oem

|/n:+/'1_'(&^n)_n:`I$'0:"i/1"
+/n@3#>n

oem

using Pipe: @pipe

getdata(file) = open(joinpath(@__DIR__, file)) do f read(f, String) end

part1(data) = @pipe data |>
              split(_, "\n\n") |>
              map(l -> replace(l, "\n" => ""), _) |>
              map(l -> split(l, ""), _) |>
              map(l -> unique(l), _) |>
              map(l -> length(l), _) |>

oem

import turicreate as tc
import os

current_dir = os.path.dirname(__file__)
model = tc.load_model(os.path.join(
    current_dir, 'model/v1.model'))

images = tc.load_images(os.path.join(current_dir, 'data/test'))
images['predictions'] = model.predict(images)
images.print_rows(num_rows=10)

oem

import turicreate as tc
import os

current_dir = os.path.dirname(__file__)
data = tc.SFrame(os.path.join(current_dir, 'data/processed/beers.sframe'))

train, test = data.random_split(0.8)

model = tc.image_classifier.create(train, target='label', max_iterations=100)

oem

import turicreate as tc
import re
import os


def path_as_label(path):
    label = re.search('(?<=raw/)(.+)?/.+$', path).group(1)
    label = label.replace('_', ' ')
    return label.title()

oem

require 'minitest/autorun'
require 'matrix'

module LinearRegression
  def predict(features, theta)
    features * theta
  end

  def cost_function(features, labels, theta)
    m = features.row_count

oem

def normal_equation(features, labels)
  (features.transpose * features).inverse * features.transpose * labels
end

oem

# features is a Matrix, labels and theta are Vectors
def cost_function(features, labels, theta)
  m = features.row_count
  predictions = predict(features, theta)
  squared_errors = (predictions - labels).map { |error| error**2 }
  (1.0 / (2.0 * m)) * squared_errors.reduce { |a, b| a + b }
end

oem

require 'matrix'
Matrix[[10, 1000, 10], [5, 800, 2], [12, 2500, 3]] * Vector[200,-0.1,-10] # => Vector[1800.0, 900.0, 2120.0]

oem

package main

import (
	"fmt"
	"time"
)

const timeFormat = "2006-01-02 15:04 MST"

func main() {