def noncentral_f(self, dfnum, dfden, nonc, size=None, dtype=float):
"""Returns an array of samples drawn from the noncentral F distribution.
.. warning::
This function may synchronize the device.
.. seealso::
:func:`cupy.random.noncentral_f` for full documentation,
:meth:`numpy.random.RandomState.noncentral_f
<numpy.random.mtrand.RandomState.noncentral_f>`
"""
dfnum, dfden, nonc = \
cupy.asarray(dfnum), cupy.asarray(dfden), cupy.asarray(nonc)
if cupy.any(dfnum <= 0): # synchronize!
raise ValueError('dfnum <= 0')
if cupy.any(dfden <= 0): # synchronize!
raise ValueError('dfden <= 0')
if cupy.any(nonc < 0): # synchronize!
raise ValueError('nonc < 0')
if size is None:
size = cupy.broadcast(dfnum, dfden, nonc).shape
y = cupy.empty(shape=size, dtype=dtype)
_kernels.noncentral_f_kernel(dfnum, dfden, nonc, self._rk_seed, y)
self._update_seed(y.size)
return y
def test_unsharp_masking_with_different_ranges(shape, offset, multichannel,
preserve):
radius = 2.0
amount = 1.0
dtype = np.int16
array = (cp.random.random(shape) * 5 + offset).astype(dtype)
negative = cp.any(array < 0)
output = unsharp_mask(array, radius, amount, multichannel, preserve)
if preserve is False:
assert cp.any(output <= 1)
assert cp.any(output >= -1)
if negative is False:
assert cp.any(output >= 0)
assert output.dtype in [np.float32, np.float64]
assert output.shape == shape
def test_percentile_memory_access(self, dtype):
# Create an allocator that guarantees array allocated in
# cupy.percentile call will be followed by a NaN
original_allocator = cuda.get_allocator()
def controlled_allocator(size):
memptr = original_allocator(size)
base_size = memptr.mem.size
assert base_size % 512 == 0
item_size = dtype().itemsize
shape = (base_size // item_size, )
x = cupy.ndarray(memptr=memptr, shape=shape, dtype=dtype)
x.fill(cupy.nan)
return memptr
# Check that percentile still returns non-NaN results
a = testing.shaped_random((5, ), cupy, dtype)
q = cupy.array((0, 100), dtype=dtype)
cuda.set_allocator(controlled_allocator)
try:
percentiles = cupy.percentile(a, q, axis=None, method='linear')
finally:
cuda.set_allocator(original_allocator)
assert not cupy.any(cupy.isnan(percentiles))
def prepare_train_data(self):
batch_size = self.train_batch_size
is_neg = cp.logical_not(self._train_labels)
# Do not store verification matrix if using the negatives generation shortcut
neg_mat = None if self.use_neg_trick else cp.array(self._neg_mat)
# If there are no negative samples in the local portion of the training data, do nothing
any_neg = cp.any(is_neg)
if any_neg:
self._train_users[is_neg], self._train_items[is_neg] = generate_negatives(
self._train_users[is_neg], neg_mat, self.num_items, use_trick=self.use_neg_trick
)
shuffled_order = cp.random.permutation(self._train_users.shape[0])
self._train_users = self._train_users[shuffled_order]
self._train_items = self._train_items[shuffled_order]
self._train_labels = self._train_labels[shuffled_order]
is_neg = cp.logical_not(self._train_labels)
# Manually create batches
split_indices = np.arange(batch_size, self._train_users.shape[0], batch_size)
self.train_users_batches = np.split(self._train_users, split_indices)
self.train_items_batches = np.split(self._train_items, split_indices)
self.train_labels_batches = np.split(self._train_labels, split_indices)
def _masked_column_median(arr, masked_value):
"""Compute the median of each column in the 2D array arr, ignoring any
instances of masked_value"""
mask = _get_mask(arr, masked_value)
if arr.size == 0:
return cp.full(arr.shape[1], cp.nan)
arr_sorted = arr.copy()
if not cp.isnan(masked_value):
# If nan is not the missing value, any column with nans should
# have a median of nan
nan_cols = cp.any(cp.isnan(arr), axis=0)
arr_sorted[mask] = cp.nan
else:
nan_cols = cp.full(arr.shape[1], False)
# nans are always sorted to end of array
arr_sorted = cp.sort(arr_sorted, axis=0)
count_missing_values = mask.sum(axis=0)
# Ignore missing values in determining "halfway" index of sorted
# array
n_elems = arr.shape[0] - count_missing_values
# If no elements remain after removing missing value, median for
# that colum is nan
nan_cols = cp.logical_or(nan_cols, n_elems <= 0)
col_index = cp.arange(arr_sorted.shape[1])
median = (arr_sorted[cp.floor_divide(n_elems - 1, 2), col_index] +
arr_sorted[cp.floor_divide(n_elems, 2), col_index]) / 2
median[nan_cols] = cp.nan
return median
def launch_superpose_deltas(positions, shape):
array = cp.zeros((shape[0], shape[1]), dtype=cp.float32)
if len(positions) == 0:
return array
if (cp.any(positions[:, 0] < 0) or cp.any(positions[:, 1] < 0) or cp.any(positions[:, 0] > shape[0]) or cp.any(
positions[:, 1] > shape[1])):
raise RuntimeError()
rounded = cp.floor(positions).astype(cp.int32)
threadsperblock = 32
blockspergrid = (positions.shape[0] + (threadsperblock - 1)) // threadsperblock
superpose_deltas[blockspergrid, threadsperblock](array, positions, rounded)
return array
def check_finite(array, force_all_finite=True):
"""Checks that the input is finite if necessary
Parameters
----------
array : object
Input object to check / convert.
force_all_finite : boolean or 'allow-nan', (default=True)
Whether to raise an error on np.inf, np.nan, pd.NA in array. The
possibilities are:
- True: Force all values of array to be finite.
- False: accepts np.inf, np.nan, pd.NA in array.
- 'allow-nan': accepts only np.nan and pd.NA values in array. Values
cannot be infinite.
``force_all_finite`` accepts the string ``'allow-nan'``.
Returns
-------
None or raise error
"""
if force_all_finite is True:
if not cp.all(cp.isfinite(array)):
raise ValueError("Non-finite value encountered in array")
elif force_all_finite == 'allow-nan':
if cp.any(cp.isinf(array)):
raise ValueError("Non-finite value encountered in array")
def hilbert2(x, N=None):
"""
Compute the '2-D' analytic signal of `x`
Parameters
----------
x : array_like
2-D signal data.
N : int or tuple of two ints, optional
Number of Fourier components. Default is ``x.shape``
Returns
-------
xa : ndarray
Analytic signal of `x` taken along axes (0,1).
References
----------
.. [1] Wikipedia, "Analytic signal",
https://en.wikipedia.org/wiki/Analytic_signal
"""
x = atleast_2d(x)
if x.ndim > 2:
raise ValueError("x must be 2-D.")
if iscomplexobj(x):
raise ValueError("x must be real.")
if N is None:
N = x.shape
elif isinstance(N, int):
if N <= 0:
raise ValueError("N must be positive.")
N = (N, N)
elif len(N) != 2 or cp.any(cp.asarray(N) <= 0):
raise ValueError(
"When given as a tuple, N must hold exactly two positive integers"
)
Xf = fftpack.fft2(x, N, axes=(0, 1))
h1 = zeros(N[0], "d")
h2 = zeros(N[1], "d")
for p in range(2):
h = eval("h%d" % (p + 1))
N1 = N[p]
if N1 % 2 == 0:
h[0] = h[N1 // 2] = 1
h[1 : N1 // 2] = 2
else:
h[0] = 1
h[1 : (N1 + 1) // 2] = 2
exec("h%d = h" % (p + 1), globals(), locals())
h = h1[:, newaxis] * h2[newaxis, :]
k = x.ndim
while k > 2:
h = h[:, newaxis]
k -= 1
x = fftpack.ifft2(Xf * h, axes=(0, 1))
return x
def test_niblack_sauvola_pathological_image():
# For certain values, floating point error can cause
# E(X^2) - (E(X))^2 to be negative, and taking the square root of this
# resulted in NaNs. Here we check that these are safely caught.
# see https://github.com/scikit-image/scikit-image/issues/3007
value = 0.03082192 + 2.19178082e-09
src_img = cp.full((4, 4), value).astype(cp.float64)
assert not cp.any(cp.isnan(threshold_niblack(src_img)))
def negative_binomial(self, n, p, size=None, dtype=int):
"""Returns an array of samples drawn from the negative binomial distribution.
.. seealso::
:func:`cupy.random.negative_binomial` for full documentation,
:meth:`numpy.random.RandomState.negative_binomial`
"""
n = cupy.asarray(n)
p = cupy.asarray(p)
if cupy.any(n <= 0):
raise ValueError("n <= 0")
if cupy.any(p < 0):
raise ValueError("p < 0")
if cupy.any(p > 1):
raise ValueError("p > 1")
y = self.gamma(n, (1 - p) / p, size)
return self.poisson(y, dtype=dtype)
def _assert_non_negative(image):
if cp.any(image < 0):
raise ValueError(
"Image Correction methods work correctly only on "
"images with non-negative values. Use "
"skimage.exposure.rescale_intensity."
)
def test_out_argument():
for func in (binary.binary_erosion, binary.binary_dilation):
strel = cp.ones((3, 3), dtype=cp.uint8)
img = cp.ones((10, 10))
out = cp.zeros_like(img)
out_saved = out.copy()
func(img, strel, out=out)
assert cp.any(out != out_saved)
testing.assert_array_equal(out, func(img, strel))
def _cholesky(B):
"""
Wrapper around `cupy.linalg.cholesky` that raises LinAlgError if there are
NaNs in the output
"""
R = cupy.linalg.cholesky(B)
if cupy.any(cupy.isnan(R)):
raise numpy.linalg.LinAlgError
return R
def triangular(self, left, mode, right, size=None, dtype=float):
"""Returns an array of samples drawn from the triangular distribution.
.. seealso::
:func:`cupy.random.triangular` for full documentation,
:meth:`numpy.random.RandomState.triangular`
"""
left, mode, right = \
cupy.asarray(left), cupy.asarray(mode), cupy.asarray(right)
if cupy.any(left > mode):
raise ValueError("left > mode")
if cupy.any(mode > right):
raise ValueError("mode > right")
if cupy.any(left == right):
raise ValueError("left == right")
if size is None:
size = cupy.broadcast(left, mode, right).shape
x = self.random_sample(size=size, dtype=dtype)
return RandomState._triangular_kernel(left, mode, right, x)
def cupy_unique_axis0(array):
# axis is still not supported for cupy.unique, this
# is a workaround
if len(array.shape) != 2:
raise ValueError("Input array must be 2D.")
sortarr = array[cp.lexsort(array.T[::-1])]
mask = cp.empty(array.shape[0], dtype=cp.bool_)
mask[0] = True
mask[1:] = cp.any(sortarr[1:] != sortarr[:-1], axis=1)
return sortarr[mask]
def noncentral_chisquare(self, df, nonc, size=None, dtype=float):
"""Returns an array of samples drawn from the noncentral chi-square
distribution.
.. seealso::
:func:`cupy.random.noncentral_chisquare` for full documentation,
:meth:`numpy.random.RandomState.noncentral_chisquare`
"""
df, nonc = cupy.asarray(df), cupy.asarray(nonc)
if cupy.any(df <= 0):
raise ValueError("df <= 0")
if cupy.any(nonc < 0):
raise ValueError("nonc < 0")
if size is None:
size = cupy.broadcast(df, nonc).shape
y = cupy.empty(shape=size, dtype=dtype)
_kernels.noncentral_chisquare_kernel(df, nonc, self.rk_seed, y)
self.rk_seed += numpy.prod(size)
return y
def logseries(self, p, size=None, dtype=int):
"""Returns an array of samples drawn from a log series distribution.
.. seealso::
:func:`cupy.random.logseries` for full documentation,
:meth:`numpy.random.RandomState.logseries`
"""
p = cupy.asarray(p)
if cupy.any(p <= 0):
raise ValueError('p <= 0.0')
if cupy.any(p >= 1):
raise ValueError('p >= 1.0')
if size is None:
size = p.shape
y = cupy.empty(shape=size, dtype=dtype)
_kernels.logseries_kernel(p, self.rk_seed, y)
self.rk_seed += numpy.prod(size)
return y
def test_csr_norms(norm, ref_norm, dtype, seed, shape):
X = np.random.RandomState(seed).randn(*shape).astype(dtype)
X_csr = sp.csr_matrix(X)
X_csr_gpu = cupyx.scipy.sparse.csr_matrix(X_csr)
norm(X_csr_gpu)
ref_norm(X_csr)
# checks that array have been changed inplace
assert cp.any(cp.not_equal(X_csr_gpu.todense(), cp.array(X)))
cp.testing.assert_array_almost_equal(X_csr_gpu.todense(), X_csr.todense())
def _preprocess(labels):
label_values, inv_idx = cp.unique(labels, return_inverse=True)
if not (label_values == 0).any():
warn('Random walker only segments unlabeled areas, where '
'labels == 0. No zero valued areas in labels were '
'found. Returning provided labels.',
stacklevel=2)
return labels, None, None, None, None
# If some labeled pixels are isolated inside pruned zones, prune them
# as well and keep the labels for the final output
null_mask = labels == 0
pos_mask = labels > 0
mask = labels >= 0
fill = ndi.binary_propagation(null_mask, mask=mask)
isolated = cp.logical_and(pos_mask, cp.logical_not(fill))
pos_mask[isolated] = False
# If the array has pruned zones, be sure that no isolated pixels
# exist between pruned zones (they could not be determined)
if label_values[0] < 0 or cp.any(isolated): # synchronize!
isolated = cp.logical_and(
cp.logical_not(ndi.binary_propagation(pos_mask, mask=mask)),
null_mask)
labels[isolated] = -1
if cp.all(isolated[null_mask]):
warn('All unlabeled pixels are isolated, they could not be '
'determined by the random walker algorithm.',
stacklevel=2)
return labels, None, None, None, None
mask[isolated] = False
mask = cp.atleast_3d(mask)
else:
mask = None
# Reorder label values to have consecutive integers (no gaps)
zero_idx = cp.searchsorted(label_values, cp.array(0))
labels = cp.atleast_3d(inv_idx.reshape(labels.shape) - zero_idx)
nlabels = label_values[zero_idx + 1:].shape[0]
inds_isolated_seeds = cp.nonzero(isolated)
isolated_values = labels[inds_isolated_seeds]
return labels, nlabels, mask, inds_isolated_seeds, isolated_values
def noncentral_f(self, dfnum, dfden, nonc, size=None, dtype=float):
"""Returns an array of samples drawn from the noncentral F distribution.
.. seealso::
:func:`cupy.random.noncentral_f` for full documentation,
:meth:`numpy.random.RandomState.noncentral_f`
"""
dfnum, dfden, nonc = \
cupy.asarray(dfnum), cupy.asarray(dfden), cupy.asarray(nonc)
if cupy.any(dfnum <= 0):
raise ValueError('dfnum <= 0')
if cupy.any(dfden <= 0):
raise ValueError('dfden <= 0')
if cupy.any(nonc < 0):
raise ValueError('nonc < 0')
if size is None:
size = cupy.broadcast(dfnum, dfden, nonc).shape
y = cupy.empty(shape=size, dtype=dtype)
_kernels.noncentral_f_kernel(dfnum, dfden, nonc, self.rk_seed, y)
self.rk_seed += numpy.prod(size, dtype=self.rk_seed.dtype)
return y
def negative_binomial(self, n, p, size=None, dtype=int):
"""Returns an array of samples drawn from the negative binomial distribution.
.. warning::
This function may synchronize the device.
.. seealso::
- :func:`cupy.random.negative_binomial` for full documentation
- :meth:`numpy.random.RandomState.negative_binomial`
"""
n = cupy.asarray(n)
p = cupy.asarray(p)
if cupy.any(n <= 0): # synchronize!
raise ValueError('n <= 0')
if cupy.any(p < 0): # synchronize!
raise ValueError('p < 0')
if cupy.any(p > 1): # synchronize!
raise ValueError('p > 1')
y = self.gamma(n, (1 - p) / p, size)
return self.poisson(y, dtype=dtype)
def test_copy_crop():
arr = cp.arange(45).reshape(9, 5)
out0 = crop(arr, 1, copy=True)
assert out0.flags.c_contiguous
out0[0, 0] = 100
assert not cp.any(arr == 100)
assert not cp.may_share_memory(arr, out0)
out1 = crop(arr, 1)
out1[0, 0] = 100
assert arr[1, 1] == 100
assert cp.may_share_memory(arr, out1)
def evaluate_individual_fitness(individual: cupy.ndarray,
formula: cupy.ndarray) -> cupy.ndarray:
"""
Evaluates the fitness of an individual as a function of how many clauses in the formula it satisfies. For example
if an individual satisfies half of the clauses, its fitness will be 0.5
:param individual: The individual for which we compute the fitness
:param formula: The CNF formula to satisfy
:return: The value of the fitness of an individual, or the ratio of clauses of the formula it satisfies
"""
return cupy.mean(cupy.any(individual == formula, axis=1),
dtype=cupy.float16)
def ndtri(self,y0):
out = cp.zeros(y0.shape)
cond1 = y0 == 0.0
cond2 = y0 == 1.0
if cp.any(cond1) == True:
y0[cond1] = -cp.inf
if cp.any(cond2) == True:
y0[cond2] = cp.inf
code = cp.ones(y0.shape, dtype=bool)
cond3 = y0 > (1.0 - 0.13533528323661269189)
if cp.any(cond3) == True:
y0[cond3] = 1.0 - y0[cond3]
code[cond3] = 0
cond4 = y0 > 0.13533528323661269189
cond5 = y0 <= 0.13533528323661269189
x = cp.sqrt(-2.0 * cp.log(y0))
cond6 = (x < 8.0) & cond5
cond7 = (x >= 8.0) & cond5
x0 = x - cp.log(x) / x
z = 1.0 / x
if cp.any(cond6) == True:
x1 = x0[cond6] - z[cond6] * cp.polyval(P1,z[cond6]) / cp.polyval(Q1, z[cond6])
out[cond6] = x1
if cp.any(cond7) == True:
x2 = x0[cond7] - z[cond7] * cp.polyval(P2, z[cond7]) / cp.polyval(Q2, z[cond7])
out[cond7] = x2
out[code] = -out[code]
if cp.any(cond4) == True:
y = y0[cond4]
y = y - 0.5
y2 = y * y
x = y + y * (y2 * cp.polyval(P0,y2) / cp.polyval(Q0, y2))
x = x * s2pi
out[cond4] = x
return out
def move_slimes():
slime_dir = cp.array(
[cp.sin(SlimeWorld.slime_angle),
cp.cos(SlimeWorld.slime_angle)]).T
SlimeWorld.slime_pos += slime_dir
# Change direction if out of bounds
xoob = (SlimeWorld.slime_pos[:, 0] < 0) | (SlimeWorld.slime_pos[:, 0] >
SlimeWorld.WIDTH - 1)
yoob = (SlimeWorld.slime_pos[:, 1] < 0) | (SlimeWorld.slime_pos[:, 1] >
SlimeWorld.HEIGHT - 1)
# Bounce off walls
if cp.any(xoob):
slime_dir[xoob, 0] *= -1
if cp.any(yoob):
slime_dir[yoob, 1] *= -1
if cp.any(xoob | yoob):
SlimeWorld.slime_angle = cp.arctan2(slime_dir[:, 0], slime_dir[:,
1])
# Clip if out of bounds
SlimeWorld.clip(SlimeWorld.slime_pos)
def weibull(self, a, size=None, dtype=float):
"""Returns an array of samples drawn from the weibull distribution.
.. seealso::
:func:`cupy.random.weibull` for full documentation,
:meth:`numpy.random.RandomState.weibull`
"""
a = cupy.asarray(a)
if cupy.any(a < 0):
raise ValueError("a < 0")
x = self.standard_exponential(size, dtype)
cupy.power(x, 1. / a, out=x)
return x
def evaluate_population(population: cupy.ndarray,
formula: cupy.ndarray) -> cupy.ndarray:
"""
Evaluates the fitness of every individual in a population and returns the fitness values in the order of the
individuals in the population matrix.
:param population: A matrix where each row represents an individual to be evaluated
:param formula: The CNF formula to satisfy
:return: The values of fitness of each individual in the input population as an array
"""
return cupy.mean(cupy.any(population[:, cupy.newaxis] == formula, axis=2),
axis=1,
dtype=cupy.float16)
def test_init(self):
cd = self.init_cuda_dict()
cd.bpg = ceil(len(self.test_keys) / cd.tpb)
assert cp.all(cd.contains(self.test_keys))
assert cp.all(cd[self.test_keys] == self.test_values)
cd.bpg = ceil(len(self.test_lookup_keys_avail) / cd.tpb)
assert cp.all(cd.contains(self.test_lookup_keys_avail))
assert cp.all(cd[self.test_lookup_keys_avail] == self.test_lookup_values_avail)
cd.bpg = ceil(len(self.test_lookup_keys_unavail) / cd.tpb)
assert cp.all(cd[self.test_lookup_keys_unavail] == self.test_default[0])
assert not cp.any(cd.contains(self.test_lookup_keys_unavail))
def _truncnorm_ppf(self,q, N):
out = cp.zeros(cp.shape(q))
delta = self._truncnorm_get_delta(N)
cond1 = delta > 0
cond2 = (delta > 0) & (self.a > 0)
cond21 = (delta > 0) & (self.a<=0)
if cp.any(cond1) == True:
sa = self.norm_sf(a[cond2])
out[:,cond2] = -self.ndtri((1 - q[:,cond2]) * sa)
if cp.any(cond21) == True:
na = norm_cdf(self.a[cond21])
out[:,cond21] = self._ndtri(q[:,cond21] + na * (1.0 - q[:,cond21]))
cond3 = ~cond1 & cp.isinf(self.b)
cond4 = ~cond1 & cp.isinf(self.a)
if cp.any(cond3) == True:
out[:,cond3] = -self._norm_ilogcdf(cp.log1p(-q[:,cond3]) + self.norm_logsf(self.a[cond3]))
if cp.any(cond4) == True:
out[:,cond4] = self._norm_ilogcdf(cp.log(q) + self.norm_logcdf(self.b))
cond5 = out < self.a
if cp.any(cond5) == True:
out[cond5] = ((cond5) * self.a)[cond5]
return out
def triangular(self, left, mode, right, size=None, dtype=float):
"""Returns an array of samples drawn from the triangular distribution.
.. warning::
This function may synchronize the device.
.. seealso::
- :func:`cupy.random.triangular` for full documentation
- :meth:`numpy.random.RandomState.triangular`
"""
left, mode, right = \
cupy.asarray(left), cupy.asarray(mode), cupy.asarray(right)
if cupy.any(left > mode): # synchronize!
raise ValueError('left > mode')
if cupy.any(mode > right): # synchronize!
raise ValueError('mode > right')
if cupy.any(left == right): # synchronize!
raise ValueError('left == right')
if size is None:
size = cupy.broadcast(left, mode, right).shape
x = self.random_sample(size=size, dtype=dtype)
return RandomState._triangular_kernel(left, mode, right, x)
