Michael A. Alcorn airalcorn2

## gdp.csv

          
            Rank
            Country
            GDP (millions of Int$)

            
              World
              119,097,427

            
              1
              China
              21,269,017

            
              —
              European Union[n 1]
              19,973,035

            
              2
              United States
              18,561,934

            
              3
              India
              8,720,514

            
              4
              Japan
              4,931,877

            
              5
              Germany
              3,979,083

            
              6
              Russia
              3,745,084

            
              7
              Brazil
              3,134,892

## d_stat_experiment.py
# Michael A. Alcorn (airalcorn2@gmail.com)

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

import numpy as np
import statsmodels.api as sm # recommended import according to the docs
import matplotlib.pyplot as plt

## difference_of_biased_estimators.py
# Michael A. Alcorn (airalcorn2@gmail.com)

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},

## pokemon_filters.py
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])

## not_space_filling.py
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.

## species_plots.py
# 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

## clade_counts.py
# 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()

## species_count.R
# 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")

## attention_grok.py
# 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.

## rtw_fatalities.py
# 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()
Rank	Country	GDP (millions of Int$)
	World	119,097,427
1	China	21,269,017
—	European Union[n 1]	19,973,035
2	United States	18,561,934
3	India	8,720,514
4	Japan	4,931,877
5	Germany	3,979,083
6	Russia	3,745,084
7	Brazil	3,134,892
	# Michael A. Alcorn (airalcorn2@gmail.com)

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

	import numpy as np
	import statsmodels.api as sm # recommended import according to the docs
	import matplotlib.pyplot as plt
	# Michael A. Alcorn (airalcorn2@gmail.com)

	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},
	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])
	# 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
	# 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()
	# 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")
	# 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.