| [package] | |
| name = "playground" | |
| version = "0.1.0" | |
| edition = "2021" | |
| [dependencies] | |
| alloy = { git = "https://github.com/alloy-rs/alloy.git", features = ["contract", "provider-ws", "rpc-types-eth"] } | |
| anyhow = "1.0.82" | |
| tokio = { version = "1.37.0", features = ["full"] } | |
| tracing = "0.1.40" |
| import json | |
| MIN_ERC20_ABI = json.loads( | |
| """ | |
| [{"constant": true, "inputs": [], "name": "name", "outputs": [ { "name": "", "type": "string" } ], "payable": false, "stateMutability": "view", "type": "function" }, { "constant": false, "inputs": [ { "name": "_spender", "type": "address" }, { "name": "_value", "type": "uint256" } ], "name": "approve", "outputs": [ { "name": "", "type": "bool" } ], "payable": false, "stateMutability": "nonpayable", "type": "function" }, { "constant": true, "inputs": [], "name": "totalSupply", "outputs": [ { "name": "", "type": "uint256" } ], "payable": false, "stateMutability": "view", "type": "function" }, { "constant": false, "inputs": [ { "name": "_from", "type": "address" }, { "name": "_to", "type": "address" }, { "name": "_value", "type": "uint256" } ], "name": "transferFrom", "outputs": [ { "name": "", "type": "bool" } ], "payable": false, "stateMutability": "nonpayable", "type": "function" }, { "constant": true, "inputs": [], "name": "decimals", "outputs": [ { "name" |
| #!/bin/bash | |
| # | |
| # convert a mysql database to sqlite3 | |
| # | |
| #see https://stackoverflow.com/questions/5164033/ | |
| # export-a-mysql-database-to-sqlite-database | |
| mysql_host=localhost | |
| mysql_user=george | |
| #mysql_passwd=**************** |
| """ | |
| preprocess-twitter.py | |
| python preprocess-twitter.py "Some random text with #hashtags, @mentions and http://t.co/kdjfkdjf (links). :)" | |
| Script for preprocessing tweets by Romain Paulus | |
| with small modifications by Jeffrey Pennington | |
| with translation to Python by Motoki Wu | |
| Translation of Ruby script to create features for GloVe vectors for Twitter data. |
| function svmStruct = best_svm_classifer_rbf(cdata,labels) | |
| %Write a function called crossfun to calculate the predicted classification yfit from a test vector | |
| %xtest, when the SVM is trained on a sample xtrain that has classification ytrain. | |
| function yfit = crossfun(xtrain,ytrain,xtest, rbf_sigma, boxconstraint) | |
| % Train the model on xtrain, ytrain, | |
| % and get predictions of class of xtest and output it as yfit |