"""
return compare_chararrays(x1, x2, '!=', True) Example 5
def greater_equal(x1, x2):
"""
Return (x1 >= x2) element-wise.
Unlike `numpy.greater_equal`, this comparison is performed by
first stripping whitespace characters from the end of the string.
This behavior is provided for backward-compatibility with
numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray or bool
Output array of bools, or a single bool if x1 and x2 are scalars.
See Also
--------
equal, not_equal, less_equal, greater, less
"""
return compare_chararrays(x1, x2, '>=', True) Example 6
def less_equal(x1, x2):
"""
Return (x1 <= x2) element-wise.
Unlike `numpy.less_equal`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray or bool
Output array of bools, or a single bool if x1 and x2 are scalars.
See Also
--------
equal, not_equal, greater_equal, greater, less
"""
return compare_chararrays(x1, x2, '<=', True) Example 7
def greater(x1, x2):
"""
Return (x1 > x2) element-wise.
Unlike `numpy.greater`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray or bool
Output array of bools, or a single bool if x1 and x2 are scalars.
See Also
--------
equal, not_equal, greater_equal, less_equal, less
"""
return compare_chararrays(x1, x2, '>', True) Example 8
def build(self, input_shape):
super().build(input_shape)
self.mask = np.ones(self.W_shape)
assert mask.shape[0] == mask.shape[1]
filter_size = self.mask.shape[0]
filter_center = filter_size / 2
self.mask[math.ceil(filter_center):] = 0
self.mask[math.floor(filter_center):, math.ceil(filter_center):] = 0
if self.mono:
if self.mask_type == 'A':
self.mask[math.floor(filter_center), math.floor(filter_center)] = 0
else:
op = np.greater_equal if self.mask_type == 'A' else np.greater
for i in range(self.n_channels):
for j in range(self.n_channels):
if op(i, j):
self.mask[math.floor(filter_center), math.floor(filter_center), i::self.n_channels, j::self.n_channels] = 0
self.mask = K.variable(self.mask) Example 9
def _reset(self):
"""Resets wait counter and cooldown counter.
"""
if self.mode not in ['auto', 'min', 'max']:
warnings.warn('Learning Rate Plateau Reducing mode %s is unknown, '
'fallback to auto mode.' % (self.mode),
RuntimeWarning)
self.mode = 'auto'
if (self.mode == 'min' or
(self.mode == 'auto' and 'acc' not in self.monitor)):
self.monitor_op = lambda a, b: np.less(a, b - self.epsilon)
self.best = np.Inf
else:
self.monitor_op = lambda a, b: np.greater(a, b + self.epsilon)
self.best = -np.Inf
self.cooldown_counter = 0
self.wait = 0
self.lr_epsilon = self.min_lr * 1e-4 Example 10
def equal(x1, x2):
"""
Return (x1 == x2) element-wise.
Unlike `numpy.equal`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray or bool
Output array of bools, or a single bool if x1 and x2 are scalars.
See Also
--------
not_equal, greater_equal, less_equal, greater, less
"""
return compare_chararrays(x1, x2, '==', True) Example 11
def not_equal(x1, x2):
"""
Return (x1 != x2) element-wise.
Unlike `numpy.not_equal`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray or bool
Output array of bools, or a single bool if x1 and x2 are scalars.
See Also
--------
equal, greater_equal, less_equal, greater, less
"""
return compare_chararrays(x1, x2, '!=', True) Example 12
def greater_equal(x1, x2):
"""
Return (x1 >= x2) element-wise.
Unlike `numpy.greater_equal`, this comparison is performed by
first stripping whitespace characters from the end of the string.
This behavior is provided for backward-compatibility with
numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray or bool
Output array of bools, or a single bool if x1 and x2 are scalars.
See Also
--------
equal, not_equal, less_equal, greater, less
"""
return compare_chararrays(x1, x2, '>=', True) Example 13
def less_equal(x1, x2):
"""
Return (x1 <= x2) element-wise.
Unlike `numpy.less_equal`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray or bool
Output array of bools, or a single bool if x1 and x2 are scalars.
See Also
--------
equal, not_equal, greater_equal, greater, less
"""
return compare_chararrays(x1, x2, '<=', True) Example 14
def greater(x1, x2):
"""
Return (x1 > x2) element-wise.
Unlike `numpy.greater`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray or bool
Output array of bools, or a single bool if x1 and x2 are scalars.
See Also
--------
equal, not_equal, greater_equal, less_equal, less
"""
return compare_chararrays(x1, x2, '>', True) Example 15
def image_series_summary(tag, imgs, max_timesteps=10):
# take only 3 items from the minibatch
imgs = imgs[:, :3]
# assume img.shape == (T, batch_size, n_obj, H, W, C)
# let's log only for 1st obj
tf.cond(tf.equal(tf.rank(imgs), 6), lambda: imgs[:, :, 0], lambda: imgs)
shape = (max_timesteps,) + tuple(imgs.get_shape()[1:])
nt = tf.shape(imgs)[0]
def pad():
paddings = tf.concat(axis=0, values=([[0, max_timesteps - nt]], tf.zeros((len(shape) - 1, 2), tf.int32)))
return tf.pad(imgs, paddings)
imgs = tf.cond(tf.greater(nt, max_timesteps), lambda: imgs[:max_timesteps], pad)
imgs.set_shape(shape)
imgs = tf.squeeze(imgs)
imgs = tf.unstack(imgs)
# concatenate along the columns
imgs = tf.concat(axis=2, values=imgs)
tf.summary.image(tag, imgs) Example 16
def get_local_maxima(x, y):
"""
This function ...
:param x:
:param y:
:return:
"""
m = argrelextrema(y, np.greater)[0].tolist()
# Find the index of the absolute maximum (should also be included, is not for example when it is at the edge)
index = np.argmax(y)
if index not in m: m.append(index)
x_maxima = [x[i] for i in m]
y_maxima = [y[i] for i in m]
return x_maxima, y_maxima
# ----------------------------------------------------------------- Example 17
def get_local_maxima(x, y):
"""
This function ...
:param x:
:param y:
:return:
"""
m = argrelextrema(y, np.greater)[0].tolist()
# Find the index of the absolute maximum (should also be included, is not for example when it is at the edge)
index = np.argmax(y)
if index not in m: m.append(index)
x_maxima = [x[i] for i in m]
y_maxima = [y[i] for i in m]
return x_maxima, y_maxima
# ----------------------------------------------------------------- Example 18
def detect_peaks(hist, count=2):
hist_copy = hist
peaks = len(argrelextrema(hist_copy, np.greater, mode="wrap")[0])
sigma = log1p(peaks)
print(peaks, sigma)
while (peaks > count):
new_hist = gaussian_filter(hist_copy, sigma=sigma)
peaks = len(argrelextrema(new_hist, np.greater, mode="wrap")[0])
if peaks < count:
peaks = count + 1
sigma = sigma * 0.5
continue
hist_copy = new_hist
sigma = log1p(peaks)
print(peaks, sigma)
return argrelextrema(hist_copy, np.greater, mode="wrap")[0] Example 19
def _reset(self):
"""Resets wait counter and cooldown counter.
"""
if self.mode not in ['auto', 'min', 'max']:
logging.warning('Learning Rate Plateau Reducing mode %s is unknown, '
'fallback to auto mode.' % (self.mode))
self.mode = 'auto'
if (self.mode == 'min' or
(self.mode == 'auto' and 'acc' not in self.monitor)):
self.monitor_op = lambda a, b: np.less(a, b - self.epsilon)
self.best = np.Inf
else:
self.monitor_op = lambda a, b: np.greater(a, b + self.epsilon)
self.best = -np.Inf
self.cooldown_counter = 0
self.wait = 0
self.lr_epsilon = self.min_lr * 1e-4 Example 20
def preprocess_labels(label, number_slices):
"""Preprocess the labels to adapt them to the loss computation requirements
Args:
Label corresponding to the input image (W,H) numpy array
Returns:
Label ready to compute the loss (1,W,H,1)
"""
labels = [[] for i in range(np.array(label).shape[0])]
for j in range(np.array(label).shape[0]):
if type(label) is not np.ndarray:
for i in range(number_slices):
labels[j].append(np.array(Image.open(label[0][i]), dtype=np.uint8))
label = np.array(labels[0])
label = label.transpose((1, 2, 0))
max_mask = np.max(label) * 0.5
label = np.greater(label, max_mask)
label = np.expand_dims(label, axis=0)
return label Example 21
def class_balanced_cross_entropy_loss(output, label):
"""Define the class balanced cross entropy loss to train the network
Args:
output: Output of the network
label: Ground truth label
Returns:
Tensor that evaluates the loss
"""
labels = tf.cast(tf.greater(label, 0.5), tf.float32)
output_gt_zero = tf.cast(tf.greater_equal(output, 0), tf.float32)
loss_val = tf.multiply(output, (labels - output_gt_zero)) - tf.log(
1 + tf.exp(output - 2 * tf.multiply(output, output_gt_zero)))
loss_pos = tf.reduce_sum(-tf.multiply(labels, loss_val))
loss_neg = tf.reduce_sum(-tf.multiply(1.0 - labels, loss_val))
final_loss = 0.931 * loss_pos + 0.069 * loss_neg
return final_loss Example 22
def dice_coef_theoretical(y_pred, y_true):
"""Define the dice coefficient
Args:
y_pred: Prediction
y_true: Ground truth Label
Returns:
Dice coefficient
"""
y_true_f = tf.cast(tf.reshape(y_true, [-1]), tf.float32)
y_pred_f = tf.nn.sigmoid(y_pred)
y_pred_f = tf.cast(tf.greater(y_pred_f, 0.5), tf.float32)
y_pred_f = tf.cast(tf.reshape(y_pred_f, [-1]), tf.float32)
intersection = tf.reduce_sum(y_true_f * y_pred_f)
union = tf.reduce_sum(y_true_f) + tf.reduce_sum(y_pred_f)
dice = (2. * intersection) / (union + 0.00001)
if (tf.reduce_sum(y_pred) == 0) and (tf.reduce_sum(y_true) == 0):
dice = 1
return dice Example 23
def preprocess_labels(label, number_slices):
"""Preprocess the labels to adapt them to the loss computation requirements
Args:
Label corresponding to the input image (W,H) numpy array
Returns:
Label ready to compute the loss (1,W,H,1)
"""
labels = [[] for i in range(np.array(label).shape[0])]
for j in range(np.array(label).shape[0]):
if type(label) is not np.ndarray:
for i in range(number_slices):
labels[j].append(np.array(Image.open(label[0][i]), dtype=np.uint8))
label = np.array(labels[0])
label = label.transpose((1,2,0))
max_mask = np.max(label) * 0.5
label = np.greater(label, max_mask)
label = np.expand_dims(label, axis=0)
return label Example 24
def class_balanced_cross_entropy_loss(output, label, results_liver):
"""Define the class balanced cross entropy loss to train the network
Args:
output: Output of the network
label: Ground truth label
Returns:
Tensor that evaluates the loss
"""
labels = tf.cast(tf.greater(label, 0.5), tf.float32)
output_gt_zero = tf.cast(tf.greater_equal(output, 0), tf.float32)
loss_val = tf.multiply(output, (labels - output_gt_zero)) - tf.log(
1 + tf.exp(output - 2 * tf.multiply(output, output_gt_zero)))
loss_pos = tf.reduce_sum(-tf.multiply(results_liver, tf.multiply(labels, loss_val)))
loss_neg = tf.reduce_sum(-tf.multiply(results_liver, tf.multiply(1.0 - labels, loss_val)))
final_loss = 0.1018*loss_neg + 0.8982*loss_pos
return final_loss Example 25
def preprocess_labels(label):
"""Preprocess the labels to adapt them to the loss computation requirements
Args:
Label corresponding to the input image (W,H) numpy array
Returns:
Label ready to compute the loss (1,W,H,1)
"""
labels = [[] for i in range(np.array(label).shape[0])]
for j in range(np.array(label).shape[0]):
if type(label) is not np.ndarray:
for i in range(3):
aux = np.array(Image.open(label[j][i]), dtype=np.uint8)
crop = aux[int(float(x_bb[j])):int((float(x_bb[j])+80)), int(float(y_bb[j])): int((float(y_bb[j])+80))]
labels[j].append(crop)
label = np.array(labels[0])
label = label.transpose((1,2,0))
label = label[:, :, ::-1]
max_mask = np.max(label) * 0.5
label = np.greater(label, max_mask)
label = np.expand_dims(label, axis=0)
return label Example 26
def iterate_until_button_press(buttons, game_state, text_ending_place, text_starting_place):
# while a button was not clicked this method checks if mouse is in the button and if it is
# changes its colour
button_clicked = 0
while button_clicked == 0:
pygame.display.update()
user_events = event.events()
# the first button is the title which is unclickable, thus iterating from 1 to len(buttons)
for num in range(1, len(buttons)):
if np.all((np.less(text_starting_place[num] - config.menu_spacing, user_events["mouse_pos"]),
np.greater(text_ending_place[num] + config.menu_spacing, user_events["mouse_pos"]))):
if user_events["clicked"]:
button_clicked = num
else:
game_state.canvas.surface.blit(
buttons[num][1], text_starting_place[num])
else:
game_state.canvas.surface.blit(
buttons[num][0], text_starting_place[num])
if user_events["closed"] or user_events["quit_to_main_menu"]:
button_clicked = len(buttons)-1
return button_clicked Example 27
def equal(x1, x2):
"""
Return (x1 == x2) element-wise.
Unlike `numpy.equal`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray or bool
Output array of bools, or a single bool if x1 and x2 are scalars.
See Also
--------
not_equal, greater_equal, less_equal, greater, less
"""
return compare_chararrays(x1, x2, '==', True) Example 28
def not_equal(x1, x2):
"""
Return (x1 != x2) element-wise.
Unlike `numpy.not_equal`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray or bool
Output array of bools, or a single bool if x1 and x2 are scalars.
See Also
--------
equal, greater_equal, less_equal, greater, less
"""
return compare_chararrays(x1, x2, '!=', True) Example 29
def greater_equal(x1, x2):
"""
Return (x1 >= x2) element-wise.
Unlike `numpy.greater_equal`, this comparison is performed by
first stripping whitespace characters from the end of the string.
This behavior is provided for backward-compatibility with
numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray or bool
Output array of bools, or a single bool if x1 and x2 are scalars.
See Also
--------
equal, not_equal, less_equal, greater, less
"""
return compare_chararrays(x1, x2, '>=', True) Example 30
def less_equal(x1, x2):
"""
Return (x1 <= x2) element-wise.
Unlike `numpy.less_equal`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray or bool
Output array of bools, or a single bool if x1 and x2 are scalars.
See Also
--------
equal, not_equal, greater_equal, greater, less
"""
return compare_chararrays(x1, x2, '<=', True) Example 31
def less(x1, x2):
"""
Return (x1 < x2) element-wise.
Unlike `numpy.greater`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray or bool
Output array of bools, or a single bool if x1 and x2 are scalars.
See Also
--------
equal, not_equal, greater_equal, less_equal, greater
"""
return compare_chararrays(x1, x2, '<', True) Example 32
def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.logical_and, np.logical_or, np.logical_xor, np.maximum,
np.minimum, np.mod
]
# These functions still return NotImplemented. Will be fixed in
# future.
# bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]
a = np.array('1')
b = 1
for f in binary_funcs:
assert_raises(TypeError, f, a, b) Example 33
def test_identity_equality_mismatch(self):
a = np.array([np.nan], dtype=object)
with warnings.catch_warnings():
warnings.filterwarnings('always', '', FutureWarning)
assert_warns(FutureWarning, np.equal, a, a)
assert_warns(FutureWarning, np.not_equal, a, a)
with warnings.catch_warnings():
warnings.filterwarnings('error', '', FutureWarning)
assert_raises(FutureWarning, np.equal, a, a)
assert_raises(FutureWarning, np.not_equal, a, a)
# And the other do not warn:
with np.errstate(invalid='ignore'):
np.less(a, a)
np.greater(a, a)
np.less_equal(a, a)
np.greater_equal(a, a) Example 34
def test_minmax_func(self):
# Tests minimum and maximum.
(x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
# max doesn't work if shaped
xr = np.ravel(x)
xmr = ravel(xm)
# following are true because of careful selection of data
assert_equal(max(xr), maximum(xmr))
assert_equal(min(xr), minimum(xmr))
assert_equal(minimum([1, 2, 3], [4, 0, 9]), [1, 0, 3])
assert_equal(maximum([1, 2, 3], [4, 0, 9]), [4, 2, 9])
x = arange(5)
y = arange(5) - 2
x[3] = masked
y[0] = masked
assert_equal(minimum(x, y), where(less(x, y), x, y))
assert_equal(maximum(x, y), where(greater(x, y), x, y))
assert_(minimum(x) == 0)
assert_(maximum(x) == 4)
x = arange(4).reshape(2, 2)
x[-1, -1] = masked
assert_equal(maximum(x), 2) Example 35
def validate(self, mb_inputs, mb_targets, mb_probs):
""""""
sents = []
mb_parse_probs, mb_rel_probs = mb_probs
for inputs, targets, parse_probs, rel_probs in zip(mb_inputs, mb_targets, mb_parse_probs, mb_rel_probs):
tokens_to_keep = np.greater(inputs[:,0], Vocab.ROOT)
length = np.sum(tokens_to_keep)
parse_preds, rel_preds = self.prob_argmax(parse_probs, rel_probs, tokens_to_keep)
sent = -np.ones( (length, 9), dtype=int)
tokens = np.arange(1, length+1)
sent[:,0] = tokens
sent[:,1:4] = inputs[tokens]
sent[:,4] = targets[tokens,0]
sent[:,5] = parse_preds[tokens]
sent[:,6] = rel_preds[tokens]
sent[:,7:] = targets[tokens, 1:]
sents.append(sent)
return sents
#============================================================= Example 36
def validate(self, mb_inputs, mb_targets, mb_probs):
""""""
sents = []
mb_parse_probs, mb_rel_probs = mb_probs
for inputs, targets, parse_probs, rel_probs in zip(mb_inputs, mb_targets, mb_parse_probs, mb_rel_probs):
tokens_to_keep = np.greater(inputs[:,0], Vocab.ROOT)
length = np.sum(tokens_to_keep)
parse_preds, rel_preds = self.prob_argmax(parse_probs, rel_probs, tokens_to_keep)
sent = -np.ones( (length, 9), dtype=int)
tokens = np.arange(1, length+1)
sent[:,0] = tokens
sent[:,1:4] = inputs[tokens]
sent[:,4] = targets[tokens,0]
sent[:,5] = parse_preds[tokens]
sent[:,6] = rel_preds[tokens]
sent[:,7:] = targets[tokens, 1:]
sents.append(sent)
return sents
#============================================================= Example 37
def equal(x1, x2):
"""
Return (x1 == x2) element-wise.
Unlike `numpy.equal`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray or bool
Output array of bools, or a single bool if x1 and x2 are scalars.
See Also
--------
not_equal, greater_equal, less_equal, greater, less
"""
return compare_chararrays(x1, x2, '==', True) Example 38
def greater_equal(x1, x2):
"""
Return (x1 >= x2) element-wise.
Unlike `numpy.greater_equal`, this comparison is performed by
first stripping whitespace characters from the end of the string.
This behavior is provided for backward-compatibility with
numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray or bool
Output array of bools, or a single bool if x1 and x2 are scalars.
See Also
--------
equal, not_equal, less_equal, greater, less
"""
return compare_chararrays(x1, x2, '>=', True) Example 39
def less_equal(x1, x2):
"""
Return (x1 <= x2) element-wise.
Unlike `numpy.less_equal`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray or bool
Output array of bools, or a single bool if x1 and x2 are scalars.
See Also
--------
equal, not_equal, greater_equal, greater, less
"""
return compare_chararrays(x1, x2, '<=', True) Example 40
def greater(x1, x2):
"""
Return (x1 > x2) element-wise.
Unlike `numpy.greater`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray or bool
Output array of bools, or a single bool if x1 and x2 are scalars.
See Also
--------
equal, not_equal, greater_equal, less_equal, less
"""
return compare_chararrays(x1, x2, '>', True) Example 41
def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.logical_and, np.logical_or, np.logical_xor, np.maximum,
np.minimum, np.mod
]
# These functions still return NotImplemented. Will be fixed in
# future.
# bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]
a = np.array('1')
b = 1
for f in binary_funcs:
assert_raises(TypeError, f, a, b) Example 42
def test_minmax_func(self):
# Tests minimum and maximum.
(x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
# max doesn't work if shaped
xr = np.ravel(x)
xmr = ravel(xm)
# following are true because of careful selection of data
assert_equal(max(xr), maximum(xmr))
assert_equal(min(xr), minimum(xmr))
assert_equal(minimum([1, 2, 3], [4, 0, 9]), [1, 0, 3])
assert_equal(maximum([1, 2, 3], [4, 0, 9]), [4, 2, 9])
x = arange(5)
y = arange(5) - 2
x[3] = masked
y[0] = masked
assert_equal(minimum(x, y), where(less(x, y), x, y))
assert_equal(maximum(x, y), where(greater(x, y), x, y))
assert_(minimum(x) == 0)
assert_(maximum(x) == 4)
x = arange(4).reshape(2, 2)
x[-1, -1] = masked
assert_equal(maximum(x), 2) Example 43
def preprocess_labels(label):
"""Preprocess the labels to adapt them to the loss computation requirements
Args:
Label corresponding to the input image (W,H) numpy array
Returns:
Label ready to compute the loss (1,W,H,1)
"""
if type(label) is not np.ndarray:
label = np.array(Image.open(label).split()[0], dtype=np.uint8)
max_mask = np.max(label) * 0.5
label = np.greater(label, max_mask)
label = np.expand_dims(np.expand_dims(label, axis=0), axis=3)
# label = tf.cast(np.array(label), tf.float32)
# max_mask = tf.multiply(tf.reduce_max(label), 0.5)
# label = tf.cast(tf.greater(label, max_mask), tf.float32)
# label = tf.expand_dims(tf.expand_dims(label, 0), 3)
return label Example 44
def class_balanced_cross_entropy_loss(output, label):
"""Define the class balanced cross entropy loss to train the network
Args:
output: Output of the network
label: Ground truth label
Returns:
Tensor that evaluates the loss
"""
labels = tf.cast(tf.greater(label, 0.5), tf.float32)
num_labels_pos = tf.reduce_sum(labels)
num_labels_neg = tf.reduce_sum(1.0 - labels)
num_total = num_labels_pos + num_labels_neg
output_gt_zero = tf.cast(tf.greater_equal(output, 0), tf.float32)
loss_val = tf.multiply(output, (labels - output_gt_zero)) - tf.log(
1 + tf.exp(output - 2 * tf.multiply(output, output_gt_zero)))
loss_pos = tf.reduce_sum(-tf.multiply(labels, loss_val))
loss_neg = tf.reduce_sum(-tf.multiply(1.0 - labels, loss_val))
final_loss = num_labels_neg / num_total * loss_pos + num_labels_pos / num_total * loss_neg
return final_loss Example 45
def class_balanced_cross_entropy_loss_theoretical(output, label):
"""Theoretical version of the class balanced cross entropy loss to train the network (Produces unstable results)
Args:
output: Output of the network
label: Ground truth label
Returns:
Tensor that evaluates the loss
"""
output = tf.nn.sigmoid(output)
labels_pos = tf.cast(tf.greater(label, 0), tf.float32)
labels_neg = tf.cast(tf.less(label, 1), tf.float32)
num_labels_pos = tf.reduce_sum(labels_pos)
num_labels_neg = tf.reduce_sum(labels_neg)
num_total = num_labels_pos + num_labels_neg
loss_pos = tf.reduce_sum(tf.multiply(labels_pos, tf.log(output + 0.00001)))
loss_neg = tf.reduce_sum(tf.multiply(labels_neg, tf.log(1 - output + 0.00001)))
final_loss = -num_labels_neg / num_total * loss_pos - num_labels_pos / num_total * loss_neg
return final_loss Example 46
def __init__(self, monitor='val_loss', patience=0, verbose=0, mode='auto'):
super(Callback, self).__init__()
self.monitor = monitor
self.patience = patience
self.verbose = verbose
self.wait = 0
self.best_epoch = 0
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:
self.monitor_op = np.greater
self.best = -np.Inf
else:
self.monitor_op = np.less
self.best = np.Inf Example 47
def handle_rolling(agg, granularity, timestamps, values, is_aggregated,
references, window):
if window > len(values):
raise exceptions.UnAggregableTimeseries(
references,
"Rolling window '%d' is greater than serie length '%d'" %
(window, len(values))
)
timestamps = timestamps[window - 1:]
values = values.T
# rigtorp.se/2011/01/01/rolling-statistics-numpy.html
shape = values.shape[:-1] + (values.shape[-1] - window + 1, window)
strides = values.strides + (values.strides[-1],)
new_values = AGG_MAP[agg](as_strided(values, shape=shape, strides=strides),
axis=-1)
return granularity, timestamps, new_values.T, is_aggregated Example 48
def infill_lines(idxs, idx_max):
if len(idxs) == 0:
return np.array([])
ends = np.array([i[[0,-1]] for i in idxs])
ends = np.roll(ends.reshape(-1), -1).reshape(-1,2)
if np.greater(*ends[-1]):
ends[-1][1] += idx_max
infill = np.diff(ends, axis=1).reshape(-1) > 0
aranges = ends[infill]
if len(aranges) == 0:
return np.array([])
result = np.vstack([pair_space(*i) for i in aranges])
result %= idx_max
return result Example 49
def __init__(self, monitor='val_loss', mode='auto', verbose=0):
super(BestWeight, self).__init__()
self.monitor = monitor
self.mode = mode
self.best_weights = None
self.verbose = verbose
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:
self.monitor_op = np.greater
self.best = -np.Inf
else:
self.monitor_op = np.less
self.best = np.Inf Example 50
def equal(x1, x2):
"""
Return (x1 == x2) element-wise.
Unlike `numpy.equal`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray or bool
Output array of bools, or a single bool if x1 and x2 are scalars.
See Also
--------
not_equal, greater_equal, less_equal, greater, less
"""
return compare_chararrays(x1, x2, '==', True) 