abali96

class AttentionWithContext(Layer):
    """
        Attention operation, with a context/query vector, for temporal data.
        Supports Masking.
        Follows the work of Yang et al. [https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf]
        "Hierarchical Attention Networks for Document Classification"
        by using a context vector to assist the attention
        # Input shape
            3D tensor with shape: `(samples, steps, features)`.
        # Output shape

abali96

#include <QGuiApplication>
#include <QQmlApplicationEngine>
#include <QQmlComponent>
#include <QDebug>
#include <QQmlProperty>
#include <QQuickView>

//class MyClass : public QObject
//{
//    Q_OBJECT

abali96

board = [["o", "a", "g", "r"], ["s", "q", "n", "t"], ["e", "i", "s", "g"]]


def spell_all_words(board, word_dictionary):
    words_found = list()
    for row_number in range(len(board)):
        for col_number in range(len(board[row_number])):
            for i_offset in range(-1, 2):  # get our adjacent characters
                if not 0 <= row_number + i_offset < len(board):
                    continue

abali96

https://gist.github.com/abali96/438adb50415384149911

abali96

  if operands.each {|operand| operand[0] == "-"}
    x = operands.shift
    puts operands
    pre_arguments = '"' + operands.join('", "').gsub(/-/, '') + '"'
    add(x, pre_arguments)
  end
  
  # the add method accepts arguments as strings like add("1", "2", "3")