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pip install logentries | |
Downloading/unpacking logentries | |
Running setup.py egg_info for package logentries | |
Traceback (most recent call last): | |
File "<string>", line 14, in <module> | |
File "/home/vagrant/build/logentries/setup.py", line 13, in <module> | |
long_description=open('README.rst').read(), | |
IOError: [Errno 2] No such file or directory: 'README.rst' | |
Complete output from command python setup.py egg_info: | |
Traceback (most recent call last): |
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Transactions: 999993 hits | |
Availability: 100.00 % | |
Elapsed time: 83.15 secs | |
Data transferred: 9.54 MB | |
Response time: 0.07 secs | |
Transaction rate: 12026.37 trans/sec | |
Throughput: 0.11 MB/sec | |
Concurrency: 830.26 | |
Successful transactions: 999993 | |
Failed transactions: 7 |
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./test_net_async.native | |
err (lib/monitor.ml.Error_ | |
((exn (Failure "SSL unsupported")) | |
(backtrace | |
("Raised by primitive operation at file \"lib/core_unix.ml\", line 111, characters 2-55" | |
"Called from file \"lib/interruptor.ml\", line 74, characters 16-132" | |
"")) | |
(monitor | |
(((name try_with) (here ()) (id 2) (has_seen_error true) | |
(is_detached true) (kill_index 0)))))). |
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{ | |
"objectType": "activity", | |
"id": "tag:search.twitter.com,2005:487332866842300416", | |
"postedTime": "2014-07-10T20:29:44.000Z", | |
"provider": { | |
"objectType": "service", | |
"displayName": "Twitter", | |
"link": "http://www.twitter.com" | |
}, | |
"link": "http://twitter.com/Danie11eWi1cox/statuses/487332866842300416", |
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Rscript classify-autosplit.R "james.txt" "/Users/travisbrady/pp/" TRUE | |
YOLoading required package: methods | |
Loaded glmnet 1.9-8 | |
randomForest 4.6-7 | |
Type rfNews() to see new features/changes/bug fixes. | |
Loading required package: rJava | |
Loading required package: iterators | |
Loading required package: parallel | |
4HI7 |
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# One pass mean and standard deviation in Ruby | |
def a_mean_std(a) | |
n = a.length.to_f | |
sum = 0.0 | |
sq_sum = 0.0 | |
for x in a | |
sum += x | |
sq_sum += x * x | |
end | |
mean = sum / n |
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def linspace(start, stop, num=50, endpoint=true) | |
start_f = start.to_f | |
stop_f = stop.to_f | |
step = (stop_f - start_f) / (num.to_f - (endpoint ? 1.0 : 0.0)) | |
i = 1 | |
n = num - 1 | |
result = [start_f] | |
while i < n | |
result.push(result.last + step) | |
i += 1 |
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################################ | |
# train | |
$ ocaml_gpr -verbose -cmd train -model foo.model < train.data | |
target variance: 53980.91115 | |
iter 1: MSLL=20677.0739875 SMSE=0.6434589 MAD=144.2473817 MAXAD=395.0879824 | |
iter 1: |gradient|=406648.85928 | |
result: MSLL=0.0000006 SMSE=1.0000000 MAD=186.1172019 MAXAD=488.0434778 | |
#################################################### | |
# Now apply, all data is just the mean of the series |
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1 290 Boston | |
2 59 Torii Hunter | |
3 55 Gawker | |
4 48 Sportsnation | |
5 45 Steve Bartman incident | |
6 32 Steve Horgan | |
7 30 Boston Red Sox | |
8 19 Chicago | |
9 17 Mike Epps | |
10 16 Colts |
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Facebook 21 | |
YouTube 12 | |
Twitter 12 | |
Lk Bajaj 10 | |
Absolut 9 | |
United States 9 | |
Adriana 8 | |
yahoo 8 | |
Linda Cunningham 6 | |
Adelaide Raiders 5 |