g-leech

library(matlib)

gamma <- 2
rho11 <- 1.15
rho22 <- 0.05
mu1 <- 0.3
r <- 0.05
delta <- 0.05

x <- rnorm(1000000)

g-leech

https://twitter.com/g_leech_/status/1728731355029393447?t=VN50RyPqwWFfcC63tMP_ng&s=19
https://twitter.com/g_leech_/status/1723630099507871780?t=YFYUi0eNkkLXOzzmbMtoGA&s=19
https://twitter.com/g_leech_/status/1623044128190697483
https://twitter.com/g_leech_/status/1639725932448456705
https://twitter.com/g_leech_/status/1630276985963556865
https://twitter.com/g_leech_/status/1622321776595189765
https://twitter.com/g_leech_/status/1668960035068694533
https://twitter.com/g_leech_/status/1715654603973312949
https://twitter.com/g_leech_/status/1713930232632135986
https://twitter.com/g_leech_/status/1711441986878644597

g-leech

import numpy as np
import gjp
# https://github.com/niplav/iqisa/blob/master/iqisa.py
import iqisa as iqs
import matplotlib.pyplot as plt

df=gjp.load_markets()

df["isCorrect"] = (df.outcome == df.answer_option).astype(float)
fig = plt.figure(figsize=(9, 9))

g-leech

g-leech

g-leech

# You can get your own copy of the MCMC trace: https://github.com/g-leech/masks_v_mandates#run
# but I've included the quantiles in this script for reproducibility. 

import numpy as np
sns.set_style("whitegrid")

def exp_reduction(a, x):
    reductions = 1 - np.exp((-1.0) * a * x)
    return reductions.mean()

g-leech

import numpy as np

# impossible 0
# v unlik 10
# unlik 25
# lik 75
# vv likely 99.5
preds = [
	[0, 1],
	[0.75, 1],

g-leech

#!/usr/bin/env python
# coding: utf-8

# ## Excess COVID-19 mortality vs reported deaths over time
#  
# split bar?
#  
# y-axis: deaths 
# x-axis: time
# 

g-leech

def handle_utterance_str(text) :
    if text[0] != "'" and text[0] != '"' :
        text = f'"{text}"'
    
    text = text.replace('"', '\"')
    text = text.replace("'", '\"')
    return "handle_utterance(1,{},Output)".format(text)

def escape_and_call_prolexa(text) :
    libPrefix = "prolexa:"

g-leech

#%tensorflow_version 2.x
import pandas as pd
import numpy as np
import re
from nltk import word_tokenize          
from nltk.stem import WordNetLemmatizer 
from scipy.sparse import hstack


from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer