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from sklearn.metrics.pairwise import pairwise_distances | |
import numpy as np | |
# X shoudl be a numpy matrix, very likely sparse matrix: http://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.sparse.csr_matrix.html#scipy.sparse.csr_matrix | |
# T1 > T2 for overlapping clusters | |
# T1 = Distance to centroid point to not include in other clusters | |
# T2 = Distance to centroid point to include in cluster | |
# T1 > T2 for overlapping clusters | |
# T1 < T2 will have points which reside in no clusters | |
# T1 == T2 will cause all points to reside in mutually exclusive clusters |
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#standardSQL | |
CREATE TEMPORARY FUNCTION geohashDecode(geohash STRING) | |
RETURNS STRUCT<bbox ARRAY<FLOAT64>, lat FLOAT64, lng FLOAT64> | |
LANGUAGE js AS """ | |
if (!geohash) return null; | |
var base32 = '0123456789bcdefghjkmnpqrstuvwxyz'; | |
geohash = geohash.toLowerCase(); | |
var evenBit = true; | |
var latMin = -90, latMax = 90; |
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#This script has been solely created under dataturks. Copyrights are reserved | |
#EXAMPLE USAGE | |
#python3 tensorflow_json_parser.py --json_file "flower.json" --dataset_path "Dataset5/" | |
import json | |
import glob | |
import urllib.request |
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license: gpl-3.0 |