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 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 Example 2
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() Example 3
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 Example 4
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() Example 5
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 Example 6
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) Example 7
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) Example 8
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 Example 9
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 Example 10
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 Example 11
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 Example 12
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 13
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 14
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 15
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 16
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 17
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)) Example 18
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() Example 19
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) Example 20
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))) Example 21
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 Example 22
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 Example 23
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]]) Example 24
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 Example 25
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 26
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 27
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 28
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 29
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 30
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)) Example 31
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() Example 32
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) Example 33
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 Example 34
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
Example 35
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)) Example 36
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 37
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 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_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)) Example 42
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() Example 43
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() Example 44
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) Example 45
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']] Example 46
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 Example 47
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 Example 48
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 49
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 50
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) 