def create(predict_fn, word_representations,
batch_size, window_size, vocabulary_size,
result_callback):
assert result_callback is not None
instance_dtype = np.min_scalar_type(vocabulary_size - 1)
logging.info('Instance elements will be stored using %s.', instance_dtype)
if result_callback.should_average_input():
batcher = EmbeddingMapper(
predict_fn,
word_representations,
result_callback)
else:
batcher = WordBatcher(
predict_fn,
batch_size, window_size,
instance_dtype,
result_callback)
return batcher
python类min_scalar_type()的实例源码
def test_usigned_shortshort(self):
dt = np.min_scalar_type(2**8-1)
wanted = np.dtype('uint8')
assert_equal(wanted, dt)
def test_usigned_short(self):
dt = np.min_scalar_type(2**16-1)
wanted = np.dtype('uint16')
assert_equal(wanted, dt)
def test_usigned_int(self):
dt = np.min_scalar_type(2**32-1)
wanted = np.dtype('uint32')
assert_equal(wanted, dt)
def test_usigned_longlong(self):
dt = np.min_scalar_type(2**63-1)
wanted = np.dtype('uint64')
assert_equal(wanted, dt)
def test_object(self):
dt = np.min_scalar_type(2**64)
wanted = np.dtype('O')
assert_equal(wanted, dt)
def test_usigned_shortshort(self):
dt = np.min_scalar_type(2**8-1)
wanted = np.dtype('uint8')
assert_equal(wanted, dt)
def test_usigned_short(self):
dt = np.min_scalar_type(2**16-1)
wanted = np.dtype('uint16')
assert_equal(wanted, dt)
def test_usigned_int(self):
dt = np.min_scalar_type(2**32-1)
wanted = np.dtype('uint32')
assert_equal(wanted, dt)
def test_usigned_longlong(self):
dt = np.min_scalar_type(2**63-1)
wanted = np.dtype('uint64')
assert_equal(wanted, dt)
def test_object(self):
dt = np.min_scalar_type(2**64)
wanted = np.dtype('O')
assert_equal(wanted, dt)
test_multiarray.py 文件源码
项目:PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda
作者: SignalMedia
项目源码
文件源码
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def test_usigned_shortshort(self):
dt = np.min_scalar_type(2**8-1)
wanted = np.dtype('uint8')
assert_equal(wanted, dt)
test_multiarray.py 文件源码
项目:PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda
作者: SignalMedia
项目源码
文件源码
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def test_usigned_short(self):
dt = np.min_scalar_type(2**16-1)
wanted = np.dtype('uint16')
assert_equal(wanted, dt)
test_multiarray.py 文件源码
项目:PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda
作者: SignalMedia
项目源码
文件源码
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def test_usigned_int(self):
dt = np.min_scalar_type(2**32-1)
wanted = np.dtype('uint32')
assert_equal(wanted, dt)
test_multiarray.py 文件源码
项目:PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda
作者: SignalMedia
项目源码
文件源码
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def test_usigned_longlong(self):
dt = np.min_scalar_type(2**63-1)
wanted = np.dtype('uint64')
assert_equal(wanted, dt)
test_multiarray.py 文件源码
项目:PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda
作者: SignalMedia
项目源码
文件源码
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def test_object(self):
dt = np.min_scalar_type(2**64)
wanted = np.dtype('O')
assert_equal(wanted, dt)
def test_usigned_shortshort(self):
dt = np.min_scalar_type(2**8-1)
wanted = np.dtype('uint8')
assert_equal(wanted, dt)
def test_usigned_short(self):
dt = np.min_scalar_type(2**16-1)
wanted = np.dtype('uint16')
assert_equal(wanted, dt)
def test_usigned_int(self):
dt = np.min_scalar_type(2**32-1)
wanted = np.dtype('uint32')
assert_equal(wanted, dt)
def test_usigned_longlong(self):
dt = np.min_scalar_type(2**63-1)
wanted = np.dtype('uint64')
assert_equal(wanted, dt)
def test_object(self):
dt = np.min_scalar_type(2**64)
wanted = np.dtype('O')
assert_equal(wanted, dt)
def test_usigned_shortshort(self):
dt = np.min_scalar_type(2**8-1)
wanted = np.dtype('uint8')
assert_equal(wanted, dt)
def test_usigned_short(self):
dt = np.min_scalar_type(2**16-1)
wanted = np.dtype('uint16')
assert_equal(wanted, dt)
def test_usigned_int(self):
dt = np.min_scalar_type(2**32-1)
wanted = np.dtype('uint32')
assert_equal(wanted, dt)
def test_usigned_longlong(self):
dt = np.min_scalar_type(2**63-1)
wanted = np.dtype('uint64')
assert_equal(wanted, dt)
def test_object(self):
dt = np.min_scalar_type(2**64)
wanted = np.dtype('O')
assert_equal(wanted, dt)
def _linear_loc(coords, shape, signed=False):
n = reduce(operator.mul, shape, 1)
if signed:
n = -n
dtype = np.min_scalar_type(n)
out = np.zeros(coords.shape[1], dtype=dtype)
tmp = np.zeros(coords.shape[1], dtype=dtype)
strides = 1
for i, d in enumerate(shape[::-1]):
# out += self.coords[-(i + 1), :].astype(dtype) * strides
np.multiply(coords[-(i + 1), :], strides, out=tmp, dtype=dtype)
np.add(tmp, out, out=out)
strides *= d
return out
def reshape(self, shape):
if self.shape == shape:
return self
if any(d == -1 for d in shape):
extra = int(np.prod(self.shape) /
np.prod([d for d in shape if d != -1]))
shape = tuple([d if d != -1 else extra for d in shape])
if self.shape == shape:
return self
if self._cache is not None:
for sh, value in self._cache['reshape']:
if sh == shape:
return value
# TODO: this np.prod(self.shape) enforces a 2**64 limit to array size
linear_loc = self.linear_loc()
coords = np.empty((len(shape), self.nnz), dtype=np.min_scalar_type(max(shape)))
strides = 1
for i, d in enumerate(shape[::-1]):
coords[-(i + 1), :] = (linear_loc // strides) % d
strides *= d
result = COO(coords, self.data, shape,
has_duplicates=self.has_duplicates,
sorted=self.sorted, cache=self._cache is not None)
if self._cache is not None:
self._cache['reshape'].append((shape, result))
return result
def rasterize_pctcover_geom(geom, shape, affine, scale=None, all_touched=False):
"""
Parameters
----------
geom: GeoJSON geometry
shape: desired shape
affine: desired transform
scale: scale at which to generate percent cover estimate
Returns
-------
ndarray: float32
"""
if scale is None:
scale = 10
min_dtype = min_scalar_type(scale ** 2)
new_affine = Affine(affine[0]/scale, 0, affine[2],
0, affine[4]/scale, affine[5])
new_shape = (shape[0] * scale, shape[1] * scale)
rv_array = rasterize_geom(geom, new_shape, new_affine, all_touched=all_touched)
rv_array = rebin_sum(rv_array, shape, min_dtype)
return rv_array.astype('float32') / (scale**2)
def test_usigned_shortshort(self):
dt = np.min_scalar_type(2**8-1)
wanted = np.dtype('uint8')
assert_equal(wanted, dt)