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# George Lesicaglesica

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Created Jan 27, 2015
Hello, world! using a goroutine
View helloworld.go
 package main import ( "fmt" ) func main() { myChan := make(chan string) go func() { fmt.Println(<-myChan)
Created Dec 22, 2014
View pojos.java
 class RequestJson { protected String firstName; protected String lastName; // Getters and setters omitted } class ResponseJson extends RequestJson { protected Integer userId;
Last active Aug 29, 2015
View comprehension.jl
 julia> typeof([i for i = 1:nrow(ddf)]) Array{Any,1} julia> typeof([i for i = 1:10]) Array{Int64,1} julia> k = 10 10 julia> typeof([i for i = 1:k])
Last active Aug 29, 2015
View cartesian_product.jl
 type Attr vals data end function cart_prod(sets...) result_size = length(sets) result_elems = reduce(*, 1, map(length, sets)) result = zeros(result_elems, result_size) scale_factor = result_elems
Last active Aug 29, 2015
Simulation demonstrating the characteristics of the Secretary Problem (https://en.wikipedia.org/wiki/Secretary_problem) using Julia (http://julialang.org/).
View secretary.jl
 # Do K simulation runs each with N items. The goal is to find the largest # item in the list, without "remembering" and carrying along some previous # maximum. N = 10000 K = 1000 diffs = zeros(Int, K) pct_diffs = zeros(Float64, K) for i = 1:K
Created Feb 3, 2014
A simple KNN implementation in Julia that allows for a reasonable level of flexibility. Written to practice using Julia.
View knn.jl
 function knn_normalize{T}(D::Array{T, 2}, mx::Array{T, 1}, mn::Array{T, 1}) return mapslices(x -> (x - mn) ./ (mx - mn), D, 2) end function knn_normalize{T}(D::Array{T, 1}, mx::Array{T, 1}, mn::Array{T, 1}) return (D - mn) ./ (mx - mn) end function knn_maxmin{T}(D::Array{T, 2}) mx = vec(mapslices(maximum, D, 1))
Created Jan 31, 2014
Minimal KNN implementation in Julia.
View knn.jl
 using StatsBase function knn(k, train, classes, obs) nearest = sortperm(vec(sqrt(sum(broadcast((a, b) -> (a-b)^2, obs, train), 2))))[1:k] return mode(classes[nearest]) end
Created Sep 22, 2013
View contour.py
 """ Contouring using the Marching Squares algorithm. George Lesica """ from cairo import SVGSurface, Context WIDTH = 8 * 72.0 HEIGHT = 8 * 72.0
Created Sep 17, 2013
Contouring algorithm in Julia.
View contour.jl
 # contour(A, v) # Implements the Marching Squares contouring algorithm (see: # https://en.wikipedia.org/wiki/Marching_squares for details). The # matrix `A` is an m x n scalar field and the scalar `v` is the # value to be contoured. The return value is an (m-1) x (n-1) matrix # of contour line type indices (see the Wikipedia article). # TODO: Implement in parallel function contour(A, v) rows, cols = size(A)
Created Apr 20, 2013
Simple drawing example using Pycairo.
View example1.py
 from math import pi from cairo import SVGSurface, Context, Matrix WIDTH = 6 * 72 HEIGHT = 4 * 72 s = SVGSurface('example1.svg', WIDTH, HEIGHT) c = Context(s) # Transform to normal cartesian coordinate system
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