def compute_error(x_obj, x_approx):
'''
Error absoluto o relativo entre x_obj y x_approx.
'''
if cp.linalg.norm(x_obj) > cp.nextafter(0, 1):
Err = cp.linalg.norm(x_obj - x_approx) / cp.linalg.norm(x_obj)
else:
Err = cp.linalg.norm(x_obj - x_approx)
return Err
def histogram(x, bins=10, range=None, weights=None, density=False):
"""Computes the histogram of a set of data.
Args:
x (cupy.ndarray): Input array.
bins (int or cupy.ndarray): If ``bins`` is an int, it represents the
number of bins. If ``bins`` is an :class:`~cupy.ndarray`, it
represents a bin edges.
range (2-tuple of float, optional): The lower and upper range of the
bins. If not provided, range is simply ``(x.min(), x.max())``.
Values outside the range are ignored. The first element of the
range must be less than or equal to the second. `range` affects the
automatic bin computation as well. While bin width is computed to
be optimal based on the actual data within `range`, the bin count
will fill the entire range including portions containing no data.
density (bool, optional): If False, the default, returns the number of
samples in each bin. If True, returns the probability *density*
function at the bin, ``bin_count / sample_count / bin_volume``.
weights (cupy.ndarray, optional): An array of weights, of the same
shape as `x`. Each value in `x` only contributes its associated
weight towards the bin count (instead of 1).
Returns:
tuple: ``(hist, bin_edges)`` where ``hist`` is a :class:`cupy.ndarray`
storing the values of the histogram, and ``bin_edges`` is a
:class:`cupy.ndarray` storing the bin edges.
.. warning::
This function may synchronize the device.
.. seealso:: :func:`numpy.histogram`
"""
if x.dtype.kind == 'c':
# TODO(unno): comparison between complex numbers is not implemented
raise NotImplementedError('complex number is not supported')
if not isinstance(x, cupy.ndarray):
raise ValueError('x must be a cupy.ndarray')
x, weights = _ravel_and_check_weights(x, weights)
bin_edges = _get_bin_edges(x, bins, range)
if weights is None:
y = cupy.zeros(bin_edges.size - 1, dtype='l')
for accelerator in _accelerator.get_routine_accelerators():
# CUB uses int for bin counts
# TODO(leofang): support >= 2^31 elements in x?
if (accelerator == _accelerator.ACCELERATOR_CUB
and x.size <= 0x7fffffff and bin_edges.size <= 0x7fffffff):
# Need to ensure the dtype of bin_edges as it's needed for both
# the CUB call and the correction later
assert isinstance(bin_edges, cupy.ndarray)
if numpy.issubdtype(x.dtype, numpy.integer):
bin_type = numpy.float
else:
bin_type = numpy.result_type(bin_edges.dtype, x.dtype)
if (bin_type == numpy.float16
and not common._is_fp16_supported()):
bin_type = numpy.float32
x = x.astype(bin_type, copy=False)
acc_bin_edge = bin_edges.astype(bin_type, copy=True)
# CUB's upper bin boundary is exclusive for all bins, including
# the last bin, so we must shift it to comply with NumPy
if x.dtype.kind in 'ui':
acc_bin_edge[-1] += 1
elif x.dtype.kind == 'f':
last = acc_bin_edge[-1]
acc_bin_edge[-1] = cupy.nextafter(last, last + 1)
if runtime.is_hip:
y = y.astype(cupy.uint64, copy=False)
y = cub.device_histogram(x, acc_bin_edge, y)
if runtime.is_hip:
y = y.astype(cupy.int64, copy=False)
break
else:
_histogram_kernel(x, bin_edges, bin_edges.size, y)
else:
simple_weights = (cupy.can_cast(weights.dtype, cupy.float64)
or cupy.can_cast(weights.dtype, cupy.complex128))
if not simple_weights:
# object dtype such as Decimal are supported in NumPy, but not here
raise NotImplementedError(
'only weights with dtype that can be cast to float or complex '
'are supported')
if weights.dtype.kind == 'c':
y = cupy.zeros(bin_edges.size - 1, dtype=cupy.complex128)
_weighted_histogram_kernel(x, bin_edges, bin_edges.size,
weights.real, y.real)
_weighted_histogram_kernel(x, bin_edges, bin_edges.size,
weights.imag, y.imag)
else:
if weights.dtype.kind in 'bui':
y = cupy.zeros(bin_edges.size - 1, dtype=int)
else:
y = cupy.zeros(bin_edges.size - 1, dtype=cupy.float64)
_weighted_histogram_kernel(x, bin_edges, bin_edges.size, weights,
y)
if density:
db = cupy.array(cupy.diff(bin_edges), cupy.float64)
return y / db / y.sum(), bin_edges
return y, bin_edges