dermesser
/ metropolishastings.jl
Last active
June 30, 2021 15:38
Very very simple implementation of multi-dimensional Metropolis-Hastings algorithm
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| ### A Pluto.jl notebook ### | |
| # v0.14.7 | |
| using Markdown | |
| using InteractiveUtils | |
| # ╔═╡ 4f4ffc64-cdf9-11eb-32ef-e5e79401898e | |
| using Random | |
| # ╔═╡ fdc3d3af-f680-41b0-903e-779be1b24206 |
dermesser
/ bioassay_metropolis.jl
Created
June 17, 2021 11:51
Replication of PyMC3's simple introduction example in Julia, for the purpose of my understanding
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| ### A Pluto.jl notebook ### | |
| # v0.14.7 | |
| using Markdown | |
| using InteractiveUtils | |
| # ╔═╡ 8ce57352-cf5b-11eb-2624-976564fd9572 | |
| using Plots | |
| # ╔═╡ 22e5afa5-03a5-4c6c-9744-7417f1176839 |
dermesser
/ GPR.jl
Last active
July 18, 2021 19:21
Julia version of the very excellent introduction to gaussian process regression at https://peterroelants.github.io/posts/gaussian-process-tutorial/
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| ### A Pluto.jl notebook ### | |
| # v0.14.7 | |
| using Markdown | |
| using InteractiveUtils | |
| # ╔═╡ 8560c782-e7f0-11eb-1b79-21d0c85978db | |
| begin | |
| import CairoMakie as CM | |
| import LinearAlgebra |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| # Author: Lewin Bormann, 2021 | |
| # A simple finite difference algorithm for differentiating a function numerically with respect to multiple dimensions. | |
| function mydiff(func, theta, releps=1e-3, reltol=1e-3) | |
| scale_eps = 1/2 | |
| grad = zeros(size(theta)) | |
| upper, lower = theta[:], theta[:] | |
| for (i, t) in enumerate(theta) | |
| count = 0 | |
| flipflop = 0 |
dermesser
/ max_likelihood.jl
Created
August 8, 2021 08:55
Simple example for fitting a normal distribution using max likelihood and gradient descent
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| ### A Pluto.jl notebook ### | |
| # v0.15.1 | |
| using Markdown | |
| using InteractiveUtils | |
| # ╔═╡ cc62dbce-f820-11eb-168f-670217a987d9 | |
| begin | |
| import Pkg | |
| import Distributions |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| ### A Pluto.jl notebook ### | |
| # v0.16.0 | |
| using Markdown | |
| using InteractiveUtils | |
| # ╔═╡ b70ce3a6-30e8-11ec-3f2a-59d54d66bfbd | |
| begin | |
| import Zygote | |
| import Plots |
dermesser
/ multibisect.py
Created
November 29, 2021 19:30
Annotated version of a multi-bisect algorithm finding all places where "performance became worse" in a commit list.
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| import numpy as np | |
| #perf = [10, 10, 10, 10, 9, 9, 9, 9, 9, 9, 8, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 3, 2, 2] | |
| perf = np.concatenate([4*np.ones(25), 2*np.ones(25), np.ones(25)]) | |
| #perf = list(range(40, 0, -1)) | |
| count = 0 | |
| def worseCommit(a, b): | |
| global count | |
| count += 1 |
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
dermesser
/ miniautodiff.py
Last active
December 22, 2021 17:28
A very simple reverse-mode automatic differentiation in Python for demo purposes.
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| import numpy as np | |
| class Expression: | |
| def __init__(self, left, right): | |
| self.l = left | |
| self.r = right | |
| self.eval_l = None | |
| self.eval_r = None | |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| import numpy as np | |
| import tensorflow as tf | |
| def LV(N1, N2, eps1, eps2, gam1, gam2): | |
| dt = tf.constant(1.) | |
| states = [(N1, N2)] | |
| for i in range(1, 13): | |
| states.append((states[i-1][0] + (states[i-1][0] * (eps1-gam1*states[i-1][1])) * dt, states[i-1][1] - states[i-1][1] * (eps2-gam2*states[i-1][0])) * dt) | |
| return states[-1] |