99 lines
2.9 KiB
Python
99 lines
2.9 KiB
Python
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import numpy as np
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import numba
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from pynndescent.utils import tau_rand_int, norm
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######################################################
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# Alternative tree approach; should be the basis
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# for a dask-distributed version of the algorithm
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######################################################
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@numba.njit(fastmath=True, nogil=True)
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def apply_hyperplane(
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data,
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hyperplane_vector,
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hyperplane_offset,
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hyperplane_node_num,
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current_num_nodes,
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data_node_loc,
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rng_state,
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):
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left_node = current_num_nodes
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right_node = current_num_nodes + 1
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for i in range(data_node_loc.shape[0]):
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if data_node_loc[i] != hyperplane_node_num:
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continue
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margin = hyperplane_offset
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for d in range(hyperplane_vector.shape[0]):
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margin += hyperplane_vector[d] * data[i, d]
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if margin == 0:
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if abs(tau_rand_int(rng_state)) % 2 == 0:
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data_node_loc[i] = left_node
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else:
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data_node_loc[i] = right_node
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elif margin > 0:
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data_node_loc[i] = left_node
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else:
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data_node_loc[i] = right_node
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return
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@numba.njit(fastmath=True, nogil=True)
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def make_euclidean_hyperplane(data, indices, rng_state):
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left_index = tau_rand_int(rng_state) % indices.shape[0]
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right_index = tau_rand_int(rng_state) % indices.shape[0]
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right_index += left_index == right_index
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right_index = right_index % indices.shape[0]
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left = indices[left_index]
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right = indices[right_index]
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# Compute the normal vector to the hyperplane (the vector between
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# the two points) and the offset from the origin
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hyperplane_offset = 0.0
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hyperplane_vector = np.empty(data.shape[1], dtype=np.float32)
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for d in range(data.shape[1]):
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hyperplane_vector[d] = data[left, d] - data[right, d]
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hyperplane_offset -= (
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hyperplane_vector[d] * (data[left, d] + data[right, d]) / 2.0
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)
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return hyperplane_vector, hyperplane_offset
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@numba.njit(fastmath=True, nogil=True)
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def make_angular_hyperplane(data, indices, rng_state):
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left_index = tau_rand_int(rng_state) % indices.shape[0]
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right_index = tau_rand_int(rng_state) % indices.shape[0]
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right_index += left_index == right_index
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right_index = right_index % indices.shape[0]
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left = indices[left_index]
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right = indices[right_index]
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left_norm = norm(data[left])
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right_norm = norm(data[right])
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if left_norm == 0.0:
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left_norm = 1.0
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if right_norm == 0.0:
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right_norm = 1.0
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# Compute the normal vector to the hyperplane (the vector between
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# the two points) and the offset from the origin
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hyperplane_offset = 0.0
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hyperplane_vector = np.empty(data.shape[1], dtype=np.float32)
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for d in range(data.shape[1]):
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hyperplane_vector[d] = (data[left, d] / left_norm) - (
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data[right, d] / right_norm
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)
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return hyperplane_vector, hyperplane_offset
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