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Platform | CPU | GOOS | GARCH | GOARM | SDK | CGO_FLAGS | CARCH | |
---|---|---|---|---|---|---|---|---|
Windows | x86_64 | windows | amd64 | x86_64 | ||||
Windows | x86_32 | windows | 386 | x86_32 | ||||
MacOS | x86_64 | darwin | amd64 | macos | x86_64 | |||
MacOS | M1 | darwin | arm64 | macos | arm64 | |||
Linux | x86_64 | linux | amd64 | x86_64 | ||||
Linux | x86_32 | linux | 386 | x86_32 | ||||
Linux | arm64 | linux | arm64 | arm64 | ||||
iOS Simulator | x86_64 | darwin | amd64 | iphonesimulator | -fembed-bitcode | x86_64 | ||
iOS | arm | darwin | arm64 | iphoneos | -fembed-bitcode | arm64 |
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#!/usr/bin/env python3 | |
import urllib.request | |
import json | |
import requests | |
full_name = "Professor John Roberts Smith Jr." | |
domain = "example.com" | |
api_key = "<neverbounce_api_key>" | |
name_parts = full_name.lower().split(" ") |
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import re | |
import requests | |
import nltk | |
from lxml import html | |
from nltk.corpus import stopwords | |
stop_words = set(stopwords.words("english")) | |
# A Company Name | |
company_name = "The Boring Company" | |
search_terms = ["flamethrower"] # The Boring Company sells flamethrowers |
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import re | |
import nltk | |
from nltk.corpus import stopwords | |
import pandas as pd | |
stop_words = set(stopwords.words("english")) | |
def get_first_title(title): | |
# keep "co-founder, co-ceo, etc" | |
title = re.sub(r"[Cc]o[\-\ ]","", title) |
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LexicalAnalyzer.prototype.generateParseError = function(lineNumber=null, message="") { | |
error = {"line": lineNumber, "message": message}; | |
return error; | |
} |
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LexicalAnalyzer.prototype.run = function(tokenLines) { | |
lexOutput = this.getClassifiedTokenFromLines(tokenLines); | |
return lexOutput; | |
}; |
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LexicalAnalyzer.prototype.getClassifiedTokenFromLines = function(tokenLines) { | |
classifiedTokens = []; | |
errors = []; | |
// loop through each line of code | |
for (var lineId = 0; lineId < tokenLines.length; lineId++) { | |
line = tokenLines[lineId]; | |
// loop through each token in each line | |
for (var tokenId = 0; tokenId < line.length; tokenId++) { | |
token = line[tokenId]; | |
classifiedToken = null; |
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function LexicalAnalyzer() { | |
this.tokenTypes = { | |
"include": /^include$/, | |
"string": /^\"[^\"]+\"$/, | |
"unaryop": /^(sin|cos|tan|exp|ln|sqrt)$/, | |
"exp": /^(\+|\-|\*|\/|\^)$/, | |
"comma": /^\,$/, | |
"openparen": /^\($/, | |
"closeparen": /^\)$/, | |
"openbracket": /^\[$/, |
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Tokenizer.prototype.run = function(text) { | |
formattedText = this.prepareCodeForTokenization(text); | |
tokens = this.getTokenLines(formattedText); | |
return tokens; | |
} | |
Tokenizer.prototype.prepareCodeForTokenization = function(text) { | |
text = this.stripComments(text); | |
text = this.trimWhitespace(text); | |
text = this.resetNewlines(text); |
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Tokenizer.prototype.getTokenLines = function(text) { | |
lines = text.split("\n"); | |
tokenLines = []; | |
for (var lineId = 0; lineId < lines.length; lineId++) { | |
line = lines[lineId]; | |
tokens = line.split(" "); | |
tokenLines.push(tokens); | |
} | |
return tokenLines; | |
} |
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