Michael A. Alcorn airalcorn2

Brute-forcing my way through life.

airalcorn2 / atan2.py

Created September 14, 2018 20:06

Testing the (cos(theta), sin(theta)) encoding of an angle discussed here --> https://stats.stackexchange.com/questions/218407/encoding-angle-data-for-neural-networ.

	import matplotlib.pyplot as plt
	import numpy as np
	import torch
	import torch.utils.data
	import torch.nn as nn

	device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")


	class Net(nn.Module):

airalcorn2 / tensorflow_vs_pytorch_scatter_nd.py

Created August 14, 2018 16:03

Replicating TensorFlow's scatter_nd function in PyTorch.

	# See: https://www.tensorflow.org/api_docs/python/tf/scatter_nd.

	# TensorFlow
	import tensorflow as tf

	sess = tf.InteractiveSession()
	indices = tf.constant([[0, 1], [2, 3]])
	updates = tf.constant([[5, 5, 5, 5],
	[6, 6, 6, 6]])
	shape = tf.constant([4, 4, 4])

airalcorn2 / rtw_fatalities.py

Created July 3, 2018 16:36

Plotting fatality rates for states that had a change in right-to-work laws from this paper --> https://oem.bmj.com/content/early/2018/06/13/oemed-2017-104747.

	# Michael A. Alcorn

	import matplotlib.pyplot as plt
	import pandas as pd

	df = pd.read_csv("repdata.tab", sep = "\t", index_col = False)
	no_law = set(df[df["RTW"] == 0]["state"])
	yes_law = set(df[df["RTW"] == 1]["state"])
	states_that_changed = list(no_law & yes_law)
	states_that_changed.sort()

airalcorn2 / attention_grok.py

Last active March 21, 2020 14:10

A barebones PyTorch implementation of a seq2seq model with attention.

	# Michael A. Alcorn

	import torch
	import torch.autograd as autograd
	import torch.nn as nn


	def create_lstm(params):
	"""Create a LSTM from a dictionary of parameters.

airalcorn2 / species_count.R

Created April 13, 2018 19:28

Code to generate the plot seen here --> https://www.facebook.com/photo.php?fbid=10103790197221251.

	# Michael A. Alcorn

	library(ggplot2)
	library(plyr)

	data <- read.csv("species.csv")
	data$Date <- as.Date(data$Date, "%Y-%m-%d")

	ggplot(data, aes(Date, Total)) + geom_line() + scale_x_date(date_breaks = "1 month")

airalcorn2 / clade_counts.py

Created April 13, 2018 18:54

A script for counting the total species in each clade in a Newick formatted file.

	# Michael A. Alcorn

	from Bio import Phylo

	tree = Phylo.read("species_newick.txt", "newick")
	clade_counts = {}
	for clade in tree.find_clades():
	if not clade.is_terminal():
	clade_counts[str(clade)] = clade.count_terminals()

airalcorn2 / species_plots.py

Last active April 13, 2018 17:40

Python code to generate the plots used to make this GIF --> http://imgur.com/BjgN6UA.

	# Michael A. Alcorn

	import matplotlib
	matplotlib.use("Agg")
	import matplotlib.pyplot as plt
	import multiprocessing
	import numpy as np
	import pandas as pd
	import seaborn as sns
	import time

airalcorn2 / not_space_filling.py

Created May 9, 2017 21:55

Investigating http://koaning.io/homogenous-autoencoder-ui.html.

	import matplotlib.pyplot as plt
	import numpy as np
	import pandas as pd
	import seaborn as sns

	from keras import backend
	from keras.layers import Input, Dense
	from keras.models import Model

	# Autoencoder.

airalcorn2 / pokemon_filters.py

Last active March 21, 2017 00:20

Visualize filters from: https://jgeekstudies.org/2017/03/12/who-is-that-neural-network/.

	kernels = model.layers[0].get_weights()[0].T

	(rows, cols) = (8, 4)
	plt.figure(figsize = (16, 24))
	gs = gridspec.GridSpec(rows, cols, wspace = 0.1, hspace = 0.1)

	for i in range(kernels.shape[0]):
	row = i // cols
	col = i % cols
	ax = plt.subplot(gs[row, col])

airalcorn2 / difference_of_biased_estimators.py

Last active December 22, 2016 00:12

	# Michael A. Alcorn ([email protected])

	import matplotlib.pyplot as plt
	import pandas as pd
	import seaborn as sns
	import random

	fixed_rates = {"p_1": 0.3, "p_2": 0.5}

	find_rates = {"biased": {"fixed": 0.7, "non_fixed": 0.9},