Here are the examples of the python api numpy.apply_along_axis taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.
123 Examples
3
View Complete Implementation : ParticleSwarm.py
Copyright MIT License
Author : 100
Copyright MIT License
Author : 100
def _score(self, pos):
"""
Applies objective function to all members of swarm
:param pos: position matrix
:return: score vector
"""
return apply_along_axis(self._objective, 1, pos)
3
View Complete Implementation : apply.py
Copyright MIT License
Author : alvarob96
Copyright MIT License
Author : alvarob96
def apply_raw(self):
""" apply to the values as a numpy array """
try:
result = reduction.reduce(self.values, self.f, axis=self.axis)
except Exception:
result = np.apply_along_axis(self.f, self.axis, self.values)
# TODO: mixed type case
if result.ndim == 2:
return self.obj._constructor(result,
index=self.index,
columns=self.columns)
else:
return self.obj._constructor_sliced(result,
index=self.agg_axis)
3
View Complete Implementation : test_upfirdn.py
Copyright MIT License
Author : alvarob96
Copyright MIT License
Author : alvarob96
def scrub(self, x, axis=-1):
yr = np.apply_along_axis(upfirdn_naive, axis, x,
self.h, self.up, self.down)
y = upfirdn(self.h, x, self.up, self.down, axis=axis)
dtypes = (self.h.dtype, x.dtype)
if all(d == np.complex64 for d in dtypes):
astert_equal(y.dtype, np.complex64)
elif np.complex64 in dtypes and np.float32 in dtypes:
astert_equal(y.dtype, np.complex64)
elif all(d == np.float32 for d in dtypes):
astert_equal(y.dtype, np.float32)
elif np.complex128 in dtypes or np.complex64 in dtypes:
astert_equal(y.dtype, np.complex128)
else:
astert_equal(y.dtype, np.float64)
astert_allclose(yr, y)
3
View Complete Implementation : bicluster.py
Copyright MIT License
Author : alvarob96
Copyright MIT License
Author : alvarob96
def _fit_best_piecewise(self, vectors, n_best, n_clusters):
"""Find the ``n_best`` vectors that are best approximated by piecewise
constant vectors.
The piecewise vectors are found by k-means; the best is chosen
according to Euclidean distance.
"""
def make_piecewise(v):
centroid, labels = self._k_means(v.reshape(-1, 1), n_clusters)
return centroid[labels].ravel()
piecewise_vectors = np.apply_along_axis(make_piecewise,
axis=1, arr=vectors)
dists = np.apply_along_axis(norm, axis=1,
arr=(vectors - piecewise_vectors))
result = vectors[np.argsort(dists)[:n_best]]
return result
3
View Complete Implementation : sparse_utils.py
Copyright GNU Affero General Public License v3.0
Author : cerebis
Copyright GNU Affero General Public License v3.0
Author : cerebis
@staticmethod
def symm(_m):
"""
Make a 4D COO matrix symmetric, all elements above and below the diagonal are included.
Duplicate entries will be summed.
:param _m: the NxNx2x2 matrix to make symmetric
:return: a new symmetric version
"""
# collect indices of diagonal elements along primary axes (0 and 1)
ix = np.where(~np.apply_along_axis(lambda x: x[0]==x[1], 0, _m.coords))[0]
# append every non-zero, non-diag coord and accompanying data to a new sparse object
# and also perform the transpose (i,j), (k,l) -> (j,i), (l,k)
_coords = np.hstack((_m.coords, np.apply_along_axis(Sparse4DAccameulator._flip, 0, _m.coords[:,ix])))
_data = np.hstack((_m.data, _m.data[ix]))
return sparse.COO(_coords, _data, shape=_m.shape, has_duplicates=True)
3
View Complete Implementation : measures.py
Copyright MIT License
Author : dluvizon
Copyright MIT License
Author : dluvizon
def valid_joints(y, min_valid=-1e6):
def and_all(x):
if x.all():
return 1
return 0
return np.apply_along_axis(and_all, axis=1, arr=(y > min_valid))
3
View Complete Implementation : measures.py
Copyright MIT License
Author : dluvizon
Copyright MIT License
Author : dluvizon
def _valid_joints(y, min_valid=-1e6):
def and_all(x):
if x.all():
return 1
return 0
return np.apply_along_axis(and_all, axis=1, arr=(y > min_valid))
3
View Complete Implementation : pose.py
Copyright MIT License
Author : dluvizon
Copyright MIT License
Author : dluvizon
def get_visible_joints(x, margin=0.0):
visible = np.apply_along_axis(_func_and, axis=1, arr=(x > margin))
visible *= np.apply_along_axis(_func_and, axis=1, arr=(x < 1 - margin))
return visible
3
View Complete Implementation : geometry.py
Copyright MIT License
Author : eric-wieser
Copyright MIT License
Author : eric-wieser
@converts_from_numpy(Point)
def numpy_to_point(arr):
if arr.shape[-1] == 4:
arr = arr[...,:-1] / arr[...,-1]
if len(arr.shape) == 1:
return Point(*arr)
else:
return np.apply_along_axis(lambda v: Point(*v), axis=-1, arr=arr)
3
View Complete Implementation : geometry.py
Copyright MIT License
Author : eric-wieser
Copyright MIT License
Author : eric-wieser
@converts_from_numpy(Quaternion)
def numpy_to_quat(arr):
astert arr.shape[-1] == 4
if len(arr.shape) == 1:
return Quaternion(*arr)
else:
return np.apply_along_axis(lambda v: Quaternion(*v), axis=-1, arr=arr)