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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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  | function declareWinner() public onlyOwner { | |
| if (charity1Votes == charity2Votes) { | |
| winner = 'Charity #1 and #2 tied!'; | |
| } | |
| if (charity1Votes > charity2Votes) { | |
| winner = 'Charity #1 won!'; | |
| } | |
| if (charity1Votes > charity2Votes) { | 
  
    
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  | let item1Votes | |
| try { | |
| item1Votes = parseInt(responses[0].data.rows[0].metricValues[0].value) | |
| } catch { | |
| item1Votes = 0 | |
| } | |
| let item2Votes | |
| try { | |
| item2Votes = parseInt(responses[1].data.rows[0].metricValues[0].value) | 
  
    
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  | const requestConfig = { | |
| method: 'post', | |
| headers: { | |
| "Authorization": `Bearer ${accessToken}`, | |
| "Accept": 'application/json', | |
| "Content-Type": 'application/json' | |
| }, | |
| data: { | |
| "metrics": [{"name":"activeUsers"}], | |
| "minuteRanges":[{"startMinutesAgo":29,"endMinutesAgo":0}] | 
  
    
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  | const tokenResponse = await Functions.makeHttpRequest({ | |
| url: 'https://oauth2.googleapis.com/token', | |
| method: 'post', | |
| data: jwtRequestString | |
| }) | |
| const accessToken = tokenResponse.data.access_token | 
  
    
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  | CREATE TEMP FUNCTION | |
| KECCAK256(data STRING) | |
| RETURNS STRING | |
| LANGUAGE js AS """ | |
| var utf8 = unescape(encodeURIComponent(data)); | |
| var arr = []; | |
| for (var i = 0; i < utf8.length; i++) { | |
| arr.push(utf8.charCodeAt(i)); | |
| } | |
| return ethers.utils.keccak256(arr) | 
  
    
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  | CREATE TEMP FUNCTION | |
| KECCAK256(data STRING) | |
| RETURNS STRING | |
| LANGUAGE js AS """ | |
| var utf8 = unescape(encodeURIComponent(data)); | |
| var arr = []; | |
| for (var i = 0; i < utf8.length; i++) { | |
| arr.push(utf8.charCodeAt(i)); | |
| } | |
| return ethers.utils.keccak256(arr) | 
      
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  | CREATE TEMP FUNCTION | |
| hex64ToInt64(hex STRING) | |
| RETURNS INT64 AS ( | |
| IF(hex < "8000000000000000", | |
| cast(concat("0x", hex) AS INT64), | |
| (SELECT (((ms32 & 0x7fffffff) << 32) | ls32) - 0x7fffffffffffffff - 1 | |
| FROM (SELECT cast(concat("0x", substr(hex, 1, 8)) AS INT64) AS ms32, | |
| cast(concat("0x", substr(hex, 9, 8)) AS INT64) AS ls32)))); | |
| CREATE TEMP FUNCTION | |
| KECCAK(data STRING) | 
  
    
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  | CREATE TEMP FUNCTION term_years(term STRING) | |
| RETURNS ARRAY<STRUCT<year INT64, term_frequency INT64>> AS ( | |
| ( | |
| SELECT years | |
| FROM `gcp-pdp-words-dev.sandbox.eng_bert_preprocessed_year_term_frequencies` | |
| WHERE preprocessed_term = term | |
| ) | |
| -- SELECT ARRAY_AGG(STRUCT<year INT64, term_frequency INT64>(z.year, z.term_frequency + IFNULL(t.term_frequency, 0))) | |
| -- FROM | |
| -- ( |