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pmadhyastha / minimal_beamsearch.py
Created May 17, 2017 14:03 — forked from kastnerkyle/minimal_beamsearch.py
Minimal beam search example
# Author: Kyle Kastner
# License: BSD 3-Clause
# See core implementations here http://geekyisawesome.blogspot.ca/2016/10/using-beam-search-to-generate-most.html
# Also includes a reduction of the post by Yoav Goldberg to a script
# markov_lm.py
# https://gist.github.com/yoavg/d76121dfde2618422139
import numpy as np
import heapq
@pmadhyastha
pmadhyastha / recover_source_code.md
Created March 12, 2017 21:24 — forked from simonw/recover_source_code.md
How to recover lost Python source code if it's still resident in-memory

How to recover lost Python source code if it's still resident in-memory

I screwed up using git ("git checkout --" on the wrong file) and managed to delete the code I had just written... but it was still running in a process in a docker container. Here's how I got it back, using https://pypi.python.org/pypi/pyrasite/ and https://pypi.python.org/pypi/uncompyle6

Attach a shell to the docker container

Install GDB (needed by pyrasite)

apt-get update && apt-get install gdb
@pmadhyastha
pmadhyastha / rank_metrics.py
Created January 10, 2016 21:10 — forked from bwhite/rank_metrics.py
Ranking Metrics
"""Information Retrieval metrics
Useful Resources:
http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt
http://www.nii.ac.jp/TechReports/05-014E.pdf
http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf
http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf
Learning to Rank for Information Retrieval (Tie-Yan Liu)
"""
import numpy as np
@pmadhyastha
pmadhyastha / internet_radio_stream_aliases.sh
Created November 12, 2015 20:39 — forked from pwenzel/internet_radio_stream_aliases.sh
Internet Radio Streams Via Command Line
# 1. Install mplayer command line (via Brew, Macports, or APT)
# 2. Add the following aliases to ~/.profile
# 3. Type `source ~/.profile`
# 3. Type `news` or `current` to listen in your terminal
alias news="mplayer -playlist http://minnesota.publicradio.org/tools/play/streams/news.pls" # MPR News
alias current="mplayer -playlist http://minnesota.publicradio.org/tools/play/streams/the_current.pls" # The Current
alias classical="mplayer -playlist http://minnesota.publicradio.org/tools/play/streams/classical.pls" # Classical MPR
alias localcurrent="mplayer -playlist http://minnesota.publicradio.org/tools/play/streams/local.pls" # Local Current
alias heartland="mplayer -playlist http://minnesota.publicradio.org/tools/play/streams/radio_heartland.pls" # MPR Radio Heartland
class avg_perceptron_vanilla:
def avg_perceptron():
for t in range(max_iter):
sentence_count = 1
argmax_time_total = 0.0
while data_pool.has_next_data():
sentence_count += 1
data_instance = data_pool.get_next_data()
gold_global_vector = data_instance.gold_global_vector
current_global_vector = f_argmax(data_instance)
@pmadhyastha
pmadhyastha / clapack.pxd
Created June 1, 2014 17:11
Computing svd of a matrix using lapack's dgesdd available here: http://www.netlib.org/lapack/explore-html/db/db4/dgesdd_8f.html
#!/usr/bin/env python
from numpy cimport int
cdef extern void dgesdd_(char *jobz, int *m, int *n,
double a[], int *lda, double s[], double u[],
int *ldu, double vt[], int *ldvt, double work[],
int *lwork, int iwork[], int *info)
#triqfreq is a collections.Counter()
def represent_ps(self, trifreq):
hashpref = defaultdict(list)
scorepref = defaultdict(list)
reversehash = defaultdict(list)
content = [word.strip() for word in open(self.target)]