def kneighbors(self, X, n_neighbors=None):
"""Finds the K-neighbors of a point.
Returns distance
Parameters
----------
X : array-like, last dimension same as that of fit data
The new point.
n_neighbors : int
Number of neighbors to get (default is the value
passed to the constructor).
Returns
-------
dist : array
Array representing the lengths to point, only present if
return_distance=True
ind : array
Indices of the nearest points in the population matrix.
"""
if n_neighbors is not None:
self.n_neighbors = n_neighbors
if isinstance(X, np.ndarray):
X = expr.from_numpy(X)
if self.algorithm in ('auto', 'brute'):
X_broadcast = expr.reshape(X, (X.shape[0], 1, X.shape[1]))
fit_X_broadcast = expr.reshape(self.X, (1, self.X.shape[0], self.X.shape[1]))
distances = expr.sum((X_broadcast - fit_X_broadcast) ** 2, axis=2)
neigh_ind = expr.argsort(distances, axis=1)
neigh_ind = neigh_ind[:, :n_neighbors].optimized().glom()
neigh_dist = expr.sort(distances, axis=1)
neigh_dist = expr.sqrt(neigh_dist[:, :n_neighbors]).optimized().glom()
return neigh_dist, neigh_ind
else:
results = self.X.foreach_tile(mapper_fn=_knn_mapper,
kw={'X': self.X, 'Q': X,
'n_neighbors': self.n_neighbors,
'algorithm': self.algorithm})
dist = None
ind = None
""" Get the KNN candidates for each tile of X, then find out the real KNN """
for k, v in results.iteritems():
if dist is None:
dist = v[0]
ind = v[1]
else:
dist = np.concatenate((dist, v[0]), axis=1)
ind = np.concatenate((ind, v[1]), axis=1)
mask = np.argsort(dist, axis=1)[:, :self.n_neighbors]
new_dist = np.array([dist[i][mask[i]] for i, r in enumerate(dist)])
new_ind = np.array([ind[i][mask[i]] for i, r in enumerate(ind)])
return new_dist, new_ind
def test_ndimension(self):
for case in xrange(5):
dim = np.random.randint(low=2, high=6)
shape = np.random.randint(low=5, high=11, size=dim)
util.log_info('Test Case #%s: DIM(%s) shape%s', case + 1, dim, shape)
na = new_ndarray(shape)
a = expr.from_numpy(na)
for axis in xrange(dim):
Assert.all_eq(expr.sort(a, axis).glom(),
np.sort(na, axis))
Assert.all_eq(expr.argsort(a, axis).glom(),
np.argsort(na, axis))
def kneighbors(self, X, n_neighbors=None):
"""Finds the K-neighbors of a point.
Returns distance
Parameters
----------
X : array-like, last dimension same as that of fit data
The new point.
n_neighbors : int
Number of neighbors to get (default is the value
passed to the constructor).
Returns
-------
dist : array
Array representing the lengths to point, only present if
return_distance=True
ind : array
Indices of the nearest points in the population matrix.
"""
if n_neighbors is not None:
self.n_neighbors = n_neighbors
if isinstance(X, np.ndarray):
X = expr.from_numpy(X)
if self.algorithm in ('auto', 'brute'):
X_broadcast = expr.reshape(X, (X.shape[0], 1, X.shape[1]))
fit_X_broadcast = expr.reshape(
self.X, (1, self.X.shape[0], self.X.shape[1]))
distances = expr.sum((X_broadcast - fit_X_broadcast)**2, axis=2)
neigh_ind = expr.argsort(distances, axis=1)
neigh_ind = neigh_ind[:, :n_neighbors].optimized().glom()
neigh_dist = expr.sort(distances, axis=1)
neigh_dist = expr.sqrt(
neigh_dist[:, :n_neighbors]).optimized().glom()
return neigh_dist, neigh_ind
else:
results = self.X.foreach_tile(mapper_fn=_knn_mapper,
kw={
'X': self.X,
'Q': X,
'n_neighbors': self.n_neighbors,
'algorithm': self.algorithm
})
dist = None
ind = None
""" Get the KNN candidates for each tile of X, then find out the real KNN """
for k, v in results.iteritems():
if dist is None:
dist = v[0]
ind = v[1]
else:
dist = np.concatenate((dist, v[0]), axis=1)
ind = np.concatenate((ind, v[1]), axis=1)
mask = np.argsort(dist, axis=1)[:, :self.n_neighbors]
new_dist = np.array([dist[i][mask[i]] for i, r in enumerate(dist)])
new_ind = np.array([ind[i][mask[i]] for i, r in enumerate(ind)])
return new_dist, new_ind
