wehub-resource-sync
34 lines
1.3 KiB
Python
34 lines
1.3 KiB
Python
import numpy as np
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def get_adjacency_matrix(distance_df, sensor_ids, normalized_k=0.1):
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"""
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:param distance_df: data frame with three columns: [from, to, distance].
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:param sensor_ids: list of sensor ids.
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:param normalized_k: entries that become lower than normalized_k after normalization are set to zero for sparsity.
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:return: adjacency matrix
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"""
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num_sensors = len(sensor_ids)
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dist_mx = np.zeros((num_sensors, num_sensors), dtype=np.float32)
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dist_mx[:] = np.inf
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# Builds sensor id to index map.
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sensor_id_to_ind = {}
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for i, sensor_id in enumerate(sensor_ids):
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sensor_id_to_ind[sensor_id] = i
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# Fills cells in the matrix with distances.
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for row in distance_df.values:
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if row[0] not in sensor_id_to_ind or row[1] not in sensor_id_to_ind:
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continue
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dist_mx[sensor_id_to_ind[row[0]], sensor_id_to_ind[row[1]]] = row[2]
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# Calculates the standard deviation as theta.
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distances = dist_mx[~np.isinf(dist_mx)].flatten()
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std = distances.std()
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adj_mx = np.exp(-np.square(dist_mx / std))
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# Make the adjacent matrix symmetric by taking the max.
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# adj_mx = np.maximum.reduce([adj_mx, adj_mx.T])
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# Sets entries that lower than a threshold, i.e., k, to zero for sparsity.
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adj_mx[adj_mx < normalized_k] = 0
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return adj_mx
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