Use k-d tree to optimize performance in visual function

src/SOM.jl

@@ -15,6 +15,7 @@ using Distances
 using ProgressMeter
 using StatsBase
 using Distributions
+using NearestNeighbors
 #using TensorToolbox
 using LinearAlgebra
 # if VERSION < v"0.7.0-DEV.5183"

src/helpers.jl

@@ -90,6 +90,14 @@ function findWinner(cod, sampl)
     return winner
 end
 
+function findWinner(kd_tree::KDTree, sampl)
+    idxs, _ = knn(kd_tree, sampl, 1)
+    idxs[1]
+end
+
+function buildKDTree(codes)
+    permutedims(codes,(2,1)) |> KDTree
+end
 
 """
     normTrainData(x, normParams)

src/soms.jl

@@ -76,16 +76,21 @@ end
 Return the index of the winner neuron for each training pattern
 in x (row-wise).
 """
-function visual(codes, x)
+
+function visualBruteForce(codes, x)
 
     vis = zeros(Int, nrow(x))
-    for i in 1:nrow(x)
+    @time for i in 1:nrow(x)
         vis[i] = findWinner(codes, [x[i, col] for col in 1:size(x, 2)])
     end
 
     return(vis)
 end
 
+function visual(codes, x)
+    kd_tree = buildKDTree(codes)
+    visualBruteForce(kd_tree, x)
+end
 
 """
     makePopulation(nCodes, vis)

test/benchmark.jl

@@ -0,0 +1,88 @@
+using Primes
+
+function generate_integer_hash_function(a,b,cardinality)
+    # cardinality must be prime number
+    # a,b: 1 ≤ a ≤ p−1  , 0 ≤ b ≤ p−1
+    @assert a < cardinality
+    @assert a > 0
+    @assert b < cardinality
+    @assert b >= 0
+    @assert isprime(cardinality)
+    m = cardinality
+    x -> (x^a%m + b)%m  #TODO use memory-efficient modular exponentiation for better performance
+end
+
+"""
+# function generate_vector_deterministic_pseudorandom
+
+Generates a pseudorandom vector. For the same hash function it always generates the same vector.
+If the hash function is an integer hash from a universal family, the elements of the vector will
+obey a continuous uniform distribution.
+# Arguments:
+- `seed`: A random number chosen by the user to ensure the result is deterministic
+- `hash_func`: An integer hash function sampled from a universal family
+- `dimension`: number of elements in the vector
+"""
+function generate_vector_deterministic_pseudorandom(seed,hash_func,dimension)
+    [hash_func(x+seed) for x in 1:dimension]
+end
+
+
+"""
+# function generate_dataset
+
+Generates a pseudorandom dataset that can be used for benchmarking SOMs.
+The elements in the vectors are distributed uniformly between 0 and `max_value`.
+
+# Arguments:
+- `number`: Number of rows/entries/vectors/individual iris plants in the dataset
+- `dimension`: Number of dimensions/elements in each vector
+- `max_value`: The maximum value that an element in a vector can have
+- `y`: an optional function to map the uniformly distributed elements to a desired non-uniform distribution
+- `params`: Randomness parameters chosen by user to ensure deterministic result
+"""
+function generate_dataset(number, dimension, max_value, y=(x->x), params = Dict(:a => 13, :b => 17, :hash_seed => 23, :prime => 1013))
+    hash_func = generate_integer_hash_function(params[:a],params[:b],params[:prime])
+
+    seed = params[:hash_seed]
+    matrix = zeros((number,dimension))
+    for i in 1:number
+        vector = generate_vector_deterministic_pseudorandom(seed,hash_func,dimension)
+        vector = map((x->x*max_value/params[:prime]),vector)
+        vector = map((x->(x+max_value)/2),vector)
+        vector = map(y,vector)
+        seed = hash_func(seed+i)
+        matrix[i,:] = vector
+    end
+    convert(DataFrame,matrix)
+end
+
+
+
+# Benchmarking functions
+# ======================
+
+function benchmark_init(train, topol; toroidal = false,
+                    normType = :zscore)
+
+    xdim = 10
+    ydim = 10
+
+    initSOM(train, xdim, ydim, norm = normType,
+                  topol = topol, toroidal = toroidal)
+end
+
+function benchmark_train(som,train, kernel = gaussianKernel)
+    trainSOM(som, train, 100000, kernelFun = kernel)
+end
+
+
+# Example
+# =======
+
+# iris = dataset("datasets", "iris")
+# train = iris[:,1:4]
+
+# train = generate_dataset(1000,100,1)
+# som = benchmark_init(train, :rectangular, toroidal = false)
+# som = benchmark_train(som,train)