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*2vec papers

1. act2vec, trace2vec, log2vec, model2vec https://link.springer.com/chapter/10.1007/978-3-319-98648-7_18
2. apk2vec https://arxiv.org/abs/1809.05693
3. app2vec http://paul.rutgers.edu/~qma/research/ma_app2vec.pdf
4. author2vec http://dl.acm.org/citation.cfm?id=2889382
5. bb2vec https://arxiv.org/pdf/1809.09621.pdf
6. behavior2vec https://dl.acm.org/citation.cfm?id=3184454
7. care2vec https://arxiv.org/abs/1812.00715
8. cat2vec http://104.155.136.4:3000/forum?id=HyNxRZ9xg

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#!/usr/bin/env python
# -*- coding:UTF-8 -*-

import torch
import torch.nn as nn
import torch.nn.init as init


def weight_init(m):
    '''

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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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NLP and source code papers, very scattered and partial listing
(collected by Nathan Schneider and Brendan O'Connor)


ICML 2014
Maddison and Tarlow
Structured Generative Models of Natural Source Code
http://jmlr.org/proceedings/papers/v32/maddison14.pdf

ACL 2013

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import types
import tensorflow as tf 
import numpy as np

# Expressions are represented as lists of lists,
# in lisp style -- the symbol name is the head (first element)
# of the list, and the arguments follow.

# add an expression to an expression list, recursively if necessary.
def add_expr_to_list(exprlist, expr):

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# Implementation of a simple MLP network with one hidden layer. Tested on the iris data set.
# Requires: numpy, sklearn>=0.18.1, tensorflow>=1.0

# NOTE: In order to make the code simple, we rewrite x * W_1 + b_1 = x' * W_1'
# where x' = [x | 1] and W_1' is the matrix W_1 appended with a new row with elements b_1's.
# Similarly, for h * W_2 + b_2
import tensorflow as tf
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

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""" Deep Auto-Encoder implementation
	
	An auto-encoder works as follows:

	Data of dimension k is reduced to a lower dimension j using a matrix multiplication:
	softmax(W*x + b)  = x'
	
	where W is matrix from R^k --> R^j

	A reconstruction matrix W' maps back from R^j --> R^k

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1. General Background and Overview

• Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
• Models and Issues in Data Stream Systems
• Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
• Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
• [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

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The easiest way to start using the LLVM C++ API by example is to have LLVM generate the API usage for a given code sample. In this example it will emit the code required to rebuild the test.c sample by using LLVM:

$ clang -c -emit-llvm test.c -o test.ll
$ llc -march=cpp test.ll -o test.cpp

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#!/bin/bash
# create .bc file
clang -emit-llvm -c testcfg.c
# create .dot file
opt -dot-cfg testcfg.bc
# cteate png,pdf 
dot -Tpng -o main.png cfg.main.dot