Umedzhon Izbasarov umedsondoniyor

MongoDB Cheat Sheet

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Postgres Cheatsheet

This is a collection of the most common commands I run while administering Postgres databases. The variables shown between the open and closed tags, "<" and ">", should be replaced with a name you choose. Postgres has multiple shortcut functions, starting with a forward slash, "". Any SQL command that is not a shortcut, must end with a semicolon, ";". You can use the keyboard UP and DOWN keys to scroll the history of previous commands you've run.

Setup

installation, Ubuntu

http://www.postgresql.org/download/linux/ubuntu/ https://help.ubuntu.com/community/PostgreSQL

PostgreSQL & PostGIS Cheatsheet

This is a collection of information on PostgreSQL and PostGIS for what I tend to use most often.

	<!-- This is the main Blade file that you want your components to show up in -->

	<!DOCTYPE html>
	<html lang="{{ config('app.locale') }}">
	<head>
	<meta charset="utf-8">
	<meta http-equiv="X-UA-Compatible" content="IE=edge">
	<meta name="viewport" content="width=device-width, initial-scale=1">

	<title>Laravel</title>

	{
	"name": "bs4starter",
	"version": "1.0.0",
	"description": "Bootstrap 4 starter workflow APAT",
	"main": "index.js",
	"scripts": {
	"start": "gulp"
	},
	"author": "Umedzhon Izbasarov",
	"license": "MIT",

	brew install Qt4
	brew install openssl
	brew install cmake
	git clone https://github.com/PySide/pyside-setup.git --recurse-submodules
	cd pyside-setup

	# The following must be executed in a bash shell
	python setup.py bdist_wheel --ignore-git --qmake=/usr/local/Cellar/qt/4.8.7_2/bin/qmake --openssl=/usr/local/Cellar/openssl/1.0.2h_1/bin/openssl --cmake=/usr/local/Cellar/cmake/3.5.2/bin/cmake

	from skimage.morphology import skeletonize
	from skimage import data
	import matplotlib.pyplot as plt
	from skimage.util import invert

	from numpy import invert

	# Invert the horse image
	image = invert(data.horse())
	print image

	metin1 = ["web", "net"]
	metin2 = ["graph", "web", "net"]
	metin3 = ["page", "web", "complex", "net"]

	voc = []
	vec = [2, 2, 2, 2, 2]


	def vocabulary(test):
	for x in test:

	import numpy as np
	import matplotlib.pyplot as plt
	from sklearn import svm

	# we create clusters with 1000 and 100 points
	rng = np.random.RandomState(0)
	n_samples_1 = 1000
	n_samples_2 = 100
	X = np.r_[1.5 * rng.randn(n_samples_1, 2),
	0.5 * rng.randn(n_samples_2, 2) + [2, 2]]