Python numpy 模块，long() 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.long()。
def _is_integer(x):
"""Determine whether some object ``x`` is an
integer type (int, long, etc). This is part of the
``fixes`` module, since Python 3 removes the long
datatype, we have to check the version major.
Parameters
----------
x : object
The item to assess whether is an integer.
Returns
-------
bool
True if ``x`` is an integer type
"""
return (not isinstance(x, (bool, np.bool))) and \
isinstance(x, (numbers.Integral, int, np.int, np.long, long)) # no long type in python 3
def test_object_array_self_reference(self):
# Object arrays with references to themselves can cause problems
a = np.array(0, dtype=object)
a[()] = a
assert_raises(TypeError, int, a)
assert_raises(TypeError, long, a)
assert_raises(TypeError, float, a)
assert_raises(TypeError, oct, a)
assert_raises(TypeError, hex, a)
# Test the same for a circular reference.
b = np.array(a, dtype=object)
a[()] = b
assert_raises(TypeError, int, a)
# Numpy has no tp_traverse currently, so circular references
# cannot be detected. So resolve it:
a[()] = 0
# This was causing a to become like the above
a = np.array(0, dtype=object)
a[...] += 1
assert_equal(a, 1)
def test_attribute_wrapper():
def attribute_value_test(attribute_value):
node = make_node('Abs', ['X'], [], name='test_node', test_attribute=attribute_value)
model = make_model(make_graph([node], 'test_graph', [
make_tensor_value_info('X', onnx.TensorProto.FLOAT, [1, 2]),
], []), producer_name='ngraph')
wrapped_attribute = ModelWrapper(model).graph.node[0].get_attribute('test_attribute')
return wrapped_attribute.get_value()
tensor = make_tensor('test_tensor', onnx.TensorProto.FLOAT, [1], [1])
assert attribute_value_test(1) == 1
assert type(attribute_value_test(1)) == np.long
assert attribute_value_test(1.0) == 1.0
assert type(attribute_value_test(1.0)) == np.float
assert attribute_value_test('test') == 'test'
assert attribute_value_test(tensor)._proto == tensor
assert attribute_value_test([1, 2, 3]) == [1, 2, 3]
assert attribute_value_test([1.0, 2.0, 3.0]) == [1.0, 2.0, 3.0]
assert attribute_value_test(['test1', 'test2']) == ['test1', 'test2']
assert attribute_value_test([tensor, tensor])[1]._proto == tensor
def test_object_array_self_reference(self):
# Object arrays with references to themselves can cause problems
a = np.array(0, dtype=object)
a[()] = a
assert_raises(TypeError, int, a)
assert_raises(TypeError, long, a)
assert_raises(TypeError, float, a)
assert_raises(TypeError, oct, a)
assert_raises(TypeError, hex, a)
# Test the same for a circular reference.
b = np.array(a, dtype=object)
a[()] = b
assert_raises(TypeError, int, a)
# Numpy has no tp_traverse currently, so circular references
# cannot be detected. So resolve it:
a[()] = 0
# This was causing a to become like the above
a = np.array(0, dtype=object)
a[...] += 1
assert_equal(a, 1)
def test_object_array_self_reference(self):
# Object arrays with references to themselves can cause problems
a = np.array(0, dtype=object)
a[()] = a
assert_raises(TypeError, int, a)
assert_raises(TypeError, long, a)
assert_raises(TypeError, float, a)
assert_raises(TypeError, oct, a)
assert_raises(TypeError, hex, a)
# Test the same for a circular reference.
b = np.array(a, dtype=object)
a[()] = b
assert_raises(TypeError, int, a)
# Numpy has no tp_traverse currently, so circular references
# cannot be detected. So resolve it:
a[()] = 0
# This was causing a to become like the above
a = np.array(0, dtype=object)
a[...] += 1
assert_equal(a, 1)
def test_object_array_self_reference(self):
# Object arrays with references to themselves can cause problems
a = np.array(0, dtype=object)
a[()] = a
assert_raises(TypeError, int, a)
assert_raises(TypeError, long, a)
assert_raises(TypeError, float, a)
assert_raises(TypeError, oct, a)
assert_raises(TypeError, hex, a)
# Test the same for a circular reference.
b = np.array(a, dtype=object)
a[()] = b
assert_raises(TypeError, int, a)
# Numpy has no tp_traverse currently, so circular references
# cannot be detected. So resolve it:
a[()] = 0
# This was causing a to become like the above
a = np.array(0, dtype=object)
a[...] += 1
assert_equal(a, 1)
def test_respect_dtype_singleton(self):
# See gh-7203
for dt in self.itype:
lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min
ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1
sample = self.rfunc(lbnd, ubnd, dtype=dt)
self.assertEqual(sample.dtype, np.dtype(dt))
for dt in (np.bool, np.int, np.long):
lbnd = 0 if dt is np.bool else np.iinfo(dt).min
ubnd = 2 if dt is np.bool else np.iinfo(dt).max + 1
# gh-7284: Ensure that we get Python data types
sample = self.rfunc(lbnd, ubnd, dtype=dt)
self.assertFalse(hasattr(sample, 'dtype'))
self.assertEqual(type(sample), dt)
def test_object_array_self_reference(self):
# Object arrays with references to themselves can cause problems
a = np.array(0, dtype=object)
a[()] = a
assert_raises(TypeError, int, a)
assert_raises(TypeError, long, a)
assert_raises(TypeError, float, a)
assert_raises(TypeError, oct, a)
assert_raises(TypeError, hex, a)
# Test the same for a circular reference.
b = np.array(a, dtype=object)
a[()] = b
assert_raises(TypeError, int, a)
# Numpy has no tp_traverse currently, so circular references
# cannot be detected. So resolve it:
a[()] = 0
# This was causing a to become like the above
a = np.array(0, dtype=object)
a[...] += 1
assert_equal(a, 1)
def test_object_array_self_reference(self):
# Object arrays with references to themselves can cause problems
a = np.array(0, dtype=object)
a[()] = a
assert_raises(TypeError, int, a)
assert_raises(TypeError, long, a)
assert_raises(TypeError, float, a)
assert_raises(TypeError, oct, a)
assert_raises(TypeError, hex, a)
# Test the same for a circular reference.
b = np.array(a, dtype=object)
a[()] = b
assert_raises(TypeError, int, a)
# NumPy has no tp_traverse currently, so circular references
# cannot be detected. So resolve it:
a[()] = 0
# This was causing a to become like the above
a = np.array(0, dtype=object)
a[...] += 1
assert_equal(a, 1)
def test_object_array_self_reference(self):
# Object arrays with references to themselves can cause problems
a = np.array(0, dtype=object)
a[()] = a
assert_raises(TypeError, int, a)
assert_raises(TypeError, long, a)
assert_raises(TypeError, float, a)
assert_raises(TypeError, oct, a)
assert_raises(TypeError, hex, a)
# Test the same for a circular reference.
b = np.array(a, dtype=object)
a[()] = b
assert_raises(TypeError, int, a)
# Numpy has no tp_traverse currently, so circular references
# cannot be detected. So resolve it:
a[()] = 0
# This was causing a to become like the above
a = np.array(0, dtype=object)
a[...] += 1
assert_equal(a, 1)
def _extract_field_names(self, event):
# extract field names from sids (price, volume etc), make sure
# every sid has the same fields.
sid_keys = []
for sid in itervalues(event.data):
keys = set([name for name, value in sid.items()
if isinstance(value,
(int,
float,
numpy.integer,
numpy.float,
numpy.long))
])
sid_keys.append(keys)
# with CUSTOM data events, there may be different fields
# per sid. So the allowable keys are the union of all events.
union = set.union(*sid_keys)
unwanted_fields = {
'portfolio',
'sid',
'dt',
'type',
'source_id',
'_initial_len',
}
return union - unwanted_fields
def fit(self, X):
_X = self.__aggregate_dataset(X)
self.polynomial = np.polyfit(_X['expenses'].astype(np.long),
_X['distance_traveled'].astype(np.long),
3)
self._polynomial_fn = np.poly1d(self.polynomial)
return self
def test_is_numeric():
assert is_numeric(1)
assert is_numeric(1.)
assert is_numeric(np.long(1))
assert is_numeric(np.int(1.0))
assert is_numeric(np.float(1))
assert is_numeric(1e-12)
assert not is_numeric('a')
assert not is_numeric(True)
def score(self, X, y=None):
"""Returns the score on the given data, if the estimator has been refit.
This uses the score defined by ``scoring`` where provided, and the
``best_estimator_.score`` method otherwise.
Parameters
----------
X : array-like or pandas DataFrame, shape = [n_samples, n_features]
Input data, where n_samples is the number of samples and
n_features is the number of features.
y : array-like, shape = [n_samples] or [n_samples, n_output], optional
Target relative to X for classification or regression;
None for unsupervised learning.
Returns
-------
score : float
Notes
-----
* The long-standing behavior of this method changed in version 0.16.
* It no longer uses the metric provided by ``estimator.score`` if the
``scoring`` parameter was set when fitting.
"""
X = _validate_X(X)
y = _validate_y(y)
if not hasattr(self, 'scorer_') or self.scorer_ is None:
raise ValueError("No score function explicitly defined, "
"and the estimator doesn't provide one %s"
% self.best_estimator_)
# we've already fit, and we have a scorer
if self.scoring is not None and hasattr(self.best_estimator_, 'score'):
warnings.warn("The long-standing behavior to use the estimator's "
"score function in {0}.score has changed. The "
"scoring parameter is now used."
"".format(self.__class__.__name__),
UserWarning)
return self.scorer_(self.best_estimator_, X, y)
def is_integer(x):
"""Determine whether some object ``x`` is an
integer type (int, long, etc).
Parameters
----------
x : object
The item to assess
Returns
-------
bool
True if ``x`` is an integer type
"""
try:
python_major_version = sys.version_info.major
assert(python_major_version == 2 or python_major_version == 3)
if python_major_version == 2:
return (not isinstance(x, (bool, np.bool))) and \
isinstance(x, (numbers.Integral, int, long, np.int, np.long))
elif python_major_version == 3:
return (not isinstance(x, (bool, np.bool))) and \
isinstance(x, (numbers.Integral, int, np.int, np.long))
except AssertionError:
_, _, tb = sys.exc_info()
traceback.print_tb(tb) # Fixed format
tb_info = traceback.extract_tb(tb)
filename, line, func, text = tb_info[-1]
print('An error occurred on line {} in statement {}'.format(line, text))
exit(1)
return _is_integer(x)
def __init__(self, weights=None, size_average=None):
super(ClassNLLCriterion, self).__init__()
if size_average:
self.size_average = size_average
else:
self.size_average = True
if weights:
# assert(weights:dim() == 1, "weights input should be 1-D Tensor")
self.weights = weights
self.output_tensor = np.zeros(1)
self.total_weight_tensor = np.ones(1)
self.target = np.zeros(1) # , dtype=np.long)
def test_signed_integer_division_overflow(self):
# Ticket #1317.
def test_type(t):
min = np.array([np.iinfo(t).min])
min //= -1
with np.errstate(divide="ignore"):
for t in (np.int8, np.int16, np.int32, np.int64, np.int, np.long):
test_type(t)
def test_array_side_effect(self):
# The second use of itemsize was throwing an exception because in
# ctors.c, discover_itemsize was calling PyObject_Length without
# checking the return code. This failed to get the length of the
# number 2, and the exception hung around until something checked
# PyErr_Occurred() and returned an error.
assert_equal(np.dtype('S10').itemsize, 10)
np.array([['abc', 2], ['long ', '0123456789']], dtype=np.string_)
assert_equal(np.dtype('S10').itemsize, 10)
def test_sequence_long(self):
assert_equal(np.array([long(4), long(4)]).dtype, np.long)
assert_equal(np.array([long(4), 2**80]).dtype, np.object)
assert_equal(np.array([long(4), 2**80, long(4)]).dtype, np.object)
assert_equal(np.array([2**80, long(4)]).dtype, np.object)
def test_matrix_multiply(self):
self.compare_matrix_multiply_results(np.long)
self.compare_matrix_multiply_results(np.double)
def test_random_integers_max_int(self):
# Tests whether random_integers can generate the
# maximum allowed Python int that can be converted
# into a C long. Previous implementations of this
# method have thrown an OverflowError when attempting
# to generate this integer.
actual = np.random.random_integers(np.iinfo('l').max,
np.iinfo('l').max)
desired = np.iinfo('l').max
np.testing.assert_equal(actual, desired)
def test_signed_integer_division_overflow(self):
# Ticket #1317.
def test_type(t):
min = np.array([np.iinfo(t).min])
min //= -1
with np.errstate(divide="ignore"):
for t in (np.int8, np.int16, np.int32, np.int64, np.int, np.long):
test_type(t)
def test_array_side_effect(self):
# The second use of itemsize was throwing an exception because in
# ctors.c, discover_itemsize was calling PyObject_Length without
# checking the return code. This failed to get the length of the
# number 2, and the exception hung around until something checked
# PyErr_Occurred() and returned an error.
assert_equal(np.dtype('S10').itemsize, 10)
np.array([['abc', 2], ['long ', '0123456789']], dtype=np.string_)
assert_equal(np.dtype('S10').itemsize, 10)
def test_sequence_long(self):
assert_equal(np.array([long(4), long(4)]).dtype, np.long)
assert_equal(np.array([long(4), 2**80]).dtype, np.object)
assert_equal(np.array([long(4), 2**80, long(4)]).dtype, np.object)
assert_equal(np.array([2**80, long(4)]).dtype, np.object)
def test_matrix_multiply(self):
self.compare_matrix_multiply_results(np.long)
self.compare_matrix_multiply_results(np.double)
def test_random_integers_max_int(self):
# Tests whether random_integers can generate the
# maximum allowed Python int that can be converted
# into a C long. Previous implementations of this
# method have thrown an OverflowError when attempting
# to generate this integer.
actual = np.random.random_integers(np.iinfo('l').max,
np.iinfo('l').max)
desired = np.iinfo('l').max
np.testing.assert_equal(actual, desired)
def get_landmarks(self,img,box=None,left=None,top=None,right=None,bottom=None):
if box is not None:
left,top,right,bottom = box
left = np.long(left)
top = np.long(top)
right = np.long(right)
bottom = np.long(bottom)
bb = dlib.rectangle(left,top,right,bottom)
landmarks = self.align_tool.findLandmarks(img,bb)
npLandmarks = np.float32(landmarks)
npLandmarkIndices = np.array(self.landmarkIndices)
return npLandmarks[npLandmarkIndices]
def test_signed_integer_division_overflow(self):
# Ticket #1317.
def test_type(t):
min = np.array([np.iinfo(t).min])
min //= -1
with np.errstate(divide="ignore"):
for t in (np.int8, np.int16, np.int32, np.int64, np.int, np.long):
test_type(t)
def test_array_side_effect(self):
# The second use of itemsize was throwing an exception because in
# ctors.c, discover_itemsize was calling PyObject_Length without
# checking the return code. This failed to get the length of the
# number 2, and the exception hung around until something checked
# PyErr_Occurred() and returned an error.
assert_equal(np.dtype('S10').itemsize, 10)
np.array([['abc', 2], ['long ', '0123456789']], dtype=np.string_)
assert_equal(np.dtype('S10').itemsize, 10)
def test_sequence_long(self):
assert_equal(np.array([long(4), long(4)]).dtype, np.long)
assert_equal(np.array([long(4), 2**80]).dtype, np.object)
assert_equal(np.array([long(4), 2**80, long(4)]).dtype, np.object)
assert_equal(np.array([2**80, long(4)]).dtype, np.object)
def test_matrix_multiply(self):
self.compare_matrix_multiply_results(np.long)
self.compare_matrix_multiply_results(np.double)
def test_signed_integer_division_overflow(self):
# Ticket #1317.
def test_type(t):
min = np.array([np.iinfo(t).min])
min //= -1
with np.errstate(divide="ignore"):
for t in (np.int8, np.int16, np.int32, np.int64, np.int, np.long):
test_type(t)
def test_array_side_effect(self):
# The second use of itemsize was throwing an exception because in
# ctors.c, discover_itemsize was calling PyObject_Length without
# checking the return code. This failed to get the length of the
# number 2, and the exception hung around until something checked
# PyErr_Occurred() and returned an error.
assert_equal(np.dtype('S10').itemsize, 10)
np.array([['abc', 2], ['long ', '0123456789']], dtype=np.string_)
assert_equal(np.dtype('S10').itemsize, 10)
def test_sequence_long(self):
assert_equal(np.array([long(4), long(4)]).dtype, np.long)
assert_equal(np.array([long(4), 2**80]).dtype, np.object)
assert_equal(np.array([long(4), 2**80, long(4)]).dtype, np.object)
assert_equal(np.array([2**80, long(4)]).dtype, np.object)
def test_matrix_multiply(self):
self.compare_matrix_multiply_results(np.long)
self.compare_matrix_multiply_results(np.double)
def test_random_integers_max_int(self):
# Tests whether random_integers can generate the
# maximum allowed Python int that can be converted
# into a C long. Previous implementations of this
# method have thrown an OverflowError when attempting
# to generate this integer.
with suppress_warnings() as sup:
w = sup.record(DeprecationWarning)
actual = mt19937.random_integers(np.iinfo('l').max,
np.iinfo('l').max)
assert_(len(w) == 1)
desired = np.iinfo('l').max
assert_equal(actual, desired)
def test_signed_integer_division_overflow(self):
# Ticket #1317.
def test_type(t):
min = np.array([np.iinfo(t).min])
min //= -1
with np.errstate(divide="ignore"):
for t in (np.int8, np.int16, np.int32, np.int64, np.int, np.long):
test_type(t)
def test_array_side_effect(self):
# The second use of itemsize was throwing an exception because in
# ctors.c, discover_itemsize was calling PyObject_Length without
# checking the return code. This failed to get the length of the
# number 2, and the exception hung around until something checked
# PyErr_Occurred() and returned an error.
assert_equal(np.dtype('S10').itemsize, 10)
np.array([['abc', 2], ['long ', '0123456789']], dtype=np.string_)
assert_equal(np.dtype('S10').itemsize, 10)
def test_sequence_long(self):
assert_equal(np.array([long(4), long(4)]).dtype, np.long)
assert_equal(np.array([long(4), 2**80]).dtype, np.object)
assert_equal(np.array([long(4), 2**80, long(4)]).dtype, np.object)
assert_equal(np.array([2**80, long(4)]).dtype, np.object)
def test_matrix_multiply(self):
self.compare_matrix_multiply_results(np.long)
self.compare_matrix_multiply_results(np.double)
def test_random_integers_max_int(self):
# Tests whether random_integers can generate the
# maximum allowed Python int that can be converted
# into a C long. Previous implementations of this
# method have thrown an OverflowError when attempting
# to generate this integer.
actual = np.random.random_integers(np.iinfo('l').max,
np.iinfo('l').max)
desired = np.iinfo('l').max
np.testing.assert_equal(actual, desired)
def test_signed_integer_division_overflow(self):
# Ticket #1317.
def test_type(t):
min = np.array([np.iinfo(t).min])
min //= -1
with np.errstate(divide="ignore"):
for t in (np.int8, np.int16, np.int32, np.int64, np.int, np.long):
test_type(t)
def test_array_side_effect(self):
# The second use of itemsize was throwing an exception because in
# ctors.c, discover_itemsize was calling PyObject_Length without
# checking the return code. This failed to get the length of the
# number 2, and the exception hung around until something checked
# PyErr_Occurred() and returned an error.
assert_equal(np.dtype('S10').itemsize, 10)
np.array([['abc', 2], ['long ', '0123456789']], dtype=np.string_)
assert_equal(np.dtype('S10').itemsize, 10)
def test_sequence_long(self):
assert_equal(np.array([long(4), long(4)]).dtype, np.long)
assert_equal(np.array([long(4), 2**80]).dtype, np.object)
assert_equal(np.array([long(4), 2**80, long(4)]).dtype, np.object)
assert_equal(np.array([2**80, long(4)]).dtype, np.object)
def test_matrix_multiply(self):
self.compare_matrix_multiply_results(np.long)
self.compare_matrix_multiply_results(np.double)
def test_random_integers_max_int(self):
# Tests whether random_integers can generate the
# maximum allowed Python int that can be converted
# into a C long. Previous implementations of this
# method have thrown an OverflowError when attempting
# to generate this integer.
with suppress_warnings() as sup:
w = sup.record(DeprecationWarning)
actual = np.random.random_integers(np.iinfo('l').max,
np.iinfo('l').max)
assert_(len(w) == 1)
desired = np.iinfo('l').max
assert_equal(actual, desired)
def assert_valid_percent(x, eq_lower=False, eq_upper=False):
# these are all castable to float
assert_is_type(x, (float, np.float, np.int, int, long, np.long))
x = float(x)
# test lower bound:
if not ((eq_lower and 0. <= x) or ((not eq_lower) and 0. < x)):
raise ValueError('Expected 0. %s x, but got x=%r'
% ('<=' if eq_lower else '<', x))
if not ((eq_upper and x <= 1.) or ((not eq_upper) and x < 1.)):
raise ValueError('Expected x %s 1., but got x=%r'
% ('<=' if eq_upper else '<', x))
return x
def get_random_state(random_state):
# if it's a seed, return a new seeded RandomState
if isinstance(random_state, (int, np.int, long, np.long, NoneType)):
return RandomState(random_state)
# if it's a RandomState, it's been initialized
elif isinstance(random_state, RandomState):
return random_state
else:
raise TypeError('cannot seed new RandomState with type=%s'
% type(random_state))
def test_signed_integer_division_overflow(self):
# Ticket #1317.
def test_type(t):
min = np.array([np.iinfo(t).min])
min //= -1
with np.errstate(divide="ignore"):
for t in (np.int8, np.int16, np.int32, np.int64, np.int, np.long):
test_type(t)
def test_array_side_effect(self):
# The second use of itemsize was throwing an exception because in
# ctors.c, discover_itemsize was calling PyObject_Length without
# checking the return code. This failed to get the length of the
# number 2, and the exception hung around until something checked
# PyErr_Occurred() and returned an error.
assert_equal(np.dtype('S10').itemsize, 10)
np.array([['abc', 2], ['long ', '0123456789']], dtype=np.string_)
assert_equal(np.dtype('S10').itemsize, 10)