Paul Adamson padamson

## PyQuante Testing.ipynb

      
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                padamson
                / PyQuante Testing.ipynb
            
            
              Created
              July 8, 2021 00:53
                — forked from rpmuller/PyQuante Testing.ipynb
            
              
                Notes, timings, and additional tests for the development of pyquante2 by comparing to to PyQuante (1.6.??).
              
          
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## acronymTable.R
output$acronyms <- DT::renderDataTable({
    firstDate <- input$timelineGroups_window[1]
    lastDate <- input$timelineGroups_window[2]
    data <- input$timelineGroups_data %>%
      select(label,start,end) %>%
      filter((is.na(end) & (start > firstDate & start < lastDate)) |
               (!is.na(end) & (start < lastDate & end > firstDate)))
    acronyms %>%
      filter(grepl(
        paste(unlist(str_split(data$label,pattern=" ")),collapse="|"),

## rwml-R_listing4_3.R
# Returns the false-positive and true-positive rates at nPoints thresholds for
# the given true and predicted labels
# trueLabels: 0=FALSE; 1=TRUE
rocCurve <- function(trueLabels, predictedProbs, nPoints=100, posClass=1){
  # Allocates the threshold and ROC lists
  thr <- seq(0,1,length=nPoints)
  tpr <- numeric(nPoints)
  fpr <- numeric(nPoints)

  # Precalculates values for the positive and negative cases, used in the loop

## rwml-R_figure4_20.R
library(ROCR)
library(dplyr)

mnistResultsDF <- data.frame(actual = mnistTest$label,
                             fit = mnist.kknn$fit,
                             as.data.frame(mnist.kknn$prob))

plotROCs <- function(df, digitList) {
  firstPlot <- TRUE
  legendList <- NULL

## rwml-R_figure4_19.R
library(caret)
library(kknn)
library(RColorBrewer)
library(cowplot)

mnist <- read.csv("data/mnist_small.csv",
                  colClasses = c(label = "factor"))

trainIndex <- createDataPartition(mnist$label, p = .8,
                                  list = FALSE,

## rwml-R_figure3_10.R
mnist <- read.csv("data/mnist_small.csv",
                  colClasses = c(label = "factor"))
displayMnistSamples <- function(x) {
  for(i in x){
  y = as.matrix(mnist[i, 2:785])
  dim(y) = c(28, 28)
  image( y[,nrow(y):1], axes = FALSE, col = gray(0:255 / 255))
  text( 0.2, 0, mnist[i,1], cex = 3, col = 2, pos = c(3,4))
  }
}

## rwml-R_figure2_12.R
library(vcd)
mosaic(
  ~ Sex + Survived,
  data = titanic,
  main = "Mosaic plot for Titanic data: Gender vs. survival",
  shade = TRUE,
  split_vertical = TRUE,
  labeling_args = list(
    set_varnames = c(
      Survived = "Survived?")

## installAllRPackages.bash
#!/bin/bash
#Install any packages required by an R file that aren't already installed

if [ $# -ne 1 ]; then
    echo $0: usage: installAllRPackages.bash file
    exit 1
fi

file=$1

## multi_smooth_ggpairs.R
require(datasets)
data("swiss")
require(GGally)
require(ggplot2)

my_fn <- function(data, mapping, ...){
  p <- ggplot(data = data, mapping = mapping) +
    geom_point() +
    geom_smooth(method=loess, fill="red", color="red", ...) +
    geom_smooth(method=lm, fill="blue", color="blue", ...)
	output$acronyms <- DT::renderDataTable({
	firstDate <- input$timelineGroups_window[1]
	lastDate <- input$timelineGroups_window[2]
	data <- input$timelineGroups_data %>%
	select(label,start,end) %>%
	filter((is.na(end) & (start > firstDate & start < lastDate)) \|
	(!is.na(end) & (start < lastDate & end > firstDate)))
	acronyms %>%
	filter(grepl(
	paste(unlist(str_split(data$label,pattern=" ")),collapse="\|"),
	# Returns the false-positive and true-positive rates at nPoints thresholds for
	# the given true and predicted labels
	# trueLabels: 0=FALSE; 1=TRUE
	rocCurve <- function(trueLabels, predictedProbs, nPoints=100, posClass=1){
	# Allocates the threshold and ROC lists
	thr <- seq(0,1,length=nPoints)
	tpr <- numeric(nPoints)
	fpr <- numeric(nPoints)

	# Precalculates values for the positive and negative cases, used in the loop
	library(ROCR)
	library(dplyr)

	mnistResultsDF <- data.frame(actual = mnistTest$label,
	fit = mnist.kknn$fit,
	as.data.frame(mnist.kknn$prob))

	plotROCs <- function(df, digitList) {
	firstPlot <- TRUE
	legendList <- NULL
	library(caret)
	library(kknn)
	library(RColorBrewer)
	library(cowplot)

	mnist <- read.csv("data/mnist_small.csv",
	colClasses = c(label = "factor"))

	trainIndex <- createDataPartition(mnist$label, p = .8,
	list = FALSE,
	mnist <- read.csv("data/mnist_small.csv",
	colClasses = c(label = "factor"))
	displayMnistSamples <- function(x) {
	for(i in x){
	y = as.matrix(mnist[i, 2:785])
	dim(y) = c(28, 28)
	image( y[,nrow(y):1], axes = FALSE, col = gray(0:255 / 255))
	text( 0.2, 0, mnist[i,1], cex = 3, col = 2, pos = c(3,4))
	}
	}
	library(vcd)
	mosaic(
	~ Sex + Survived,
	data = titanic,
	main = "Mosaic plot for Titanic data: Gender vs. survival",
	shade = TRUE,
	split_vertical = TRUE,
	labeling_args = list(
	set_varnames = c(
	Survived = "Survived?")
	#!/bin/bash
	#Install any packages required by an R file that aren't already installed

	if [ $# -ne 1 ]; then
	echo $0: usage: installAllRPackages.bash file
	exit 1
	fi

	file=$1
	require(datasets)
	data("swiss")
	require(GGally)
	require(ggplot2)

	my_fn <- function(data, mapping, ...){
	p <- ggplot(data = data, mapping = mapping) +
	geom_point() +
	geom_smooth(method=loess, fill="red", color="red", ...) +
	geom_smooth(method=lm, fill="blue", color="blue", ...)