glesica

package main

import (
	"fmt"
	"sync"
)

func main() {
	numbers := make(chan int, 10)
	squares := make(chan int, 10)

glesica

package main

import (
	"fmt"
)

func main() {
	myChan := make(chan string)
	go func() {
		fmt.Println(<-myChan)

glesica

class RequestJson {
  protected String firstName;
  protected String lastName;
  
  // Getters and setters omitted
}

class ResponseJson extends RequestJson {
  protected Integer userId;
  

glesica

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])

glesica

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

# 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

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

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

glesica

"""
Contouring using the Marching Squares algorithm.
George Lesica
"""

from cairo import SVGSurface, Context
 
WIDTH = 8 * 72.0
HEIGHT = 8 * 72.0

glesica

# 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)