jatinjindalj

CVE-2019-8331

Bootstrap < 3.4.1 || < 4.3.1

✔️ CSP strict-dynamic bypass

➖ Requires user interaction

➖ Requires $('[data-toggle="tooltip"]').tooltip();

jatinjindalj

Big List of Real Estate APIs

Listings / Property Data

####Rets Rabbit http://www.retsrabbit.com

Rets Rabbit removes the nightmare of importing thousands of real estate listings and photos from RETS or ListHub and gives you an easy to use import and Web API server so you can focus on building your listing search powered website or app.

jatinjindalj

WITH gen_series AS (
  SELECT 
    i, 
    count(*) over () as rows 
  FROM generate_series(1,100000) tbl(i)
), gen_year AS (
  SELECT 
    i,
    CASE WHEN i <= rows * 0.25 THEN 2022 
         WHEN i <= rows * 0.5 THEN 2023 

jatinjindalj

Python

This is my recommended Python setup, as of Fall 2022. The Python landscape can be a confusing mess of overlapping tools that sometimes don't work well together. This is an effort to standardize our approach and environments.

Tools and helpful links:

• Python docs: https://docs.python.org/3/
• Python Standard Library: https://docs.python.org/3/library/index.html - Start here when you're trying to solve a specific problem
• pip: https://pip.pypa.io/en/stable/ - Python's standard package installer, included with the language itself
• pipx:  - Global script installer, built on top of pip

jatinjindalj

#
# Reload localhost tabs. Sample usage:
#
# $ brew install watchexec
# $ watchexec --watch <DIRNAME> reload.osa
#

tell application "Google Chrome"
	set window_list to every window
	repeat with the_window in window_list

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# how to run this thingy
# create a file on your mac called setup.sh
# run it from terminal with: sh setup.sh

# heavily inspired by https://twitter.com/damcclean
# https://github.com/damcclean/dotfiles/blob/master/install.sh

#!/bin/bash
set -euo pipefail

jatinjindalj

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
 Denoising Autoencoders (dA)

 References :
   - P. Vincent, H. Larochelle, Y. Bengio, P.A. Manzagol: Extracting and
   Composing Robust Features with Denoising Autoencoders, ICML'08, 1096-1103,
   2008

jatinjindalj

#coding: utf8
"""
1. Download this gist.
2. Get the MNIST data. 
    wget http://deeplearning.net/data/mnist/mnist.pkl.gz 
3. Run this code.
    python autoencoder.py 100 -e 1 -b 20 -v 

Wait about a minute ... and get a vialization of weights.
"""

jatinjindalj

# Author: Jean-Remi King <jeanremi.king@gmail.com>
"""
    Illustrate how a hinge loss and a log loss functions 
    typically used in SVM and Logistic Regression 
    respectively focus on a variable number of samples.
    For simplification purposes, we won't consider the
    regularization or penalty (C) factors.
"""
import numpy as np
import matplotlib.animation as animation

jatinjindalj

# http://stackoverflow.com/questions/6645895/calculating-the-percentage-of-variance-measure-for-k-means

cents = [km.kmeans.cluster_centers_ for km in kms]
D_k = [cdist(rid_brand_pca, cent, 'euclidean') for cent in cents]

# 最も近い中心との距離
dist = [np.min(D,axis=1) for D in D_k]
avgWithinSS = [sum(d)/rid_brand_pca.shape[0] for d in dist]

# elbow curve