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ProjectEstimate.R
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147 lines (102 loc) · 4.96 KB
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#Deploy command rsconnect::deployApp("DeployShinyApp")
library(shiny)
library(tidyverse)
priors <- dplyr::tibble(workItem = c(1, 2, 3, 4, 5), estmimates = c(20,40, 80, 90, 120), price = c(5000, 7000, 10500, 12000, 15000))
n = 5
#LearningRate = 0.0001
#calcErrorsAndGradients <- function(houseData, weight, bias) {
# houseData_Errors_Gradients <- houseData %>%
# mutate(prediction = weight * squareSpace + bias) %>%
# mutate(error = prediction - price) %>%
# mutate(weightGradient = error * squareSpace) %>%
# mutate(gradientBias = error) %>%
# mutate(errrorSquared = error ^ 2)
#
# return (houseData_Errors_Gradients)
#}
#calcIncrementWeight <- function(houseData_Errors_Gradients, weight) {
#
# increment <- houseData_Errors_Gradients %>%
# summarize(incrementWeight = -sum(houseData_Errors_Gradients$weightGradient) / n * LearningRate)
#
# return(increment$incrementWeight)
#}
#calcIncrementBias <- function(houseData_Errors_Gradients, bias) {
#
# increment <- houseData_Errors_Gradients %>%
# summarize(incrementBias = -sum(houseData_Errors_Gradients$gradientBias) / n * LearningRate * 1000)
#
# return(increment$incrementBias)
#}
#houseData_Errors_Gradients <- calcErrorsAndGradients(houseData, 3, 40)
ui <- fluidPage(
sidebarLayout(
sidebarPanel(
# --- Update model ----
tags$div( # headline
HTML('<h4 style="color:#000;margin-left:0px;">Träna modell</h4>')
),
#tags$div( # partition
# HTML('<hr style="height:1px; border:none; color:#000; background-color:#000;">')
#),
htmlOutput(outputId = "updateModelText"),
actionButton("train_button", "Uppdatera modell", width = 150)
),
mainPanel(
# --- Visualize model training
htmlOutput(outputId = "modelText"),
plotOutput(outputId = "plotByHouseNo"), #, width = "100%", height = "200px"),
htmlOutput(outputId = "roundText"),
actionButton("reset_button", "Återställ modell", width = 150)
)
)
)
server <- function(input, output, session) {
# --- reactive expressions
modelData <- reactiveValues(increment = 0, houseData_Errors_Gradients = houseData, weight = 40, bias = 2000) # Defining & initializing the reactiveValues object
incrementWieght <- reactive({
calcIncrementWeight(modelData$houseData_Errors_Gradients, modelData$weight)
})
incrementBias <- reactive({
calcIncrementBias(modelData$houseData_Errors_Gradients, modelData$weight)
})
# --- Visualize model
output$modelText <- renderUI({
modelData$houseData_Errors_Gradients <- calcErrorsAndGradients(houseData, modelData$weight, modelData$bias)
text0 <- paste("Modell:")
text1 <- paste("Pris = k * boyta + m")
text2 <- paste("k = ", format((modelData$weight), digits = 2), "",' ', "m = ", format((modelData$bias), digits = 0))
HTML("<font face='Courier New' style = font-size:16px>","<font color='#00BFFF'>", "<b>", text0,"</font>", "</b>", text1, "<br>", text2, "<br>", "<font color='#27e833'>", "<b>", "Träningsdata", "</b>", "</font>")
})
output$plotByHouseNo <-renderPlot({
t1 <- ggplot(modelData$houseData_Errors_Gradients, aes(x = houseNumber))
t2 <- t1 + geom_point(color='#00BFFF', aes(y = prediction, size = 30))
t3 <- t2 + geom_point(color = '#27e833', aes(y = price, size = 30)) + xlim(1,5) + ylim(1000, 16000) + theme_bw()
t4 <- t3 + labs( x = "Hus", y = "Pris")
t4 + theme(legend.position = "none", text = element_text(size=16))
})
output$roundText <- renderUI({
text <- paste("<font face='Courier New' style = font-size:16px>", "Träningsrunda: ", modelData$increment)
HTML(text)
})
#--- Update model ---
output$updateModelText <- renderUI({
modelData$houseData_Errors_Gradients <- calcErrorsAndGradients(houseData, modelData$weight, modelData$bias)
weightText <- paste("Justera k med:",format(incrementWieght(), digits = 2)) #, "<br>")#, "Nytt värde för k", format(incrementWieght(), digits = 2), "+", format(modelData$weight, digits = 2), "<b>", "=", format(modelData$weight +incrementWieght(), digits = 2), "</b>")
biasText <- paste("Justera m med:",format(incrementBias(), digits = 2),"<br>")#, "Nytt värde för m", format(incrementBias(), digits = 2), "+", format(modelData$bias, digits = 2), "=", "<b>", format(modelData$bias +incrementBias(), digits = 2), "</b>")
HTML("<font face='Courier New' style = font-size:16px>", weightText, "<br>", biasText )
})
observeEvent(input$train_button, {
modelData$houseData_Errors_Gradients <- calcErrorsAndGradients(houseData, modelData$weight, modelData$bias)
modelData$weight = modelData$weight + incrementWieght()
modelData$bias = modelData$bias + incrementBias()
modelData$increment = modelData$increment + 1
})
observeEvent(input$reset_button, {
modelData$bias = 2000
modelData$weight = 40
modelData$increment = 0
})
}
# Create a Shiny app object
shinyApp(ui = ui, server = server)