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#!/usr/bin/env bash
cat <<EOF
{
 "cells": [],
 "metadata": {},
 "nbformat": 4,
 "nbformat_minor": 2
}
EOF

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#!/usr/bin/env python
import sys
from pdb import set_trace
import sympy as ss
from mpmath import *
import matplotlib.pyplot as plt
from matplotlib import cm
import numpy as np
import argparse
from sympy.abc import x,y

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# remap prefix from 'C-b' to 'C-o'
unbind C-b
set-option -g prefix C-o
bind-key C-o send-prefix
bind-key A rename-session
unbind ,
# split panes using | and -
bind | split-window -h
bind - split-window -v
unbind '"'

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#!/usr/bin/env python3
import subprocess, os
import sys
ERR1 = 'Latexmk: Errors, so I did not complete making targets'
ERR2 = 'pdflatex: Command for \'pdflatex\' gave return code 256'
def main():
    proc = subprocess.Popen(['latexmk', '-pvc'] + sys.argv[1:],
                            stdin=subprocess.PIPE,
                            stdout=subprocess.PIPE,
                            stderr=subprocess.STDOUT,

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--   Usecase for a script runner.
--   Author: Pushpendre Rastogi
--   Date: 8 July 2017
-- Multiple small changes in code are a norm when developing machine learning software
-- in dynamic languages, such as lua, python, because of a large number of hyper-parameters
-- in machine learning code, and large number of typos because of lack of compile time
-- type checking. For example, lua does not even enforce the number of arguments to a
-- function to be the same as its signature. Because of this ML code typically needs to be
-- run multiple times during development with small changes/fixes.
-- ML code also commonly has costly processing that must be done on a dataset.

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diff --git a/mtrand/mtrand.pyx b/mtrand/mtrand.pyx
index 65c1954..79d0d70 100755
--- a/mtrand/mtrand.pyx
+++ b/mtrand/mtrand.pyx
@@ -71,6 +71,7 @@ cdef extern from "randomkit.h":
     rk_error rk_altfill(void *buffer, size_t size, int strong,
             rk_state *state) nogil
     double rk_gauss(rk_state *state) nogil
+    float rk_gauss_32bit(rk_state *state) nogil
     void rk_random_uint64(npy_uint64 off, npy_uint64 rng, npy_intp cnt,

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COMCAST: Thank you for patiently waiting.  I can certainly understand your
concern regarding the price increase in monthly statement. The promotional price
of service has ended and now you are paying regular charges for the current
services.  I have checked that you are currently paying $49.99 + taxes.
_Customer_: Yes, that is correct

COMCAST: I have a wonderful deal for you where you will be getting 20 + cable
channels . Along with this , you will be getting 25 MBPS of internet speed along
with one cable box (at no monthly rental ).  By bundling the deal you will get

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"""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

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# -*- coding: utf-8 -*-
'''
| Filename    : adaptive_inference.py
| Description : Implementation of core concepts from "Learning Adaptive Value of Information for Structured Prediction"
| Author      : Pushpendre Rastogi
| Created     : Wed Sep 16 12:29:09 2015 (-0400)
| Last-Updated: Thu Sep 17 03:13:58 2015 (-0400)
|           By: Pushpendre Rastogi
|     Update #: 73
'''

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'''
| Filename    : shift-reduce-ppda.py
| Description : A shift Reduce PPDA generator
| Author      : Pushpendre Rastogi
| Created     : Tue Sep  8 20:42:22 2015 (-0400)
| Last-Updated: Tue Sep  8 23:09:28 2015 (-0400)
|           By: Pushpendre Rastogi
|     Update #: 33
The paper "Relating Probabilistic Grammar and Automata" compare PCFGs and PPDAs.
They also explain how a PPDA produces a sentence. This class implements a PPDA