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@glesica
glesica / helloworld.go
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)
View pojos.java
class RequestJson {
protected String firstName;
protected String lastName;
// Getters and setters omitted
}
class ResponseJson extends RequestJson {
protected Integer userId;
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])
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
@glesica
glesica / secretary.jl
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
@glesica
glesica / knn.jl
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))
@glesica
glesica / knn.jl
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
View contour.py
"""
Contouring using the Marching Squares algorithm.
George Lesica
"""
from cairo import SVGSurface, Context
WIDTH = 8 * 72.0
HEIGHT = 8 * 72.0
@glesica
glesica / contour.jl
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)
@glesica
glesica / example1.py
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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