jhihn

class RemoteCloud {
  QNetworkAccessManger m_nam;
  
  QString URL(QString url); // left as an exercise to the reader, this just maps a short name to a server/versioned endpoint
  QNetworkReplay *get(const QString& url);
  QNetworkReply *post(const QString& url, const QVariantMap& object);
  
public:
  QNetworkReply *get(const QString& endpoint, std::function<void(QNetworkReply*)> lambda,  bool sync=false);
  QNetworkReply *post(const QString& endpoint,

jhihn

import json

def load_keras_model(filename):
	with open(filename) as file:
		data = file.read()
	json_object = json.loads(data)
	model = model_from_json(data)
	layer_arrays = []
	for layer in json_object["weights"]:
		layer_arrays.append(np.array(layer))

jhihn

# 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, ",

jhihn

function mergeObjects(){
	var out = {};
	var attrname;
	for (var i =0; i< arguments.length; i++)
	for (attrname in arguments[i]) { out[attrname] = arguments[i][attrname]; }
	return out;
}

console.log(mergeObjects({a:1}, {b:2}))

jhihn

function validateObjStructure(obj, structure) {
	if (structure.length > 0)
		while (structure[0] == '.')
			structure = structure.substr(1)
	console.log('Call:', JSON.stringify(obj), structure)

	var dot = structure.indexOf('.')
	var bracket = structure.indexOf('[')
	var prop;
	if (dot === -1 && bracket === -1) { // a