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package main | |
// This file create a new server which you can send requests like http://localhost:4999/https://google.com (url starts after | |
// port) and it will return the response with all cors headers and save the response in a file, next time if the url file | |
// exists, the file will be returned and no request will be sent | |
import ( | |
"crypto/md5" | |
"encoding/hex" |
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####################################### | |
# HOW TO COMPILE TRINITYCORE ON LINUX # | |
####################################### | |
##################### | |
# TRINITYCORELEGACY # | |
##################### | |
########################### | |
# TRINITYCORE SETUP GUIDE # |
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""" | |
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
BSD License | |
""" | |
import numpy as np | |
# data I/O | |
data = open('input.txt', 'r').read() # should be simple plain text file | |
chars = list(set(data)) | |
data_size, vocab_size = len(data), len(chars) |
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import cv2 | |
import numpy as np | |
img = cv2.imread('images/input.jpg') | |
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) | |
rows,cols = img.shape[:2] | |
cv2.imshow('Original',img) |
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from sklearn.metrics import confusion_matrix,classification_report | |
import keras.backend as K | |
from keras.engine.topology import Layer, InputSpec | |
from keras.layers import Dense, Input | |
from keras.models import Model | |
from keras.optimizers import SGD | |
from keras import callbacks | |
from keras.initializers import VarianceScaling | |
from sklearn.cluster import KMeans |
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package cryptoUtils | |
import ( | |
"github.com/dgrijalva/jwt-go" | |
"time" | |
) | |
type Credentials struct { | |
Password string `json:"password"` | |
Username string `json:"username"` |
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package ciphers | |
import ( | |
"crypto/rand" | |
"crypto/rsa" | |
"crypto/sha512" | |
"crypto/x509" | |
"encoding/pem" | |
"log" | |
) |
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package main | |
import ( | |
"flag" | |
"io" | |
"log" | |
"net" | |
"net/http" | |
"strings" | |
) |