The following are code examples for showing how to use . They are extracted from open source Python projects. You can vote up the examples you like or vote down the exmaples you don’t like. You can also save this page to your account.
Example 1
def reset_index(self):
"""Reset index to range based
"""
dfs = self.to_delayed()
sizes = np.asarray(compute(*map(delayed(len), dfs)))
prefixes = np.zeros_like(sizes)
prefixes[1:] = np.cumsum(sizes[:-1])
@delayed
def fix_index(df, startpos):
return df.set_index(np.arange(start=startpos,
stop=startpos + len(df),
dtype=np.intp))
outdfs = [fix_index(df, startpos)
for df, startpos in zip(dfs, prefixes)]
return from_delayed(outdfs) Example 2
def __init__(self, data, bucket_size=128):
if bucket_size < 1:
raise ValueError("A minimum bucket size of 1 is expected.")
self._data = data
self._n, self._k = self._data.shape
self._nodes = None
self._buckets = []
self._bucket_size = bucket_size
self._node_dtype = numpy.dtype([
('size', numpy.intp),
('bucket', numpy.intp),
('lower_bounds', (numpy.float_, self._k)),
('upper_bounds', (numpy.float_, self._k)),
])
self._neighbour_dtype = numpy.dtype([
('squared_distance', numpy.float_),
('index', numpy.intp),
])
self._build() Example 3
def test_reverse_strides_and_subspace_bufferinit(self):
# This tests that the strides are not reversed for simple and
# subspace fancy indexing.
a = np.ones(5)
b = np.zeros(5, dtype=np.intp)[::-1]
c = np.arange(5)[::-1]
a[b] = c
# If the strides are not reversed, the 0 in the arange comes last.
assert_equal(a[0], 0)
# This also tests that the subspace buffer is initialized:
a = np.ones((5, 2))
c = np.arange(10).reshape(5, 2)[::-1]
a[b, :] = c
assert_equal(a[0], [0, 1]) Example 4
def test_unaligned(self):
v = (np.zeros(64, dtype=np.int8) + ord('a'))[1:-7]
d = v.view(np.dtype("S8"))
# unaligned source
x = (np.zeros(16, dtype=np.int8) + ord('a'))[1:-7]
x = x.view(np.dtype("S8"))
x[...] = np.array("b" * 8, dtype="S")
b = np.arange(d.size)
#trivial
assert_equal(d[b], d)
d[b] = x
# nontrivial
# unaligned index array
b = np.zeros(d.size + 1).view(np.int8)[1:-(np.intp(0).itemsize - 1)]
b = b.view(np.intp)[:d.size]
b[...] = np.arange(d.size)
assert_equal(d[b.astype(np.int16)], d)
d[b.astype(np.int16)] = x
# boolean
d[b % 2 == 0]
d[b % 2 == 0] = x[::2] Example 5
def test_count_func(self):
# Tests count
assert_equal(1, count(1))
assert_equal(0, array(1, mask=[1]))
ott = array([0., 1., 2., 3.], mask=[1, 0, 0, 0])
res = count(ott)
self.assertTrue(res.dtype.type is np.intp)
assert_equal(3, res)
ott = ott.reshape((2, 2))
res = count(ott)
assert_(res.dtype.type is np.intp)
assert_equal(3, res)
res = count(ott, 0)
assert_(isinstance(res, ndarray))
assert_equal([1, 2], res)
assert_(getmask(res) is nomask)
ott = array([0., 1., 2., 3.])
res = count(ott, 0)
assert_(isinstance(res, ndarray))
assert_(res.dtype.type is np.intp)
assert_raises(IndexError, ott.count, 1) Example 6
def check_function(self, function, sz):
from threading import Thread
out1 = np.empty((len(self.seeds),) + sz)
out2 = np.empty((len(self.seeds),) + sz)
# threaded generation
t = [Thread(target=function, args=(np.random.RandomState(s), o))
for s, o in zip(self.seeds, out1)]
[x.start() for x in t]
[x.join() for x in t]
# the same serial
for s, o in zip(self.seeds, out2):
function(np.random.RandomState(s), o)
# these platforms change x87 fpu precision mode in threads
if (np.intp().dtype.itemsize == 4 and sys.platform == "win32"):
np.testing.assert_array_almost_equal(out1, out2)
else:
np.testing.assert_array_equal(out1, out2) Example 7
def csc_matvec(mat_csc, vec, dense_output=True, dtype=None):
v_nnz = vec.indices
v_val = vec.data
m_val = mat_csc.data
m_ind = mat_csc.indices
m_ptr = mat_csc.indptr
res_dtype = dtype or np.result_type(mat_csc.dtype, vec.dtype)
if dense_output:
res = np.zeros((mat_csc.shape[0],), dtype=res_dtype)
matvec2dense(m_ptr, m_ind, m_val, v_nnz, v_val, res)
else:
sizes = m_ptr.take(v_nnz+1) - m_ptr.take(v_nnz)
sizes = np.concatenate(([0], np.cumsum(sizes)))
n = sizes[-1]
data = np.empty((n,), dtype=res_dtype)
indices = np.empty((n,), dtype=np.intp)
indptr = np.array([0, n], dtype=np.intp)
matvec2sparse(m_ptr, m_ind, m_val, v_nnz, v_val, sizes, indices, data)
res = sp.sparse.csr_matrix((data, indices, indptr), shape=(1, mat_csc.shape[0]), dtype=res_dtype)
res.sum_duplicates() # expensive operation
return res Example 8
def to_coo(self, tensor_mode=False):
userid, itemid, feedback = self.fields
user_item_data = self.training[[userid, itemid]].values
if tensor_mode:
# TODO this recomputes feedback data every new functon call,
# but if data has not changed - no need for this, make a property
new_feedback, feedback_transform = self.reindex(self.training, feedback, inplace=False)
self.index = self.index._replace(feedback=feedback_transform)
idx = np.hstack((user_item_data, new_feedback[:, np.newaxis]))
idx = np.ascontiguousarray(idx)
val = np.ones(self.training.shape[0],)
else:
idx = user_item_data
val = self.training[feedback].values
shp = tuple(idx.max(axis=0) + 1)
idx = idx.astype(np.intp)
val = np.ascontiguousarray(val)
return idx, val, shp Example 9
def test_to_coo(self, tensor_mode=False):
userid, itemid, feedback = self.fields
test_data = self.test.testset
user_idx = test_data[userid].values.astype(np.intp)
item_idx = test_data[itemid].values.astype(np.intp)
fdbk_val = test_data[feedback].values
if tensor_mode:
fdbk_idx = self.index.feedback.set_index('old').loc[fdbk_val, 'new'].values
if np.isnan(fdbk_idx).any():
raise NotImplementedError('Not all values of feedback are present in training data')
else:
fdbk_idx = fdbk_idx.astype(np.intp)
test_coo = (user_idx, item_idx, fdbk_idx)
else:
test_coo = (user_idx, item_idx, fdbk_val)
return test_coo Example 10
def _compile_and_prepare_functions(self, **kwargs):
module_text = _module_reader(find_kernel('lomb'), self._cpp_defs)
self.module = SourceModule(module_text, options=self.module_options)
self.dtypes = dict(
lomb=[np.intp, np.intp, np.intp, np.intp, np.int32,
self.real_type, self.real_type, np.int32, np.int32],
lomb_dirsum=[np.intp, np.intp, np.intp, np.intp, np.intp,
np.int32, np.int32, self.real_type, self.real_type,
self.real_type, self.real_type, np.int32]
)
self.nfft_proc._compile_and_prepare_functions(**kwargs)
for fname, dtype in self.dtypes.items():
func = self.module.get_function(fname)
self.prepared_functions[fname] = func.prepare(dtype)
self.function_tuple = tuple(self.prepared_functions[fname]
for fname in sorted(self.dtypes.keys())) Example 11
def default(self, obj):
# convert dates and numpy objects in a json serializable format
if isinstance(obj, datetime):
return obj.strftime('%Y-%m-%dT%H:%M:%SZ')
elif isinstance(obj, date):
return obj.strftime('%Y-%m-%d')
elif type(obj) in (np.int_, np.intc, np.intp, np.int8, np.int16,
np.int32, np.int64, np.uint8, np.uint16,
np.uint32, np.uint64):
return int(obj)
elif type(obj) in (np.bool_,):
return bool(obj)
elif type(obj) in (np.float_, np.float16, np.float32, np.float64,
np.complex_, np.complex64, np.complex128):
return float(obj)
# Let the base class default method raise the TypeError
return json.JSONEncoder.default(self, obj) Example 12
def test_reverse_strides_and_subspace_bufferinit(self):
# This tests that the strides are not reversed for simple and
# subspace fancy indexing.
a = np.ones(5)
b = np.zeros(5, dtype=np.intp)[::-1]
c = np.arange(5)[::-1]
a[b] = c
# If the strides are not reversed, the 0 in the arange comes last.
assert_equal(a[0], 0)
# This also tests that the subspace buffer is initialized:
a = np.ones((5, 2))
c = np.arange(10).reshape(5, 2)[::-1]
a[b, :] = c
assert_equal(a[0], [0, 1]) Example 13
def test_unaligned(self):
v = (np.zeros(64, dtype=np.int8) + ord('a'))[1:-7]
d = v.view(np.dtype("S8"))
# unaligned source
x = (np.zeros(16, dtype=np.int8) + ord('a'))[1:-7]
x = x.view(np.dtype("S8"))
x[...] = np.array("b" * 8, dtype="S")
b = np.arange(d.size)
#trivial
assert_equal(d[b], d)
d[b] = x
# nontrivial
# unaligned index array
b = np.zeros(d.size + 1).view(np.int8)[1:-(np.intp(0).itemsize - 1)]
b = b.view(np.intp)[:d.size]
b[...] = np.arange(d.size)
assert_equal(d[b.astype(np.int16)], d)
d[b.astype(np.int16)] = x
# boolean
d[b % 2 == 0]
d[b % 2 == 0] = x[::2] Example 14
def test_count_func(self):
# Tests count
assert_equal(1, count(1))
assert_equal(0, array(1, mask=[1]))
ott = array([0., 1., 2., 3.], mask=[1, 0, 0, 0])
res = count(ott)
self.assertTrue(res.dtype.type is np.intp)
assert_equal(3, res)
ott = ott.reshape((2, 2))
res = count(ott)
assert_(res.dtype.type is np.intp)
assert_equal(3, res)
res = count(ott, 0)
assert_(isinstance(res, ndarray))
assert_equal([1, 2], res)
assert_(getmask(res) is nomask)
ott = array([0., 1., 2., 3.])
res = count(ott, 0)
assert_(isinstance(res, ndarray))
assert_(res.dtype.type is np.intp)
assert_raises(IndexError, ott.count, 1) Example 15
def check_function(self, function, sz):
from threading import Thread
out1 = np.empty((len(self.seeds),) + sz)
out2 = np.empty((len(self.seeds),) + sz)
# threaded generation
t = [Thread(target=function, args=(np.random.RandomState(s), o))
for s, o in zip(self.seeds, out1)]
[x.start() for x in t]
[x.join() for x in t]
# the same serial
for s, o in zip(self.seeds, out2):
function(np.random.RandomState(s), o)
# these platforms change x87 fpu precision mode in threads
if (np.intp().dtype.itemsize == 4 and sys.platform == "win32"):
np.testing.assert_array_almost_equal(out1, out2)
else:
np.testing.assert_array_equal(out1, out2) Example 16
def nn_vec_basic(arr1, arr2, topn, sort=True, return_sims=False, nthreads=8):
"""
For each row in arr1 (m1 x d) find topn most similar rows from arr2 (m2 x d). Similarity is defined as dot product.
Please note, that in the case of normalized rows in arr1 and arr2 dot product will be equal to cosine and will be
monotonically decreasing function of Eualidean distance.
:param arr1: array of vectors to find nearest neighbours for
:param arr2: array of vectors to search for nearest neighbours in
:param topn: number of nearest neighbours
:param sort: indices in i-th row of returned array should sort corresponding rows of arr2 in descending order of
similarity to i-th row of arr2
:param return_sims: return similarities along with indices of nearest neighbours
:param nthreads:
:return: array (m1 x topn) where i-th row contains indices of rows in arr2 most similar to i-th row of m1, and, if
return_sims=True, an array (m1 x topn) of corresponding similarities.
"""
sims = np.dot(arr1, arr2.T)
best_inds = argmaxk_rows(sims, topn, sort=sort, nthreads=nthreads)
if not return_sims:
return best_inds
# generate row indices corresponding to best_inds (just current row id in each row) (m x k)
rows = np.arange(best_inds.shape[0], dtype=np.intp)[:, np.newaxis].repeat(best_inds.shape[1], axis=1)
return best_inds, sims[rows, best_inds] Example 17
def argmaxk_rows_opt1(arr, k=10, sort=False):
"""
Optimized implementation. When sort=False it is equal to argmaxk_rows_basic. When sort=True and k << arr.shape[1],
it is should be faster, because we argsort only subarray of k max elements from each row of arr (arr.shape[0] x k) instead of
the whole array arr (arr.shape[0] x arr.shape[1]).
"""
best_inds = np.argpartition(arr, kth=-k, axis=1)[:, -k:] # column indices of k max elements in each row (m x k)
if not sort:
return best_inds
# generate row indices corresponding to best_ids (just current row id in each row) (m x k)
rows = np.arange(best_inds.shape[0], dtype=np.intp)[:, np.newaxis].repeat(best_inds.shape[1], axis=1)
best_elems = arr[rows, best_inds] # select k max elements from each row using advanced indexing (m x k)
# indices which sort each row of best_elems in descending order (m x k)
best_elems_inds = np.argsort(best_elems, axis=1)[:, ::-1]
# reorder best_indices so that arr[i, sorted_best_inds[i,:]] will be sorted in descending order
sorted_best_inds = best_inds[rows, best_elems_inds]
return sorted_best_inds Example 18
def test_reverse_strides_and_subspace_bufferinit(self):
# This tests that the strides are not reversed for simple and
# subspace fancy indexing.
a = np.ones(5)
b = np.zeros(5, dtype=np.intp)[::-1]
c = np.arange(5)[::-1]
a[b] = c
# If the strides are not reversed, the 0 in the arange comes last.
assert_equal(a[0], 0)
# This also tests that the subspace buffer is initialized:
a = np.ones((5, 2))
c = np.arange(10).reshape(5, 2)[::-1]
a[b, :] = c
assert_equal(a[0], [0, 1]) Example 19
def test_unaligned(self):
v = (np.zeros(64, dtype=np.int8) + ord('a'))[1:-7]
d = v.view(np.dtype("S8"))
# unaligned source
x = (np.zeros(16, dtype=np.int8) + ord('a'))[1:-7]
x = x.view(np.dtype("S8"))
x[...] = np.array("b" * 8, dtype="S")
b = np.arange(d.size)
#trivial
assert_equal(d[b], d)
d[b] = x
# nontrivial
# unaligned index array
b = np.zeros(d.size + 1).view(np.int8)[1:-(np.intp(0).itemsize - 1)]
b = b.view(np.intp)[:d.size]
b[...] = np.arange(d.size)
assert_equal(d[b.astype(np.int16)], d)
d[b.astype(np.int16)] = x
# boolean
d[b % 2 == 0]
d[b % 2 == 0] = x[::2] Example 20
def test_count_func(self):
# Tests count
assert_equal(1, count(1))
assert_equal(0, array(1, mask=[1]))
ott = array([0., 1., 2., 3.], mask=[1, 0, 0, 0])
res = count(ott)
self.assertTrue(res.dtype.type is np.intp)
assert_equal(3, res)
ott = ott.reshape((2, 2))
res = count(ott)
assert_(res.dtype.type is np.intp)
assert_equal(3, res)
res = count(ott, 0)
assert_(isinstance(res, ndarray))
assert_equal([1, 2], res)
assert_(getmask(res) is nomask)
ott = array([0., 1., 2., 3.])
res = count(ott, 0)
assert_(isinstance(res, ndarray))
assert_(res.dtype.type is np.intp)
assert_raises(IndexError, ott.count, 1) Example 21
def check_function(self, function, sz):
from threading import Thread
out1 = np.empty((len(self.seeds),) + sz)
out2 = np.empty((len(self.seeds),) + sz)
# threaded generation
t = [Thread(target=function, args=(np.random.RandomState(s), o))
for s, o in zip(self.seeds, out1)]
[x.start() for x in t]
[x.join() for x in t]
# the same serial
for s, o in zip(self.seeds, out2):
function(np.random.RandomState(s), o)
# these platforms change x87 fpu precision mode in threads
if (np.intp().dtype.itemsize == 4 and sys.platform == "win32"):
np.testing.assert_array_almost_equal(out1, out2)
else:
np.testing.assert_array_equal(out1, out2) Example 22
def test_reverse_strides_and_subspace_bufferinit(self):
# This tests that the strides are not reversed for simple and
# subspace fancy indexing.
a = np.ones(5)
b = np.zeros(5, dtype=np.intp)[::-1]
c = np.arange(5)[::-1]
a[b] = c
# If the strides are not reversed, the 0 in the arange comes last.
assert_equal(a[0], 0)
# This also tests that the subspace buffer is initialized:
a = np.ones((5, 2))
c = np.arange(10).reshape(5, 2)[::-1]
a[b, :] = c
assert_equal(a[0], [0, 1]) Example 23
def test_unaligned(self):
v = (np.zeros(64, dtype=np.int8) + ord('a'))[1:-7]
d = v.view(np.dtype("S8"))
# unaligned source
x = (np.zeros(16, dtype=np.int8) + ord('a'))[1:-7]
x = x.view(np.dtype("S8"))
x[...] = np.array("b" * 8, dtype="S")
b = np.arange(d.size)
#trivial
assert_equal(d[b], d)
d[b] = x
# nontrivial
# unaligned index array
b = np.zeros(d.size + 1).view(np.int8)[1:-(np.intp(0).itemsize - 1)]
b = b.view(np.intp)[:d.size]
b[...] = np.arange(d.size)
assert_equal(d[b.astype(np.int16)], d)
d[b.astype(np.int16)] = x
# boolean
d[b % 2 == 0]
d[b % 2 == 0] = x[::2] Example 24
def test_count_func(self):
# Tests count
assert_equal(1, count(1))
assert_equal(0, array(1, mask=[1]))
ott = array([0., 1., 2., 3.], mask=[1, 0, 0, 0])
res = count(ott)
self.assertTrue(res.dtype.type is np.intp)
assert_equal(3, res)
ott = ott.reshape((2, 2))
res = count(ott)
assert_(res.dtype.type is np.intp)
assert_equal(3, res)
res = count(ott, 0)
assert_(isinstance(res, ndarray))
assert_equal([1, 2], res)
assert_(getmask(res) is nomask)
ott = array([0., 1., 2., 3.])
res = count(ott, 0)
assert_(isinstance(res, ndarray))
assert_(res.dtype.type is np.intp)
assert_raises(IndexError, ott.count, 1) Example 25
def check_function(self, function, sz):
from threading import Thread
out1 = np.empty((len(self.seeds),) + sz)
out2 = np.empty((len(self.seeds),) + sz)
# threaded generation
t = [Thread(target=function, args=(np.random.RandomState(s), o))
for s, o in zip(self.seeds, out1)]
[x.start() for x in t]
[x.join() for x in t]
# the same serial
for s, o in zip(self.seeds, out2):
function(np.random.RandomState(s), o)
# these platforms change x87 fpu precision mode in threads
if (np.intp().dtype.itemsize == 4 and sys.platform == "win32"):
np.testing.assert_array_almost_equal(out1, out2)
else:
np.testing.assert_array_equal(out1, out2) Example 26
def check_function(self, function, sz):
from threading import Thread
out1 = np.empty((len(self.seeds),) + sz)
out2 = np.empty((len(self.seeds),) + sz)
# threaded generation
t = [Thread(target=function, args=(mt19937.RandomState(s), o))
for s, o in zip(self.seeds, out1)]
[x.start() for x in t]
[x.join() for x in t]
# the same serial
for s, o in zip(self.seeds, out2):
function(mt19937.RandomState(s), o)
# these platforms change x87 fpu precision mode in threads
if np.intp().dtype.itemsize == 4 and sys.platform == "win32":
assert_array_almost_equal(out1, out2)
else:
assert_array_equal(out1, out2) Example 27
def test_reverse_strides_and_subspace_bufferinit(self):
# This tests that the strides are not reversed for simple and
# subspace fancy indexing.
a = np.ones(5)
b = np.zeros(5, dtype=np.intp)[::-1]
c = np.arange(5)[::-1]
a[b] = c
# If the strides are not reversed, the 0 in the arange comes last.
assert_equal(a[0], 0)
# This also tests that the subspace buffer is initialized:
a = np.ones((5, 2))
c = np.arange(10).reshape(5, 2)[::-1]
a[b, :] = c
assert_equal(a[0], [0, 1]) Example 28
def test_unaligned(self):
v = (np.zeros(64, dtype=np.int8) + ord('a'))[1:-7]
d = v.view(np.dtype("S8"))
# unaligned source
x = (np.zeros(16, dtype=np.int8) + ord('a'))[1:-7]
x = x.view(np.dtype("S8"))
x[...] = np.array("b" * 8, dtype="S")
b = np.arange(d.size)
#trivial
assert_equal(d[b], d)
d[b] = x
# nontrivial
# unaligned index array
b = np.zeros(d.size + 1).view(np.int8)[1:-(np.intp(0).itemsize - 1)]
b = b.view(np.intp)[:d.size]
b[...] = np.arange(d.size)
assert_equal(d[b.astype(np.int16)], d)
d[b.astype(np.int16)] = x
# boolean
d[b % 2 == 0]
d[b % 2 == 0] = x[::2] Example 29
def test_count_func(self):
# Tests count
assert_equal(1, count(1))
assert_equal(0, array(1, mask=[1]))
ott = array([0., 1., 2., 3.], mask=[1, 0, 0, 0])
res = count(ott)
self.assertTrue(res.dtype.type is np.intp)
assert_equal(3, res)
ott = ott.reshape((2, 2))
res = count(ott)
assert_(res.dtype.type is np.intp)
assert_equal(3, res)
res = count(ott, 0)
assert_(isinstance(res, ndarray))
assert_equal([1, 2], res)
assert_(getmask(res) is nomask)
ott = array([0., 1., 2., 3.])
res = count(ott, 0)
assert_(isinstance(res, ndarray))
assert_(res.dtype.type is np.intp)
assert_raises(ValueError, ott.count, axis=1) Example 30
def check_function(self, function, sz):
from threading import Thread
out1 = np.empty((len(self.seeds),) + sz)
out2 = np.empty((len(self.seeds),) + sz)
# threaded generation
t = [Thread(target=function, args=(np.random.RandomState(s), o))
for s, o in zip(self.seeds, out1)]
[x.start() for x in t]
[x.join() for x in t]
# the same serial
for s, o in zip(self.seeds, out2):
function(np.random.RandomState(s), o)
# these platforms change x87 fpu precision mode in threads
if (np.intp().dtype.itemsize == 4 and sys.platform == "win32"):
np.testing.assert_array_almost_equal(out1, out2)
else:
np.testing.assert_array_equal(out1, out2) Example 31
def default(self, obj):
# convert dates and numpy objects in a json serializable format
if isinstance(obj, datetime):
return obj.strftime('%Y-%m-%dT%H:%M:%SZ')
elif isinstance(obj, date):
return obj.strftime('%Y-%m-%d')
elif type(obj) in [np.int_, np.intc, np.intp, np.int8, np.int16,
np.int32, np.int64, np.uint8, np.uint16,
np.uint32, np.uint64]:
return int(obj)
elif type(obj) in [np.bool_]:
return bool(obj)
elif type(obj) in [np.float_, np.float16, np.float32, np.float64,
np.complex_, np.complex64, np.complex128]:
return float(obj)
# Let the base class default method raise the TypeError
return json.JSONEncoder.default(self, obj) Example 32
def test_reverse_strides_and_subspace_bufferinit(self):
# This tests that the strides are not reversed for simple and
# subspace fancy indexing.
a = np.ones(5)
b = np.zeros(5, dtype=np.intp)[::-1]
c = np.arange(5)[::-1]
a[b] = c
# If the strides are not reversed, the 0 in the arange comes last.
assert_equal(a[0], 0)
# This also tests that the subspace buffer is initialized:
a = np.ones((5, 2))
c = np.arange(10).reshape(5, 2)[::-1]
a[b, :] = c
assert_equal(a[0], [0, 1]) Example 33
def test_unaligned(self):
v = (np.zeros(64, dtype=np.int8) + ord('a'))[1:-7]
d = v.view(np.dtype("S8"))
# unaligned source
x = (np.zeros(16, dtype=np.int8) + ord('a'))[1:-7]
x = x.view(np.dtype("S8"))
x[...] = np.array("b" * 8, dtype="S")
b = np.arange(d.size)
#trivial
assert_equal(d[b], d)
d[b] = x
# nontrivial
# unaligned index array
b = np.zeros(d.size + 1).view(np.int8)[1:-(np.intp(0).itemsize - 1)]
b = b.view(np.intp)[:d.size]
b[...] = np.arange(d.size)
assert_equal(d[b.astype(np.int16)], d)
d[b.astype(np.int16)] = x
# boolean
d[b % 2 == 0]
d[b % 2 == 0] = x[::2] Example 34
def test_big_indices(self):
# ravel_multi_index for big indices (issue #7546)
if np.intp == np.int64:
arr = ([1, 29], [3, 5], [3, 117], [19, 2],
[2379, 1284], [2, 2], [0, 1])
assert_equal(
np.ravel_multi_index(arr, (41, 7, 120, 36, 2706, 8, 6)),
[5627771580, 117259570957])
# test overflow checking for too big array (issue #7546)
dummy_arr = ([0],[0])
half_max = np.iinfo(np.intp).max // 2
assert_equal(
np.ravel_multi_index(dummy_arr, (half_max, 2)), [0])
assert_raises(ValueError,
np.ravel_multi_index, dummy_arr, (half_max+1, 2))
assert_equal(
np.ravel_multi_index(dummy_arr, (half_max, 2), order='F'), [0])
assert_raises(ValueError,
np.ravel_multi_index, dummy_arr, (half_max+1, 2), order='F') Example 35
def test_count_func(self):
# Tests count
assert_equal(1, count(1))
assert_equal(0, array(1, mask=[1]))
ott = array([0., 1., 2., 3.], mask=[1, 0, 0, 0])
res = count(ott)
self.assertTrue(res.dtype.type is np.intp)
assert_equal(3, res)
ott = ott.reshape((2, 2))
res = count(ott)
assert_(res.dtype.type is np.intp)
assert_equal(3, res)
res = count(ott, 0)
assert_(isinstance(res, ndarray))
assert_equal([1, 2], res)
assert_(getmask(res) is nomask)
ott = array([0., 1., 2., 3.])
res = count(ott, 0)
assert_(isinstance(res, ndarray))
assert_(res.dtype.type is np.intp)
assert_raises(ValueError, ott.count, axis=1) Example 36
def check_function(self, function, sz):
from threading import Thread
out1 = np.empty((len(self.seeds),) + sz)
out2 = np.empty((len(self.seeds),) + sz)
# threaded generation
t = [Thread(target=function, args=(np.random.RandomState(s), o))
for s, o in zip(self.seeds, out1)]
[x.start() for x in t]
[x.join() for x in t]
# the same serial
for s, o in zip(self.seeds, out2):
function(np.random.RandomState(s), o)
# these platforms change x87 fpu precision mode in threads
if np.intp().dtype.itemsize == 4 and sys.platform == "win32":
assert_array_almost_equal(out1, out2)
else:
assert_array_equal(out1, out2) Example 37
def predict(self, X):
if not hasattr(self, "classes_"):
raise ValueError('fit')
if self.normalize_:
X = self._sc_X.fit_transform(X)
X_ = self.transform(X)
y_pred = self.estimator.predict(X_)
return self.classes_.take(np.asarray(y_pred, dtype=np.intp))
# elif self.predict_with == 'all':
#
# predict_ = []
#
# for mask in self.mask_:
# self.estimator.fit(X=self.transform(self.X_, mask=mask), y=self.y_)
# X_ = self.transform(X, mask=mask)
# y_pred = self.estimator.predict(X_)
# predict_.append(self.classes_.take(np.asarray(y_pred, dtype=np.intp)))
# return np.asarray(predict_) Example 38
def predict(self, X):
if not hasattr(self, "classes_"):
raise ValueError('fit')
if self.normalize_:
X = self._sc_X.fit_transform(X)
X_ = self.transform(X)
y_pred = self.estimator.predict(X_)
return self.classes_.take(np.asarray(y_pred, dtype=np.intp))
# elif self.predict_with == 'all':
#
# predict_ = []
#
# for mask in self.mask_:
# self.estimator.fit(X=self.transform(self.X_, mask=mask), y=self.y_)
# X_ = self.transform(X, mask=mask)
# y_pred = self.estimator.predict(X_)
# predict_.append(self.classes_.take(np.asarray(y_pred, dtype=np.intp)))
# return np.asarray(predict_) Example 39
def get_curand_int_func():
code = """
#include "curand_kernel.h"
extern "C" {
__global__ void
rand_setup(curandStateXORWOW_t* state, int size, unsigned long long seed)
{
int tid = threadIdx.x + blockIdx.x * blockDim.x;
int total_threads = blockDim.x * gridDim.x;
for(int i = tid; i < size; i+=total_threads)
{
curand_init(seed, i, 0, &state[i]);
}
}
}
"""
mod = SourceModule(code, no_extern_c = True)
func = mod.get_function("rand_setup")
func.prepare('PiL')#[np.intp, np.int32, np.uint64])
return func Example 40
def get_fill_function(dtype, pitch = True):
type_dst = dtype_to_ctype(dtype)
name = "fill"
if pitch:
func = SourceModule(
fill_pitch_template % {
"name": name,
"type_dst": type_dst
}, options=["--ptxas-options=-v"]).get_function(name)
func.prepare('iiPi'+np.dtype(dtype).char)
# [np.int32, np.int32, np.intp, np.int32, _get_type(dtype)])
else:
func = SourceModule(
fill_nonpitch_template % {
"name": name,
"type_dst": type_dst
},
options=["--ptxas-options=-v"]).get_function(name)
func.prepare('iP'+np.dtype(dtype).char)#[np.int32, np.intp, _get_type(dtype)])
return func Example 41
def get_transpose_function(dtype, conj = False):
src_type = dtype_to_ctype(dtype)
name = "trans"
operation = ""
if conj:
if dtype == np.complex128:
operation = "pycuda::conj"
elif dtype == np.complex64:
operation = "pycuda::conj"
func = SourceModule(
transpose_template % {
"name": name,
"type": src_type,
"operation": operation
},
options=["--ptxas-options=-v"]).get_function(name)
func.prepare('iiPiPi')#[np.int32, np.int32, np.intp,
# np.int32, np.intp, np.int32])
return func Example 42
def npy2py_type(npy_type):
int_types = [
np.int_, np.intc, np.intp, np.int8, np.int16, np.int32, np.int64,
np.uint8, np.uint16, np.uint32, np.uint64
]
float_types = [np.float_, np.float16, np.float32, np.float64]
bytes_types = [np.str_, np.string_]
if npy_type in int_types:
return int
if npy_type in float_types:
return float
if npy_type in bytes_types:
return bytes
if hasattr(npy_type, 'char'):
if npy_type.char in ['S', 'a']:
return bytes
raise TypeError
return npy_type Example 43
def test_multinomial_binary():
# Test multinomial LR on a binary problem.
target = (iris.target > 0).astype(np.intp)
target = np.array(["setosa", "not-setosa"])[target]
for solver in ['lbfgs', 'newton-cg', 'sag']:
clf = LogisticRegression(solver=solver, multi_class='multinomial',
random_state=42, max_iter=2000)
clf.fit(iris.data, target)
assert_equal(clf.coef_.shape, (1, iris.data.shape[1]))
assert_equal(clf.intercept_.shape, (1,))
assert_array_equal(clf.predict(iris.data), target)
mlr = LogisticRegression(solver=solver, multi_class='multinomial',
random_state=42, fit_intercept=False)
mlr.fit(iris.data, target)
pred = clf.classes_[np.argmax(clf.predict_log_proba(iris.data),
axis=1)]
assert_greater(np.mean(pred == target), .9) Example 44
def test_int_float_dict():
rng = np.random.RandomState(0)
keys = np.unique(rng.randint(100, size=10).astype(np.intp))
values = rng.rand(len(keys))
d = IntFloatDict(keys, values)
for key, value in zip(keys, values):
assert_equal(d[key], value)
assert_equal(len(d), len(keys))
d.append(120, 3.)
assert_equal(d[120], 3.0)
assert_equal(len(d), len(keys) + 1)
for i in xrange(2000):
d.append(i + 1000, 4.0)
assert_equal(d[1100], 4.0) Example 45
def get_indices(self, i):
"""Row and column indices of the i'th bicluster.
Only works if ``rows_`` and ``columns_`` attributes exist.
Returns
-------
row_ind : np.array, dtype=np.intp
Indices of rows in the dataset that belong to the bicluster.
col_ind : np.array, dtype=np.intp
Indices of columns in the dataset that belong to the bicluster.
"""
rows = self.rows_[i]
columns = self.columns_[i]
return np.nonzero(rows)[0], np.nonzero(columns)[0] Example 46
def predict(self, X):
"""Perform classification on samples in X.
For an one-class model, +1 or -1 is returned.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
For kernel="precomputed", the expected shape of X is
[n_samples_test, n_samples_train]
Returns
-------
y_pred : array, shape (n_samples,)
Class labels for samples in X.
"""
y = super(BaseSVC, self).predict(X)
return self.classes_.take(np.asarray(y, dtype=np.intp))
# Hacky way of getting predict_proba to raise an AttributeError when
# probability=False using properties. Do not use this in new code; when
# probabilities are not available depending on a setting, introduce two
# estimators. Example 47
def test_reverse_strides_and_subspace_bufferinit(self):
# This tests that the strides are not reversed for simple and
# subspace fancy indexing.
a = np.ones(5)
b = np.zeros(5, dtype=np.intp)[::-1]
c = np.arange(5)[::-1]
a[b] = c
# If the strides are not reversed, the 0 in the arange comes last.
assert_equal(a[0], 0)
# This also tests that the subspace buffer is initialized:
a = np.ones((5, 2))
c = np.arange(10).reshape(5, 2)[::-1]
a[b, :] = c
assert_equal(a[0], [0, 1]) Example 48
def test_unaligned(self):
v = (np.zeros(64, dtype=np.int8) + ord('a'))[1:-7]
d = v.view(np.dtype("S8"))
# unaligned source
x = (np.zeros(16, dtype=np.int8) + ord('a'))[1:-7]
x = x.view(np.dtype("S8"))
x[...] = np.array("b" * 8, dtype="S")
b = np.arange(d.size)
#trivial
assert_equal(d[b], d)
d[b] = x
# nontrivial
# unaligned index array
b = np.zeros(d.size + 1).view(np.int8)[1:-(np.intp(0).itemsize - 1)]
b = b.view(np.intp)[:d.size]
b[...] = np.arange(d.size)
assert_equal(d[b.astype(np.int16)], d)
d[b.astype(np.int16)] = x
# boolean
d[b % 2 == 0]
d[b % 2 == 0] = x[::2] Example 49
def fit(self, X, y, **kwargs):
# Determine output settings
n_samples, self.n_features_ = X.shape
if self.max_features is None:
self.max_features = 'auto'
y = np.atleast_1d(y)
if y.ndim == 1:
# reshape is necessary to preserve the data contiguity against vs
# [:, np.newaxis] that does not.
y = np.reshape(y, (-1, 1))
self.n_outputs_ = y.shape[1]
self.classes_ = [None] * self.n_outputs_
self.n_classes_ = [1] * self.n_outputs_
self.n_classes_ = np.array(self.n_classes_, dtype=np.intp)
if getattr(y, "dtype", None) != DOUBLE or not y.flags.contiguous:
y = np.ascontiguousarray(y, dtype=DOUBLE)
if len(y) != n_samples:
raise ValueError(
"Number of labels=%d does not match number of samples=%d"
% (len(y), n_samples))
# Build tree
self.tree_ = ExtraTree(
self.max_features, self.min_samples_split, self.n_classes_,
self.n_outputs_, self.classification)
self.tree_.build(X, y)
if self.n_outputs_ == 1:
self.n_classes_ = self.n_classes_[0]
self.classes_ = self.classes_[0]
return self Example 50
def test_intp(self,level=rlevel):
# Ticket #99
i_width = np.int_(0).nbytes*2 - 1
np.intp('0x' + 'f'*i_width, 16)
self.assertRaises(OverflowError, np.intp, '0x' + 'f'*(i_width+1), 16)
self.assertRaises(ValueError, np.intp, '0x1', 32)
assert_equal(255, np.intp('0xFF', 16))
assert_equal(1024, np.intp(1024)) 