def make_angular_hyperplane(data, indices, rng_state):
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_norm = norm(data[left])
right_norm = norm(data[right])
if left_norm == 0.0:
left_norm = 1.0
if right_norm == 0.0:
right_norm = 1.0
# Compute the normal vector to the hyperplane (the vector between
# the two points) and the offset from the origin
hyperplane_offset = 0.0
hyperplane_vector = np.empty(data.shape[1], dtype=np.float32)
for d in range(data.shape[1]):
hyperplane_vector[d] = (data[left, d] / left_norm) - (data[right, d] /
right_norm)
return hyperplane_vector, hyperplane_offset
def sparse_correlation(ind1, data1, ind2, data2, n_features):
mu_x = 0.0
mu_y = 0.0
dot_product = 0.0
if ind1.shape[0] == 0 and ind2.shape[0] == 0:
return 0.0
elif ind1.shape[0] == 0 or ind2.shape[0] == 0:
return 1.0
for i in range(data1.shape[0]):
mu_x += data1[i]
for i in range(data2.shape[0]):
mu_y += data2[i]
mu_x /= n_features
mu_y /= n_features
shifted_data1 = np.empty(data1.shape[0], dtype=np.float32)
shifted_data2 = np.empty(data2.shape[0], dtype=np.float32)
for i in range(data1.shape[0]):
shifted_data1[i] = data1[i] - mu_x
for i in range(data2.shape[0]):
shifted_data2[i] = data2[i] - mu_y
norm1 = np.sqrt(
(norm(shifted_data1) ** 2) + (n_features - ind1.shape[0]) * (mu_x ** 2)
)
norm2 = np.sqrt(
(norm(shifted_data2) ** 2) + (n_features - ind2.shape[0]) * (mu_y ** 2)
)
dot_prod_inds, dot_prod_data = sparse_mul(ind1, shifted_data1, ind2, shifted_data2)
common_indices = set(dot_prod_inds)
for val in dot_prod_data:
dot_product += val
for i in range(ind1.shape[0]):
if ind1[i] not in common_indices:
dot_product -= shifted_data1[i] * (mu_y)
for i in range(ind2.shape[0]):
if ind2[i] not in common_indices:
dot_product -= shifted_data2[i] * (mu_x)
all_indices = arr_union(ind1, ind2)
dot_product += mu_x * mu_y * (n_features - all_indices.shape[0])
if norm1 == 0.0 and norm2 == 0.0:
return 0.0
elif dot_product == 0.0:
return 1.0
else:
return 1.0 - (dot_product / (norm1 * norm2))
def sparse_wasserstein_1d(ind1, data1, ind2, data2, p=1):
result = 0.0
old_ind = 0
delta = 0.0
i1 = 0
i2 = 0
cdf1 = 0.0
cdf2 = 0.0
l1_norm_x = np.sum(data1)
l1_norm_y = np.sum(data2)
norm = lambda x, p: np.power(np.abs(x), p)
# pass through both index lists
while i1 < ind1.shape[0] and i2 < ind2.shape[0]:
j1 = ind1[i1]
j2 = ind2[i2]
if j1 == j2:
result += delta * (j1 - old_ind)
cdf1 += data1[i1] / l1_norm_x
cdf2 += data2[i2] / l1_norm_y
delta = norm(cdf1 - cdf2, p)
old_ind = j1
i1 += 1
i2 += 1
elif j1 < j2:
result += delta * (j1 - old_ind)
cdf1 += data1[i1] / l1_norm_x
delta = norm(cdf1 - cdf2, p)
old_ind = j1
i1 += 1
else:
result += delta * (j2 - old_ind)
cdf2 += data2[i2] / l1_norm_y
delta = norm(cdf1 - cdf2, p)
old_ind = j2
i2 += 1
# pass over the tails
while i1 < ind1.shape[0]:
j1 = ind1[i1]
result += delta * (j1 - old_ind)
cdf1 += data1[i1] / l1_norm_x
delta = norm(cdf1 - cdf2, p)
old_ind = j1
i1 += 1
while i2 < ind2.shape[0]:
j2 = ind2[i2]
result += delta * (j2 - old_ind)
cdf2 += data2[i2] / l1_norm_y
delta = norm(cdf1 - cdf2, p)
old_ind = j2
i2 += 1
return np.power(result, 1.0 / p)
def sparse_cosine(ind1, data1, ind2, data2):
_, aux_data = sparse_mul(ind1, data1, ind2, data2)
result = 0.0
norm1 = norm(data1)
norm2 = norm(data2)
for val in aux_data:
result += val
if norm1 == 0.0 and norm2 == 0.0:
return 0.0
elif norm1 == 0.0 or norm2 == 0.0:
return 1.0
else:
return 1.0 - (result / (norm1 * norm2))
def sparse_cosine(ind1, data1, ind2, data2):
_, aux_data = sparse_mul(ind1, data1, ind2, data2)
result = 0.0
norm1 = norm(data1)
norm2 = norm(data2)
for i in range(aux_data.shape[0]):
result += aux_data[i]
if norm1 == 0.0 and norm2 == 0.0:
return 0.0
elif norm1 == 0.0 or norm2 == 0.0:
return 1.0
else:
return 1.0 - (result / (norm1 * norm2))
def sparse_alternative_cosine(ind1, data1, ind2, data2):
_, aux_data = sparse_mul(ind1, data1, ind2, data2)
result = 0.0
norm_x = norm(data1)
norm_y = norm(data2)
dim = len(aux_data)
for i in range(dim):
result += aux_data[i]
if norm_x == 0.0 and norm_y == 0.0:
return 0.0
elif norm_x == 0.0 or norm_y == 0.0:
return FLOAT32_MAX
elif result <= 0.0:
return FLOAT32_MAX
else:
return 0.5 * (np.log(norm_x) + np.log(norm_y)) - np.log(result)
def angular_random_projection_split(data, indices, rng_state):
"""Given a set of ``graph_indices`` for graph_data points from ``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
----------
data: array of shape (n_samples, n_features)
The original graph_data to be split
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.
"""
dim = data.shape[1]
# 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_norm = norm(data[left])
right_norm = norm(data[right])
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)
hyperplane_vector = np.empty(dim, dtype=np.float32)
for d in range(dim):
hyperplane_vector[d] = (data[left, d] / left_norm) - (data[right, d] /
right_norm)
hyperplane_norm = norm(hyperplane_vector)
if abs(hyperplane_norm) < EPS:
hyperplane_norm = 1.0
for d in range(dim):
hyperplane_vector[d] = hyperplane_vector[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
for d in range(dim):
margin += hyperplane_vector[d] * data[indices[i], 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.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
return indices_left, indices_right, hyperplane_vector, 0.0
def sparse_angular_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 = 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).astype(np.float32)
normalized_right_data = (right_data / right_norm).astype(np.float32)
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 val in mul_data:
margin += val
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.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, 0.0