samehkamaleldin

from collections import Counter
from typing import Dict


def main():
    # ----------------- Configuration ----------------- #
    fasta_filepath = "./data/Hsym_primary_v1.0.aa.fasta"
    window_size = 16
    slide_size = 4
    aa_min_occurrences = {'R': 5, 'G': 3}

samehkamaleldin

# -*- coding: utf-8 -*-

import os
import numpy as np
import gzip
import math


def export_entry(entry, out_fd):
    """

samehkamaleldin

\documentclass{standalone}
\usepackage{times}
\usepackage{tikz}

\begin{document}
		\begin{tikzpicture}[scale=2]
		%\fontfamily{times}
		\tikzstyle{rect}=[rectangle,draw=black!75,fill=black!20,minimum size=0mm, node distance=7.5pt]
		\tikzstyle{circ}=[circle,thick,draw=black!75,fill=blue!30,minimum size=0mm, node distance=80pt]
		

samehkamaleldin

import os
from datetime import datetime, timedelta
from shutil import copyfile

# ------------------------------------------------------------------------------------
# [Things to Change] please input exact format of the date and time like the example
# ------------------------------------------------------------------------------------
records_directory = "/Users/sameh/Downloads/records"
site_name = "Cork_7"
first_date = "21/5/2012 14:31:51"

samehkamaleldin

Awesome Knowledge Graph Embedding Approaches

Libraries

• Embedding framework (5 algorithms) @ https://github.com/BookmanHan/Embedding
• KB2EP collection (5 algorithms) @ https://github.com/thunlp/KB2EP
• scikit-kge (3 algorithms) @ https://github.com/mnick/scikit-kge
• scikit-tensor (5 algorithms) @ https://github.com/mnick/scikit-tensor
• SME (4 algorithms) @ https://github.com/glorotxa/SME

samehkamaleldin

########## Start of header ################
# Title: Data Analysis Assignment [SeaWeed dataset]
#
# Description:
# Analysis of SeaWeed dataset
# extracted of from a field experiment on an island off Connemara
#
# Author: 
# Date: 20.07.2017
########## End of header ###################

samehkamaleldin

import scipy.io
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D

# (get the data from files)
# read train data into python from .mat file
mat = scipy.io.loadmat('classifier_data.mat')
x_train = mat['TrainData_2d']

samehkamaleldin

#include <iostream>
#include <vector>
#include <map>
#include <string>
#include <memory>
#include <functional>

using namespace std;

// usage of auto as function return type that depends on template params

samehkamaleldin

import json
# from tqdm import tqdm
link_json_fd = open('link.json')
nolink_json_fd = open('nolink.json')

link_json = json.load(link_json_fd)
nolink_json = json.load(nolink_json_fd)

link_json = [
    {"title": v["title"], "url": v["doc - url"], "abstract": v["abstract"][0:20]}

samehkamaleldin

tmux cheatsheet

As configured in my dotfiles.

start new:

tmux

start new with session name: