jihunchoi

def _sequence_mask(sequence_length, max_len=None):
    if max_len is None:
        max_len = sequence_length.data.max()
    batch_size = sequence_length.size(0)
    seq_range = torch.range(0, max_len - 1).long()
    seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len)
    seq_range_expand = Variable(seq_range_expand)
    if sequence_length.is_cuda:
        seq_range_expand = seq_range_expand.cuda()
    seq_length_expand = (sequence_length.unsqueeze(1)

volkancirik

"""
TreeLSTM[1] implementation in Pytorch

Based on dynet benchmarks : 
  https://github.com/neulab/dynet-benchmark/blob/master/dynet-py/treenn.py
  https://github.com/neulab/dynet-benchmark/blob/master/chainer/treenn.py
Other References:
  https://github.com/pytorch/examples/tree/master/word_language_model
  https://github.com/pfnet/chainer/blob/29c67fe1f2140fa8637201505b4c5e8556fad809/chainer/functions/activation/slstm.py
  https://github.com/stanfordnlp/treelstm

progrium

#!/bin/bash

cmd-hello() {
  declare desc="Displays a friendly hello"
  declare firstname="$1" lastname="$2"
  echo "Hello, $firstname $lastname."
}

cmd-help() {
  declare desc="Shows help information for a command"

jal278

#### Simple genetic algorithm
#### Joel Lehman

import random
errors=[]

def equation_error(x):
 left_side = 2.0*x-10.0
 right_side = 3.0
 return abs(left_side-right_side)