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import torch | |
from torch import LongTensor | |
from torch.nn import Embedding, LSTM | |
from torch.autograd import Variable | |
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence | |
## We want to run LSTM on a batch of 3 character sequences ['long_str', 'tiny', 'medium'] | |
# | |
# Step 1: Construct Vocabulary | |
# Step 2: Load indexed data (list of instances, where each instance is list of character indices) |
We can make this file beautiful and searchable if this error is corrected: No tabs found in this TSV file in line 0.
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7 | |
2 | |
1 | |
0 | |
4 | |
1 | |
4 | |
9 | |
5 | |
9 |
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function getUserId(fbid) { | |
return fbid.split(':')[1]; | |
} | |
requireLazy( | |
['MercuryTypingReceiver', 'MercuryThreads', 'ShortProfiles'], | |
(MercuryTypingReceiver, MercuryThreads, ShortProfiles) => { | |
MercuryTypingReceiver | |
.get() |
Picking the right architecture = Picking the right battles + Managing trade-offs
- Clarify and agree on the scope of the system
- User cases (description of sequences of events that, taken together, lead to a system doing something useful)
- Who is going to use it?
- How are they going to use it?
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using System; | |
using System.Collections.Generic; | |
using System.IO; | |
using System.Linq; | |
using System.Text; | |
namespace SuffixTreeAlgorithm | |
{ | |
public class SuffixTree | |
{ |