text:
### A Pluto.jl notebook ###
# v0.17.7

using Markdown
using InteractiveUtils

# ╔═╡ 64bba790-89ad-11ec-272d-af9bb791e63d
begin
	using Turing, MCMCChains
	using Distributions, StatsPlots, Random
end

# ╔═╡ dc1177eb-66ec-4b82-ad0d-5e76539faa24
md"""
### Gaussian Mixture Model

In this post we see an example of gaussian mixture model, which is a clustering model of unsupervised learning.
As stated, gaussian mixture model has several normal distributions. The aim of this model is to find optimal value of parameters(μ, Σ, π) that describe clusters.

Let's say we have n data and assumes K clusters.
- θ parameters
- Z latent variables(hidden variables)
- X data

What are the parameters here?
- θ = {μ(μ₁ . . . μₖ), Σ(Σ₁ . . . Σₖ), π(π₁ . . . πₖ)}
- μₖ is mean of kᵗʰ cluster
- Σₖ is covariance matrix of kᵗʰ cluster
- πₖ represent for probability of selecting kᵗʰ cluster

We assume that we have n data.
- {(X₁, Z₁) . . . (Xₙ, Zₙ)}
- Z can be interpreted as one-hot vector with only 1 in kᵗʰ position and 0 others.

"""

# ╔═╡ fc380f54-24c7-440a-9cfd-04d3a8ea3184
# Set a random seed.
Random.seed!(3)

# ╔═╡ 38643e87-ee2d-4aae-83f7-abc6199f54aa
# Construct 30 data points for each cluster.
N = 30

# ╔═╡ b7acb16e-b6d1-4e53-80c4-0819259a6130
# Parameters for each cluster, we assume that each cluster is Gaussian distributed in the example.
μs = [4.0, 1.0]

# ╔═╡ 24553bda-3ce4-4156-bf8a-0a1fe3df2afb
# Construct the data points.
x = mapreduce(c -> rand(MvNormal([μs[c], μs[c]], 1.), N), hcat, 1:2)

# ╔═╡ a0871b5d-02f4-4f16-9bcd-2e85264b0f41
gr()

# ╔═╡ b5f72bd8-97b7-435f-8940-950af86aed91
# Visualization.
scatter(x[1,:], x[2,:], legend = false, title = "Dataset", fmt="png")

# ╔═╡ 00f15e65-5133-4453-88af-c9479c8e3f9e
@model function GaussianMixtureModel(x)

    D, N = size(x)

    # Draw the parameters for cluster 1.
    μ1 ~ Normal()

    # Draw the parameters for cluster 2.
    μ2 ~ Normal()

    μ = [μ1, μ2]

    # draw the weights for the K clusters from a Dirichlet distribution.

    α = 1.0
	π ~ Dirichlet(D, α)

    # Draw assignments for each datum and generate it from a multivariate normal.
    k = Vector{Int}(undef, N)
    for i in 1:N
        k[i] ~ Categorical(π)
        x[:,i] ~ MvNormal([μ[k[i]], μ[k[i]]], 1.)
    end
    return k
end;

# ╔═╡ a45b70a7-acb0-48f1-8ae5-31c63992cbfa
gmm_model = GaussianMixtureModel(x);

# ╔═╡ 7fe105ae-7d5c-4dd8-8104-fdaa9a18ea25
gmm_sampler = Gibbs(PG(100, :k), HMC(0.05, 10, :μ1, :μ2))

# ╔═╡ c30df2ab-45c2-42c3-9e72-2c4759b9b640
tchain = sample(gmm_model, gmm_sampler, MCMCThreads(), 200, 3)

# ╔═╡ 9f18a980-5277-4419-912b-e261451427b0
summarystats(tchain)

# ╔═╡ c726de5c-e241-4fa5-984d-c4184a626e52
names(tchain)

# ╔═╡ 166d94ed-1e70-4ef6-8b78-f5b566216246
ids = findall(map(name -> occursin("μ", string(name)), names(tchain)))

# ╔═╡ 29c24b11-fad4-4a2f-afbe-9f6afaab82f1
p = plot(tchain[:, ids, :]; legend=true, labels=["Mu 1" "Mu 2"], colordim=:parameter)

# ╔═╡ 40214128-1ed7-4b37-b19e-78fafdebbb0b
tchain2 = tchain[:, :, 3];

# ╔═╡ 2c2ea945-795a-4e0a-bc16-c49fa15bee1c
# Helper function for plotting
function predict(x, y, π, μ)

    return Turing.logaddexp(
        log(π[1]) + logpdf(MvNormal([μ[1], μ[1]], 1.), [x, y]),
        log(π[2]) + logpdf(MvNormal([μ[2], μ[2]], 1.), [x, y])
    )
end

# ╔═╡ 60d2ae42-7e56-418e-88a9-2ad16e84555e
# plotting contour plot
begin
	contour(range(-2, stop = 8), range(-2, stop = 8),
	    (x, y) -> predict(x, y, [mean(tchain2[:"π[1]"]), mean(tchain2[:"π[2]"])], 	[mean(tchain2[:μ1]), mean(tchain2[:μ2])])
	)
	scatter!(x[1,:], x[2,:]; legend=false, title="Dataset")

end

# ╔═╡ 88c6f515-b4d9-4e85-9157-2fc1e81483a2
wireframe(range(-2, stop = 8), range(-2, stop = 8),
	    (x, y) -> predict(x, y, [mean(tchain2[:"π[1]"]), mean(tchain2[:"π[2]"])], 	[mean(tchain2[:μ1]), mean(tchain2[:μ2])]))

# ╔═╡ 63703e6a-7adb-43c3-82aa-34f5aa746017
mean(MCMCChains.group(tchain2, :k))

# ╔═╡ b81eb0e7-d3cd-49aa-ae14-b8436dc515a4
assignments = round.(mean(MCMCChains.group(tchain2, :k)).nt.mean)

# ╔═╡ 60b70f36-ffac-45c3-a3d4-78991ab57bab
scatter(x[1,:], x[2,:]; legend=false,
    title="Assignments on Dataset", zcolor=assignments)

# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
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Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"

[compat]
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"""

# ╔═╡ 00000000-0000-0000-0000-000000000002
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[[GLFW_jll]]
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[[GR]]
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[[LeftChildRightSiblingTrees]]
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"""

# ╔═╡ Cell order:
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# ╟─00000000-0000-0000-0000-000000000001
# ╟─00000000-0000-0000-0000-000000000002

max_stars_repo_name: delphinH/imagetoy

max_issues_repo_name: delphinH/imagetoy

max_forks_repo_name: delphinH/imagetoy


text:
include(joinpath(dirname(@__FILE__), "config.jl"))
include(joinpath(dirname(@__FILE__), "lstm.jl"))
include(joinpath(dirname(@__FILE__), "seq-data.jl"))

using StatsBase
using MXNet

# load vocabulary
vocab   = build_vocabulary(INPUT_FILE, VOCAB_FILE)
n_class = length(vocab)

# prepare data provider
jl_data = Pair[(symbol(NAME, "_data_$t") => zeros(mx.MX_float, (length(vocab), BATCH_SIZE_SMP)))
               for t = 1:1]
jl_c    = Pair[(symbol(NAME, "_l$(l)_init_c") => zeros(mx.MX_float, (DIM_HIDDEN, BATCH_SIZE_SMP)))
               for l = 1:LSTM_N_LAYER]
jl_h    = Pair[(symbol(NAME, "_l$(l)_init_h") => zeros(mx.MX_float, (DIM_HIDDEN, BATCH_SIZE_SMP)))
               for l = 1:LSTM_N_LAYER]

# the first input in the sequence
jl_data_start = jl_data[1].second
jl_data_start[char_idx(vocab, SAMPLE_START),:] = 1

# define a LSTM with sequence length 1, also output states so that we could manually copy the states
# when sampling the next char
lstm  = LSTM(LSTM_N_LAYER, 1, DIM_HIDDEN, DIM_EMBED, n_class, name=NAME, output_states=true)
model = mx.FeedForward(lstm, context=mx.cpu())

# load parameters from traind LSTM, though the sequence length is different, since the weights are shared
# over time, this should be compatible.
model = mx.load_checkpoint(model, CKPOINT_PREFIX, N_EPOCH, allow_different_arch=true)

# prepare outputs
Base.zero(::Type{Char}) = Char(0)
output_samples = zeros(Char, (SAMPLE_LENGTH, BATCH_SIZE_SMP))
output_samples[1, :] = SAMPLE_START

# build inverse vocabulary for convenience
inv_vocab = Dict([v => k for (k,v) in vocab])

# do prediction and sampling step by step
for t = 2:SAMPLE_LENGTH-1
  data    = mx.ArrayDataProvider(jl_data ∪ jl_c ∪ jl_h)
  preds   = mx.predict(model, data)

  # the first output is prediction
  outputs = preds[1]

  # do sampling and init the next inputs
  jl_data_start[:] = 0
  for i = 1:BATCH_SIZE_SMP
    prob = WeightVec(outputs[:, i])
    k    = sample(prob)
    output_samples[t, i] = inv_vocab[k]
    jl_data_start[k, i]  = 1
  end

  # copy the states over
  for l = 1:LSTM_N_LAYER
    copy!(jl_c[l][2], preds[1+l])
    copy!(jl_h[l][2], preds[1+LSTM_N_LAYER+l])
  end
end

output_texts = [join(output_samples[:,i]) for i = 1:BATCH_SIZE_SMP]
output_texts = [replace(x, UNKNOWN_CHAR, '?') for x in output_texts]

for (i, text) in enumerate(output_texts)
  println("## Sample $i")
  println(text)
  println()
end

ext: jl


text:
# This file was generated by the Julia Swagger Code Generator
# Do not modify this file directly. Modify the swagger specification instead.

mutable struct UpdateFirewallRuleParameters <: SwaggerModel
    properties::Any # spec type: Union{ Nothing, UpdateFirewallRuleProperties } # spec name: properties

    function UpdateFirewallRuleParameters(;properties=nothing)
        o = new()
        validate_property(UpdateFirewallRuleParameters, Symbol("properties"), properties)
        setfield!(o, Symbol("properties"), properties)
        o
    end
end # type UpdateFirewallRuleParameters

const _property_map_UpdateFirewallRuleParameters = Dict{Symbol,Symbol}(Symbol("properties")=>Symbol("properties"))
const _property_types_UpdateFirewallRuleParameters = Dict{Symbol,String}(Symbol("properties")=>"UpdateFirewallRuleProperties")
Base.propertynames(::Type{ UpdateFirewallRuleParameters }) = collect(keys(_property_map_UpdateFirewallRuleParameters))
Swagger.property_type(::Type{ UpdateFirewallRuleParameters }, name::Symbol) = Union{Nothing,eval(Base.Meta.parse(_property_types_UpdateFirewallRuleParameters[name]))}
Swagger.field_name(::Type{ UpdateFirewallRuleParameters }, property_name::Symbol) =  _property_map_UpdateFirewallRuleParameters[property_name]

function check_required(o::UpdateFirewallRuleParameters)
    true
end

function validate_property(::Type{ UpdateFirewallRuleParameters }, name::Symbol, val)
end


text:
struct FixtureFactory
    channel_func :: Function
    instant_teardown :: Bool
end

struct RunningFixtureFactory
    task :: Task
    channel :: Channel
    instant_teardown :: Bool
end

instant_teardown(rff::RunningFixtureFactory) = rff.instant_teardown

struct LabeledValue
    value :: Any
    label :: String

    LabeledValue(value, label=nothing) = new(value, string(label === nothing ? value : label))
end

Base.:(==)(lval1::LabeledValue, lval2::LabeledValue) =
    (lval1.value == lval2.value) && (lval1.label == lval2.label)

unwrap_value(lval::LabeledValue) = lval.value

unwrap_label(lval::LabeledValue) = lval.label

function fixture_factory(producer_func; instant_teardown=false)
    channel_func = function(c, args)
        produce = function(value, label=nothing)
            put!(c, LabeledValue(value, label))
            if !instant_teardown
                # block until the caller's signal
                take!(c)
            end
        end
        producer_func(produce, args...)
    end

    FixtureFactory(channel_func, instant_teardown)
end

function setup(ff::FixtureFactory, args)
    # Currently we cannot `wait()` on a channel that gets closed during waiting,
    # because an exception gets thrown.
    # We do not want to do the dirty hack from base/channels.jl
    # where they wrap `wait()` in a try/catch and check the resulting exception.
    # So we create a task explicitly. A `Task` object can be waited on safely.
    channel = Channel(0) # an unbuffered channel
    task = Task(() -> ff.channel_func(channel, args))
    schedule(task)
    ret = take!(channel)
    rff = RunningFixtureFactory(task, channel, ff.instant_teardown)
    ret, rff
end

function teardown(rff::RunningFixtureFactory)
    if !rff.instant_teardown
        put!(rff.channel, nothing)
    end
    if !istaskdone(rff.task)
        fetch(rff.task)
    end
end