add heatmap for visualization
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@@ -8,6 +8,7 @@ library(ggplot2)
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library(tidyr)
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library(tidyr)
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library(dplyr)
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library(dplyr)
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# Line plots -------------------------------------------------------------------
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# Create a grid of a‑values
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# Create a grid of a‑values
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a_grid <- seq(-20, 20, length.out = 200)
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a_grid <- seq(-20, 20, length.out = 200)
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@@ -43,3 +44,39 @@ ggplot(df_entries, aes(x = a, y = value, colour = entry, linetype = entry)) +
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colour = "Entry"
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colour = "Entry"
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) +
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) +
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theme_minimal()
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theme_minimal()
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# Heat map for a single larger matrix ------------------------------------------
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# Choose a value of a
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a0 <- -10
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M0 <- compute_matrix(seed=1L,
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a= a0,
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n = 50,
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K = 50,
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sample_X_fn = function(n) {matrix(rnorm(n), ncol = 1L)},
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fv = function(x) {dnorm(x, mean=0, sd=1)},
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Fv = function(x) {pnorm(x, mean=0, sd=1)},
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guard = 1e-12)
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# Convert to a tidy data frame for ggplot
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df_heat <- as.data.frame(M0) %>%
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mutate(row = row_number()) %>%
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pivot_longer(-row, names_to = "col", values_to = "value") %>%
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mutate(
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col = as.integer(gsub("V", "", col)), # turn "V1","V2" into 1,2
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row = factor(row, levels = rev(unique(row))) # reverse y‑axis for proper orientation
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)
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ggplot(df_heat, aes(x = col, y = row, fill = value)) +
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geom_tile(colour = "grey30", size = 0.5) +
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scale_fill_gradient2(
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low = "steelblue", mid = "white", high = "tomato",
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midpoint = 0.5, limits = c(min(df_heat$value), max(df_heat$value))
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) +
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labs(
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title = sprintf("Heat‑map of the matrix at a = %.2f", a0),
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x = "Column", y = "Row", fill = "Value"
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) +
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theme_minimal() +
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theme(axis.text = element_blank(),
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axis.ticks = element_blank())
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