kailaix’s gists

kailaix / Burgers.jl

Created April 7, 2020 04:43

Finite difference for Burger's equation

kailaix / pwd.cpp

Created April 17, 2019 22:54

PWD in C++

	std::string survey_fname;
	std::string dir(__FILE__);
	dir = dir.substr(0, dir.find_last_of("\\/"));

kailaix / run.sh

Created February 2, 2019 21:22

Running jobs in parallel

	for n in 50 60 70 80 90 100
	do
	julia euro_main.jl $n 2>&1 \| tee -a euro$n.log &
	done

	wait %1 %2 %3 %4 %5 %6

	for n in 50 70 90 110 130 150
	do
	julia flap_main.jl $n 2>&1 \| tee -a flap$n.log &

kailaix / cir.jl

Last active January 13, 2019 22:59

CIR Model

	using LinearAlgebra
	using PyPlot
	using SparseArrays
	# https://uu.diva-portal.org/smash/get/diva2:1148496/FULLTEXT01.pdf page 8
	# https://regulation.pstat.ucsb.edu/sites/sa-wcmf6/5-Peng_ECIR_Model_Qidi.pdf page 13

	# params
	r0 = 0.5
	L = 4.0
	a = 1.0

kailaix / ppl.py

Last active December 29, 2018 20:07

A working example of Probabilistic Programming using Edward

	# modified version from http://edwardlib.org/tutorials/
	from tensorflow_probability import edward2 as ed
	import tensorflow as tf
	import tensorflow_probability as tfp
	import numpy as np

	def logistic_regression(features):
	coeffs = ed.Normal(loc=0., scale=1.,
	sample_shape=features.shape[1], name="coeffs")
	outcomes = ed.Bernoulli(logits=tf.tensordot(features, coeffs, [[1], [0]]),

kailaix / CO2.png

Last active December 28, 2018 02:54

Predicting CO2 using PyTensorFlow

kailaix / mlp.jl

Last active December 25, 2018 21:58

Multilayer Perceptron in Julia

	using TensorFlow

	num_layers = 3 # number of hidden layer
	nh = 20 # number of neurons per hidden layer
	"""
	neural(x, scope="default", reuse=false)

	A Multilayer Perceptron with `num_layers` hidden layers. The last layer is a linear layer.
	The neural network is enough to perform many useful approximations
	"""

kailaix / wave.jl

Last active July 28, 2022 11:05

Example of 1D Wave Equation with Absorbing Boundary Condition

	# solve 1D wave equation with ABC
	using PyPlot
	using Plots
	using LinearAlgebra


	N = 100
	x = LinRange(-1.,1.,N+1)
	Δx = x[2]-x[1]
	NT = 200

kailaix / planestress.jl

Created December 22, 2018 04:40

Plane Stress: An Example of Assembler Programming

	# This example solves the plane stress problem
	# E/(2(1+ν)) ∇^2 u_1 + E/(2(1-ν)) * ∂/∂x ( ∂u_1/∂x + ∂u_2/∂y ) = f1
	# E/(2(1+ν)) ∇^2 u_2 + E/(2(1-ν)) * ∂/∂y ( ∂u_1/∂x + ∂u_2/∂y ) = f2

	# Exact solution = sin(πx)sin(πy)
	# f1, f2, σ can be evaluated analytically

	using LinearAlgebra
	using PyPlot
	import Plots

kailaix / assembler.jl

Created December 22, 2018 02:57

Assembler Programming

	# This is an example of solving the Poisson problem using the Assembler Programming method
	# (-Δ) u(x) = f(x)
	# u(x) = 0, x ∈ [0,1]^2
	# The exact solution is u(x,y) = sin(pix)sin(piy)
	# and f(x,y) = 2pi^2 * sin(pix)sin(piy)

	using PyPlot
	import Plots
	using PyCall
	using SparseArrays