adding sliced algo

Project.toml

@@ -9,17 +9,25 @@ CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 ColorSchemes = "35d6a980-a343-548e-a6ea-1d62b119f2f4"
 Colors = "5ae59095-9a9b-59fe-a467-6f913c188581"
 Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
+Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 Formatting = "59287772-0a20-5a39-b81b-1366585eb4c0"
+GR = "28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71"
 HypothesisTests = "09f84164-cd44-5f33-b23f-e6b0d136a0d5"
+ImageTransformations = "02fcd773-0e25-5acc-982a-7f6622650795"
+Images = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
+KernelDensity = "5ab0869b-81aa-558d-bb23-cbf5423bbe9b"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
 MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
 Parameters = "d96e819e-fc66-5662-9728-84c9c7592b0a"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
 StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 ThreadsX = "ac1d9e8a-700a-412c-b207-f0111f4b6c0d"
+Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [extras]
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

examples/Learning1d_distribution/benchmark_unimodal.jl

@@ -9,11 +9,13 @@ include("../utils.jl")
         n_samples = 10000
 
         @test_experiments "N(0,1) to N(23,1)" begin
-            gen = Chain(Dense(1, 7), elu, Dense(7, 13), elu, Dense(13, 7), elu, Dense(7, 1))
+            gen = Chain(Dense(2, 7), elu, Dense(7, 13), elu, Dense(13, 7), elu, Dense(7, 2))
             dscr = Chain(
                 Dense(1, 11), elu, Dense(11, 29), elu, Dense(29, 11), elu, Dense(11, 1, σ)
             )
-            target_model = Normal(4.0f0, 2.0f0)
+            target_model = MixtureModel([
+                Normal(5.0f0, 2.0f0), Pareto(5.0f0,1.0f0),
+            ])
             hparams = HyperParamsVanillaGan(;
                 data_size=100,
                 batch_size=1,
@@ -29,17 +31,16 @@ include("../utils.jl")
             train_vanilla_gan(dscr, gen, hparams)
 
             hparams = AutoISLParams(;
-                max_k=10, samples=1000, epochs=1000, η=1e-2, transform=noise_model
+                max_k=10, samples=1000, epochs=100, η=1e-2, transform=noise_model
             )
 
-            train_set = Float32.(rand(target_model, hparams.samples))
-            loader = Flux.DataLoader(train_set; batchsize=-1, shuffle=true, partial=false)
-
+            train_set = Float32.(rand(target_model, hparams.samples * hparams.epochs))
+            loader = Flux.DataLoader(train_set; batchsize=hparams.samples, shuffle=true, partial=false)
             auto_invariant_statistical_loss(gen, loader, hparams)
         end
 
         @test_experiments "N(0,1) to Uniform(22,24)" begin
-            gen = Chain(Dense(1, 7), elu, Dense(7, 13), elu, Dense(13, 7), elu, Dense(7, 1))
+            gen = Chain(Dense(1, 7), elu, Dense(7, 13), elu, Dense(13, 7), elu, Dense(7, 2))
             dscr = Chain(
                 Dense(1, 11), elu, Dense(11, 29), elu, Dense(29, 11), elu, Dense(11, 1, σ)
             )
@@ -59,7 +60,7 @@ include("../utils.jl")
             train_vanilla_gan(dscr, gen, hparams)
 
             hparams = AutoISLParams(;
-                max_k=10, samples=1000, epochs=1000, η=1e-2, transform=noise_model
+                max_k=10, samples=1000, epochs=100, η=1e-2, transform=noise_model
             )
             train_set = Float32.(rand(target_model, hparams.samples))
             loader = Flux.DataLoader(train_set; batchsize=-1, shuffle=true, partial=false)
@@ -73,7 +74,7 @@ include("../utils.jl")
                 gen,
                 n_samples,
                 (-3:0.1:3),
-                (0:0.1:10),
+                (0:0.1:30),
             )
         end
 
@@ -100,9 +101,9 @@ include("../utils.jl")
             train_vanilla_gan(dscr, gen, hparams)
 
             hparams = AutoISLParams(;
-                max_k=10, samples=1000, epochs=1000, η=1e-2, transform=noise_model
+                max_k=10, samples=1000, epochs=100, η=1e-2, transform=noise_model
             )
-            train_set = Float32.(rand(target_model, hparams.samples))
+            train_set = Float32.(rand(target_model, hparams.sample * hparams.epochs))
             loader = Flux.DataLoader(train_set; batchsize=-1, shuffle=true, partial=false)
 
             auto_invariant_statistical_loss(gen, loader, hparams)
@@ -122,7 +123,7 @@ include("../utils.jl")
                 gen,
                 n_samples,
                 (-3:0.1:3),
-                (0:0.1:10),
+                (-20:0.1:20),
             )
         end
 

examples/Sliced_ISL/MNIST_sliced.jl

@@ -0,0 +1,63 @@
+using ISL
+using Flux
+using MLDatasets
+using Images
+using ImageTransformations  # For resizing images if necessary
+
+function load_mnist(digit::Int)
+    # Load MNIST data
+    train_x, train_y = MLDatasets.MNIST.traindata()
+    test_x, test_y = MLDatasets.MNIST.testdata()
+
+    # Find indices where the label is digit
+    selected_indices = findall(x -> x == digit, train_y)
+
+    selected_images = train_x[:, :, selected_indices]
+
+    return (reshape(Float32.(selected_images), 784, :), train_y)#, (test_x, test_y)
+end
+
+(train_x, train_y) = load_mnist(5)
+
+
+model = Chain(
+    Dense(3, 512, relu),
+    Dense(512, 28*28, sigmoid)
+)
+
+model = Chain(
+    Dense(3, 256, relu),
+    #BatchNorm(256),
+    Dense(256, 512, relu),
+    #BatchNorm(512, relu),
+    Dense(512, 28*28, identity),
+    x -> reshape(x, 28, 28, 1, :),
+    Conv((3, 3), 1=>16, relu),
+    MaxPool((2,2)),
+    x -> reshape(x, :, size(x, 4)),
+    Flux.flatten,
+    Dense(2704, 28*28)
+)
+
+# Define hyperparameters
+noise_model = MvNormal([0.0, 0.0, 0.0], [1.0 0.0 0.0; 0.0 1.0 0.0; 0.0 0.0 1.0])
+n_samples = 10000
+
+hparams = HyperParamsSlicedISL(;
+    K=10, samples=1000, epochs=5, η=1e-2, noise_model=noise_model, m=20
+)
+
+# Create a data loader for training
+batch_size = 1000
+train_loader = DataLoader(train_x; batchsize=batch_size, shuffle=false, partial=false)
+
+total_loss = []
+for _ in 1:10
+    append!(total_loss, sliced_invariant_statistical_loss(model, train_loader, hparams))
+end
+
+img = model(Float32.(rand(hparams.noise_model, 1)))
+img2 = reshape(img, 28, 28)
+display(Gray.(img2))
+transformed_matrix = Float32.(img2 .> 0.1)
+display(Gray.(transformed_matrix))