From 5dec1e78ffa7944add0153f4c181182a1d293aa1 Mon Sep 17 00:00:00 2001
From: Jonathan Berrisch <Jonathan@Berrisch.biz>
Date: Sat, 24 May 2025 00:06:24 +0200
Subject: [PATCH] Make curves smoooooooth

---
 25_07_phd_defense/app.qmd                  |  2 +-
 25_07_phd_defense/assets/make_knots_data.R | 38 ++++++++++++++--------
 2 files changed, 25 insertions(+), 15 deletions(-)

diff --git a/25_07_phd_defense/app.qmd b/25_07_phd_defense/app.qmd
index 5ae4e20..9d893fa 100644
--- a/25_07_phd_defense/app.qmd
+++ b/25_07_phd_defense/app.qmd
@@ -206,7 +206,7 @@ chart = {
    const line = d3.line()
      .x(d => x(d.x))
      .y(d => y(d.y))
-     .curve(d3.curveLinear);
+     .curve(d3.curveBasis);
    
    // Group to contain the basis function lines
    const linesGroup = g.append("g")
diff --git a/25_07_phd_defense/assets/make_knots_data.R b/25_07_phd_defense/assets/make_knots_data.R
index caf24df..86c8031 100644
--- a/25_07_phd_defense/assets/make_knots_data.R
+++ b/25_07_phd_defense/assets/make_knots_data.R
@@ -8,7 +8,7 @@ library(readr)
 # Creating faceted plots for different knot values and mu values
 # Create a function to generate the data for a given number of knots and mu value
 generate_basis_data <- function(num_knots, mu_value, sig_value, nonc_value, tailw_value, deg_value) {
-  grid <- seq(from = 0.01, to = 0.99, length.out = 99)
+  grid <- seq(from = 0.01, to = 0.99, length.out = 50)
   # Use provided degree
   B <- profoc:::make_basis_matrix(grid,
     profoc::make_knots(
@@ -21,9 +21,9 @@ generate_basis_data <- function(num_knots, mu_value, sig_value, nonc_value, tail
     ),
     deg = deg_value
   )
-  B_mat <- round(as.matrix(B), 2)
+  B_mat <- round(as.matrix(B), 3)
   df <- as.data.frame(B_mat)
-  df$x <- round(grid, 2)
+  df$x <- grid
   df_long <- pivot_longer(df,
     cols = -x,
     names_to = "b",
@@ -39,7 +39,6 @@ generate_basis_data <- function(num_knots, mu_value, sig_value, nonc_value, tail
 }
 
 # Generate data for each combination of knot, mu, sig, nonc, tailw, and deg
-knot_values <- c(10)
 mu_values <- seq(0.1, 0.9, by = 0.2)
 sig_values <- 2^(-2:2)
 nonc_values <- c(-4, -2, 0, 2, 4)
@@ -50,16 +49,14 @@ all_data <- list()
 counter <- 1
 
 # Nested loops to cover all parameter combinations
-for (k in knot_values) {
-  print(paste("Processing knots:", k))
-  for (m in mu_values) {
-    print(paste("Processing mu:", m))
-    for (s in sig_values) {
-      for (nc in nonc_values) {
-        for (tw in tailw_values) {
-          all_data[[counter]] <- generate_basis_data(5, m, s, nc, tw, 2)
-          counter <- counter + 1
-        }
+print(paste("Processing knots:", k))
+for (m in mu_values) {
+  print(paste("Processing mu:", m))
+  for (s in sig_values) {
+    for (nc in nonc_values) {
+      for (tw in tailw_values) {
+        all_data[[counter]] <- generate_basis_data(5, m, s, nc, tw, 2)
+        counter <- counter + 1
       }
     }
   }
@@ -71,3 +68,16 @@ all_data <- bind_rows(all_data)
 write_csv(all_data, "25_07_phd_defense/assets/mcrps_learning/basis_functions.csv")
 
 # %%
+all_data %>%
+  filter(mu == 0.1) %>%
+  filter(sig == 0.25) %>%
+  filter(nonc == -4) %>%
+  filter(tailw == 0.25) %>%
+  ggplot(aes(x = x, y = y, col = b)) +
+  geom_line(size = 2) +
+  labs(
+    title = "Basis Functions for Different Knot Values",
+    x = "x",
+    y = "y"
+  ) +
+  theme_minimal()