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### Library
library(limSolve)

### Functions

# Make the matrix A from the puzzle data table
training.to.matrix.A <- function(train)
{
    output<-matrix(nrow=nrow(train),ncol=10)
    for (i in 1:nrow(train))

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#!/usr/bin/env bash

for a in {1..3}; do perf -ROC < kddb.t_eval.subsample.txt &gt; /dev/null; done
time for a in {1..10}; do perf -ROC < kddb.t_eval.subsample.txt; done

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# http://ryankuhn.net/blog/How-To-Use-Plotly-With-Jekyll
devtools::install_github("brendan-R/brocks")

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library(data.table)
library(ggplot2)

thm <- theme_bw()  
thm <- thm + theme(axis.line = element_line(colour = "black"),
                   panel.grid.major = element_blank(),
                   panel.grid.minor = element_blank(),
                   panel.border = element_blank(),
                   panel.background = element_blank()) 

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data
*~

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#!/usr/bin/env bash

mkdir -p data
cd data
wget https://archive.ics.uci.edu/ml/machine-learning-databases/00380/YouTube-Spam-Collection-v1.zip
unzip YouTube-Spam-Collection-v1.zip

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# Make a directory you can blow away in full later
mkdir -p aicamp_demo && cd aicamp_demo

# Clone and pin the fwhigh/metaflow-helper git repo
git clone https://github.com/fwhigh/metaflow-helper.git
cd metaflow-helper
git checkout v0.0.1

# Set up and active a virtual environment
# Install the metaflow-helper package in editable model and dependencies

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from metaflow import Flow


def get_generate_data_run():
    return Flow('GenerateData').latest_successful_run


def get_train_run(tags: list):
    print(f'Retrieving run data with tags {tags}')
    run_list = [