Michael Griffiths msjgriffiths
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| using Flux | |
| using Random | |
| mutable struct SRUCell{M, V} | |
| """ | |
| W, Wⱼ, Wᵣ are the parameter matrices, and | |
| vⱼ, vᵣ, bⱼ, bᵣ are the parameter vectors to be | |
| learnt during training. | |
| Note we change f -> j in paper for notational convenience | |
| (there is no unicode subscript for f). |
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| library(tidyverse) | |
| library(patchwork) | |
| # CDC Covid-19 Excess Deaths | |
| # https://data.cdc.gov/NCHS/Excess-Deaths-Associated-with-COVID-19/xkkf-xrst/ | |
| df <- read_csv("https://data.cdc.gov/resource/xkkf-xrst.csv") | |
| df %>% | |
| filter(Outcome == "All causes", Type == "Unweighted") -> | |
| df |
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| #' Lift to AUC | |
| #' | |
| #' @param percentiles | |
| #' @param conversion_rate | |
| #' | |
| #' @return list | |
| #' | |
| #' @description | |
| #' | |
| #' This function takes in percentiles and the conversion rate, |
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| using Flux | |
| # Input data, of (features, examples, time_step) | |
| # We ignore masking | |
| x = randn(Float32, 32, 100, 12) | |
| hidden_states = Chain( | |
| # A 4-layer stacked LSTM | |
| LSTM(32, 64), | |
| LSTM(64, 64), |
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| library(tidyverse) | |
| library(patchwork) | |
| crashes <- read_csv("~/Downloads/Motor_Vehicle_Collisions_-_Crashes.csv") | |
| vehicles <- read_csv("~/Downloads/Motor_Vehicle_Collisions_-_Vehicles.csv") | |
| vehicles %>% | |
| group_by(COLLISION_ID) %>% | |
| summarise( | |
| out_of_state = sum(STATE_REGISTRATION != "NY", na.rm = TRUE), |
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| library(tidyverse) | |
| df <- read_csv("https://raw.githubusercontent.com/nychealth/coronavirus-data/master/latest/now-cases-by-day.csv") | |
| df %>% | |
| mutate(date = as.Date(date_of_interest, "%m/%d/%Y")) ->df | |
| df %>% | |
| ggplot(aes(date, BK_CASE_COUNT)) + | |
| geom_point() |
msjgriffiths
/ imdb_quick_logit.py
Created
March 7, 2019 04:11
Quick check of scores on baseline logit
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| from tensorflow.keras.datasets import imdb | |
| from sklearn.feature_extraction.text import TfidfVectorizer | |
| from sklearn.linear_model import LogisticRegression | |
| from sklearn.metrics import roc_auc_score | |
| (x_train, y_train), (x_test, y_test) = imdb.load_data() | |
| w2i = imdb.get_word_index() | |
| i2w = {v: k for k, v in w2i.items()} | |
| review_train = [" ".join([i2w[i] if i in i2w else '' for i in x]) for x in x_train] |
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| library(tidyverse) | |
| library(alfred) | |
| series <- tribble( | |
| ~Series, ~Name, | |
| "CEU3000000001", "All Employees - Manufacturing", | |
| "JTU3000HIR", "Hires", | |
| "JTU3000LDR", "Layoffs", | |
| "JTU3000QUR", "Quits" | |
| ) |
msjgriffiths
/ chart.R
Last active
May 22, 2016 08:36
Partial Replication of Climate Radar Chart (http://www.climate-lab-book.ac.uk/2016/spiralling-global-temperatures/)
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| library(needs) | |
| # gganimate: devtools::install_github("dgrtwo/gganimate") | |
| needs(ggplot2, gganimate, dplyr, viridis) | |
| # HadCRUT4.4 | |
| file_url = "http://www.metoffice.gov.uk/hadobs/hadcrut4/data/current/time_series/HadCRUT.4.4.0.0.monthly_ns_avg.txt" | |
| if(!file.exists("data.txt")) download.file(file_url, "data.txt") | |
| read.table("data.txt", "", header = F, stringsAsFactors = F) %>% | |
| mutate(Date = as.Date(sprintf("%s/01", V1), "%Y/%m/%d")) %>% |