cshjin

''' Go through each of the subfolder and run git fetch if the folder is a git repo.'''

import os
import subprocess

def check_update():
    ''' Go through each of the subfolder and run git fetch if the folder is a git repo.'''
    for root, dirs, files in os.walk('.'):
        if '.git' in dirs:
            print('Fetching %s' % root)

cshjin

# OEIS sequence id A360497: https://oeis.org/A360497
# 
# Author: Hongwei Jin <[email protected]>
# Date: 2023-07-26

from sympy import isprime
import matplotlib.pyplot as plt
import networkx as nx

cshjin

# Dataset

cshjin

# defaultSettings.yaml for latexindent.pl, version 3.7.1, 2019-09-07
#                      a script that aims to
#                      beautify .tex, .sty, .cls files
#
# (or latexindent.exe if you're on Windows)
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

cshjin

try:
    # import tf 2.X
    import tensorflow.compat.v2 as tf
except Exception:
    # import tf 1.X
    import tensorflow as tf
    tf.logging.set_verbosity(tf.logging.ERROR)
print('Using TF {}'.format(tf.__version__))

# set logging level for tf

cshjin

Collection of Coding Practice

This is a collection of online programming practice. Several website provide online judge, some provide problem list and some others provide only result judge.

List of online resources

Website	OJ	RJ	Practice	Challenge	Interviews	Languages
CareerCup	❌	❌	❌	⭕	⭕	C, C++, Java, Python, etc.
CodeChef	⭕	❌	⭕	⭕	⭕	C, C++, Java, Python, etc.

cshjin

# -*- coding: utf-8 -*-
#!/bin/env python
import numpy as np
import unittest


def softmax(x):
    """ Given a vector, apply the softmax activation function 

    Parameters

cshjin

""" Solve a minimax problem 
    The problme is defined as
    $$ \min_{x} \max_{y} f(x, y) = x^2(y+1) + y^2(x+1)$$
    
    The first order gradient is 
    $$ \frac{\partial f}{\partial x} = 2x(y+1) + y^2 $$
    $$ \frac{\partial f}{\partial y} = x^2 + 2y(x+1) $$
    
    From the first order optimality condition, the alternatively solver 
        should solve the problem and converge to a stationary point.

cshjin

yalmip('clear')

T = [0 1 0 1; 1 0 1 0; 0 1 0 1; 1 0 1 0];

x = sdpvar(4, 1);
y = sdpvar(4, 1);
assign(x, randn(4, 1));
assign(y, randn(4, 1));
const = [x'*x <= 1; y'*y <=1];
% const = [];

cshjin

import numpy as np
import pickle
import matplotlib.pyplot as plt
import scipy.io as sio
import tensorflow as tf

tf.enable_eager_execution()
tmm = tf.matmul

def _test():