From 4dce3b1319213fc77a03192437cc7c38aed65a9f Mon Sep 17 00:00:00 2001 From: eltrompetero Date: Thu, 29 Apr 2021 20:29:25 +0200 Subject: [PATCH] compiled for pypi upload --- docs/_build/html/.buildinfo | 2 +- docs/_build/html/_static/basic.css | 167 ++++++++++--- docs/_build/html/_static/doctools.js | 14 +- docs/_build/html/_static/jquery.js | 4 +- docs/_build/html/_static/pygments.css | 7 +- docs/_build/html/_static/searchtools.js | 39 +-- docs/_build/html/_static/underscore.js | 37 +-- .../html/coniii_rst/coniii.enumerate.html | 28 +-- .../coniii_rst/coniii.general_model_rmc.html | 6 +- docs/_build/html/coniii_rst/coniii.html | 6 +- .../coniii_rst/coniii.ising.automaton.html | 14 +- docs/_build/html/coniii_rst/coniii.ising.html | 6 +- .../coniii.ising.test_automaton.html | 6 +- .../html/coniii_rst/coniii.mc_hist.html | 6 +- .../coniii_rst/coniii.mean_field_ising.html | 82 +++--- .../coniii.pseudo_inverse_ising.html | 20 +- .../html/coniii_rst/coniii.samplers.html | 138 +++++------ .../html/coniii_rst/coniii.solvers.html | 234 +++++++++++++----- .../html/coniii_rst/coniii.test_samplers.html | 16 +- .../html/coniii_rst/coniii.test_solvers.html | 18 +- .../html/coniii_rst/coniii.test_utils.html | 26 +- docs/_build/html/coniii_rst/coniii.utils.html | 70 +++--- docs/_build/html/coniii_rst/modules.html | 6 +- docs/_build/html/genindex.html | 17 +- docs/_build/html/index.html | 6 +- docs/_build/html/objects.inv | Bin 2501 -> 2513 bytes docs/_build/html/py-modindex.html | 6 +- docs/_build/html/search.html | 7 +- docs/_build/html/searchindex.js | 2 +- 29 files changed, 594 insertions(+), 396 deletions(-) diff --git a/docs/_build/html/.buildinfo b/docs/_build/html/.buildinfo index a0a16e0..6658e90 100644 --- a/docs/_build/html/.buildinfo +++ b/docs/_build/html/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: d582b8ee7aca1b1fc78d792917fbc3cb +config: 6427726ec3df5aec97e66ae0847071db tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/_build/html/_static/basic.css b/docs/_build/html/_static/basic.css index 0119285..b3bdc00 100644 --- a/docs/_build/html/_static/basic.css +++ b/docs/_build/html/_static/basic.css @@ -4,7 +4,7 @@ * * Sphinx stylesheet -- basic theme. * - * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ @@ -15,6 +15,12 @@ div.clearer { clear: both; } +div.section::after { + display: block; + content: ''; + clear: left; +} + /* -- relbar ---------------------------------------------------------------- */ div.related { @@ -271,25 +277,25 @@ p.rubric { font-weight: bold; } -img.align-left, .figure.align-left, object.align-left { +img.align-left, figure.align-left, .figure.align-left, object.align-left { clear: left; float: left; margin-right: 1em; } -img.align-right, .figure.align-right, object.align-right { +img.align-right, figure.align-right, .figure.align-right, object.align-right { clear: right; float: right; margin-left: 1em; } -img.align-center, .figure.align-center, object.align-center { +img.align-center, figure.align-center, .figure.align-center, object.align-center { display: block; margin-left: auto; margin-right: auto; } -img.align-default, .figure.align-default { +img.align-default, figure.align-default, .figure.align-default { display: block; margin-left: auto; margin-right: auto; @@ -313,24 +319,31 @@ img.align-default, .figure.align-default { /* -- sidebars -------------------------------------------------------------- */ -div.sidebar { +div.sidebar, +aside.sidebar { margin: 0 0 0.5em 1em; border: 1px solid #ddb; - padding: 7px 7px 0 7px; + padding: 7px; background-color: #ffe; width: 40%; float: right; + clear: right; + overflow-x: auto; } p.sidebar-title { font-weight: bold; } +div.admonition, div.topic, blockquote { + clear: left; +} + /* -- topics ---------------------------------------------------------------- */ div.topic { border: 1px solid #ccc; - padding: 7px 7px 0 7px; + padding: 7px; margin: 10px 0 10px 0; } @@ -352,10 +365,6 @@ div.admonition dt { font-weight: bold; } -div.admonition dl { - margin-bottom: 0; -} - p.admonition-title { margin: 0px 10px 5px 0px; font-weight: bold; @@ -366,9 +375,30 @@ div.body p.centered { margin-top: 25px; } +/* -- content of sidebars/topics/admonitions -------------------------------- */ + +div.sidebar > :last-child, +aside.sidebar > :last-child, +div.topic > :last-child, +div.admonition > :last-child { + margin-bottom: 0; +} + +div.sidebar::after, +aside.sidebar::after, +div.topic::after, +div.admonition::after, +blockquote::after { + display: block; + content: ''; + clear: both; +} + /* -- tables ---------------------------------------------------------------- */ table.docutils { + margin-top: 10px; + margin-bottom: 10px; border: 0; border-collapse: collapse; } @@ -416,32 +446,34 @@ table.citation td { border-bottom: none; } -th > p:first-child, -td > p:first-child { +th > :first-child, +td > :first-child { margin-top: 0px; } -th > p:last-child, -td > p:last-child { +th > :last-child, +td > :last-child { margin-bottom: 0px; } /* -- figures --------------------------------------------------------------- */ -div.figure { +div.figure, figure { margin: 0.5em; padding: 0.5em; } -div.figure p.caption { +div.figure p.caption, figcaption { padding: 0.3em; } -div.figure p.caption span.caption-number { +div.figure p.caption span.caption-number, +figcaption span.caption-number { font-style: italic; } -div.figure p.caption span.caption-text { +div.figure p.caption span.caption-text, +figcaption span.caption-text { } /* -- field list styles ----------------------------------------------------- */ @@ -468,6 +500,10 @@ table.field-list td, table.field-list th { /* -- hlist styles ---------------------------------------------------------- */ +table.hlist { + margin: 1em 0; +} + table.hlist td { vertical-align: top; } @@ -495,17 +531,37 @@ ol.upperroman { list-style: upper-roman; } -li > p:first-child { +:not(li) > ol > li:first-child > :first-child, +:not(li) > ul > li:first-child > :first-child { margin-top: 0px; } -li > p:last-child { +:not(li) > ol > li:last-child > :last-child, +:not(li) > ul > li:last-child > :last-child { margin-bottom: 0px; } +ol.simple ol p, +ol.simple ul p, +ul.simple ol p, +ul.simple ul p { + margin-top: 0; +} + +ol.simple > li:not(:first-child) > p, +ul.simple > li:not(:first-child) > p { + margin-top: 0; +} + +ol.simple p, +ul.simple p { + margin-bottom: 0; +} + dl.footnote > dt, dl.citation > dt { float: left; + margin-right: 0.5em; } dl.footnote > dd, @@ -546,7 +602,7 @@ dl { margin-bottom: 15px; } -dd > p:first-child { +dd > :first-child { margin-top: 0px; } @@ -560,6 +616,11 @@ dd { margin-left: 30px; } +dl > dd:last-child, +dl > dd:last-child > :last-child { + margin-bottom: 0; +} + dt:target, span.highlighted { background-color: #fbe54e; } @@ -637,6 +698,10 @@ pre { overflow-y: hidden; /* fixes display issues on Chrome browsers */ } +pre, div[class*="highlight-"] { + clear: both; +} + span.pre { -moz-hyphens: none; -ms-hyphens: none; @@ -644,22 +709,57 @@ span.pre { hyphens: none; } +div[class*="highlight-"] { + margin: 1em 0; +} + td.linenos pre { - padding: 5px 0px; border: 0; background-color: transparent; color: #aaa; } table.highlighttable { - margin-left: 0.5em; + display: block; +} + +table.highlighttable tbody { + display: block; +} + +table.highlighttable tr { + display: flex; } table.highlighttable td { - padding: 0 0.5em 0 0.5em; + margin: 0; + padding: 0; +} + +table.highlighttable td.linenos { + padding-right: 0.5em; +} + +table.highlighttable td.code { + flex: 1; + overflow: hidden; +} + +.highlight .hll { + display: block; +} + +div.highlight pre, +table.highlighttable pre { + margin: 0; +} + +div.code-block-caption + div { + margin-top: 0; } div.code-block-caption { + margin-top: 1em; padding: 2px 5px; font-size: small; } @@ -668,10 +768,8 @@ div.code-block-caption code { background-color: transparent; } -div.code-block-caption + div > div.highlight > pre { - margin-top: 0; -} - +table.highlighttable td.linenos, +span.linenos, div.doctest > div.highlight span.gp { /* gp: Generic.Prompt */ user-select: none; } @@ -685,11 +783,7 @@ div.code-block-caption span.caption-text { } div.literal-block-wrapper { - padding: 1em 1em 0; -} - -div.literal-block-wrapper div.highlight { - margin: 0; + margin: 1em 0; } code.descname { @@ -740,8 +834,7 @@ span.eqno { } span.eqno a.headerlink { - position: relative; - left: 0px; + position: absolute; z-index: 1; } diff --git a/docs/_build/html/_static/doctools.js b/docs/_build/html/_static/doctools.js index daccd20..61ac9d2 100644 --- a/docs/_build/html/_static/doctools.js +++ b/docs/_build/html/_static/doctools.js @@ -4,7 +4,7 @@ * * Sphinx JavaScript utilities for all documentation. * - * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ @@ -29,9 +29,14 @@ if (!window.console || !console.firebug) { /** * small helper function to urldecode strings + * + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL */ jQuery.urldecode = function(x) { - return decodeURIComponent(x).replace(/\+/g, ' '); + if (!x) { + return x + } + return decodeURIComponent(x.replace(/\+/g, ' ')); }; /** @@ -285,9 +290,10 @@ var Documentation = { initOnKeyListeners: function() { $(document).keydown(function(event) { var activeElementType = document.activeElement.tagName; - // don't navigate when in search box or textarea + // don't navigate when in search box, textarea, dropdown or button if (activeElementType !== 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https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions + */ + escapeRegExp : function(string) { + return string.replace(/[.*+\-?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string + }, + /** * search for full-text terms in the index */ @@ -403,13 +409,14 @@ var Search = { ]; // add support for partial matches if (word.length > 2) { + var word_regex = this.escapeRegExp(word); for (var w in terms) { - if (w.match(word) && !terms[word]) { + if (w.match(word_regex) && !terms[word]) { _o.push({files: terms[w], score: Scorer.partialTerm}) } } for (var w in titleterms) { - if (w.match(word) && !titleterms[word]) { + if (w.match(word_regex) && !titleterms[word]) { _o.push({files: titleterms[w], score: Scorer.partialTitle}) } } diff --git a/docs/_build/html/_static/underscore.js b/docs/_build/html/_static/underscore.js index 5b55f32..166240e 100644 --- a/docs/_build/html/_static/underscore.js +++ b/docs/_build/html/_static/underscore.js @@ -1,31 +1,6 @@ -// 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    coniii.enumerate module

    -coniii.enumerate.fast_logsumexp(X, coeffs=None)
    +coniii.enumerate.fast_logsumexp(X, coeffs=None)

    Simplified version of logsumexp to do correlation calculation in Ising equation files. Scipy’s logsumexp can be around 10x slower in comparison.

    @@ -55,7 +55,7 @@
    -coniii.enumerate.get_3idx(n)
    +coniii.enumerate.get_3idx(n)

    Get binary 3D matrix with truth values where index values correspond to the index of all possible ijk parameters. We can do this by recognizing that the pattern along each plane in the third dimension is like the upper triangle pattern that just moves @@ -64,7 +64,7 @@

    -coniii.enumerate.get_nidx(k, n)
    +coniii.enumerate.get_nidx(k, n)

    Get the kth order indices corresponding to all the states in which k elements are firing up out of n spins. The ordering correspond to that returned by bin_states().

    @@ -75,25 +75,25 @@
    -coniii.enumerate.get_terms(subix, prefix, binstate, br, ix0)
    +coniii.enumerate.get_terms(subix, prefix, binstate, br, ix0)

    Spins are put in explicitly

    -coniii.enumerate.get_terms01(subix, prefix, binstate, br, ix0)
    +coniii.enumerate.get_terms01(subix, prefix, binstate, br, ix0)

    Specific to {0,1}.

    -coniii.enumerate.get_terms11(subix, prefix, binstate, br, ix0)
    +coniii.enumerate.get_terms11(subix, prefix, binstate, br, ix0)

    Specific to {-1,1}.

    -coniii.enumerate.mp_fast_logsumexp(X, coeffs=None)
    +coniii.enumerate.mp_fast_logsumexp(X, coeffs=None)

    fast_logsumexp for high precision numbers using mpmath.

    Xndarray

    Terms inside logs.

    @@ -111,7 +111,7 @@
    -coniii.enumerate.pairwise(n, sym=0, **kwargs)
    +coniii.enumerate.pairwise(n, sym=0, **kwargs)

    Wrapper for writing pairwise maxent model (Ising) files.

    nint

    System size.

    @@ -125,7 +125,7 @@
    -coniii.enumerate.triplet(n, sym=0, **kwargs)
    +coniii.enumerate.triplet(n, sym=0, **kwargs)

    Wrapper for writing triplet-order maxent model.

    nint

    System size.

    @@ -139,7 +139,7 @@
    -coniii.enumerate.write_eqns(n, sym, corrTermsIx, suffix='', high_prec=False)
    +coniii.enumerate.write_eqns(n, sym, corrTermsIx, suffix='', high_prec=False)

    Create strings for writing out the equations and then write them to file.

    TODO: This code needs some cleanup.

    @@ -160,7 +160,7 @@
    -coniii.enumerate.write_py(n, sym, contraintTermsIx, signs, expterms, Z, extra='', suffix='', high_prec=False)
    +coniii.enumerate.write_py(n, sym, contraintTermsIx, signs, expterms, Z, extra='', suffix='', high_prec=False)

    Write out Ising equations for Python.

    nint

    System size.

    @@ -250,7 +250,7 @@

    Quick search

    ©2018, Edward D. Lee, Bryan C. Daniels. | - Powered by Sphinx 3.0.3 + Powered by Sphinx 3.5.4 & Alabaster 0.7.12 | diff --git a/docs/_build/html/coniii_rst/coniii.general_model_rmc.html b/docs/_build/html/coniii_rst/coniii.general_model_rmc.html index 8933619..66c6268 100644 --- a/docs/_build/html/coniii_rst/coniii.general_model_rmc.html +++ b/docs/_build/html/coniii_rst/coniii.general_model_rmc.html @@ -4,14 +4,14 @@ + coniii.general_model_rmc module — ConIII 1.1.9 documentation - + - @@ -94,7 +94,7 @@

    Quick search

    ©2018, Edward D. Lee, Bryan C. Daniels. | - Powered by Sphinx 3.0.3 + Powered by Sphinx 3.5.4 & Alabaster 0.7.12 | diff --git a/docs/_build/html/coniii_rst/coniii.html b/docs/_build/html/coniii_rst/coniii.html index 6b10ad0..7e8082c 100644 --- a/docs/_build/html/coniii_rst/coniii.html +++ b/docs/_build/html/coniii_rst/coniii.html @@ -4,14 +4,14 @@ + coniii package — ConIII 1.1.9 documentation - + - @@ -140,7 +140,7 @@

    Quick search

    ©2018, Edward D. Lee, Bryan C. Daniels. | - Powered by Sphinx 3.0.3 + Powered by Sphinx 3.5.4 & Alabaster 0.7.12 | diff --git a/docs/_build/html/coniii_rst/coniii.ising.automaton.html b/docs/_build/html/coniii_rst/coniii.ising.automaton.html index 133e0a4..3ccc59f 100644 --- a/docs/_build/html/coniii_rst/coniii.ising.automaton.html +++ b/docs/_build/html/coniii_rst/coniii.ising.automaton.html @@ -4,14 +4,14 @@ + coniii.ising.automaton module — ConIII 1.1.9 documentation - + - @@ -36,13 +36,13 @@

    coniii.ising.automaton module

    -class coniii.ising.automaton.Ising2D(dim, J, h=0, rng=None)
    +class coniii.ising.automaton.Ising2D(dim, J, h=0, rng=None)

    Bases: object

    Simulation of the ferromagnetic Ising model on a 2D periodic lattice with quenched disorder in the local fields.

    -static flip_metropolis(i, j, h, J, lattice)
    +static flip_metropolis(i, j, h, J, lattice)

    Flip a single lattice spin using Metropolis sampling.

    i : int j : int

    @@ -50,7 +50,7 @@
    -iterate(n_iters, systematic=True)
    +iterate(n_iters, systematic=True)

    n_iters : int systematic : bool,True

    @@ -62,7 +62,7 @@
    -coniii.ising.automaton.coarse_grain(lattice, factor)
    +coniii.ising.automaton.coarse_grain(lattice, factor)

    Block spin renormalization with majority rule.

    latticendarray

    +/-1

    @@ -142,7 +142,7 @@

    Quick search

    ©2018, Edward D. Lee, Bryan C. Daniels. | - Powered by Sphinx 3.0.3 + Powered by Sphinx 3.5.4 & Alabaster 0.7.12 | diff --git a/docs/_build/html/coniii_rst/coniii.ising.html b/docs/_build/html/coniii_rst/coniii.ising.html index f5b521b..fc37766 100644 --- a/docs/_build/html/coniii_rst/coniii.ising.html +++ b/docs/_build/html/coniii_rst/coniii.ising.html @@ -4,14 +4,14 @@ + coniii.ising package — ConIII 1.1.9 documentation - + - @@ -114,7 +114,7 @@

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    ©2018, Edward D. Lee, Bryan C. Daniels. | - Powered by Sphinx 3.0.3 + Powered by Sphinx 3.5.4 & Alabaster 0.7.12 | diff --git a/docs/_build/html/coniii_rst/coniii.ising.test_automaton.html b/docs/_build/html/coniii_rst/coniii.ising.test_automaton.html index 267d717..c779ed2 100644 --- a/docs/_build/html/coniii_rst/coniii.ising.test_automaton.html +++ b/docs/_build/html/coniii_rst/coniii.ising.test_automaton.html @@ -4,14 +4,14 @@ + coniii.ising.test_automaton module — ConIII 1.1.9 documentation - + - @@ -104,7 +104,7 @@

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    ©2018, Edward D. Lee, Bryan C. Daniels. | - Powered by Sphinx 3.0.3 + Powered by Sphinx 3.5.4 & Alabaster 0.7.12 | diff --git a/docs/_build/html/coniii_rst/coniii.mc_hist.html b/docs/_build/html/coniii_rst/coniii.mc_hist.html index ad5cdd2..aafbf57 100644 --- a/docs/_build/html/coniii_rst/coniii.mc_hist.html +++ b/docs/_build/html/coniii_rst/coniii.mc_hist.html @@ -4,14 +4,14 @@ + coniii.mc_hist module — ConIII 1.1.9 documentation - + - @@ -94,7 +94,7 @@

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    ©2018, Edward D. Lee, Bryan C. Daniels. | - Powered by Sphinx 3.0.3 + Powered by Sphinx 3.5.4 & Alabaster 0.7.12 | diff --git a/docs/_build/html/coniii_rst/coniii.mean_field_ising.html b/docs/_build/html/coniii_rst/coniii.mean_field_ising.html index 0b17611..6b03244 100644 --- a/docs/_build/html/coniii_rst/coniii.mean_field_ising.html +++ b/docs/_build/html/coniii_rst/coniii.mean_field_ising.html @@ -4,14 +4,14 @@ + coniii.mean_field_ising module — ConIII 1.1.9 documentation - + - @@ -34,7 +34,7 @@

    coniii.mean_field_ising module

    -coniii.mean_field_ising.FHomogeneous(h, J, N, m)
    +coniii.mean_field_ising.FHomogeneous(h, J, N, m)

    Use Hubbard-Stratonovich (auxiliary field) to calculate the (free energy?) of a homogeneous system as a function of the field m (m equals the mean field as N -> infinity?).

    @@ -42,27 +42,27 @@
    -coniii.mean_field_ising.JfullFromCluster(Jcluster, cluster, N)
    +coniii.mean_field_ising.JfullFromCluster(Jcluster, cluster, N)

    NOTE: There is perhaps a faster way of doing this?

    -coniii.mean_field_ising.JmeanField(coocMat, **kwargs)
    +coniii.mean_field_ising.JmeanField(coocMat, **kwargs)

    See SmeanField for important optional arguments, including noninteracting prior weighting.

    -coniii.mean_field_ising.SHomogeneous(h, J, N)
    +coniii.mean_field_ising.SHomogeneous(h, J, N)

    Use Hubbard-Stratonovich (auxiliary field) to numerically calculate entropy of a homogeneous system.

    -coniii.mean_field_ising.SmeanField(cluster, coocMat, meanFieldPriorLmbda=0.0, numSamples=None, indTerm=True, alternateEnt=False, useRegularizedEq=True)
    +coniii.mean_field_ising.SmeanField(cluster, coocMat, meanFieldPriorLmbda=0.0, numSamples=None, indTerm=True, alternateEnt=False, useRegularizedEq=True)

    meanFieldPriorLmbda (0.): 3.23.2014 indTerm (True) : As of 2.19.2014, I’m not

    @@ -79,7 +79,7 @@
    -coniii.mean_field_ising.aboveDiagFlat(mat, keepDiag=False, offDiagMult=None)
    +coniii.mean_field_ising.aboveDiagFlat(mat, keepDiag=False, offDiagMult=None)

    Return a flattened list of all elements of the matrix above the diagonal.

    Use offDiagMult = 2 for symmetric J matrix.

    @@ -87,45 +87,45 @@
    -coniii.mean_field_ising.analyticEntropy(J)
    +coniii.mean_field_ising.analyticEntropy(J)

    In nats.

    -coniii.mean_field_ising.avgE(h, J, ell, T)
    +coniii.mean_field_ising.avgE(h, J, ell, T)
    -coniii.mean_field_ising.avgmHomogeneous(h, J, N)
    +coniii.mean_field_ising.avgmHomogeneous(h, J, N)
    -coniii.mean_field_ising.avgxHomogeneous(h, J, N)
    +coniii.mean_field_ising.avgxHomogeneous(h, J, N)
    -coniii.mean_field_ising.coocCluster(coocMat, cluster)
    +coniii.mean_field_ising.coocCluster(coocMat, cluster)

    Sort coocMat by the cluster indices

    -coniii.mean_field_ising.coocExpectations(J, hext=0, zeroBelowDiag=True, minSize=0)
    +coniii.mean_field_ising.coocExpectations(J, hext=0, zeroBelowDiag=True, minSize=0)
    -coniii.mean_field_ising.coocMatBayesianMean(coocMat, numFights)
    +coniii.mean_field_ising.coocMatBayesianMean(coocMat, numFights)

    Using “Laplace’s method”

    -coniii.mean_field_ising.coocSampleCovariance(samples, bayesianMean=True, includePrior=True)
    +coniii.mean_field_ising.coocSampleCovariance(samples, bayesianMean=True, includePrior=True)
    includePrior (True)Include diagonal component corresponding

    to ell*(ell-1)/2 prior residuals for interaction parameters

    @@ -135,14 +135,14 @@
    -coniii.mean_field_ising.coocStdevsFlat(coocMat, numFights)
    +coniii.mean_field_ising.coocStdevsFlat(coocMat, numFights)

    Returns a flattened expected standard deviation matrix used to divide deltaCooc to turn it into z scores.

    -coniii.mean_field_ising.cooccurrence_matrix(samples, keep_diag=True)
    +coniii.mean_field_ising.cooccurrence_matrix(samples, keep_diag=True)

    Matrix of pairwise correlations. Only upper right triangle is filled.

    samples : ndarray keep_diag : bool, True

    @@ -154,117 +154,117 @@
    -coniii.mean_field_ising.dFdT(h, J, N, m)
    +coniii.mean_field_ising.dFdT(h, J, N, m)
    -coniii.mean_field_ising.diagFlatIndex(i, j, ell)
    +coniii.mean_field_ising.diagFlatIndex(i, j, ell)

    Should have j>=i…

    -coniii.mean_field_ising.dmdT(h, J, ell, T)
    +coniii.mean_field_ising.dmdT(h, J, ell, T)
    -coniii.mean_field_ising.fightPossibilities(ell, minSize=0)
    +coniii.mean_field_ising.fightPossibilities(ell, minSize=0)
    -coniii.mean_field_ising.findJmatrixAnalytic_CoocMat(coocMatData, Jinit=None, bayesianMean=False, numSamples=None, priorLmbda=0.0, minSize=0)
    +coniii.mean_field_ising.findJmatrixAnalytic_CoocMat(coocMatData, Jinit=None, bayesianMean=False, numSamples=None, priorLmbda=0.0, minSize=0)
    -coniii.mean_field_ising.fourthOrderCoocMat(samples, slowMethod=True)
    +coniii.mean_field_ising.fourthOrderCoocMat(samples, slowMethod=True)
    -coniii.mean_field_ising.independentEntropyHomogeneous(h, J, N)
    +coniii.mean_field_ising.independentEntropyHomogeneous(h, J, N)
    -coniii.mean_field_ising.independentEntropyHomogeneous2(h, J, N)
    +coniii.mean_field_ising.independentEntropyHomogeneous2(h, J, N)
    -coniii.mean_field_ising.isingDeltaCooc(isingSamples, coocMatDesired)
    +coniii.mean_field_ising.isingDeltaCooc(isingSamples, coocMatDesired)
    -coniii.mean_field_ising.logCosh(x)
    +coniii.mean_field_ising.logCosh(x)
    -coniii.mean_field_ising.m(h, J, ell, T)
    +coniii.mean_field_ising.m(h, J, ell, T)

    Careful if T is small for loss of precision?

    -coniii.mean_field_ising.meanFieldStability(J, freqs)
    +coniii.mean_field_ising.meanFieldStability(J, freqs)
    -coniii.mean_field_ising.multiInfoHomogeneous(h, J, N)
    +coniii.mean_field_ising.multiInfoHomogeneous(h, J, N)
    -coniii.mean_field_ising.replaceDiag(mat, lst)
    +coniii.mean_field_ising.replaceDiag(mat, lst)
    -coniii.mean_field_ising.seedGenerator(seedStart, deltaSeed)
    +coniii.mean_field_ising.seedGenerator(seedStart, deltaSeed)
    -coniii.mean_field_ising.specificHeat(h, J, ell, T)
    +coniii.mean_field_ising.specificHeat(h, J, ell, T)
    -coniii.mean_field_ising.susc(h, J, ell, T)
    +coniii.mean_field_ising.susc(h, J, ell, T)
    -coniii.mean_field_ising.symmetrizeUsingUpper(mat)
    +coniii.mean_field_ising.symmetrizeUsingUpper(mat)
    -coniii.mean_field_ising.unflatten(flatList, ell, symmetrize=False)
    +coniii.mean_field_ising.unflatten(flatList, ell, symmetrize=False)

    Inverse of aboveDiagFlat with keepDiag=True.

    -coniii.mean_field_ising.unsummedLogZ(J, hext=0, minSize=0)
    +coniii.mean_field_ising.unsummedLogZ(J, hext=0, minSize=0)

    J should have h on the diagonal.

    -coniii.mean_field_ising.unsummedZ(J, hext=0, minSize=0)
    +coniii.mean_field_ising.unsummedZ(J, hext=0, minSize=0)

    J should have h on the diagonal.

    -coniii.mean_field_ising.zeroDiag(mat)
    +coniii.mean_field_ising.zeroDiag(mat)
    @@ -329,7 +329,7 @@

    Quick search

    ©2018, Edward D. Lee, Bryan C. Daniels. | - Powered by Sphinx 3.0.3 + Powered by Sphinx 3.5.4 & Alabaster 0.7.12 | diff --git a/docs/_build/html/coniii_rst/coniii.pseudo_inverse_ising.html b/docs/_build/html/coniii_rst/coniii.pseudo_inverse_ising.html index 7630a83..fa611c6 100644 --- a/docs/_build/html/coniii_rst/coniii.pseudo_inverse_ising.html +++ b/docs/_build/html/coniii_rst/coniii.pseudo_inverse_ising.html @@ -4,14 +4,14 @@ + coniii.pseudo_inverse_ising module — ConIII 1.1.9 documentation - + - @@ -34,7 +34,7 @@

    coniii.pseudo_inverse_ising module

    -coniii.pseudo_inverse_ising.conditionalHessian(r, samples, Jr, minSize=0, pairCoocRhat=None)
    +coniii.pseudo_inverse_ising.conditionalHessian(r, samples, Jr, minSize=0, pairCoocRhat=None)

    Returns d^2 conditionalLogLikelihood / d Jri d Jrj, with shape (dimension of system)x(dimension of system)

    @@ -48,14 +48,14 @@
    -coniii.pseudo_inverse_ising.conditionalJacobian(r, samples, Jr, minSize=0)
    +coniii.pseudo_inverse_ising.conditionalJacobian(r, samples, Jr, minSize=0)

    Returns d conditionalLogLikelihood / d Jr, with shape (dimension of system)

    -coniii.pseudo_inverse_ising.conditionalLogLikelihood(r, samples, Jr, minSize=0)
    +coniii.pseudo_inverse_ising.conditionalLogLikelihood(r, samples, Jr, minSize=0)

    (Equals -L_r from my notes.)

    r : individual index samples : binary matrix, (# samples) x (dimension of system) @@ -65,7 +65,7 @@

    -coniii.pseudo_inverse_ising.pairCoocMat(samples)
    +coniii.pseudo_inverse_ising.pairCoocMat(samples)

    Returns matrix of shape (ell)x(# samples)x(ell).

    For use with conditionalHessian.

    Slow because I haven’t thought of a better way of doing it yet.

    @@ -73,7 +73,7 @@
    -coniii.pseudo_inverse_ising.pseudoInverseIsing(samples, minSize=0)
    +coniii.pseudo_inverse_ising.pseudoInverseIsing(samples, minSize=0)
    minSize (0)minimum number of participants per sample

    (set to 2 for fights)

    @@ -82,7 +82,7 @@
    -coniii.pseudo_inverse_ising.pseudoLogLikelihood(samples, J, minSize=0)
    +coniii.pseudo_inverse_ising.pseudoLogLikelihood(samples, J, minSize=0)

    samples : binary matrix, (# samples) x (dimension of system) J : (dimension of system) x (dimension of system)

    @@ -93,7 +93,7 @@
    -coniii.pseudo_inverse_ising.testDerivatives(r, i, samples, J, minSize=0, deltaMax=1)
    +coniii.pseudo_inverse_ising.testDerivatives(r, i, samples, J, minSize=0, deltaMax=1)
    @@ -158,7 +158,7 @@

    Quick search

    ©2018, Edward D. Lee, Bryan C. Daniels. | - Powered by Sphinx 3.0.3 + Powered by Sphinx 3.5.4 & Alabaster 0.7.12 | diff --git a/docs/_build/html/coniii_rst/coniii.samplers.html b/docs/_build/html/coniii_rst/coniii.samplers.html index 0a5c4fc..a0e7a88 100644 --- a/docs/_build/html/coniii_rst/coniii.samplers.html +++ b/docs/_build/html/coniii_rst/coniii.samplers.html @@ -4,14 +4,14 @@ + coniii.samplers module — ConIII 1.1.9 documentation - + - @@ -36,18 +36,18 @@

    coniii.samplers module

    -class coniii.samplers.HamiltonianMC(n, theta, calc_e, random_sample, grad_e=None, dt=0.01, leapfrogN=20, nCpus=0)
    +class coniii.samplers.HamiltonianMC(n, theta, calc_e, random_sample, grad_e=None, dt=0.01, leapfrogN=20, nCpus=0)

    Bases: coniii.samplers.Sampler

    -generate_samples(nSamples, nBurn=100, fast=True, x0=None)
    +generate_samples(nSamples, nBurn=100, fast=True, x0=None)

    Generate nSamples from this Hamiltonian starting from random initial conditions from each sample.

    -sample(x0, nBurn, saveHistory=False)
    +sample(x0, nBurn, saveHistory=False)

    Get a single sample by MC sampling from this Hamiltonian. Slow method

    @@ -55,7 +55,7 @@
    -class coniii.samplers.Heisenberg3DSampler(J, calc_e, random_sample)
    +class coniii.samplers.Heisenberg3DSampler(J, calc_e, random_sample)

    Bases: coniii.samplers.Sampler

    Simple MC Sampling from Heisenberg model with a lot of helpful functions.

    generate_samples() @@ -64,18 +64,18 @@ sample_energy_min()

    -equilibrate_samples(samples, n_iters, method='mc', nCpus=0)
    +equilibrate_samples(samples, n_iters, method='mc', nCpus=0)
    -generate_samples(nSamples, n_iters=100, **kwargs)
    +generate_samples(nSamples, n_iters=100, **kwargs)

    sample_size : int

    -grad_E(X)
    +grad_E(X)

    Gradient wrt theta and phi.

    Xndarray

    with dims (nSpins,2) with angles theta and phi

    @@ -85,7 +85,7 @@
    -sample_energy_min(nFixed=0, rng=<mtrand.RandomState object>, initialState=None, method='powell', **kwargs)
    +sample_energy_min(nFixed=0, rng=RandomState(MT19937) at 0x7F7901D07E20, initialState=None, method='powell', **kwargs)

    Find local energy minimum given state in angular form. Angular representation makes it easy to be explicit about constraints on the vectors.

    @@ -98,7 +98,7 @@
    -sample_metropolis(oldState, E0)
    +sample_metropolis(oldState, E0)
    sndarray

    State to perturb randomly.

    @@ -109,7 +109,7 @@
    -sample_nearby_sample(X, **kwargs)
    +sample_nearby_sample(X, **kwargs)

    Randomly move given state around for new metropolis sample. Question is whether it is more efficient to push only one of the many vectors around or all of them simultaneously.

    @@ -117,7 +117,7 @@
    -sample_nearby_vector(v, nSamples=1, otheta=None, ophi=None, sigma=0.1)
    +sample_nearby_vector(v, nSamples=1, otheta=None, ophi=None, sigma=0.1)

    Sample random vector that is nearby. It is important how you choose the width sigma. NOTE: code might be simplified by using arctan2 instead of arctan

    @@ -136,7 +136,7 @@
    -classmethod to_dict(data, names)
    +classmethod to_dict(data, names)

    Convenience function taking 3d array of of samples and arranging them into n x 3 arrays in a dictionary.

    @@ -145,11 +145,11 @@
    -class coniii.samplers.Metropolis(n, theta, calc_e=None, n_cpus=None, rng=None, boost=True, iprint=True)
    +class coniii.samplers.Metropolis(n, theta, calc_e=None, n_cpus=None, rng=None, boost=True, iprint=True)

    Bases: coniii.samplers.Sampler

    -generate_cond_samples(sample_size, fixed_subset, burn_in=1000, n_cpus=None, initial_sample=None, systematic_iter=False, parallel=True)
    +generate_cond_samples(sample_size, fixed_subset, burn_in=1000, n_cpus=None, initial_sample=None, systematic_iter=False, parallel=True)

    Generate samples from conditional distribution (while a subset of the spins are held fixed). Samples are generated in parallel.

    NOTE: There is a bug with multiprocess where many calls to the parallel sampling @@ -182,7 +182,7 @@

    -generate_samples_boost(sample_size, n_iters=1000, burn_in=None, systematic_iter=False)
    +generate_samples_boost(sample_size, n_iters=1000, burn_in=None, systematic_iter=False)

    Generate Metropolis samples using C++ and boost.

    sample_sizeint

    Number of samples.

    @@ -202,7 +202,7 @@
    -generate_samples_parallel_boost(sample_size, n_iters=1000, burn_in=None, systematic_iter=False)
    +generate_samples_parallel_boost(sample_size, n_iters=1000, burn_in=None, systematic_iter=False)

    Generate samples in parallel. Each replica in self._samples runs on its own thread and a sample is generated every n_iters.

    In order to control the random number generator, we pass in seeds that are samples @@ -222,7 +222,7 @@

    -generate_samples_parallel_py(sample_size, n_iters=1000, burn_in=None, initial_sample=None, systematic_iter=False)
    +generate_samples_parallel_py(sample_size, n_iters=1000, burn_in=None, initial_sample=None, systematic_iter=False)

    Generate samples in parallel. Each replica in self._samples runs on its own thread and a sample is generated every n_iters.

    In order to control the random number generator, we pass in seeds that are samples @@ -244,7 +244,7 @@

    -generate_samples_py(sample_size, n_iters=1000, burn_in=None, systematic_iter=False, saveHistory=False, initial_sample=None)
    +generate_samples_py(sample_size, n_iters=1000, burn_in=None, systematic_iter=False, saveHistory=False, initial_sample=None)

    Generate Metropolis samples using a for loop.

    sample_sizeint

    Number of samples.

    @@ -269,12 +269,12 @@
    -random_sample(n_samples)
    +random_sample(n_samples)
    -sample_metropolis(sample0, E0, rng=None, flip_site=None, calc_e=None)
    +sample_metropolis(sample0, E0, rng=None, flip_site=None, calc_e=None)

    Metropolis sampling given an arbitrary energy function.

    sample0ndarray

    Sample to start with. Passed by ref and changed.

    @@ -298,17 +298,17 @@
    -class coniii.samplers.ParallelTempering(n, theta, calc_e, n_replicas, Tbds=1.0, 3.0, sample_size=1000, replica_burnin=None, rep_ex_burnin=None, n_cpus=None, rng=None)
    +class coniii.samplers.ParallelTempering(n, theta, calc_e, n_replicas, Tbds=(1.0, 3.0), sample_size=1000, replica_burnin=None, rep_ex_burnin=None, n_cpus=None, rng=None)

    Bases: coniii.samplers.Sampler

    -burn_and_exchange(pool)
    +burn_and_exchange(pool)

    pool : mp.multiprocess.Pool

    -burn_in_replicas(pool=None, close_pool=True, n_iters=None)
    +burn_in_replicas(pool=None, close_pool=True, n_iters=None)

    Run each replica separately.

    pool : multiprocess.Pool, None close_pool : bool, True

    @@ -323,7 +323,7 @@
    -generate_samples(sample_size, save_exchange_trajectory=False)
    +generate_samples(sample_size, save_exchange_trajectory=False)

    Burn in, run replica exchange simulation, then sample.

    sample_sizeint

    Number of samples to take for each replica.

    @@ -341,13 +341,13 @@
    -static initialize_beta(b0, b1, n_replicas)
    +static initialize_beta(b0, b1, n_replicas)

    Use linear interpolation of temperature range.

    -static iterate_beta(beta, acceptance_ratio)
    +static iterate_beta(beta, acceptance_ratio)

    Apply algorithm from Hukushima but reversed to maintain one replica at T=1.

    betandarray

    Inverse temperature.

    @@ -363,7 +363,7 @@
    -optimize_beta(n_samples, n_iters, tol=0.01, max_iter=10)
    +optimize_beta(n_samples, n_iters, tol=0.01, max_iter=10)

    Find suitable temperature range for replicas. Sets self.beta.

    n_samplesint

    Number of samples to use to estimate acceptance ratio. Acceptance ratio is @@ -381,7 +381,7 @@

    -setup_replicas()
    +setup_replicas()

    Initialise a set of replicas at different temperatures using the Metropolis algorithm and optimize the temperatures. Replicas are burned in and ready to sample.

    @@ -389,7 +389,7 @@
    -update_replica_parameters()
    +update_replica_parameters()

    Update parameters for each replica. Remember that the parameters include the factor of beta.

    @@ -398,11 +398,11 @@
    -class coniii.samplers.Potts3(n, theta, calc_e=None, n_cpus=None, rng=None, boost=True)
    +class coniii.samplers.Potts3(n, theta, calc_e=None, n_cpus=None, rng=None, boost=True)

    Bases: coniii.samplers.Metropolis

    -generate_samples_parallel_boost(sample_size, n_iters=1000, burn_in=None, systematic_iter=False)
    +generate_samples_parallel_boost(sample_size, n_iters=1000, burn_in=None, systematic_iter=False)

    Generate samples in parallel. Each replica in self._samples runs on its own thread and a sample is generated every n_iters.

    In order to control the random number generator, we pass in seeds that are samples @@ -422,12 +422,12 @@

    -random_sample(n_samples)
    +random_sample(n_samples)
    -sample_metropolis(sample0, E0, rng=None, flip_site=None, calc_e=None)
    +sample_metropolis(sample0, E0, rng=None, flip_site=None, calc_e=None)

    Metropolis sampling given an arbitrary sampling function.

    sample0ndarray

    Sample to start with. Passed by ref and changed.

    @@ -451,11 +451,11 @@
    -class coniii.samplers.SWIsing(n, theta, calc_e, nCpus=None, rng=None)
    +class coniii.samplers.SWIsing(n, theta, calc_e, nCpus=None, rng=None)

    Bases: coniii.samplers.Sampler

    -generate_sample(n_samples, n_iters, initial_state=None)
    +generate_sample(n_samples, n_iters, initial_state=None)

    n_samples n_iters initial_state : ndarray,None

    @@ -463,7 +463,7 @@
    -generate_sample_parallel(n_samples, n_iters, initial_state=None, n_cpus=None)
    +generate_sample_parallel(n_samples, n_iters, initial_state=None, n_cpus=None)

    n_samples n_iters initial_state : ndarray,None

    @@ -471,47 +471,47 @@
    -get_clusters(state)
    +get_clusters(state)

    Get a random sample of clusters.

    -one_step(state)
    +one_step(state)
    -print_cluster_size(n_iters)
    +print_cluster_size(n_iters)
    -randomly_flip_clusters(state, clusters)
    +randomly_flip_clusters(state, clusters)
    -class coniii.samplers.Sampler(n, theta, **kwargs)
    +class coniii.samplers.Sampler(n, theta, **kwargs)

    Bases: object

    Base class for MCMC sampling.

    -generate_samples(sample_size, **kwargs)
    +generate_samples(sample_size, **kwargs)

    sample_size : int

    -generate_samples_parallel(sample_size, **kwargs)
    +generate_samples_parallel(sample_size, **kwargs)

    sample_size : int

    -sample_metropolis(s, energy)
    +sample_metropolis(s, energy)
    sndarray

    State to perturb randomly.

    @@ -522,24 +522,24 @@
    -update_parameters(new_parameters)
    +update_parameters(new_parameters)
    -class coniii.samplers.WolffIsing(J, h)
    +class coniii.samplers.WolffIsing(J, h)

    Bases: coniii.samplers.Sampler

    -build_cluster(state, initialsite)
    +build_cluster(state, initialsite)

    Grow cluster from initial site.

    -find_neighbors(state, site, alreadyMarked)
    +find_neighbors(state, site, alreadyMarked)

    Return neighbors of given site that need to be visited excluding sites that have already been visited. This is the implementation of the Wolff algorithm for finding neighbors such that detailed balance is satisfied. I have modified to @@ -552,33 +552,33 @@

    -generate_sample(samplesize, n_iters, initialSample=None, save_history=False)
    +generate_sample(samplesize, n_iters, initialSample=None, save_history=False)

    Generate samples by starting from random initial states.

    -generate_sample_parallel(samplesize, n_iters, initialSample=None)
    +generate_sample_parallel(samplesize, n_iters, initialSample=None)

    Generate samples by starting from random or given initial states.

    -one_step(state, initialsite=None)
    +one_step(state, initialsite=None)

    Run one iteration of the Wolff algorithm that involves finding a cluster and possibly flipping it.

    -update_parameters(J, h)
    +update_parameters(J, h)
    -coniii.samplers.calc_e(theta, x)
    +coniii.samplers.calc_e(theta, x)

    Heisenberg model.

    thetandarray

    List of couplings Jij

    @@ -590,7 +590,7 @@
    -coniii.samplers.check_e_logp(sample, calc_e)
    +coniii.samplers.check_e_logp(sample, calc_e)

    Boltzmann type model with discrete state space should have E propto -logP. Calculate these quantities for comparison.

    sample @@ -599,37 +599,37 @@

    -coniii.samplers.cross(vec1, vec2)
    +coniii.samplers.cross(vec1, vec2)

    Calculate the cross product of two 3d vectors.

    -coniii.samplers.cross_(vec1, vec2, result)
    +coniii.samplers.cross_(vec1, vec2, result)

    Calculate the cross product of two 3d vectors.

    -coniii.samplers.grad_e(theta, x)
    +coniii.samplers.grad_e(theta, x)

    Derivatives wrt the angles of the spins.

    -coniii.samplers.grad_e_theta(theta, x)
    +coniii.samplers.grad_e_theta(theta, x)

    Derivatives wrt the couplings theta.

    -coniii.samplers.iter_cluster(adj)
    +coniii.samplers.iter_cluster(adj)

    Cycle through all spins to get clusters.

    -coniii.samplers.iterate_neighbors(n, ix, expdJ, r)
    +coniii.samplers.iterate_neighbors(n, ix, expdJ, r)

    Iterate through all neighbors of a particular site and see if a bond should be formed between them.

    @@ -646,7 +646,7 @@
    -coniii.samplers.jit_sample(theta, x0, nBurn, dt, leapfrogN, randNormal, randUnif)
    +coniii.samplers.jit_sample(theta, x0, nBurn, dt, leapfrogN, randNormal, randUnif)

    Get a single sample by MC sampling from this Hamiltonian.

    thetandarray

    Parameters

    @@ -669,17 +669,17 @@
    -coniii.samplers.jit_sample_nearby_vector(rseed, v, nSamples, otheta, ophi, sigma)
    +coniii.samplers.jit_sample_nearby_vector(rseed, v, nSamples, otheta, ophi, sigma)
    -coniii.samplers.pairwise_prod(state)
    +coniii.samplers.pairwise_prod(state)
    -coniii.samplers.sample_bonds(p, r, state, J)
    +coniii.samplers.sample_bonds(p, r, state, J)
    pndarray

    Probability of bond formation.

    @@ -692,7 +692,7 @@
    -coniii.samplers.sample_ising(multipliers, n_samples, n_cpus=None, seed=None, generate_samples_kw={})
    +coniii.samplers.sample_ising(multipliers, n_samples, n_cpus=None, seed=None, generate_samples_kw={})

    Easy way to Metropolis sample from Ising model.

    multipliersndarray

    N individual fields followed by N(N-1)/2 pairwise couplings.

    @@ -720,7 +720,7 @@
    -coniii.samplers.spec_cluster(L, exact=True)
    +coniii.samplers.spec_cluster(L, exact=True)
    Lndarray

    Graph Laplacian

    @@ -791,7 +791,7 @@

    Quick search

    ©2018, Edward D. Lee, Bryan C. Daniels. | - Powered by Sphinx 3.0.3 + Powered by Sphinx 3.5.4 & Alabaster 0.7.12 | diff --git a/docs/_build/html/coniii_rst/coniii.solvers.html b/docs/_build/html/coniii_rst/coniii.solvers.html index be6a34a..afbe44c 100644 --- a/docs/_build/html/coniii_rst/coniii.solvers.html +++ b/docs/_build/html/coniii_rst/coniii.solvers.html @@ -4,14 +4,14 @@ + coniii.solvers module — ConIII 1.1.9 documentation - + - @@ -36,14 +36,14 @@

    coniii.solvers module

    -class coniii.solvers.ClusterExpansion(sample, model=None, calc_observables=None, sample_size=1000, **default_model_kwargs)
    +class coniii.solvers.ClusterExpansion(sample, model=None, calc_observables=None, sample_size=1000, iprint=True, **default_model_kwargs)

    Bases: coniii.solvers.Solver

    Implementation of Adaptive Cluster Expansion for solving the inverse Ising problem, as described in John Barton and Simona Cocco, J. of Stat. Mech. P03002 (2013).

    Specific to pairwise Ising constraints.

    -S(cluster, coocMat, deltaJdict={}, useAnalyticResults=False, priorLmbda=0.0, numSamples=None)
    +S(cluster, coocMat, deltaJdict={}, useAnalyticResults=False, priorLmbda=0.0, numSamples=None)

    Calculate pairwise entropy of cluster. (First fits pairwise Ising model.)

    clusterlist

    List of indices belonging to each cluster.

    @@ -65,7 +65,7 @@
    -Sindependent(cluster, coocMat)
    +Sindependent(cluster, coocMat)

    Entropy approximation assuming that each cluster appears independently of the others.

    cluster : list @@ -83,12 +83,12 @@

    -clusterID(cluster)
    +clusterID(cluster)
    -deltaS(cluster, coocMat, deltaSdict=None, deltaJdict=None, verbose=True, meanFieldRef=False, priorLmbda=0.0, numSamples=None, independentRef=False, meanFieldPriorLmbda=None)
    +deltaS(cluster, coocMat, deltaSdict=None, deltaJdict=None, iprint=True, meanFieldRef=False, priorLmbda=0.0, numSamples=None, independentRef=False, meanFieldPriorLmbda=None)
    clusterlist

    List of indices in cluster

    @@ -96,7 +96,7 @@

    coocMat : ndarray deltaSdict : dict, None deltaJdict : dict, None -verbose : bool, True +iprint : bool, True meanFieldRef : bool, False numSamples : int, None independentRef : bool, False

    @@ -117,7 +117,7 @@
    -solve(threshold, cluster=None, deltaSdict=None, deltaJdict=None, verbose=True, priorLmbda=0.0, numSamples=None, meanFieldRef=False, independentRef=True, veryVerbose=False, meanFieldPriorLmbda=None, full_output=False)
    +solve(threshold, cluster=None, deltaSdict=None, deltaJdict=None, iprint=True, priorLmbda=0.0, numSamples=None, meanFieldRef=False, independentRef=True, veryVerbose=False, meanFieldPriorLmbda=None, full_output=False)

    threshold : float meanFieldRef : bool, False

    @@ -152,7 +152,7 @@
    -subsets(thisSet, size, sort=False)
    +subsets(thisSet, size, sort=False)

    Given a list, returns a list of all unique subsets of that list with given size.

    thisSet : list @@ -168,13 +168,13 @@

    -class coniii.solvers.Enumerate(sample=None, model=None, calc_observables=None, **default_model_kwargs)
    +class coniii.solvers.Enumerate(sample=None, model=None, calc_observables=None, iprint=True, **default_model_kwargs)

    Bases: coniii.solvers.Solver

    Class for solving fully-connected inverse Ising model problem by enumeration of the partition function and then using gradient descent.

    -solve(initial_guess=None, constraints=None, max_param_value=50, full_output=False, use_root=True, scipy_solver_kwargs={'method': 'krylov', 'options': {'fatol': 1e-13, 'xatol': 1e-13}})
    +solve(initial_guess=None, constraints=None, max_param_value=50, full_output=False, use_root=True, scipy_solver_kwargs={'method': 'krylov', 'options': {'fatol': 1e-13, 'xatol': 1e-13}})

    Must specify either constraints (the correlations) or samples from which the correlations will be calculated using self.calc_observables. This routine by default uses scipy.optimize.root to find the solution. This is MUCH faster than @@ -218,14 +218,14 @@

    -class coniii.solvers.MCH(sample, model=None, calc_observables=None, sample_size=1000, sample_method='metropolis', mch_approximation=None, **default_model_kwargs)
    +class coniii.solvers.MCH(sample, model=None, calc_observables=None, sample_size=1000, sample_method='metropolis', mch_approximation=None, iprint=True, sampler_kw={}, **default_model_kwargs)

    Bases: coniii.solvers.Solver

    Class for solving maxent problems using the Monte Carlo Histogram method.

    Broderick, T., Dudik, M., Tkacik, G., Schapire, R. E. & Bialek, W. Faster solutions of the inverse pairwise Ising problem. arXiv 1-8 (2007).

    -estimate_jac(eps=0.001)
    +estimate_jac(eps=0.001)

    Approximation Jacobian using the MCH approximation.

    eps : float, 1e-3

    @@ -237,12 +237,17 @@
    -learn_parameters_mch(estConstraints, maxdlamda=1, maxdlamdaNorm=1, maxLearningSteps=50, eta=1)
    +learn_parameters_mch(estConstraints, constraints, maxdlamda=1, maxdlamdaNorm=1, maxLearningSteps=50, eta=1)
    estConstraintsndarray

    Constraints estimated from MCH approximation.

    -
    maxdlamdafloat, 1

    Max allowed magnitude for any element of dlamda vector before exiting.

    -
    +
    +

    constraints : ndarray +maxdlamda : float, 1

    +
    +

    Max allowed magnitude for any element of dlamda vector before exiting.

    +
    +
    maxdlamdaNormfloat, 1

    Max allowed norm of dlamda vector before exiting.

    maxLearningStepsint

    max learning steps before ending MCH

    @@ -258,7 +263,7 @@
    -solve(initial_guess=None, constraints=None, tol=None, tolNorm=None, n_iters=30, burn_in=30, maxiter=10, custom_convergence_f=None, iprint=False, full_output=False, learn_params_kwargs={'eta': 1, 'maxdlamda': 1}, generate_kwargs={})
    +solve(initial_guess=None, constraints=None, tol=None, tolNorm=None, n_iters=30, burn_in=30, maxiter=10, custom_convergence_f=None, iprint=False, full_output=False, learn_params_kwargs={'eta': 1, 'maxdlamda': 1}, generate_kwargs={})

    Solve for maxent model parameters using MCH routine.

    initial_guessndarray, None

    Initial starting point.

    @@ -324,7 +329,7 @@
    -class coniii.solvers.MCHIncompleteData(*args, **kwargs)
    +class coniii.solvers.MCHIncompleteData(*args, **kwargs)

    Bases: coniii.solvers.MCH

    Class for solving maxent problems using the Monte Carlo Histogram method on incomplete data where some spins may not be visible.

    @@ -336,7 +341,7 @@
    -generate_samples(n_iters, burn_in, uIncompleteStates=None, f_cond_sample_size=None, f_cond_sample_iters=None, sample_size=None, sample_method=None, initial_sample=None, run_regular_sampler=True, run_cond_sampler=True, disp=0, generate_kwargs={})
    +generate_samples(n_iters, burn_in, uIncompleteStates=None, f_cond_sample_size=None, f_cond_sample_iters=None, sample_size=None, sample_method=None, initial_sample=None, run_regular_sampler=True, run_cond_sampler=True, disp=0, generate_kwargs={})

    Wrapper around generate_samples_parallel() from available samplers.

    n_iters : int burn_in : int

    @@ -360,7 +365,7 @@
    -learn_parameters_mch(estConstraints, fullFraction, uIncompleteStates, uIncompleteStatesCount, maxdlamda=1, maxdlamdaNorm=1, maxLearningSteps=50, eta=1)
    +learn_parameters_mch(estConstraints, fullFraction, uIncompleteStates, uIncompleteStatesCount, maxdlamda=1, maxdlamdaNorm=1, maxLearningSteps=50, eta=1)

    Update parameters with MCH step. Update is proportional to the difference between the observables and the predicted observables after a small change to the parameters. This is calculated from likelihood maximization, and for the incomplete data points this corresponds @@ -392,7 +397,7 @@

    -solve(X=None, constraints=None, initial_guess=None, cond_sample_size=100, cond_sample_iters=100, tol=None, tolNorm=None, n_iters=30, burn_in=30, maxiter=10, disp=False, full_output=False, learn_params_kwargs={}, generate_kwargs={})
    +solve(X=None, constraints=None, initial_guess=None, cond_sample_size=100, cond_sample_iters=100, tol=None, tolNorm=None, n_iters=30, burn_in=30, maxiter=10, disp=False, full_output=False, learn_params_kwargs={}, generate_kwargs={})

    Solve for parameters using MCH routine.

    X : ndarray constraints : ndarray

    @@ -442,11 +447,11 @@
    -class coniii.solvers.MPF(sample, model=None, calc_observables=None, calc_de=None, adj=None, **default_model_kwargs)
    +class coniii.solvers.MPF(sample, model=None, calc_observables=None, calc_de=None, adj=None, iprint=True, **default_model_kwargs)

    Bases: coniii.solvers.Solver

    -K(Xuniq, Xcount, adjacentStates, params)
    +K(Xuniq, Xcount, adjacentStates, params)

    Compute objective function.

    Xuniqndarray

    (ndata x ndims) @@ -464,7 +469,7 @@

    -list_adjacent_states(Xuniq, all_connected)
    +list_adjacent_states(Xuniq, all_connected)

    Use self.adj to evaluate all adjacent states in Xuniq.

    Xuniq : ndarray all_connected : bool

    @@ -473,7 +478,7 @@
    -logK(Xuniq, Xcount, adjacentStates, params)
    +logK(Xuniq, Xcount, adjacentStates, params)

    Compute log of objective function.

    Xuniqndarray

    (n_samples, n_dim) @@ -491,7 +496,7 @@

    -solve(initial_guess=None, method='L-BFGS-B', full_output=False, all_connected=True, parameter_limits=100, solver_kwargs={'disp': False, 'ftol': 1e-15, 'maxiter': 100}, uselog=True)
    +solve(initial_guess=None, method='L-BFGS-B', full_output=False, all_connected=True, parameter_limits=100, solver_kwargs={'disp': False, 'ftol': 1e-15, 'maxiter': 100}, uselog=True)

    Minimize MPF objective function using scipy.optimize.minimize.

    initial_guess : ndarray, None method : str, ‘L-BFGS-B’

    @@ -525,26 +530,26 @@
    -static worker_objective_task(s, Xcount, adjacentStates, params, calc_e)
    +static worker_objective_task(s, Xcount, adjacentStates, params, calc_e)
    -coniii.solvers.MonteCarloHistogram
    +coniii.solvers.MonteCarloHistogram

    alias of coniii.solvers.MCH

    -class coniii.solvers.Pseudo(sample, model=None, calc_observables=None, get_multipliers_r=None, calc_observables_r=None, k=2, **default_model_kwargs)
    +class coniii.solvers.Pseudo(sample, model=None, calc_observables=None, get_multipliers_r=None, calc_observables_r=None, k=2, iprint=True, **default_model_kwargs)

    Bases: coniii.solvers.Solver

    Pseudolikelihood approximation to solving the inverse Ising problem as described in Aurell and Ekeberg, PRL 108, 090201 (2012).

    -cond_hess(r, X, Jr, pairCoocRhat=None)
    +cond_hess(r, X, Jr, pairCoocRhat=None)

    Returns d^2 cond_log_likelihood / d Jri d Jrj, with shape (dimension of system)x(dimension of system)

    Current implementation uses more memory for speed. For large sample size, it may @@ -558,14 +563,14 @@

    -cond_jac(r, X, Jr)
    +cond_jac(r, X, Jr)

    Returns d cond_log_likelihood / d Jr, with shape (dimension of system)

    Deprecated.

    -cond_log_likelihood(r, X, Jr)
    +cond_log_likelihood(r, X, Jr)

    Equals the conditional log likelihood -L_r.

    Deprecated.

    @@ -581,7 +586,7 @@
    -pair_cooc_mat(X)
    +pair_cooc_mat(X)

    Returns matrix of shape (self.n)x(# X)x(self.n).

    For use with cond_hess.

    Slow because I haven’t thought of a better way of doing it yet.

    @@ -590,7 +595,7 @@
    -pseudo_log_likelihood(X, J)
    +pseudo_log_likelihood(X, J)

    TODO: Could probably be made more efficient.

    Deprecated.

    @@ -604,7 +609,7 @@
    -solve(force_general=False, **kwargs)
    +solve(force_general=False, **kwargs)

    Uses a general all-purpose optimization to solve the problem using functions defined in self.get_multipliers_r and self.calc_observables_r.

    @@ -626,7 +631,7 @@
    -class coniii.solvers.RegularizedMeanField(sample, model=None, calc_observables=None, sample_size=1000, verbose=False, **default_model_kwargs)
    +class coniii.solvers.RegularizedMeanField(sample, model=None, calc_observables=None, sample_size=1000, iprint=False, **default_model_kwargs)

    Bases: coniii.solvers.Solver

    Implementation of regularized mean field method for solving the inverse Ising problem, as described in Daniels, Bryan C., David C. Krakauer, and Jessica C. Flack. @@ -635,7 +640,7 @@

    Specific to pairwise Ising constraints.

    -bracket1d(xList, funcList)
    +bracket1d(xList, funcList)

    Assumes xList is monotonically increasing

    Get bracketed interval (a,b,c) with a < b < c, and f(b) < f(a) and f(c). (Choose b and c to make f(b) and f(c) as small as possible.)

    @@ -644,7 +649,7 @@
    -solve(n_grid_points=200, min_size=0, reset_rng=True, min_covariance=False, min_independent=True, cooc_cov=None, priorLmbda=0.0, bracket=None)
    +solve(n_grid_points=200, min_size=0, reset_rng=True, min_covariance=False, min_independent=True, cooc_cov=None, priorLmbda=0.0, bracket=None)

    Varies the strength of regularization on the mean field J to best fit given cooccurrence data.

    @@ -685,13 +690,13 @@
    -class coniii.solvers.Solver
    +class coniii.solvers.Solver

    Bases: object

    Base class for declaring common methods and attributes for inverse maxent algorithms.

    -basic_setup(sample_or_n=None, model=None, calc_observables=None, model_kwargs={})
    +basic_setup(sample_or_n=None, model=None, calc_observables=None, iprint=True, model_kwargs={})

    General routine for setting up a Solver instance.

    sample_or_nndarray or int, None

    If ndarray, of dimensions (samples, dimension).

    @@ -703,6 +708,8 @@
    calc_observablesfunction, None

    For calculating observables from a set of samples.

    +
    iprintstr, True

    If empty, do not display warning messages.

    +
    model_kwargsdict, {}

    Additional arguments that will be passed to Ising class. These only matter if model is None. Important ones include “n_cpus” and “rng”.

    @@ -710,24 +717,8 @@
    -
    -solve()
    -

    To be defined in derivative classes.

    -
    - -
    - -
    -
    -class coniii.solvers.SparseEnumerate(sample=None, model=None, calc_observables=None, parameter_ix=None, **default_model_kwargs)
    -

    Bases: coniii.solvers.Solver

    -

    Class for solving Ising model with a sparse parameter set by enumeration of -the partition function and then using gradient descent. Unspecified parameters are -implicitly fixed to be zero, which corresponds to leaving the corresponding -correlation function unconstrained.

    -
    -
    -fill_in(x, fill_value=0)
    +
    +fill_in(x, fill_value=0)

    Helper function for filling in missing parameter values.

    x : ndarray fill_value : float, 0

    @@ -738,15 +729,31 @@
    -
    -set_insertion_ix()
    +
    +set_insertion_ix()

    Calculate indices to fill in with zeros to “fool” code that takes full set of params.

    +
    +
    +solve()
    +

    To be defined in derivative classes.

    +
    + +
    + +
    +
    +class coniii.solvers.SparseEnumerate(sample=None, model=None, calc_observables=None, parameter_ix=None, iprint=True, **default_model_kwargs)
    +

    Bases: coniii.solvers.Solver

    +

    Class for solving Ising model with a sparse parameter set by enumeration of +the partition function and then using gradient descent. Unspecified parameters are +implicitly fixed to be zero, which corresponds to leaving the corresponding +correlation function unconstrained.

    -solve(initial_guess=None, constraints=None, max_param_value=50, full_output=False, use_root=True, scipy_solver_kwargs={'method': 'krylov', 'options': {'fatol': 1e-13, 'xatol': 1e-13}})
    +solve(initial_guess=None, constraints=None, max_param_value=50, full_output=False, use_root=True, scipy_solver_kwargs={'method': 'krylov', 'options': {'fatol': 1e-13, 'xatol': 1e-13}})

    Must specify either constraints (the correlations) or samples from which the correlations will be calculated using self.calc_observables. This routine by default uses scipy.optimize.root to find the solution. This is MUCH faster than @@ -787,9 +794,108 @@

    +
    +
    +class coniii.solvers.SparseMCH(sample, model=None, calc_observables=None, sample_size=1000, sample_method='metropolis', mch_approximation=None, parameter_ix=None, iprint=True, sampler_kw={}, **default_model_kwargs)
    +

    Bases: coniii.solvers.Solver

    +

    Class for solving maxent problems on sparse constraints using the Monte Carlo +Histogram method.

    +

    See MCH class.

    +
    +
    +learn_parameters_mch(estConstraints, constraints, maxdlamda=1, maxdlamdaNorm=1, maxLearningSteps=50, eta=1)
    +
    +
    estConstraintsndarray

    Constraints estimated from MCH approximation.

    +
    +
    +

    constraints : ndarray +maxdlamda : float, 1

    +
    +

    Max allowed magnitude for any element of dlamda vector before exiting.

    +
    +
    +
    maxdlamdaNormfloat, 1

    Max allowed norm of dlamda vector before exiting.

    +
    +
    maxLearningStepsint

    max learning steps before ending MCH

    +
    +
    etafloat, 1

    factor for changing dlamda

    +
    +
    +
    +
    ndarray

    MCH estimate for constraints from parameters lamda+dlamda.

    +
    +
    +
    + +
    +
    +solve(initial_guess=None, constraints=None, tol=None, tolNorm=None, n_iters=30, burn_in=30, maxiter=10, custom_convergence_f=None, iprint=False, full_output=False, learn_params_kwargs={'eta': 1, 'maxdlamda': 1}, generate_kwargs={})
    +

    Solve for maxent model parameters using MCH routine.

    +
    +
    initial_guessndarray, None

    Initial starting point.

    +
    +
    constraintsndarray, None

    For debugging! +Vector of correlations to fit.

    +
    +
    tolfloat, None

    Maximum error allowed in any observable.

    +
    +
    tolNormfloat, None

    Norm error allowed in found solution.

    +
    +
    n_itersint, 30

    Number of iterations to make between samples in MCMC sampling.

    +
    +
    burn_inint, 30

    Initial burn in from random sample when MC sampling.

    +
    +
    max_iterint, 10

    Max number of iterations of MC sampling and MCH approximation.

    +
    +
    custom_convergence_ffunction, None

    Function for determining convergence criterion. At each iteration, this +function should return the next set of learn_params_kwargs and optionally the +sample size.

    +

    As an example: +def learn_settings(i):

    +
    +

    ‘’’ +Take in the iteration counter and set the maximum change allowed in any +given parameter (maxdlamda) and the multiplicative factor eta, where +d(parameter) = (error in observable) * eta.

    +

    Additional option is to also return the sample size for that step by +returning a tuple. Larger sample sizes are necessary for higher accuracy. +‘’’ +if i<10:

    +
    +

    return {‘maxdlamda’:1,’eta’:1}

    +
    +
    +
    else:

    return {‘maxdlamda’:.05,’eta’:.05}

    +
    +
    +
    +
    +
    +

    iprint : bool, False +full_output : bool, False

    +
    +

    If True, also return the errflag and error history.

    +
    +

    learn_parameters_kwargs : dict, {‘maxdlamda’:1,’eta’:1} +generate_kwargs : dict, {}

    +
    +
    ndarray

    Solved multipliers (parameters). For Ising problem, these can be converted +into matrix format using utils.vec2mat.

    +
    +
    int

    Error flag. +0, converged within given criterion +1, max iterations reached

    +
    +
    ndarray

    Log of errors in matching constraints at each step of iteration.

    +
    +
    +
    + +
    +
    -coniii.solvers.unwrap_self_worker_obj(arg, **kwarg)
    +coniii.solvers.unwrap_self_worker_obj(arg, **kwarg)
    @@ -856,7 +962,7 @@

    Quick search

    ©2018, Edward D. Lee, Bryan C. Daniels. | - Powered by Sphinx 3.0.3 + Powered by Sphinx 3.5.4 & Alabaster 0.7.12 | diff --git a/docs/_build/html/coniii_rst/coniii.test_samplers.html b/docs/_build/html/coniii_rst/coniii.test_samplers.html index df23c8f..5b1508b 100644 --- a/docs/_build/html/coniii_rst/coniii.test_samplers.html +++ b/docs/_build/html/coniii_rst/coniii.test_samplers.html @@ -4,14 +4,14 @@ + coniii.test_samplers module — ConIII 1.1.9 documentation - + - @@ -34,27 +34,27 @@

    coniii.test_samplers module

    -coniii.test_samplers.compare_samplers()
    +coniii.test_samplers.compare_samplers()
    -coniii.test_samplers.test_Metropolis(run_timing=False)
    +coniii.test_samplers.test_Metropolis(run_timing=False)
    -coniii.test_samplers.test_ParallelTempering()
    +coniii.test_samplers.test_ParallelTempering()
    -coniii.test_samplers.test_Potts3()
    +coniii.test_samplers.test_Potts3()
    -coniii.test_samplers.test_sample_ising()
    +coniii.test_samplers.test_sample_ising()
    @@ -119,7 +119,7 @@

    Quick search

    ©2018, Edward D. Lee, Bryan C. Daniels. | - Powered by Sphinx 3.0.3 + Powered by Sphinx 3.5.4 & Alabaster 0.7.12 | diff --git a/docs/_build/html/coniii_rst/coniii.test_solvers.html b/docs/_build/html/coniii_rst/coniii.test_solvers.html index decd683..580406d 100644 --- a/docs/_build/html/coniii_rst/coniii.test_solvers.html +++ b/docs/_build/html/coniii_rst/coniii.test_solvers.html @@ -4,14 +4,14 @@ + coniii.test_solvers module — ConIII 1.1.9 documentation - + - @@ -34,34 +34,34 @@

    coniii.test_solvers module

    -coniii.test_solvers.test_Enumerate()
    +coniii.test_solvers.test_Enumerate()
    -coniii.test_solvers.test_MPF()
    +coniii.test_solvers.test_MPF()

    Check MPF.

    -coniii.test_solvers.test_Pseudo()
    +coniii.test_solvers.test_Pseudo()
    -coniii.test_solvers.test_SparseEnumerate()
    +coniii.test_solvers.test_SparseEnumerate()
    -coniii.test_solvers.test_init()
    +coniii.test_solvers.test_init()

    Check that all derived Solver classes can be initialized.

    -coniii.test_solvers.test_pickling()
    +coniii.test_solvers.test_pickling()
    @@ -126,7 +126,7 @@

    Quick search

    ©2018, Edward D. Lee, Bryan C. Daniels. | - Powered by Sphinx 3.0.3 + Powered by Sphinx 3.5.4 & Alabaster 0.7.12 | diff --git a/docs/_build/html/coniii_rst/coniii.test_utils.html b/docs/_build/html/coniii_rst/coniii.test_utils.html index 1cca2ed..262153c 100644 --- a/docs/_build/html/coniii_rst/coniii.test_utils.html +++ b/docs/_build/html/coniii_rst/coniii.test_utils.html @@ -4,14 +4,14 @@ + coniii.test_utils module — ConIII 1.1.9 documentation - + - @@ -34,53 +34,53 @@

    coniii.test_utils module

    -coniii.test_utils.test_adj()
    +coniii.test_utils.test_adj()
    -coniii.test_utils.test_base_repr()
    +coniii.test_utils.test_base_repr()
    -coniii.test_utils.test_calc_de()
    +coniii.test_utils.test_calc_de()
    -coniii.test_utils.test_convert_corr()
    +coniii.test_utils.test_convert_corr()
    -coniii.test_utils.test_convert_params()
    +coniii.test_utils.test_convert_params()
    -coniii.test_utils.test_define_ising_helper_functions()
    +coniii.test_utils.test_define_ising_helper_functions()
    -coniii.test_utils.test_pair_corr()
    +coniii.test_utils.test_pair_corr()
    -coniii.test_utils.test_state_gen_and_count()
    +coniii.test_utils.test_state_gen_and_count()

    Test generation of binary states using bin_states() and xbin_states().

    -coniii.test_utils.test_sub_to_ind()
    +coniii.test_utils.test_sub_to_ind()
    -coniii.test_utils.test_vec2mat()
    +coniii.test_utils.test_vec2mat()
    @@ -145,7 +145,7 @@

    Quick search

    ©2018, Edward D. Lee, Bryan C. Daniels. | - Powered by Sphinx 3.0.3 + Powered by Sphinx 3.5.4 & Alabaster 0.7.12 | diff --git a/docs/_build/html/coniii_rst/coniii.utils.html b/docs/_build/html/coniii_rst/coniii.utils.html index 0f6badb..826e671 100644 --- a/docs/_build/html/coniii_rst/coniii.utils.html +++ b/docs/_build/html/coniii_rst/coniii.utils.html @@ -4,14 +4,14 @@ + coniii.utils module — ConIII 1.1.9 documentation - + - @@ -35,7 +35,7 @@

    coniii.utils module

    -coniii.utils.adj(s, n_random_neighbors=0)
    +coniii.utils.adj(s, n_random_neighbors=0)

    Return one-flip neighbors and a set of random neighbors. This is written to be used with the solvers.MPF class. Use adj_sym() if symmetric spins in {-1,1} are needed.

    NOTE: For random neighbors, there is no check to make sure neighbors don’t repeat but @@ -57,13 +57,13 @@

    -coniii.utils.adj_sym(s, n_random_neighbors=False)
    +coniii.utils.adj_sym(s, n_random_neighbors=False)

    Symmetric version of adj() where spins are in {-1,1}.

    -coniii.utils.base_repr(i, base)
    +coniii.utils.base_repr(i, base)

    Return decimal number in given base as list.

    i : int base : int

    @@ -72,7 +72,7 @@
    -coniii.utils.bin_states(n, sym=False)
    +coniii.utils.bin_states(n, sym=False)

    Generate all possible binary spin states.

    nint

    Number of spins.

    @@ -85,7 +85,7 @@
    -coniii.utils.calc_de(s, i)
    +coniii.utils.calc_de(s, i)

    Calculate the derivative of the energy wrt parameters given the state and index of the parameter. In this case, the parameters are the concatenated vector of {h_i,J_ij}.

    @@ -102,7 +102,7 @@
    -coniii.utils.calc_overlap(sample, ignore_zeros=False)
    +coniii.utils.calc_overlap(sample, ignore_zeros=False)

    <si_a si_b> between all pairs of replicas a and b

    sample ignore_zeros (bool=False)

    @@ -114,7 +114,7 @@
    -coniii.utils.coarse_grain_with_func(X, n_times, sim_func, coarse_func)
    +coniii.utils.coarse_grain_with_func(X, n_times, sim_func, coarse_func)

    Iteratively coarse-grain X by combining pairs with the highest similarity. Both the function to measure similarity and to implement the coarse-graining must be supplied.

    @@ -139,7 +139,7 @@
    -coniii.utils.convert_corr(si, sisj, convert_to, concat=False, **kwargs)
    +coniii.utils.convert_corr(si, sisj, convert_to, concat=False, **kwargs)

    Convert single spin means and pairwise correlations between {0,1} and {-1,1} formulations.

    @@ -164,7 +164,7 @@
    -coniii.utils.convert_params(h, J, convert_to, concat=False)
    +coniii.utils.convert_params(h, J, convert_to, concat=False)

    Convert Ising model fields and couplings from {0,1} basis to {-1,1} and vice versa.

    hndarray

    Fields.

    @@ -186,7 +186,7 @@
    -coniii.utils.define_ising_helper_functions()
    +coniii.utils.define_ising_helper_functions()

    Functions for plugging into solvers for +/-1 Ising model with fields h_i and couplings J_ij.

    @@ -201,7 +201,7 @@
    -coniii.utils.define_ising_helper_functions_sym()
    +coniii.utils.define_ising_helper_functions_sym()

    Functions for plugging into solvers for +/-1 Ising model with couplings J_ij and no fields.

    @@ -216,7 +216,7 @@
    -coniii.utils.define_potts_helper_functions(k)
    +coniii.utils.define_potts_helper_functions(k)

    Helper functions for calculating quantities in k-state Potts model.

    kint

    Number of possible states.

    @@ -234,7 +234,7 @@
    -coniii.utils.define_pseudo_ising_helper_functions(N)
    +coniii.utils.define_pseudo_ising_helper_functions(N)

    Define helper functions for using Pseudo method on Ising model.

    Nint

    System size.

    @@ -250,7 +250,7 @@
    -coniii.utils.define_pseudo_potts_helper_functions(n, k)
    +coniii.utils.define_pseudo_potts_helper_functions(n, k)

    Define helper functions for using Pseudo method on Potts model with simple form for couplings that are only nonzero when the spins are occupying the same state.

    @@ -269,17 +269,17 @@
    -coniii.utils.define_ternary_helper_functions()
    +coniii.utils.define_ternary_helper_functions()
    -coniii.utils.define_triplet_helper_functions()
    +coniii.utils.define_triplet_helper_functions()
    -coniii.utils.ind_to_sub(n, ix)
    +coniii.utils.ind_to_sub(n, ix)

    Convert index from flattened upper triangular matrix to pair subindex.

    nint

    Dimension size of square array.

    @@ -295,7 +295,7 @@
    -coniii.utils.ising_convert_params(oparams, convert_to, concat=False)
    +coniii.utils.ising_convert_params(oparams, convert_to, concat=False)

    General conversion of parameters from 01 to 11 basis.

    Take set of Ising model parameters up to nth order interactions in either {0,1} or {-1,1} basis and convert to other basis.

    @@ -316,7 +316,7 @@
    -coniii.utils.k_corr(X, k, weights=None, exclude_empty=False)
    +coniii.utils.k_corr(X, k, weights=None, exclude_empty=False)

    Calculate kth order correlations of spins.

    Xndarray

    Dimensions (n_samples, n_dim).

    @@ -339,7 +339,7 @@
    -coniii.utils.mat2vec(multipliers)
    +coniii.utils.mat2vec(multipliers)

    Convert matrix form of Ising parameters to a vector.

    This is specific to the Ising model.

    @@ -354,12 +354,12 @@
    -coniii.utils.multinomial(*args)
    +coniii.utils.multinomial(*args)
    -coniii.utils.pair_corr(X, weights=None, concat=False, exclude_empty=False, subtract_mean=False, laplace_count=False)
    +coniii.utils.pair_corr(X, weights=None, concat=False, exclude_empty=False, subtract_mean=False, laplace_count=False)

    Calculate averages and pairwise correlations of spins.

    Xndarray

    Dimensions (n_samples,n_dim).

    @@ -387,7 +387,7 @@
    -coniii.utils.replace_diag(mat, newdiag)
    +coniii.utils.replace_diag(mat, newdiag)

    Replace diagonal entries of square matrix.

    mat : ndarray newdiag : ndarray

    @@ -396,7 +396,7 @@
    -coniii.utils.split_concat_params(p, n)
    +coniii.utils.split_concat_params(p, n)

    Split parameters for Ising model that have all been concatenated together into a single list into separate lists. Assumes that the parameters are increasing in order of interaction and that all parameters are present.

    @@ -409,7 +409,7 @@
    -coniii.utils.state_probs(v, allstates=None, weights=None, normalized=True)
    +coniii.utils.state_probs(v, allstates=None, weights=None, normalized=True)

    Get probability of unique states. There is an option to allow for weighted counting.

    @@ -434,7 +434,7 @@
    -coniii.utils.sub_to_ind(n, i, j)
    +coniii.utils.sub_to_ind(n, i, j)

    Convert pair of coordinates of a symmetric square array into consecutive index of flattened upper triangle. This is slimmed down so it won’t throw errors like if i>n or j>n or if they’re negative. Only checking for if the returned index is negative which @@ -450,7 +450,7 @@

    -coniii.utils.unique_rows(mat, return_inverse=False)
    +coniii.utils.unique_rows(mat, return_inverse=False)

    Return unique rows indices of a numeric numpy array.

    mat : ndarray return_inverse : bool

    @@ -468,7 +468,7 @@
    -coniii.utils.unravel_index(ijk, n)
    +coniii.utils.unravel_index(ijk, n)

    Unravel multi-dimensional index to flattened index but specifically for multi-dimensional analog of an upper triangular array (lower triangle indices are not counted).

    @@ -486,7 +486,7 @@
    -coniii.utils.vec2mat(multipliers, separate_fields=False)
    +coniii.utils.vec2mat(multipliers, separate_fields=False)

    Convert vector of parameters containing fields and couplings to a matrix where the diagonal elements are the fields and the remaining elements are the couplings. Fields can be returned separately with the separate_fields keyword argument.

    @@ -507,7 +507,7 @@
    -coniii.utils.xbin_states(n, sym=False)
    +coniii.utils.xbin_states(n, sym=False)

    Generator for iterating through all possible binary states.

    nint

    Number of spins.

    @@ -520,7 +520,7 @@
    -coniii.utils.xpotts_states(n, k)
    +coniii.utils.xpotts_states(n, k)

    Generator for iterating through all states for Potts model with k distinct states. This is a faster version of calling xbin_states(n, False) except with strings returned as elements instead of integers.

    @@ -536,7 +536,7 @@
    -coniii.utils.zero_diag(mat)
    +coniii.utils.zero_diag(mat)

    Replace diagonal entries of square matrix with zeros.

    mat : ndarray

    ndarray

    @@ -605,7 +605,7 @@

    Quick search

    ©2018, Edward D. Lee, Bryan C. Daniels. | - Powered by Sphinx 3.0.3 + Powered by Sphinx 3.5.4 & Alabaster 0.7.12 | diff --git a/docs/_build/html/coniii_rst/modules.html b/docs/_build/html/coniii_rst/modules.html index af9cab8..22ede16 100644 --- a/docs/_build/html/coniii_rst/modules.html +++ b/docs/_build/html/coniii_rst/modules.html @@ -4,14 +4,14 @@ + coniii — ConIII 1.1.9 documentation - + - @@ -129,7 +129,7 @@

    Quick search

    ©2018, Edward D. Lee, Bryan C. Daniels. | - Powered by Sphinx 3.0.3 + Powered by Sphinx 3.5.4 & Alabaster 0.7.12 | diff --git a/docs/_build/html/genindex.html b/docs/_build/html/genindex.html index ea343d9..1328310 100644 --- a/docs/_build/html/genindex.html +++ b/docs/_build/html/genindex.html @@ -1,18 +1,17 @@ - + Index — ConIII 1.1.9 documentation - + - @@ -312,7 +311,7 @@

    F

  • fightPossibilities() (in module coniii.mean_field_ising)
    • -
  • fill_in() (coniii.solvers.SparseEnumerate method) +
  • fill_in() (coniii.solvers.Solver method)
    • @@ -649,7 +650,7 @@

    S

  • seedGenerator() (in module coniii.mean_field_ising)
    • -
  • set_insertion_ix() (coniii.solvers.SparseEnumerate method) +
  • set_insertion_ix() (coniii.solvers.Solver method)
  • setup_replicas() (coniii.samplers.ParallelTempering method)
    • @@ -677,6 +678,8 @@

    S

  • (coniii.solvers.Solver method)
  • (coniii.solvers.SparseEnumerate method) +
    • +
  • (coniii.solvers.SparseMCH method)
    • @@ -684,6 +687,8 @@

    S

  • Solver (class in coniii.solvers)
  • SparseEnumerate (class in coniii.solvers) +
    • +
  • SparseMCH (class in coniii.solvers)
  • spec_cluster() (in module coniii.samplers)
    • @@ -903,7 +908,7 @@

    Quick search

    ©2018, Edward D. Lee, Bryan C. Daniels. | - Powered by Sphinx 3.0.3 + Powered by Sphinx 3.5.4 & Alabaster 0.7.12 diff --git a/docs/_build/html/index.html b/docs/_build/html/index.html index e1119fc..c858658 100644 --- a/docs/_build/html/index.html +++ b/docs/_build/html/index.html @@ -4,14 +4,14 @@ + Welcome to ConIII’s documentation! — ConIII 1.1.9 documentation - + - @@ -119,7 +119,7 @@

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