cheeyeo

#image processing resources
from skimage.io import imread, imshow
from skimage.filters import gaussian, threshold_otsu
from skimage.feature import canny
from skimage.transform import probabilistic_hough_line, rotate

#testing 
import numpy as np
import os

cheeyeo

# Change these variables as necessary.
MAIN_PACKAGE_PATH := ./cmd/example
BINARY_NAME := example

# ==================================================================================== #
# HELPERS
# ==================================================================================== #

## help: print this help message
.PHONY: help

cheeyeo

// Interval time to check if runtime is disconnected
interval = 1000 * 60;

// Busy/Reconnect button top-right
reloadButton = document.querySelector('#connect > paper-button > span')

setInterval(() => {
    if (reloadButton.innerText == 'Reconnect') {
        reloadButton.click();
        console.log('Restarting');

cheeyeo

# Keras tokenizer lacks serialization. Therefore I created the below to address this without changing the API.
# (Since I don't know how long it'll take for keras to support it)
# The Tokenizer __init__ should be modified to take the word_stats dictionary as a kwarg, 
# and a method added to the class to return the stats
# Expiermentally this works, but I am not sure of any nuances in the Tokenizer class.

def test_tokenizer():
	texts = ["It was the best of times, it was the worst of times, it was the age of wisdom",
      "it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, ",
      "it was the season of Light, it was the season of Darkness, it was the spring of hope, ",

cheeyeo

from keras.models import Sequential
from keras.layers import Dense
from keras.utils.io_utils import HDF5Matrix
import numpy as np

def create_dataset():
    import h5py
    X = np.random.randn(200,10).astype('float32')
    y = np.random.randint(0, 2, size=(200,1))
    f = h5py.File('test.h5', 'w')

cheeyeo

# Create a Person class which will print the following output when run:
# Jane Doe
# John Smith

# YOUR CODE GOES HERE

class Person
  attr_accessor :first_name, :last_name

  def initialize(first_name="", last_name="")

cheeyeo

// === Arrays

var [a, b] = [1, 2];
console.log(a, b);
//=> 1 2


// Use from functions, only select from pattern
var foo = () => [1, 2, 3];

cheeyeo

import numpy as np
from keras.models import Sequential
from keras.layers.core import Activation, Dense
from keras.optimizers import SGD

X = np.array([[0,0],[0,1],[1,0],[1,1]], "float32")
y = np.array([[0],[1],[1],[0]], "float32")

model = Sequential()
model.add(Dense(2, input_dim=2, activation='sigmoid'))

cheeyeo

require 'oat/adapters/hal'
class BooticAdapter < Oat::Adapters::HAL
  def type(*types)
    property :_class, *types
  end

  def curie(link_opts)
    data[:_links][:curies] ||= []
    data[:_links][:curies] << link_opts
  end

cheeyeo

defmodule Expng do

  defstruct [:width, :height, :bit_depth, :color_type, :compression, :filter, :interlace, :chunks]

  def png_parse(<<
  0x89, 0x50, 0x4E, 0x47, 0x0D, 0x0A, 0x1A, 0x0A,
                 _length :: size(32),
                 "IHDR",
                 width :: size(32),
                 height :: size(32),