4u9u5t 4u9u5tsong

Useful Spacemacs commands

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

DRAMSim2/Makefile: comment -DLOG_OUTPUT for extra logs

	Latency Comparison Numbers
	--------------------------
	L1 cache reference 0.5 ns
	Branch mispredict 5 ns
	L2 cache reference 7 ns 14x L1 cache
	Mutex lock/unlock 25 ns
	Main memory reference 100 ns 20x L2 cache, 200x L1 cache
	Compress 1K bytes with Zippy 3,000 ns 3 us
	Send 1K bytes over 1 Gbps network 10,000 ns 10 us
	Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD

	def float64Tobinary(num):
	return ''.join(bin(ord(c)).replace('0b', '').rjust(8, '0') for c in
	struct.pack('!d', num))


	def convertTo1DIntArray(d, start, end, patternwidth, f):
	sview = 'S' + str(patternwidth)
	return f(d[start:end]).view(sview)

	# Got an answer from the Scipy user group:
	# http://stackoverflow.com/questions/8955448/save-load-scipy-sparse-csr-matrix-in-portable-data-format
	# A csr_matrix has 3 data attributes that matter: .data, .indices, and .indptr. All are simple ndarrays, so numpy.save will work on them. Save the three arrays with numpy.save or numpy.savez, load them back with numpy.load, and then recreate the sparse matrix object with:

	# new_csr = csr_matrix((data, indices, indptr), shape=(M, N))
	# So for example:

	def save_sparse_csr(filename,array):
	np.savez(filename,data = array.data ,indices=array.indices,
	indptr =array.indptr, shape=array.shape )

	#!/usr/bin/env python

	import struct
	import scipy as sp
	import numpy as np


	def binary(num):
	return ''.join(bin(ord(c)).replace('0b', '').rjust(8, '0') for c in struct.pack('!d', num))

	ifstream pbinfile(argv[2]);

	vector<unordered_set<int> > numbers;
	while(getline(pbinfile, stemp)){
	istringstream buffer(stemp);
	vector<int> line((istream_iterator<int>(buffer)),
	istream_iterator<int>());
	unordered_set<int> s(line.begin(), line.end());
	numbers.push_back(s);
	}

	inpstream = subprocess.check_output(cmdstr).split('\n')
	data = sp.genfromtxt(inpstream, dtype=np.uint64)