def values_eq_approx(a, b,
allow_remove_inf=False, allow_remove_nan=False,
rtol=None, atol=None):
if a.shape != b.shape or a.dtype != b.dtype:
return False
if 'int' in str(a.dtype):
return GpuArrayType.values_eq(a, b)
else:
if allow_remove_inf or allow_remove_nan:
raise NotImplementedError(
"GpuArrayType.values_eq_approx() don't implemented the"
" allow_remove_inf and allow_remove_nan parameter")
atol_, rtol_ = theano.tensor.basic._get_atol_rtol(a, b)
if rtol is not None:
rtol_ = rtol
if atol is not None:
atol_ = atol
res = elemwise2(a, '', b, a, odtype=numpy.dtype('bool'),
op_tmpl="res = (fabs(a - b) <"
"(%(atol_)s + %(rtol_)s * fabs(b)))" %
locals())
ret = numpy.asarray(res).all()
if ret:
return True
# maybe the trouble is that there are NaNs
an = numpy.asarray(a)
bn = numpy.asarray(b)
return tensor.TensorType.values_eq_approx(
an, bn, allow_remove_inf=allow_remove_inf,
allow_remove_nan=allow_remove_nan, rtol=rtol, atol=atol)
def values_eq_approx(a, b,
allow_remove_inf=False, allow_remove_nan=False,
rtol=None, atol=None):
if a.shape != b.shape or a.dtype != b.dtype:
return False
if 'int' in str(a.dtype):
return GpuArrayType.values_eq(a, b)
else:
if allow_remove_inf or allow_remove_nan:
raise NotImplementedError(
"GpuArrayType.values_eq_approx() don't implemented the"
" allow_remove_inf and allow_remove_nan parameter")
atol_, rtol_ = theano.tensor.basic._get_atol_rtol(a, b)
if rtol is not None:
rtol_ = rtol
if atol is not None:
atol_ = atol
res = elemwise2(a, '', b, a, odtype=numpy.dtype('bool'),
op_tmpl="res = (fabs(a - b) <"
"(%(atol_)s + %(rtol_)s * fabs(b)))" %
locals())
ret = numpy.asarray(res).all()
if ret:
return True
# maybe the trouble is that there are NaNs
an = numpy.asarray(a)
bn = numpy.asarray(b)
return tensor.TensorType.values_eq_approx(
an, bn, allow_remove_inf=allow_remove_inf,
allow_remove_nan=allow_remove_nan, rtol=rtol, atol=atol)
def values_eq_approx(a,
b,
allow_remove_inf=False,
allow_remove_nan=False,
rtol=None,
atol=None):
if a.shape != b.shape or a.dtype != b.dtype:
return False
if 'int' in str(a.dtype):
return GpuArrayType.values_eq(a, b)
else:
if allow_remove_inf or allow_remove_nan:
raise NotImplementedError(
"GpuArrayType.values_eq_approx() don't implemented the"
" allow_remove_inf and allow_remove_nan parameter")
narrow = 'float32', 'complex64'
if (str(a.dtype) in narrow) or (str(b.dtype) in narrow):
atol_ = theano.tensor.basic.float32_atol
rtol_ = theano.tensor.basic.float32_rtol
else:
atol_ = theano.tensor.basic.float64_atol
rtol_ = theano.tensor.basic.float64_rtol
if rtol is not None:
rtol_ = rtol
if atol is not None:
atol_ = atol
res = elemwise2(a,
'',
b,
a,
odtype=numpy.dtype('bool'),
op_tmpl="res[i] = (fabs(%%(a)s - %%(b)s) <"
"(%(atol_)s + %(rtol_)s * fabs(%%(b)s)))" %
locals())
return numpy.asarray(res).all()
def values_eq_approx(a, b,
allow_remove_inf=False, allow_remove_nan=False,
rtol=None, atol=None):
if a.shape != b.shape or a.dtype != b.dtype:
return False
if 'int' in str(a.dtype):
return GpuArrayType.values_eq(a, b)
else:
if allow_remove_inf or allow_remove_nan:
raise NotImplementedError(
"GpuArrayType.values_eq_approx() don't implemented the"
" allow_remove_inf and allow_remove_nan parameter")
narrow = 'float32', 'complex64'
if (str(a.dtype) in narrow) or (str(b.dtype) in narrow):
atol_ = theano.tensor.basic.float32_atol
rtol_ = theano.tensor.basic.float32_rtol
else:
atol_ = theano.tensor.basic.float64_atol
rtol_ = theano.tensor.basic.float64_rtol
if rtol is not None:
rtol_ = rtol
if atol is not None:
atol_ = atol
res = elemwise2(a, '', b, a, odtype=numpy.dtype('bool'),
op_tmpl="res[i] = (fabs(%%(a)s - %%(b)s) <"
"(%(atol_)s + %(rtol_)s * fabs(%%(b)s)))" %
locals())
return numpy.asarray(res).all()
def values_eq_approx(a, b, allow_remove_inf=False, allow_remove_nan=False,
rtol=None, atol=None):
if a.shape != b.shape or a.dtype != b.dtype:
return False
if str(a.dtype) in theano.tensor.discrete_dtypes:
return GpuArrayType.values_eq(a, b)
else:
if not (allow_remove_inf or allow_remove_nan):
atol_, rtol_ = theano.tensor.basic._get_atol_rtol(a, b)
if rtol is not None:
rtol_ = rtol
if atol is not None:
atol_ = atol
res = elemwise2(a, '', b, a, odtype=np.dtype('bool'),
op_tmpl="res = (fabs(a - b) <"
"(%(atol_)s + %(rtol_)s * fabs(b)))" %
locals())
ret = np.asarray(res).all()
if ret:
return True
an = np.asarray(a)
bn = np.asarray(b)
return tensor.TensorType.values_eq_approx(
an, bn, allow_remove_inf=allow_remove_inf,
allow_remove_nan=allow_remove_nan, rtol=rtol, atol=atol)
def values_eq_approx(a, b):
if a.shape != b.shape or a.dtype != b.dtype:
return False
if 'int' in str(a.dtype):
return GpuArrayType.values_eq(a, b)
else:
res = elemwise2(a, '', b, a, odtype=numpy.dtype('bool'),
op_tmpl="res[i] = ((%(a)s - %(b)s) <"
"(1e-8 + 1e-5 * fabs(%(b)s)))")
return numpy.asarray(res).all()
def values_eq_approx(a,
b,
allow_remove_inf=False,
allow_remove_nan=False,
rtol=None,
atol=None):
if a.shape != b.shape or a.dtype != b.dtype:
return False
if str(a.dtype) in theano.tensor.discrete_dtypes:
return GpuArrayType.values_eq(a, b)
else:
if not (allow_remove_inf or allow_remove_nan):
atol_, rtol_ = theano.tensor.basic._get_atol_rtol(a, b)
if rtol is not None:
rtol_ = rtol
if atol is not None:
atol_ = atol
res = elemwise2(
a,
"",
b,
a,
odtype=np.dtype("bool"),
op_tmpl="res = (fabs(a - b) <"
"(%(atol_)s + %(rtol_)s * fabs(b)))" % locals(),
)
ret = np.asarray(res).all()
if ret:
return True
an = np.asarray(a)
bn = np.asarray(b)
return tensor.TensorType.values_eq_approx(
an,
bn,
allow_remove_inf=allow_remove_inf,
allow_remove_nan=allow_remove_nan,
rtol=rtol,
atol=atol,
)
