marty1885

#include <htm/algorithms/TemporalMemory.hpp>
#include <htm/encoders/ScalarEncoder.hpp>
using namespace htm;

float benchmarkTemporalMemory(const std::vector<UInt>& out_shape, const std::vector<SDR>& x, size_t num_epoch)
{
	TemporalMemory tm;
        tm.initialize(out_shape, 16, 2, 0.21, 0.1, 1, 1024, 0.1, 0.1, 0, 42, 1, 1024, false);
	// sp.permanences_ = sp.permanences_.cast(DType::Half);

marty1885

import torch
from torch import nn, optim
import torch.functional as F

import matplotlib.pyplot as plt
import numpy as np

# Use ROOT as a binding between C++ and Python
import ROOT
gROOT = ROOT.gROOT

marty1885

void missing_creator_pct()
{
	// Now we can use the new and improved RDataFrame interface
	auto rdf = ROOT::RDataFrame("library", "library.root");
	auto rdf_with_year = rdf.Filter("publication_year != 0x7fffffff");
	auto rdf_missing = rdf_with_year.Filter("creator != \"\"");
	std::map<std::string, int> books;
	std::map<std::string, int> all_books;
	rdf_with_year.Foreach([&](std::string title, int year) {all_books[title] = year;}, {"title", "publication_year"});
	rdf_missing.Foreach([&](std::string title, int year) {books[title] = year;}, {"title", "publication_year"});

marty1885

void missing_creator()
{
	// Now we can use the new and improved RDataFrame interface
	auto rdf = ROOT::RDataFrame("library", "library.root");
	auto rdf_missing = rdf.Filter("creator != \"\" && publication_year != 0x7fffffff");
	std::map<std::string, int> books;
	rdf_missing.Foreach([&](std::string title, int year) {books[title] = year;}, {"title", "publication_year"});
	auto h1 = new TH1F("h1", "books with missing creator", 100, 1850, 2019);
	for(auto [title, year] : books)
		h1->Fill(year);

marty1885

// A basic parser, it might get stuff wrong, but good enought for EDA
Int_t parse_year(std::string year)
{
	if(year == "")
		throw std::runtime_error("year is empty");
	if(year[0] == '[' || year[0] == 'c' || year[0] == 'p') // Handle format of [2000], c2000 and p2000
		return std::stoi(std::string(year.begin()+1, year.begin()+5));
	else if(isdigit(year[0]) == true)
		return std::stoi(std::string(year.begin(), year.begin()+4));
	throw std::runtime_error("cannot parse format");

marty1885

void count_zeros()
{
	// Now we can use the new and improved RDataFrame interface
	auto rdf = ROOT::RDataFrame("library_raw", "library.root");

	auto columns = rdf.GetColumnNames();
	auto empty_lst = std::map<std::string, size_t>();

	ProgressDisplay disp(*rdf.Count()*columns.size());

marty1885

void to_root_raw()
{
	// Create a reader from CSV
	csv::CSVReader reader("checkouts-by-title.csv");

	// Create a ROOT File. For the first pass, we'll be saving everything as a string
	auto f = new TFile("library.root", "recreate");
	TTree *t = new TTree("library_raw","raw data from csv file");
	auto fields = reader.get_col_names();
	auto buf = std::vector<std::string>(fields.size());

marty1885

//Released under BSD-3 Cluse licsence
//How to run: root -b stackedsp.cpp
//or: c++ stackedsp.cpp -o stackedsp -O3 -lEtaler `root-config --cflags --ldflags --glibs` && ./stackedsp
//Assuming you have Etaler installed in /use/local/lib. Change this if you have it in other places
#pragma cling load("/usr/local/lib/libEtaler.so")
#include <Etaler/Etaler.hpp>
#include <Etaler/Algorithms/SpatialPooler.hpp>
#include <Etaler/Encoders/GridCell1d.hpp>
#include <Etaler/Encoders/Scalar.hpp>
#include <Etaler/Backends/OpenCLBackend.hpp>

marty1885

marty1885

#include <iostream>
#include <vector>
#include <algorithm>

int main()
{
	size_t n = 0;
	size_t count = 0;
	while(std::cin >> n) {
		std::vector<size_t> vec(n);