Python numpy.core.umath.absolute() Examples
The following are 30
code examples of numpy.core.umath.absolute().
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Example #1
| Source File: testutils.py From coffeegrindsize with MIT License | 6 votes |
|
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 #2
| Source File: testutils.py From Fluid-Designer with GNU General Public License v3.0 | 6 votes |
|
def approx (a, b, fill_value=True, rtol=1e-5, atol=1e-8):
"""Returns true if all components of a and b are equal subject 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 #3
| Source File: testutils.py From pySINDy with MIT License | 6 votes |
|
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 #4
| Source File: testutils.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
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 #5
| Source File: testutils.py From twitter-stock-recommendation with MIT License | 6 votes |
|
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 #6
| Source File: testutils.py From GraphicDesignPatternByPython with MIT License | 6 votes |
|
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 #7
| Source File: testutils.py From mxnet-lambda with Apache License 2.0 | 6 votes |
|
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 #8
| Source File: testutils.py From Mastering-Elasticsearch-7.0 with MIT License | 6 votes |
|
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 #9
| Source File: testutils.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 6 votes |
|
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 #10
| Source File: test_umath.py From Computable with MIT License | 6 votes |
|
def test_abs_blocked(self):
"simd tests on abs"
for dt in [np.float32, np.float64]:
for out, inp, msg in _gen_alignment_data(dtype=dt, type='unary',
max_size=17):
tgt = [ncu.absolute(i) for i in inp]
np.absolute(inp, out=out)
assert_equal(out, tgt, err_msg=msg)
self.assertTrue((out >= 0).all())
# will throw invalid flag depending on compiler optimizations
with np.errstate(invalid='ignore'):
for v in [np.nan, -np.inf, np.inf]:
for i in range(inp.size):
d = np.arange(inp.size, dtype=dt)
inp[:] = -d
inp[i] = v
d[i] = -v if v == -np.inf else v
assert_array_equal(np.abs(inp), d, err_msg=msg)
np.abs(inp, out=out)
assert_array_equal(out, d, err_msg=msg)
Example #11
| Source File: ma.py From Computable with MIT License | 6 votes |
|
def masked_values (data, value, rtol=1.e-5, atol=1.e-8, copy=1):
"""
masked_values(data, value, rtol=1.e-5, atol=1.e-8)
Create a masked array; mask is nomask if possible.
If copy==0, and otherwise possible, result
may share data values with original array.
Let d = filled(data, value). Returns d
masked where abs(data-value)<= atol + rtol * abs(value)
if d is of a floating point type. Otherwise returns
masked_object(d, value, copy)
"""
abs = umath.absolute
d = filled(data, value)
if issubclass(d.dtype.type, numeric.floating):
m = umath.less_equal(abs(d-value), atol+rtol*abs(value))
m = make_mask(m, flag=1)
return array(d, mask = m, copy=copy,
fill_value=value)
else:
return masked_object(d, value, copy=copy)
Example #12
| Source File: testutils.py From ImageFusion with MIT License | 6 votes |
|
def approx (a, b, fill_value=True, rtol=1e-5, atol=1e-8):
"""Returns true if all components of a and b are equal subject 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 #13
| Source File: testutils.py From Carnets with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
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 #14
| Source File: ma.py From Computable with MIT License | 6 votes |
|
def default_fill_value (obj):
"Function to calculate default fill value for an object."
if isinstance(obj, float):
return default_real_fill_value
elif isinstance(obj, int) or isinstance(obj, long):
return default_integer_fill_value
elif isinstance(obj, bytes):
return default_character_fill_value
elif isinstance(obj, complex):
return default_complex_fill_value
elif isinstance(obj, MaskedArray) or isinstance(obj, ndarray):
x = obj.dtype.char
if x in typecodes['Float']:
return default_real_fill_value
if x in typecodes['Integer']:
return default_integer_fill_value
if x in typecodes['Complex']:
return default_complex_fill_value
if x in typecodes['Character']:
return default_character_fill_value
if x in typecodes['UnsignedInteger']:
return umath.absolute(default_integer_fill_value)
return default_object_fill_value
else:
return default_object_fill_value
Example #15
| Source File: testutils.py From Computable with MIT License | 6 votes |
|
def approx (a, b, fill_value=True, rtol=1e-5, atol=1e-8):
"""Returns true if all components of a and b are equal subject 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 #16
| Source File: testutils.py From keras-lambda with MIT License | 6 votes |
|
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 #17
| Source File: testutils.py From Splunking-Crime with GNU Affero General Public License v3.0 | 6 votes |
|
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 #18
| Source File: testutils.py From vnpy_crypto with MIT License | 6 votes |
|
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 #19
| Source File: testutils.py From elasticintel with GNU General Public License v3.0 | 6 votes |
|
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 #20
| Source File: testutils.py From auto-alt-text-lambda-api with MIT License | 6 votes |
|
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 #21
| Source File: testutils.py From lambda-packs with MIT License | 6 votes |
|
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 #22
| Source File: testutils.py From lambda-packs with MIT License | 6 votes |
|
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 #23
| Source File: testutils.py From recruit with Apache License 2.0 | 6 votes |
|
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 #24
| Source File: test_umath.py From ImageFusion with MIT License | 5 votes |
|
def test_abs_neg_blocked(self):
# simd tests on abs, test all alignments for vz + 2 * (vs - 1) + 1
for dt, sz in [(np.float32, 11), (np.float64, 5)]:
for out, inp, msg in _gen_alignment_data(dtype=dt, type='unary',
max_size=sz):
tgt = [ncu.absolute(i) for i in inp]
np.absolute(inp, out=out)
assert_equal(out, tgt, err_msg=msg)
self.assertTrue((out >= 0).all())
tgt = [-1*(i) for i in inp]
np.negative(inp, out=out)
assert_equal(out, tgt, err_msg=msg)
# will throw invalid flag depending on compiler optimizations
with np.errstate(invalid='ignore'):
for v in [np.nan, -np.inf, np.inf]:
for i in range(inp.size):
d = np.arange(inp.size, dtype=dt)
inp[:] = -d
inp[i] = v
d[i] = -v if v == -np.inf else v
assert_array_equal(np.abs(inp), d, err_msg=msg)
np.abs(inp, out=out)
assert_array_equal(out, d, err_msg=msg)
assert_array_equal(-inp, -1*inp, err_msg=msg)
np.negative(inp, out=out)
assert_array_equal(out, -1*inp, err_msg=msg)
Example #25
| Source File: test_umath.py From mxnet-lambda with Apache License 2.0 | 5 votes |
|
def test_abs_neg_blocked(self):
# simd tests on abs, test all alignments for vz + 2 * (vs - 1) + 1
for dt, sz in [(np.float32, 11), (np.float64, 5)]:
for out, inp, msg in _gen_alignment_data(dtype=dt, type='unary',
max_size=sz):
tgt = [ncu.absolute(i) for i in inp]
np.absolute(inp, out=out)
assert_equal(out, tgt, err_msg=msg)
self.assertTrue((out >= 0).all())
tgt = [-1*(i) for i in inp]
np.negative(inp, out=out)
assert_equal(out, tgt, err_msg=msg)
for v in [np.nan, -np.inf, np.inf]:
for i in range(inp.size):
d = np.arange(inp.size, dtype=dt)
inp[:] = -d
inp[i] = v
d[i] = -v if v == -np.inf else v
assert_array_equal(np.abs(inp), d, err_msg=msg)
np.abs(inp, out=out)
assert_array_equal(out, d, err_msg=msg)
assert_array_equal(-inp, -1*inp, err_msg=msg)
d = -1 * inp
np.negative(inp, out=out)
assert_array_equal(out, d, err_msg=msg)
Example #26
| Source File: test_umath.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def test_abs_neg_blocked(self):
# simd tests on abs, test all alignments for vz + 2 * (vs - 1) + 1
for dt, sz in [(np.float32, 11), (np.float64, 5)]:
for out, inp, msg in _gen_alignment_data(dtype=dt, type='unary',
max_size=sz):
tgt = [ncu.absolute(i) for i in inp]
np.absolute(inp, out=out)
assert_equal(out, tgt, err_msg=msg)
self.assertTrue((out >= 0).all())
tgt = [-1*(i) for i in inp]
np.negative(inp, out=out)
assert_equal(out, tgt, err_msg=msg)
for v in [np.nan, -np.inf, np.inf]:
for i in range(inp.size):
d = np.arange(inp.size, dtype=dt)
inp[:] = -d
inp[i] = v
d[i] = -v if v == -np.inf else v
assert_array_equal(np.abs(inp), d, err_msg=msg)
np.abs(inp, out=out)
assert_array_equal(out, d, err_msg=msg)
assert_array_equal(-inp, -1*inp, err_msg=msg)
d = -1 * inp
np.negative(inp, out=out)
assert_array_equal(out, d, err_msg=msg)
Example #27
| Source File: test_umath.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
|
def test_abs_neg_blocked(self):
# simd tests on abs, test all alignments for vz + 2 * (vs - 1) + 1
for dt, sz in [(np.float32, 11), (np.float64, 5)]:
for out, inp, msg in _gen_alignment_data(dtype=dt, type='unary',
max_size=sz):
tgt = [ncu.absolute(i) for i in inp]
np.absolute(inp, out=out)
assert_equal(out, tgt, err_msg=msg)
assert_((out >= 0).all())
tgt = [-1*(i) for i in inp]
np.negative(inp, out=out)
assert_equal(out, tgt, err_msg=msg)
for v in [np.nan, -np.inf, np.inf]:
for i in range(inp.size):
d = np.arange(inp.size, dtype=dt)
inp[:] = -d
inp[i] = v
d[i] = -v if v == -np.inf else v
assert_array_equal(np.abs(inp), d, err_msg=msg)
np.abs(inp, out=out)
assert_array_equal(out, d, err_msg=msg)
assert_array_equal(-inp, -1*inp, err_msg=msg)
d = -1 * inp
np.negative(inp, out=out)
assert_array_equal(out, d, err_msg=msg)
Example #28
| Source File: test_umath.py From pySINDy with MIT License | 5 votes |
|
def test_abs_neg_blocked(self):
# simd tests on abs, test all alignments for vz + 2 * (vs - 1) + 1
for dt, sz in [(np.float32, 11), (np.float64, 5)]:
for out, inp, msg in _gen_alignment_data(dtype=dt, type='unary',
max_size=sz):
tgt = [ncu.absolute(i) for i in inp]
np.absolute(inp, out=out)
assert_equal(out, tgt, err_msg=msg)
assert_((out >= 0).all())
tgt = [-1*(i) for i in inp]
np.negative(inp, out=out)
assert_equal(out, tgt, err_msg=msg)
for v in [np.nan, -np.inf, np.inf]:
for i in range(inp.size):
d = np.arange(inp.size, dtype=dt)
inp[:] = -d
inp[i] = v
d[i] = -v if v == -np.inf else v
assert_array_equal(np.abs(inp), d, err_msg=msg)
np.abs(inp, out=out)
assert_array_equal(out, d, err_msg=msg)
assert_array_equal(-inp, -1*inp, err_msg=msg)
d = -1 * inp
np.negative(inp, out=out)
assert_array_equal(out, d, err_msg=msg)
Example #29
| Source File: test_umath.py From coffeegrindsize with MIT License | 5 votes |
|
def test_abs_neg_blocked(self):
# simd tests on abs, test all alignments for vz + 2 * (vs - 1) + 1
for dt, sz in [(np.float32, 11), (np.float64, 5)]:
for out, inp, msg in _gen_alignment_data(dtype=dt, type='unary',
max_size=sz):
tgt = [ncu.absolute(i) for i in inp]
np.absolute(inp, out=out)
assert_equal(out, tgt, err_msg=msg)
assert_((out >= 0).all())
tgt = [-1*(i) for i in inp]
np.negative(inp, out=out)
assert_equal(out, tgt, err_msg=msg)
for v in [np.nan, -np.inf, np.inf]:
for i in range(inp.size):
d = np.arange(inp.size, dtype=dt)
inp[:] = -d
inp[i] = v
d[i] = -v if v == -np.inf else v
assert_array_equal(np.abs(inp), d, err_msg=msg)
np.abs(inp, out=out)
assert_array_equal(out, d, err_msg=msg)
assert_array_equal(-inp, -1*inp, err_msg=msg)
d = -1 * inp
np.negative(inp, out=out)
assert_array_equal(out, d, err_msg=msg)
Example #30
| Source File: test_umath.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_abs_neg_blocked(self):
# simd tests on abs, test all alignments for vz + 2 * (vs - 1) + 1
for dt, sz in [(np.float32, 11), (np.float64, 5)]:
for out, inp, msg in _gen_alignment_data(dtype=dt, type='unary',
max_size=sz):
tgt = [ncu.absolute(i) for i in inp]
np.absolute(inp, out=out)
assert_equal(out, tgt, err_msg=msg)
assert_((out >= 0).all())
tgt = [-1*(i) for i in inp]
np.negative(inp, out=out)
assert_equal(out, tgt, err_msg=msg)
for v in [np.nan, -np.inf, np.inf]:
for i in range(inp.size):
d = np.arange(inp.size, dtype=dt)
inp[:] = -d
inp[i] = v
d[i] = -v if v == -np.inf else v
assert_array_equal(np.abs(inp), d, err_msg=msg)
np.abs(inp, out=out)
assert_array_equal(out, d, err_msg=msg)
assert_array_equal(-inp, -1*inp, err_msg=msg)
d = -1 * inp
np.negative(inp, out=out)
assert_array_equal(out, d, err_msg=msg)
