julia project setup

Author: lambdaofgod

JuliaExamples/Manifest.toml

@@ -19,9 +19,14 @@ Manopt = "0fc0a36d-df90-57f3-8f93-d78a9fc72bb5"
 MappedArrays = "dbb5928d-eab1-5f90-85c2-b9b0edb7c900"
 MultivariateStats = "6f286f6a-111f-5878-ab1e-185364afe411"
 OpenSSL = "4d8831e6-92b7-49fb-bdf8-b643e874388c"
+PersistenceDiagrams = "90b4794c-894b-4756-a0f8-5efeb5ddf7ae"
 PlotThemes = "ccf2f8ad-2431-5c83-bf29-c5338b663b6a"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 Pluto = "c3e4b0f8-55cb-11ea-2926-15256bba5781"
+ProgressLogging = "33c8b6b6-d38a-422a-b730-caa89a2f386c"
 Ripserer = "aa79e827-bd0b-42a8-9f10-2b302677a641"
 SimpleWeightedGraphs = "47aef6b3-ad0c-573a-a1e2-d07658019622"
 StatisticalGraphics = "8341d8ab-e5ae-4f11-aa21-e11c5cc71259"
+StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
+UMAP = "c4f8c510-2410-5be4-91d7-4fbaeb39457e"
+VegaLite = "112f6efa-9a02-5b7d-90c0-432ed331239a"

JuliaExamples/Project.toml

@@ -0,0 +1,21 @@
+[deps]
+ArgParse = "c7e460c6-2fb9-53a9-8c5b-16f535851c63"
+ColorSchemes = "35d6a980-a343-548e-a6ea-1d62b119f2f4"
+DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+FileIO = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549"
+GLMakie = "e9467ef8-e4e7-5192-8a1a-b1aee30e663a"
+ImageIO = "82e4d734-157c-48bb-816b-45c225c6df19"
+Images = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
+InMemoryDatasets = "5c01b14b-ab03-46ff-b164-14c663efdd9f"
+JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
+LightGraphs = "093fc24a-ae57-5d10-9952-331d41423f4d"
+ManifoldLearning = "06eb3307-b2af-5a2a-abea-d33192699d32"
+Manopt = "0fc0a36d-df90-57f3-8f93-d78a9fc72bb5"
+MappedArrays = "dbb5928d-eab1-5f90-85c2-b9b0edb7c900"
+MultivariateStats = "6f286f6a-111f-5878-ab1e-185364afe411"
+OpenSSL = "4d8831e6-92b7-49fb-bdf8-b643e874388c"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+Pluto = "c3e4b0f8-55cb-11ea-2926-15256bba5781"
+Ripserer = "aa79e827-bd0b-42a8-9f10-2b302677a641"
+SimpleWeightedGraphs = "47aef6b3-ad0c-573a-a1e2-d07658019622"
+StatisticalGraphics = "8341d8ab-e5ae-4f11-aa21-e11c5cc71259"

JuliaExamples/Project.toml.full

@@ -14,11 +14,11 @@ end
 
 function load_dataset(root)
     files = map(x->joinpath(root, x), readdir(root))
-    return mappedarray(p -> load_image(p), files)
+    mappedarray(p -> load_image(p), files)
 end
 
 function images_dataset_to_vectors_dataset(img_dataset, img_size=(32,32))
 	img_flat_size = img_size[1] * img_size[2]
-	resized_img_coil_dataset = map(img -> imresize(img, img_size), img_dataset)
-	map(img -> real.(reshape(float(img), img_flat_size)), resized_img_coil_dataset)
+	resized_img_coil_dataset = mappedarray(img -> imresize(img, img_size), img_dataset)
+	mappedarray(img -> real.(reshape(float(img), img_flat_size)), resized_img_coil_dataset)
 end

JuliaExamples/src/image_data.jl

@@ -1,5 +1,5 @@
 ### A Pluto.jl notebook ###
-# v0.19.29
+# v0.19.9
 
 using Markdown
 using InteractiveUtils
@@ -35,6 +35,12 @@ md"""
 ## Setup
 """
 
+# ╔═╡ 5f72520b-017a-4428-836a-b4665e97fa43
+2 + 2
+
+# ╔═╡ 943ace02-4e25-4474-95a4-500c796daf3e
+
+
 # ╔═╡ 59a31fc2-a9d1-4857-b18e-a2591f4327b0
 md"""
 ## Imports
@@ -128,6 +134,17 @@ struct ManifoldLearner
 	reduced_df :: Union{DataFrame, Nothing}
 end
 
+# ╔═╡ d6dda024-56c1-4614-aa7d-ce721e09a0f7
+function fit_manifold_learner(args :: ManifoldLearnerArgs, data)
+	(reducer, reduced_df) = fit_manifold_learner_impl(args.method, args.dim, args.k, data)
+	ManifoldLearner(
+		reducer,
+		args,
+		data,
+		reduced_df
+	)
+end
+
 # ╔═╡ 2557d853-06fb-4ffd-8310-f9fc6abe3f47
 begin 
 	function make_reduced_dataframe(X_reduced)
@@ -156,17 +173,6 @@ function fit_manifold_learner_impl(method :: ManifoldLearningType, dim, k, data)
 	(reducer, make_reduced_dataframe(ManifoldLearning.predict(reducer)'))
 end
 
-# ╔═╡ d6dda024-56c1-4614-aa7d-ce721e09a0f7
-function fit_manifold_learner(args :: ManifoldLearnerArgs, data)
-	(reducer, reduced_df) = fit_manifold_learner_impl(args.method, args.dim, args.k, data)
-	ManifoldLearner(
-		reducer,
-		args,
-		data,
-		reduced_df
-	)
-end
-
 # ╔═╡ c5b61420-7cfe-4091-82eb-d270c690456a
 function add_classes(df, classes)
 	df[!, "class"] = classes
@@ -309,6 +315,8 @@ end
 # ╔═╡ Cell order:
 # ╠═c7fdaf8f-5d97-4a71-b478-002d60b9e741
 # ╠═e3e020ac-6aaf-11ee-0d9a-85e0beb5ca6e
+# ╠═5f72520b-017a-4428-836a-b4665e97fa43
+# ╠═943ace02-4e25-4474-95a4-500c796daf3e
 # ╠═59a31fc2-a9d1-4857-b18e-a2591f4327b0
 # ╠═70d7489f-45fc-4677-9fbf-3a6916130530
 # ╠═94c2fb01-09db-4a90-b824-28cac32619cf

JuliaExamples/src/manifold_learning.jl

@@ -1,5 +1,5 @@
 ### A Pluto.jl notebook ###
-# v0.19.29
+# v0.19.9
 
 using Markdown
 using InteractiveUtils
@@ -16,9 +16,12 @@ end
 begin 
 	using Ripserer
 	using Plots, PlotThemes
-
+	using Random
 	using Images, FileIO
+	using LinearAlgebra
 	theme(:juno)
+
+	rng = MersenneTwister(42)
 end
 
 # ╔═╡ 10481fbc-6a08-11ee-1c4f-2b0f04f82767
@@ -36,20 +39,21 @@ md"""
 ## Persistent homology with Ripser
 """
 
+# ╔═╡ 3d29a334-cece-4ba4-b38b-ec97eecff935
+
+
 # ╔═╡ 4ae94cd9-2b69-4e22-8dde-67a1a0cb9b4d
 function noisy_circle(n; r1=1, r2=1, noise=0.1)
-    points = NTuple{2,Float64}[]
-    for _ in 1:n
-        θ = 2π * rand()
-        point = (
-            r1 * sin(θ) + noise * rand() - noise / 2,
-            r2 * cos(θ) + noise * rand() - noise / 2,
-        )
-        push!(points, point)
-    end
-    return points
+    X = randn(rng, Float64, (n, 3))
+	points = Tuple{Float64,3}[]
+	for row in eachrow(X)
+		push!(points, Tuple(row))
+	end
 end
 
+# ╔═╡ e22bb3a8-0185-45e6-8824-365eb52c6640
+
+
 # ╔═╡ b95d2a4e-b5d8-4ec3-8baa-23bc7db5beea
 begin
 	points = noisy_circle(100; noise=0)
@@ -61,13 +65,17 @@ begin
         aspect_ratio=1,
         xlim=(-2.2, 2.2),
         ylim=(-2.2, 2.2),
+		zlim=(-2.2, 2.2)
         title="Data",
     )
     plt_diag = plot(result; infinity=3)
 
     plot(plt_pts, plt_diag; size=(800, 400))
 end
 
+# ╔═╡ 3f9f4667-4321-4d4e-b6c5-ae30c5d690b5
+
+
 # ╔═╡ fa674ef0-aab6-432b-a5ac-4e3bffa37a80
 md"""
 ## COIL20
@@ -103,7 +111,7 @@ end
 typeof(images[1])
 
 # ╔═╡ 6070ff3e-6d8d-48eb-86c6-a96623f74014
-
+1 + 2
 
 # ╔═╡ d2c84a1e-c7ad-4178-a6dd-b59d70385da9
 images[1] - 1
@@ -120,14 +128,45 @@ ceil((images[1] + images[2]) / 2)
 # ╔═╡ cdc1ca21-3568-4f08-b144-cc79a77f72a1
 
 
+# ╔═╡ 1f5ba155-abbc-4f1a-b5fa-a0051f89dbd4
+begin
+	points = noisy_circle(100; noise=0)
+    result = ripserer(points)
+
+    plt_pts = scatter(
+        points;
+        legend=false,
+        aspect_ratio=1,
+        xlim=(-2.2, 2.2),
+        ylim=(-2.2, 2.2),
+		zlim=(-2.2, 2.2),
+        title="Data",
+    )
+    plt_diag = plot(result; infinity=3)
+
+    plot(plt_pts, plt_diag; size=(800, 400))
+end
+
+# ╔═╡ 81a5a99b-dc3d-4643-a256-f83f0c0874a2
+begin
+	X = randn(rng, Float64, (n, 3))
+	points = Tuple{Float64,3}[]
+	points
+end
+
 # ╔═╡ Cell order:
 # ╠═10481fbc-6a08-11ee-1c4f-2b0f04f82767
 # ╠═6078059a-176d-4e95-92b8-7b39bdeff7cb
 # ╠═ec68f1f5-13ad-450f-8bd4-cfb15adb0e6c
 # ╠═b3ed9787-f333-4d4a-96e5-56a6541201fe
 # ╠═33fc86a7-f9ef-4ace-8593-54d1a7eeb4b9
+# ╠═81a5a99b-dc3d-4643-a256-f83f0c0874a2
+# ╠═3d29a334-cece-4ba4-b38b-ec97eecff935
 # ╠═4ae94cd9-2b69-4e22-8dde-67a1a0cb9b4d
+# ╠═e22bb3a8-0185-45e6-8824-365eb52c6640
+# ╠═1f5ba155-abbc-4f1a-b5fa-a0051f89dbd4
 # ╠═b95d2a4e-b5d8-4ec3-8baa-23bc7db5beea
+# ╠═3f9f4667-4321-4d4e-b6c5-ae30c5d690b5
 # ╠═fa674ef0-aab6-432b-a5ac-4e3bffa37a80
 # ╠═4adadda3-9459-4122-9417-917bd32e73f9
 # ╠═e4467766-25f7-44f7-b028-33ff8aac7306