cosmozhang

'''
Original implementation
https://github.com/clab/dynet_tutorial_examples/blob/master/tutorial_parser.ipynb

The code structure and variable names are similar for better reference.

Not for serious business, just for some comparison between PyTorch and DyNet
(and I still prefer PyTorch)
'''
import torch as T

Tushar-N

import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence

seqs = ['gigantic_string','tiny_str','medium_str']

# make <pad> idx 0
vocab = ['<pad>'] + sorted(set(''.join(seqs)))

# make model

paclopes

// Fast Block Distributed CUDA Implementation of the Hungarian Algorithm
//
// Annex to the paper:
// Paulo A. C. Lopes, Satyendra Singh Yadav, Aleksandar Ilic, Sarat Kumar Patra , 
// "Fast Block Distributed CUDA Implementation of the Hungarian Algorithm",
// Parallel Computing
//
// Hungarian algorithm:
// (This algorithm was modified to result in an efficient GPU implementation, see paper)
//

brentp

# -*- coding: utf-8 -*-

u"""
Beta regression for modeling rates and proportions.

References
----------
Grün, Bettina, Ioannis Kosmidis, and Achim Zeileis. Extended beta regression
in R: Shaken, stirred, mixed, and partitioned. No. 2011-22. Working Papers in
Economics and Statistics, 2011.