Python numpy 模块，equal() 实例源码
我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用numpy.equal()。
def go_nn_kdtree(eps=0, parallel=True):
"""
Using a specialized data structure, the KDTree
This is not as performant because we're in a high dimensional space
0.777 accuracy? Should be 0.794
"""
n_jobs = 1
if parallel:
n_jobs = -1
neighbors = tree.query(Xtest, eps=eps, n_jobs=n_jobs)
predictions = ytrain[neighbors[1]]
acc = np.equal(predictions, ytest).mean()
return acc
def updateSpots(self, dataSet=None):
if dataSet is None:
dataSet = self.data
invalidate = False
if self.opts['pxMode']:
mask = np.equal(dataSet['sourceRect'], None)
if np.any(mask):
invalidate = True
opts = self.getSpotOpts(dataSet[mask])
sourceRect = self.fragmentAtlas.getSymbolCoords(opts)
dataSet['sourceRect'][mask] = sourceRect
self.fragmentAtlas.getAtlas() # generate atlas so source widths are available.
dataSet['width'] = np.array(list(imap(QtCore.QRectF.width, dataSet['sourceRect'])))/2
dataSet['targetRect'] = None
self._maxSpotPxWidth = self.fragmentAtlas.max_width
else:
self._maxSpotWidth = 0
self._maxSpotPxWidth = 0
self.measureSpotSizes(dataSet)
if invalidate:
self.invalidate()
def getSpotOpts(self, recs, scale=1.0):
if recs.ndim == 0:
rec = recs
symbol = rec['symbol']
if symbol is None:
symbol = self.opts['symbol']
size = rec['size']
if size < 0:
size = self.opts['size']
pen = rec['pen']
if pen is None:
pen = self.opts['pen']
brush = rec['brush']
if brush is None:
brush = self.opts['brush']
return (symbol, size*scale, fn.mkPen(pen), fn.mkBrush(brush))
else:
recs = recs.copy()
recs['symbol'][np.equal(recs['symbol'], None)] = self.opts['symbol']
recs['size'][np.equal(recs['size'], -1)] = self.opts['size']
recs['size'] *= scale
recs['pen'][np.equal(recs['pen'], None)] = fn.mkPen(self.opts['pen'])
recs['brush'][np.equal(recs['brush'], None)] = fn.mkBrush(self.opts['brush'])
return recs
def updateSpots(self, dataSet=None):
if dataSet is None:
dataSet = self.data
invalidate = False
if self.opts['pxMode']:
mask = np.equal(dataSet['sourceRect'], None)
if np.any(mask):
invalidate = True
opts = self.getSpotOpts(dataSet[mask])
sourceRect = self.fragmentAtlas.getSymbolCoords(opts)
dataSet['sourceRect'][mask] = sourceRect
self.fragmentAtlas.getAtlas() # generate atlas so source widths are available.
dataSet['width'] = np.array(list(imap(QtCore.QRectF.width, dataSet['sourceRect'])))/2
dataSet['targetRect'] = None
self._maxSpotPxWidth = self.fragmentAtlas.max_width
else:
self._maxSpotWidth = 0
self._maxSpotPxWidth = 0
self.measureSpotSizes(dataSet)
if invalidate:
self.invalidate()
def getSpotOpts(self, recs, scale=1.0):
if recs.ndim == 0:
rec = recs
symbol = rec['symbol']
if symbol is None:
symbol = self.opts['symbol']
size = rec['size']
if size < 0:
size = self.opts['size']
pen = rec['pen']
if pen is None:
pen = self.opts['pen']
brush = rec['brush']
if brush is None:
brush = self.opts['brush']
return (symbol, size*scale, fn.mkPen(pen), fn.mkBrush(brush))
else:
recs = recs.copy()
recs['symbol'][np.equal(recs['symbol'], None)] = self.opts['symbol']
recs['size'][np.equal(recs['size'], -1)] = self.opts['size']
recs['size'] *= scale
recs['pen'][np.equal(recs['pen'], None)] = fn.mkPen(self.opts['pen'])
recs['brush'][np.equal(recs['brush'], None)] = fn.mkBrush(self.opts['brush'])
return recs
def calc_metrics(self, data_gen, history, dataset, logs):
y_true = []
predictions = []
for i in range(data_gen.steps):
if self.verbose == 1:
print "\r\tdone {}/{}".format(i, data_gen.steps),
(x,y) = next(data_gen)
pred = self.model.predict(x, batch_size=self.batch_size)
if isinstance(x, list) and len(x) == 2: # deep supervision
for m, t, p in zip(x[1].flatten(), y.flatten(), pred.flatten()):
if np.equal(m, 1):
y_true.append(t)
predictions.append(p)
else:
y_true += list(y.flatten())
predictions += list(pred.flatten())
print "\n"
predictions = np.array(predictions)
predictions = np.stack([1-predictions, predictions], axis=1)
ret = metrics.print_metrics_binary(y_true, predictions)
for k, v in ret.iteritems():
logs[dataset + '_' + k] = v
history.append(ret)
def add(self, output, target):
if torch.is_tensor(output):
output = output.cpu().squeeze().numpy()
if torch.is_tensor(target):
target = target.cpu().squeeze().numpy()
elif isinstance(target, numbers.Number):
target = np.asarray([target])
assert np.ndim(output) == 1, \
'wrong output size (1D expected)'
assert np.ndim(target) == 1, \
'wrong target size (1D expected)'
assert output.shape[0] == target.shape[0], \
'number of outputs and targets does not match'
assert np.all(np.add(np.equal(target, 1), np.equal(target, 0))), \
'targets should be binary (0, 1)'
self.scores = np.append(self.scores, output)
self.targets = np.append(self.targets, target)
def validate_transitions_cpu_old(transitions, **kwargs):
pre = np.array(transitions[0])
suc = np.array(transitions[1])
base = setting['base']
width = pre.shape[1] // base
height = pre.shape[1] // base
load(width,height)
pre_validation = validate_states(pre, **kwargs)
suc_validation = validate_states(suc, **kwargs)
results = []
for pre, suc, pre_validation, suc_validation in zip(pre, suc, pre_validation, suc_validation):
if pre_validation and suc_validation:
c = to_configs(np.array([pre, suc]), verbose=False)
succs = successors(c[0], width, height)
results.append(np.any(np.all(np.equal(succs, c[1]), axis=1)))
else:
results.append(False)
return results
def setup():
setting['base'] = 14
def loader(width,height):
from ..util.mnist import mnist
base = setting['base']
x_train, y_train, _, _ = mnist()
filters = [ np.equal(i,y_train) for i in range(9) ]
imgs = [ x_train[f] for f in filters ]
panels = [ imgs[0].reshape((28,28)) for imgs in imgs ]
panels[8] = imgs[8][3].reshape((28,28))
panels[1] = imgs[8][3].reshape((28,28))
panels = np.array(panels)
stepy = panels.shape[1]//base
stepx = panels.shape[2]//base
# unfortunately the method below generates "bolder" fonts
# panels = panels[:,:stepy*base,:stepx*base,]
# panels = panels.reshape((panels.shape[0],base,stepy,base,stepx))
# panels = panels.mean(axis=(2,4))
# panels = panels.round()
panels = panels[:,::stepy,::stepx][:,:base,:base].round()
panels = preprocess(panels)
return panels
setting['loader'] = loader
def validate_transitions(transitions, check_states=True, **kwargs):
pre = np.array(transitions[0])
suc = np.array(transitions[1])
if check_states:
pre_validation = validate_states(pre, verbose=False, **kwargs)
suc_validation = validate_states(suc, verbose=False, **kwargs)
pre_configs = to_configs(pre, verbose=False, **kwargs)
suc_configs = to_configs(suc, verbose=False, **kwargs)
results = []
if check_states:
for pre_c, suc_c, pre_validation, suc_validation in zip(pre_configs, suc_configs, pre_validation, suc_validation):
if pre_validation and suc_validation:
succs = successors(pre_c)
results.append(np.any(np.all(np.equal(succs, suc_c), axis=1)))
else:
results.append(False)
else:
for pre_c, suc_c in zip(pre_configs, suc_configs):
succs = successors(pre_c)
results.append(np.any(np.all(np.equal(succs, suc_c), axis=1)))
return results
def validate_transitions(transitions, check_states=True, **kwargs):
pre = np.array(transitions[0])
suc = np.array(transitions[1])
if check_states:
pre_validation = validate_states(pre, verbose=False, **kwargs)
suc_validation = validate_states(suc, verbose=False, **kwargs)
pre_configs = to_configs(pre, verbose=False, **kwargs)
suc_configs = to_configs(suc, verbose=False, **kwargs)
results = []
if check_states:
for pre_c, suc_c, pre_validation, suc_validation in zip(pre_configs, suc_configs, pre_validation, suc_validation):
if pre_validation and suc_validation:
succs = successors(pre_c)
results.append(np.any(np.all(np.equal(succs, suc_c), axis=1)))
else:
results.append(False)
else:
for pre_c, suc_c in zip(pre_configs, suc_configs):
succs = successors(pre_c)
results.append(np.any(np.all(np.equal(succs, suc_c), axis=1)))
return results
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)
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)
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)
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)
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)
def test_scalar_none_comparison(self):
# Scalars should still just return False and not give a warnings.
# The comparisons are flagged by pep8, ignore that.
with warnings.catch_warnings(record=True) as w:
warnings.filterwarnings('always', '', FutureWarning)
assert_(not np.float32(1) == None)
assert_(not np.str_('test') == None)
# This is dubious (see below):
assert_(not np.datetime64('NaT') == None)
assert_(np.float32(1) != None)
assert_(np.str_('test') != None)
# This is dubious (see below):
assert_(np.datetime64('NaT') != None)
assert_(len(w) == 0)
# For documentation purposes, this is why the datetime is dubious.
# At the time of deprecation this was no behaviour change, but
# it has to be considered when the deprecations are done.
assert_(np.equal(np.datetime64('NaT'), None))
def almost(a, b, decimal=6, fill_value=True):
"""
Returns True if a and b are equal up to decimal places.
If fill_value is True, masked values considered equal. Otherwise,
masked values are considered unequal.
"""
m = mask_or(getmask(a), getmask(b))
d1 = filled(a)
d2 = filled(b)
if d1.dtype.char == "O" or d2.dtype.char == "O":
return np.equal(d1, d2).ravel()
x = filled(masked_array(d1, copy=False, mask=m), fill_value).astype(float_)
y = filled(masked_array(d2, copy=False, mask=m), 1).astype(float_)
d = np.around(np.abs(x - y), decimal) <= 10.0 ** (-decimal)
return d.ravel()
def fail_if_equal(actual, desired, err_msg='',):
"""
Raises an assertion error if two items are equal.
"""
if isinstance(desired, dict):
if not isinstance(actual, dict):
raise AssertionError(repr(type(actual)))
fail_if_equal(len(actual), len(desired), err_msg)
for k, i in desired.items():
if k not in actual:
raise AssertionError(repr(k))
fail_if_equal(actual[k], desired[k], 'key=%r\n%s' % (k, err_msg))
return
if isinstance(desired, (list, tuple)) and isinstance(actual, (list, tuple)):
fail_if_equal(len(actual), len(desired), err_msg)
for k in range(len(desired)):
fail_if_equal(actual[k], desired[k], 'item=%r\n%s' % (k, err_msg))
return
if isinstance(actual, np.ndarray) or isinstance(desired, np.ndarray):
return fail_if_array_equal(actual, desired, err_msg)
msg = build_err_msg([actual, desired], err_msg)
if not desired != actual:
raise AssertionError(msg)
def categorical_accuracy(y_true, y_pred, mask=True):
'''
categorical_accuracy adjusted for padding mask
'''
# if mask is not None:
print y_true
print y_pred
eval_shape = (reduce(mul, y_true.shape[:-1]), y_true.shape[-1])
print eval_shape
y_true_ = np.reshape(y_true, eval_shape)
y_pred_ = np.reshape(y_pred, eval_shape)
flat_mask = np.flatten(mask)
comped = np.equal(np.argmax(y_true_, axis=-1),
np.argmax(y_pred_, axis=-1))
## not sure how to do this in tensor flow
good_entries = flat_mask.nonzero()[0]
return np.mean(np.gather(comped, good_entries))
# else:
# return K.mean(K.equal(K.argmax(y_true, axis=-1),
# K.argmax(y_pred, axis=-1)))
def __estimate_entropy__(self):
counts = self.feature_vector_counts #Counter(self.timeline_feature_vectors)
#print counts
#N = float(sum(counts.values()))
N = float(len(self.timeline) + 1)
max_H = np.log(float(len(list(filter(lambda x: x, counts)))))
if np.equal(max_H, 0.0):
return 0.0
entropy = 0.0
for key in counts.keys():
if counts[key] > 0:
key_probability = counts[key] / N
entropy += -(key_probability * np.log(key_probability))
entropy /= max_H
#print u'N={0}, |counts|={3}, max_H={1}, entropy={2}, counter={4}'.format(N, max_H, entropy, len(counts), counts)
return entropy
def _f_dice(a, b):
"""DICE between two segmentations.
Args:
a: [..., H, W], binary mask
b: [..., H, W], binary mask
Returns:
dice: [...]
"""
card_a = a.sum(axis=-1).sum(axis=-1)
card_b = b.sum(axis=-1).sum(axis=-1)
card_ab = (a * b).sum(axis=-1).sum(axis=-1)
card_sum = card_a + card_b
dice = 2 * card_ab / (card_sum + np.equal(card_sum, 0).astype('float32'))
return dice
def test_accuracy():
def cat_acc(y_pred, y_true):
return np.expand_dims(np.equal(np.argmax(y_pred, axis=-1), np.argmax(y_true, axis=-1)), -1),
objectives_test(objectives.accuracy,
cat_acc,
np_pred=[[0,0,.9], [0,.9,0], [.9,0,0]],
np_true=[[0,0,1], [0,0,1], [0,0,1]])
def bi_acc(y_pred, y_true):
return np.equal(np.round(y_pred), y_true)
objectives_test(objectives.accuracy,
bi_acc,
np_pred=[[0], [0.6], [0.7]],
np_true=[[0], [1], [1]])
def test_convolutional_embedding_encoder(config, out_data_shape, out_data_length, out_seq_len):
conv_embed = sockeye.encoder.ConvolutionalEmbeddingEncoder(config)
data_nd = mx.nd.random_normal(shape=(_BATCH_SIZE, _SEQ_LEN, _NUM_EMBED))
data = mx.sym.Variable("data", shape=data_nd.shape)
data_length = mx.sym.Variable("data_length", shape=_DATA_LENGTH_ND.shape)
(encoded_data,
encoded_data_length,
encoded_seq_len) = conv_embed.encode(data=data, data_length=data_length, seq_len=_SEQ_LEN)
exe = encoded_data.simple_bind(mx.cpu(), data=data_nd.shape)
exe.forward(data=data_nd)
assert exe.outputs[0].shape == out_data_shape
exe = encoded_data_length.simple_bind(mx.cpu(), data_length=_DATA_LENGTH_ND.shape)
exe.forward(data_length=_DATA_LENGTH_ND)
assert np.equal(exe.outputs[0].asnumpy(), np.asarray(out_data_length)).all()
assert encoded_seq_len == out_seq_len
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)
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)
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)
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)
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)
def test_scalar_none_comparison(self):
# Scalars should still just return False and not give a warnings.
# The comparisons are flagged by pep8, ignore that.
with warnings.catch_warnings(record=True) as w:
warnings.filterwarnings('always', '', FutureWarning)
assert_(not np.float32(1) == None)
assert_(not np.str_('test') == None)
# This is dubious (see below):
assert_(not np.datetime64('NaT') == None)
assert_(np.float32(1) != None)
assert_(np.str_('test') != None)
# This is dubious (see below):
assert_(np.datetime64('NaT') != None)
assert_(len(w) == 0)
# For documentation purposes, this is why the datetime is dubious.
# At the time of deprecation this was no behaviour change, but
# it has to be considered when the deprecations are done.
assert_(np.equal(np.datetime64('NaT'), None))
def almost(a, b, decimal=6, fill_value=True):
"""
Returns True if a and b are equal up to decimal places.
If fill_value is True, masked values considered equal. Otherwise,
masked values are considered unequal.
"""
m = mask_or(getmask(a), getmask(b))
d1 = filled(a)
d2 = filled(b)
if d1.dtype.char == "O" or d2.dtype.char == "O":
return np.equal(d1, d2).ravel()
x = filled(masked_array(d1, copy=False, mask=m), fill_value).astype(float_)
y = filled(masked_array(d2, copy=False, mask=m), 1).astype(float_)
d = np.around(np.abs(x - y), decimal) <= 10.0 ** (-decimal)
return d.ravel()
def fail_if_equal(actual, desired, err_msg='',):
"""
Raises an assertion error if two items are equal.
"""
if isinstance(desired, dict):
if not isinstance(actual, dict):
raise AssertionError(repr(type(actual)))
fail_if_equal(len(actual), len(desired), err_msg)
for k, i in desired.items():
if k not in actual:
raise AssertionError(repr(k))
fail_if_equal(actual[k], desired[k], 'key=%r\n%s' % (k, err_msg))
return
if isinstance(desired, (list, tuple)) and isinstance(actual, (list, tuple)):
fail_if_equal(len(actual), len(desired), err_msg)
for k in range(len(desired)):
fail_if_equal(actual[k], desired[k], 'item=%r\n%s' % (k, err_msg))
return
if isinstance(actual, np.ndarray) or isinstance(desired, np.ndarray):
return fail_if_array_equal(actual, desired, err_msg)
msg = build_err_msg([actual, desired], err_msg)
if not desired != actual:
raise AssertionError(msg)
def grayscaleimage(self, value):
try:
if value.ndim == 2:
self._grayscaleimage = value
if (_np.equal(self._x,None).any() or
_np.equal(self._y,None).any() or
self._x.size != value.shape[1] or
self._y.size != value.shape[0]):
self._x = _np.linspace(1, value.shape[1], value.shape[1])
self._y = _np.linspace(1, value.shape[0], value.shape[0])
self.xunits = self.XUNITS
self.yunits = self.YUNITS
else:
pass
except:
pass
def paren_data(T, n_data):
MAX_COUNT = 10
n_paren = 10
n_noise = 10
inputs = (np.random.rand(T, n_data)* (n_paren * 2 + n_noise)).astype(np.int32)
counts = np.zeros((n_data, n_paren), dtype=np.int32)
targets = np.zeros((T, n_data, n_paren), dtype = np.int32)
opening_parens = (np.arange(0, n_paren)*2)[None, :]
closing_parens = opening_parens + 1
for i in range(T):
opened = np.equal(inputs[i, :, None], opening_parens)
counts = np.minimum(MAX_COUNT, counts + opened)
closed = np.equal(inputs[i, :, None], closing_parens)
counts = np.maximum(0, counts - closed)
targets[i, :, :] = counts
x = np.transpose(inputs, [1,0])
y = np.transpose(targets, [1,0,2])
return x, y
def is_connect_exist_nn(node_in, node_out, nn):
"""
check if the connection between node_in and node_out exists
:param node_in:
:param node_out:
:param nn: Neural network instance
:return: True if exists, False if DNE
"""
assert type(nn) == NeuralNetwork, "nn must be an instance of Neural Network"
if nn.connect_genes is None:
return False
connect = [node_in, node_out]
history = nn.connect_genes[:, :2]
return any(np.equal(connect, history).all(1))
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)
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)
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)
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)
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)
def test_scalar_none_comparison(self):
# Scalars should still just return false and not give a warnings.
# The comparisons are flagged by pep8, ignore that.
with warnings.catch_warnings(record=True) as w:
warnings.filterwarnings('always', '', FutureWarning)
assert_(not np.float32(1) == None)
assert_(not np.str_('test') == None)
# This is dubious (see below):
assert_(not np.datetime64('NaT') == None)
assert_(np.float32(1) != None)
assert_(np.str_('test') != None)
# This is dubious (see below):
assert_(np.datetime64('NaT') != None)
assert_(len(w) == 0)
# For documentaiton purpose, this is why the datetime is dubious.
# At the time of deprecation this was no behaviour change, but
# it has to be considered when the deprecations is done.
assert_(np.equal(np.datetime64('NaT'), None))
def approx(a, b, fill_value=True, rtol=1e-5, atol=1e-8):
"""
Returns true if all components of a and b are equal to given tolerances.
If fill_value is True, masked values considered equal. Otherwise,
masked values are considered unequal. The relative error rtol should
be positive and << 1.0 The absolute error atol comes into play for
those elements of b that are very small or zero; it says how small a
must be also.
"""
m = mask_or(getmask(a), getmask(b))
d1 = filled(a)
d2 = filled(b)
if d1.dtype.char == "O" or d2.dtype.char == "O":
return np.equal(d1, d2).ravel()
x = filled(masked_array(d1, copy=False, mask=m), fill_value).astype(float_)
y = filled(masked_array(d2, copy=False, mask=m), 1).astype(float_)
d = np.less_equal(umath.absolute(x - y), atol + rtol * umath.absolute(y))
return d.ravel()
def almost(a, b, decimal=6, fill_value=True):
"""
Returns True if a and b are equal up to decimal places.
If fill_value is True, masked values considered equal. Otherwise,
masked values are considered unequal.
"""
m = mask_or(getmask(a), getmask(b))
d1 = filled(a)
d2 = filled(b)
if d1.dtype.char == "O" or d2.dtype.char == "O":
return np.equal(d1, d2).ravel()
x = filled(masked_array(d1, copy=False, mask=m), fill_value).astype(float_)
y = filled(masked_array(d2, copy=False, mask=m), 1).astype(float_)
d = np.around(np.abs(x - y), decimal) <= 10.0 ** (-decimal)
return d.ravel()
def fail_if_equal(actual, desired, err_msg='',):
"""
Raises an assertion error if two items are equal.
"""
if isinstance(desired, dict):
if not isinstance(actual, dict):
raise AssertionError(repr(type(actual)))
fail_if_equal(len(actual), len(desired), err_msg)
for k, i in desired.items():
if k not in actual:
raise AssertionError(repr(k))
fail_if_equal(actual[k], desired[k], 'key=%r\n%s' % (k, err_msg))
return
if isinstance(desired, (list, tuple)) and isinstance(actual, (list, tuple)):
fail_if_equal(len(actual), len(desired), err_msg)
for k in range(len(desired)):
fail_if_equal(actual[k], desired[k], 'item=%r\n%s' % (k, err_msg))
return
if isinstance(actual, np.ndarray) or isinstance(desired, np.ndarray):
return fail_if_array_equal(actual, desired, err_msg)
msg = build_err_msg([actual, desired], err_msg)
if not desired != actual:
raise AssertionError(msg)
def get_same_status(pairs, items, target):
text_compare = pairs
item1 = items[['itemID', target]]
item1 = item1.rename(
columns={
'itemID': 'itemID_1',
target: target + '_1',
}
)
text_compare = pd.merge(text_compare, item1, how='left', on='itemID_1', left_index=True)
item2 = items[['itemID', target]]
item2 = item2.rename(
columns={
'itemID': 'itemID_2',
target: target + '_2',
}
)
text_compare = pd.merge(text_compare, item2, how='left', on='itemID_2', left_index=True)
text_compare[target + '_same'] = np.equal(text_compare[target + '_1'], text_compare[target + '_2']).astype(np.int32)
# print(text_compare[target + '_same'].describe())
return text_compare[['id', target + '_same']]
def gen_hull(p, p_mask, f_encode, f_probi, options):
# p: n_sizes * n_samples * data_dim
n_sizes = p.shape[0]
n_samples = p.shape[1] if p.ndim == 3 else 1
hprev = f_encode(p_mask, p) # n_sizes * n_samples * data_dim
points = numpy.zeros((n_samples, n_sizes), dtype='int64')
h = hprev[-1]
c = numpy.zeros((n_samples, options['dim_proj']), dtype=config.floatX)
xi = numpy.zeros((n_samples,), dtype='int64')
xi_mask = numpy.ones((n_samples,), dtype=config.floatX)
for i in range(n_sizes):
h, c, probi = f_probi(p_mask[i], xi, h, c, hprev, p_mask, p)
xi = probi.argmax(axis=0)
xi *= xi_mask.astype(numpy.int64) # Avoid compatibility problem in numpy 1.10
xi_mask = (numpy.not_equal(xi, 0)).astype(config.floatX)
if numpy.equal(xi_mask, 0).all():
break
points[:, i] = xi
return points
def VOCap(rec,prec):
mpre = np.zeros([1,2+len(prec)])
mpre[0,1:len(prec)+1] = prec
mrec = np.zeros([1,2+len(rec)])
mrec[0,1:len(rec)+1] = rec
mrec[0,len(rec)+1] = 1.0
for i in range(mpre.size-2,-1,-1):
mpre[0,i] = max(mpre[0,i],mpre[0,i+1])
i = np.argwhere( ~np.equal( mrec[0,1:], mrec[0,:mrec.shape[1]-1]) )+1
i = i.flatten()
# compute area under the curve
ap = np.sum( np.multiply( np.subtract( mrec[0,i], mrec[0,i-1]), mpre[0,i] ) )
return ap
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)
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)
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)