---
title: "Hyperparameter Tuning and Sensitivity Analyses"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{tuning}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup}
library(discent)
```


hyperparameter tuning of learning rate, lambda  
  add some text about adam optimizer and more conservative vs aggressive inputs
sensitivity analysis for starting parameter guesses 


```{r, eval=FALSE}
library(ggplot2)

# volcano is a matrix; convert to data frame
volcano_df <- as.data.frame(as.table(volcano))
names(volcano_df) <- c("x", "y", "z")

ggplot(volcano_df, aes(x, y, z = z)) +
  geom_contour_filled(bins = 15) +
  labs(fill = "Elevation") +
  theme_minimal()

# need to convert loss from final fis and M at various different levels
# and capture that landscape
# from your expand.grid start params 


```