propagation.hpp

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/*
 * Copyright (C) 1998-2018 ALPS Collaboration. See COPYRIGHT.TXT
 * All rights reserved. Use is subject to license terms. See LICENSE.TXT
 * For use in publications, see ACKNOWLEDGE.TXT
 */
#pragma once

#include <Eigen/Core>

#include <alps/alea/complex_op.hpp>

// TODO maybe a better way?
#include <alps/alea/mean.hpp>
#include <alps/alea/variance.hpp>
#include <alps/alea/covariance.hpp>
#include <alps/alea/autocorr.hpp>
#include <alps/alea/batch.hpp>

namespace alps { namespace alea {

struct no_prop { };

struct linear_prop
{
    linear_prop() : dx_(0) { }

    linear_prop(double dx) : dx_(dx) { assert(dx >= 0); }

    double dx() const { return dx_; }

private:
    double dx_;
};

struct sampling_prop
{
    sampling_prop(size_t nsamples=1024) : nsamples_(nsamples) { }

    size_t nsamples() const { return nsamples_; }

private:
    size_t nsamples_;
};

struct jackknife_prop { };

struct bootstrap_prop
{
    bootstrap_prop(size_t nsamples=1024) : nsamples_(nsamples) { }

    size_t nsamples() const { return nsamples_; }

private:
    size_t nsamples_;
};

template <typename T>
typename eigen<T>::matrix jacobian(const transformer<T> &f, column<T> x, double dx);


template <typename T>
batch_data<T> jackknife(const batch_data<T> &in, const transformer<T> &tf);

}}