def sparse_select_side(hyperplane, offset, point_inds, point_data, rng_state):
margin = offset
hyperplane_size = hyperplane.shape[1]
while hyperplane[0, hyperplane_size - 1] < 0.0:
hyperplane_size -= 1
hyperplane_inds = hyperplane[0, :hyperplane_size].astype(np.int32)
hyperplane_data = hyperplane[1, :hyperplane_size]
_, aux_data = sparse_mul(hyperplane_inds, hyperplane_data, point_inds,
point_data)
for val in aux_data:
margin += val
if abs(margin) < EPS:
side = tau_rand_int(rng_state) % 2
if side == 0:
return 0
else:
return 1
elif margin > 0:
return 0
else:
return 1
def sparse_select_side(hyperplane, offset, point_inds, point_data, rng_state):
margin = offset
hyperplane_inds = arr_unique(hyperplane[0])
hyperplane_data = hyperplane[1, : hyperplane_inds.shape[0]]
_, aux_data = sparse_mul(hyperplane_inds, hyperplane_data, point_inds, point_data)
for d in range(aux_data.shape[0]):
margin += aux_data[d]
if abs(margin) < EPS:
side = tau_rand_int(rng_state) % 2
if side == 0:
return 0
else:
return 1
elif margin > 0:
return 0
else:
return 1
def sparse_euclidean_random_projection_split(inds, indptr, data, indices,
rng_state):
"""Given a set of ``graph_indices`` for graph_data points from a sparse graph_data set
presented in csr sparse format as inds, graph_indptr and graph_data, create
a random hyperplane to split the graph_data, returning two arrays graph_indices
that fall on either side of the hyperplane. This is the basis for a
random projection tree, which simply uses this splitting recursively.
This particular split uses cosine distance to determine the hyperplane
and which side each graph_data sample falls on.
Parameters
----------
inds: array
CSR format index array of the matrix
indptr: array
CSR format index pointer array of the matrix
data: array
CSR format graph_data array of the matrix
indices: array of shape (tree_node_size,)
The graph_indices of the elements in the ``graph_data`` array that are to
be split in the current operation.
rng_state: array of int64, shape (3,)
The internal state of the rng
Returns
-------
indices_left: array
The elements of ``graph_indices`` that fall on the "left" side of the
random hyperplane.
indices_right: array
The elements of ``graph_indices`` that fall on the "left" side of the
random hyperplane.
"""
# Select two random points, set the hyperplane between them
left_index = np.abs(tau_rand_int(rng_state)) % indices.shape[0]
right_index = np.abs(tau_rand_int(rng_state)) % indices.shape[0]
right_index += left_index == right_index
right_index = right_index % indices.shape[0]
left = indices[left_index]
right = indices[right_index]
left_inds = inds[indptr[left]:indptr[left + 1]]
left_data = data[indptr[left]:indptr[left + 1]]
right_inds = inds[indptr[right]:indptr[right + 1]]
right_data = data[indptr[right]:indptr[right + 1]]
# Compute the normal vector to the hyperplane (the vector between
# the two points) and the offset from the origin
hyperplane_offset = 0.0
hyperplane_inds, hyperplane_data = sparse_diff(left_inds, left_data,
right_inds, right_data)
offset_inds, offset_data = sparse_sum(left_inds, left_data, right_inds,
right_data)
offset_data = offset_data / 2.0
offset_inds, offset_data = sparse_mul(hyperplane_inds, hyperplane_data,
offset_inds,
offset_data.astype(np.float32))
for val in offset_data:
hyperplane_offset -= val
# For each point compute the margin (project into normal vector, add offset)
# If we are on lower side of the hyperplane put in one pile, otherwise
# put it in the other pile (if we hit hyperplane on the nose, flip a coin)
n_left = 0
n_right = 0
side = np.empty(indices.shape[0], np.int8)
for i in range(indices.shape[0]):
margin = hyperplane_offset
i_inds = inds[indptr[indices[i]]:indptr[indices[i] + 1]]
i_data = data[indptr[indices[i]]:indptr[indices[i] + 1]]
_, mul_data = sparse_mul(hyperplane_inds, hyperplane_data, i_inds,
i_data)
for val in mul_data:
margin += val
if abs(margin) < EPS:
side[i] = abs(tau_rand_int(rng_state)) % 2
if side[i] == 0:
n_left += 1
else:
n_right += 1
elif margin > 0:
side[i] = 0
n_left += 1
else:
side[i] = 1
n_right += 1
# Now that we have the counts allocate arrays
indices_left = np.empty(n_left, dtype=np.int32)
indices_right = np.empty(n_right, dtype=np.int32)
# Populate the arrays with graph_indices according to which side they fell on
n_left = 0
n_right = 0
for i in range(side.shape[0]):
if side[i] == 0:
indices_left[n_left] = indices[i]
n_left += 1
else:
indices_right[n_right] = indices[i]
n_right += 1
hyperplane = np.vstack((hyperplane_inds, hyperplane_data))
return indices_left, indices_right, hyperplane, hyperplane_offset
def sparse_angular_random_projection_split(inds, indptr, data, indices,
rng_state):
"""Given a set of ``indices`` for data points from a sparse data set
presented in csr sparse format as inds, indptr and data, create
a random hyperplane to split the data, returning two arrays indices
that fall on either side of the hyperplane. This is the basis for a
random projection tree, which simply uses this splitting recursively.
This particular split uses cosine distance to determine the hyperplane
and which side each data sample falls on.
Parameters
----------
inds: array
CSR format index array of the matrix
indptr: array
CSR format index pointer array of the matrix
data: array
CSR format data array of the matrix
indices: array of shape (tree_node_size,)
The indices of the elements in the ``data`` array that are to
be split in the current operation.
rng_state: array of int64, shape (3,)
The internal state of the rng
Returns
-------
indices_left: array
The elements of ``indices`` that fall on the "left" side of the
random hyperplane.
indices_right: array
The elements of ``indices`` that fall on the "left" side of the
random hyperplane.
"""
# Select two random points, set the hyperplane between them
left_index = tau_rand_int(rng_state) % indices.shape[0]
right_index = tau_rand_int(rng_state) % indices.shape[0]
right_index += left_index == right_index
right_index = right_index % indices.shape[0]
left = indices[left_index]
right = indices[right_index]
left_inds = inds[indptr[left]:indptr[left + 1]]
left_data = data[indptr[left]:indptr[left + 1]]
right_inds = inds[indptr[right]:indptr[right + 1]]
right_data = data[indptr[right]:indptr[right + 1]]
left_norm = norm(left_data)
right_norm = norm(right_data)
if abs(left_norm) < EPS:
left_norm = 1.0
if abs(right_norm) < EPS:
right_norm = 1.0
# Compute the normal vector to the hyperplane (the vector between
# the two points)
normalized_left_data = left_data / left_norm
normalized_right_data = right_data / right_norm
hyperplane_inds, hyperplane_data = sparse_diff(left_inds,
normalized_left_data,
right_inds,
normalized_right_data)
hyperplane_norm = norm(hyperplane_data)
if abs(hyperplane_norm) < EPS:
hyperplane_norm = 1.0
for d in range(hyperplane_data.shape[0]):
hyperplane_data[d] = hyperplane_data[d] / hyperplane_norm
# For each point compute the margin (project into normal vector)
# If we are on lower side of the hyperplane put in one pile, otherwise
# put it in the other pile (if we hit hyperplane on the nose, flip a coin)
n_left = 0
n_right = 0
side = np.empty(indices.shape[0], np.int8)
for i in range(indices.shape[0]):
margin = 0.0
i_inds = inds[indptr[indices[i]]:indptr[indices[i] + 1]]
i_data = data[indptr[indices[i]]:indptr[indices[i] + 1]]
_, mul_data = sparse_mul(hyperplane_inds, hyperplane_data, i_inds,
i_data)
for d in range(mul_data.shape[0]):
margin += mul_data[d]
if abs(margin) < EPS:
side[i] = tau_rand_int(rng_state) % 2
if side[i] == 0:
n_left += 1
else:
n_right += 1
elif margin > 0:
side[i] = 0
n_left += 1
else:
side[i] = 1
n_right += 1
# Now that we have the counts allocate arrays
indices_left = np.empty(n_left, dtype=np.int64)
indices_right = np.empty(n_right, dtype=np.int64)
# Populate the arrays with indices according to which side they fell on
n_left = 0
n_right = 0
for i in range(side.shape[0]):
if side[i] == 0:
indices_left[n_left] = indices[i]
n_left += 1
else:
indices_right[n_right] = indices[i]
n_right += 1
hyperplane = np.vstack((hyperplane_inds, hyperplane_data))
return indices_left, indices_right, hyperplane, None
