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urlscan Pro
140.82.121.3
Public Scan
Submitted URL: http://sandwood.org/
Effective URL: https://github.com/oracle/sandwood/
Submission: On September 23 via api from US — Scanned from AT
Effective URL: https://github.com/oracle/sandwood/
Submission: On September 23 via api from US — Scanned from AT
Form analysis 3 forms found in the DOM
GET
<form id="query-builder-test-form" action="" accept-charset="UTF-8" method="get">
<query-builder data-target="qbsearch-input.queryBuilder" id="query-builder-query-builder-test" data-filter-key=":" data-view-component="true" class="QueryBuilder search-query-builder" data-min-width="300" data-catalyst="">
<div class="FormControl FormControl--fullWidth">
<label id="query-builder-test-label" for="query-builder-test" class="FormControl-label sr-only"> Search </label>
<div class="QueryBuilder-StyledInput width-fit " data-target="query-builder.styledInput">
<span id="query-builder-test-leadingvisual-wrap" class="FormControl-input-leadingVisualWrap QueryBuilder-leadingVisualWrap">
<svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-search FormControl-input-leadingVisual">
<path d="M10.68 11.74a6 6 0 0 1-7.922-8.982 6 6 0 0 1 8.982 7.922l3.04 3.04a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215ZM11.5 7a4.499 4.499 0 1 0-8.997 0A4.499 4.499 0 0 0 11.5 7Z"></path>
</svg>
</span>
<div data-target="query-builder.styledInputContainer" class="QueryBuilder-StyledInputContainer">
<div aria-hidden="true" class="QueryBuilder-StyledInputContent" data-target="query-builder.styledInputContent"></div>
<div class="QueryBuilder-InputWrapper">
<div aria-hidden="true" class="QueryBuilder-Sizer" data-target="query-builder.sizer"><span></span></div>
<input id="query-builder-test" name="query-builder-test" value="" autocomplete="off" type="text" role="combobox" spellcheck="false" aria-expanded="false" aria-describedby="validation-ac8b4496-44c2-488e-8020-35657f8e4fc8"
data-target="query-builder.input" data-action="
input:query-builder#inputChange
blur:query-builder#inputBlur
keydown:query-builder#inputKeydown
focus:query-builder#inputFocus
" data-view-component="true" class="FormControl-input QueryBuilder-Input FormControl-medium" aria-controls="query-builder-test-results" aria-autocomplete="list" aria-haspopup="listbox" style="width: 300px;">
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</div>
<span class="sr-only" id="query-builder-test-clear">Clear</span>
<button role="button" id="query-builder-test-clear-button" aria-labelledby="query-builder-test-clear query-builder-test-label" data-target="query-builder.clearButton" data-action="
click:query-builder#clear
focus:query-builder#clearButtonFocus
blur:query-builder#clearButtonBlur
" variant="small" hidden="" type="button" data-view-component="true" class="Button Button--iconOnly Button--invisible Button--medium mr-1 px-2 py-0 d-flex flex-items-center rounded-1 color-fg-muted"> <svg aria-hidden="true" height="16"
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<path
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<template id="search-icon">
<svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-search">
<path d="M10.68 11.74a6 6 0 0 1-7.922-8.982 6 6 0 0 1 8.982 7.922l3.04 3.04a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215ZM11.5 7a4.499 4.499 0 1 0-8.997 0A4.499 4.499 0 0 0 11.5 7Z"></path>
</svg>
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<template id="code-icon">
<svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-code">
<path
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<template id="file-code-icon">
<svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-file-code">
<path
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<template id="history-icon">
<svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-history">
<path
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<template id="repo-icon">
<svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo">
<path
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</template>
<template id="bookmark-icon">
<svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-bookmark">
<path
d="M3 2.75C3 1.784 3.784 1 4.75 1h6.5c.966 0 1.75.784 1.75 1.75v11.5a.75.75 0 0 1-1.227.579L8 11.722l-3.773 3.107A.751.751 0 0 1 3 14.25Zm1.75-.25a.25.25 0 0 0-.25.25v9.91l3.023-2.489a.75.75 0 0 1 .954 0l3.023 2.49V2.75a.25.25 0 0 0-.25-.25Z">
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</template>
<template id="plus-circle-icon">
<svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-plus-circle">
<path d="M8 0a8 8 0 1 1 0 16A8 8 0 0 1 8 0ZM1.5 8a6.5 6.5 0 1 0 13 0 6.5 6.5 0 0 0-13 0Zm7.25-3.25v2.5h2.5a.75.75 0 0 1 0 1.5h-2.5v2.5a.75.75 0 0 1-1.5 0v-2.5h-2.5a.75.75 0 0 1 0-1.5h2.5v-2.5a.75.75 0 0 1 1.5 0Z"></path>
</svg>
</template>
<template id="circle-icon">
<svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill">
<path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path>
</svg>
</template>
<template id="trash-icon">
<svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-trash">
<path
d="M11 1.75V3h2.25a.75.75 0 0 1 0 1.5H2.75a.75.75 0 0 1 0-1.5H5V1.75C5 .784 5.784 0 6.75 0h2.5C10.216 0 11 .784 11 1.75ZM4.496 6.675l.66 6.6a.25.25 0 0 0 .249.225h5.19a.25.25 0 0 0 .249-.225l.66-6.6a.75.75 0 0 1 1.492.149l-.66 6.6A1.748 1.748 0 0 1 10.595 15h-5.19a1.75 1.75 0 0 1-1.741-1.575l-.66-6.6a.75.75 0 1 1 1.492-.15ZM6.5 1.75V3h3V1.75a.25.25 0 0 0-.25-.25h-2.5a.25.25 0 0 0-.25.25Z">
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</template>
<template id="team-icon">
<svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-people">
<path
d="M2 5.5a3.5 3.5 0 1 1 5.898 2.549 5.508 5.508 0 0 1 3.034 4.084.75.75 0 1 1-1.482.235 4 4 0 0 0-7.9 0 .75.75 0 0 1-1.482-.236A5.507 5.507 0 0 1 3.102 8.05 3.493 3.493 0 0 1 2 5.5ZM11 4a3.001 3.001 0 0 1 2.22 5.018 5.01 5.01 0 0 1 2.56 3.012.749.749 0 0 1-.885.954.752.752 0 0 1-.549-.514 3.507 3.507 0 0 0-2.522-2.372.75.75 0 0 1-.574-.73v-.352a.75.75 0 0 1 .416-.672A1.5 1.5 0 0 0 11 5.5.75.75 0 0 1 11 4Zm-5.5-.5a2 2 0 1 0-.001 3.999A2 2 0 0 0 5.5 3.5Z">
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</template>
<template id="project-icon">
<svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-project">
<path
d="M1.75 0h12.5C15.216 0 16 .784 16 1.75v12.5A1.75 1.75 0 0 1 14.25 16H1.75A1.75 1.75 0 0 1 0 14.25V1.75C0 .784.784 0 1.75 0ZM1.5 1.75v12.5c0 .138.112.25.25.25h12.5a.25.25 0 0 0 .25-.25V1.75a.25.25 0 0 0-.25-.25H1.75a.25.25 0 0 0-.25.25ZM11.75 3a.75.75 0 0 1 .75.75v7.5a.75.75 0 0 1-1.5 0v-7.5a.75.75 0 0 1 .75-.75Zm-8.25.75a.75.75 0 0 1 1.5 0v5.5a.75.75 0 0 1-1.5 0ZM8 3a.75.75 0 0 1 .75.75v3.5a.75.75 0 0 1-1.5 0v-3.5A.75.75 0 0 1 8 3Z">
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</template>
<template id="pencil-icon">
<svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-pencil">
<path
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</template>
<template id="copilot-icon">
<svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copilot">
<path
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</template>
<template id="copilot-error-icon">
<svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copilot-error">
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<template id="workflow-icon">
<svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-workflow">
<path
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<template id="book-icon">
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<path
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<template id="code-review-icon">
<svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-code-review">
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<template id="codespaces-icon">
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<path
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<template id="comment-icon">
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<template id="rocket-icon">
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<template id="shield-check-icon">
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<path
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<template id="heart-icon">
<svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-heart">
<path
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</svg>
</template>
<template id="server-icon">
<svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-server">
<path
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</template>
<template id="globe-icon">
<svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-globe">
<path
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<template id="issue-opened-icon">
<svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-issue-opened">
<path d="M8 9.5a1.5 1.5 0 1 0 0-3 1.5 1.5 0 0 0 0 3Z"></path>
<path d="M8 0a8 8 0 1 1 0 16A8 8 0 0 1 8 0ZM1.5 8a6.5 6.5 0 1 0 13 0 6.5 6.5 0 0 0-13 0Z"></path>
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</template>
<template id="device-mobile-icon">
<svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-device-mobile">
<path
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<template id="package-icon">
<svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-package">
<path
d="m8.878.392 5.25 3.045c.54.314.872.89.872 1.514v6.098a1.75 1.75 0 0 1-.872 1.514l-5.25 3.045a1.75 1.75 0 0 1-1.756 0l-5.25-3.045A1.75 1.75 0 0 1 1 11.049V4.951c0-.624.332-1.201.872-1.514L7.122.392a1.75 1.75 0 0 1 1.756 0ZM7.875 1.69l-4.63 2.685L8 7.133l4.755-2.758-4.63-2.685a.248.248 0 0 0-.25 0ZM2.5 5.677v5.372c0 .09.047.171.125.216l4.625 2.683V8.432Zm6.25 8.271 4.625-2.683a.25.25 0 0 0 .125-.216V5.677L8.75 8.432Z">
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<template id="credit-card-icon">
<svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-credit-card">
<path d="M10.75 9a.75.75 0 0 0 0 1.5h1.5a.75.75 0 0 0 0-1.5h-1.5Z"></path>
<path
d="M0 3.75C0 2.784.784 2 1.75 2h12.5c.966 0 1.75.784 1.75 1.75v8.5A1.75 1.75 0 0 1 14.25 14H1.75A1.75 1.75 0 0 1 0 12.25ZM14.5 6.5h-13v5.75c0 .138.112.25.25.25h12.5a.25.25 0 0 0 .25-.25Zm0-2.75a.25.25 0 0 0-.25-.25H1.75a.25.25 0 0 0-.25.25V5h13Z">
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<template id="play-icon">
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<path d="M8 0a8 8 0 1 1 0 16A8 8 0 0 1 8 0ZM1.5 8a6.5 6.5 0 1 0 13 0 6.5 6.5 0 0 0-13 0Zm4.879-2.773 4.264 2.559a.25.25 0 0 1 0 .428l-4.264 2.559A.25.25 0 0 1 6 10.559V5.442a.25.25 0 0 1 .379-.215Z"></path>
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<template id="gift-icon">
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<path
d="M0 1.75C0 .784.784 0 1.75 0h12.5C15.216 0 16 .784 16 1.75v12.5A1.75 1.75 0 0 1 14.25 16H1.75A1.75 1.75 0 0 1 0 14.25Zm1.75-.25a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h12.5a.25.25 0 0 0 .25-.25V1.75a.25.25 0 0 0-.25-.25Zm7.47 3.97a.75.75 0 0 1 1.06 0l2 2a.75.75 0 0 1 0 1.06l-2 2a.749.749 0 0 1-1.275-.326.749.749 0 0 1 .215-.734L10.69 8 9.22 6.53a.75.75 0 0 1 0-1.06ZM6.78 6.53 5.31 8l1.47 1.47a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215l-2-2a.75.75 0 0 1 0-1.06l2-2a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042Z">
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<template id="device-desktop-icon">
<svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-device-desktop">
<path
d="M14.25 1c.966 0 1.75.784 1.75 1.75v7.5A1.75 1.75 0 0 1 14.25 12h-3.727c.099 1.041.52 1.872 1.292 2.757A.752.752 0 0 1 11.25 16h-6.5a.75.75 0 0 1-.565-1.243c.772-.885 1.192-1.716 1.292-2.757H1.75A1.75 1.75 0 0 1 0 10.25v-7.5C0 1.784.784 1 1.75 1ZM1.75 2.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h12.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25ZM9.018 12H6.982a5.72 5.72 0 0 1-.765 2.5h3.566a5.72 5.72 0 0 1-.765-2.5Z">
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<div class="position-relative">
<ul role="listbox" class="ActionListWrap QueryBuilder-ListWrap" aria-label="Suggestions" data-action="
combobox-commit:query-builder#comboboxCommit
mousedown:query-builder#resultsMousedown
" data-target="query-builder.resultsList" data-persist-list="false" id="query-builder-test-results"></ul>
</div>
<div class="FormControl-inlineValidation" id="validation-ac8b4496-44c2-488e-8020-35657f8e4fc8" hidden="hidden">
<span class="FormControl-inlineValidation--visual">
<svg aria-hidden="true" height="12" viewBox="0 0 12 12" version="1.1" width="12" data-view-component="true" class="octicon octicon-alert-fill">
<path d="M4.855.708c.5-.896 1.79-.896 2.29 0l4.675 8.351a1.312 1.312 0 0 1-1.146 1.954H1.33A1.313 1.313 0 0 1 .183 9.058ZM7 7V3H5v4Zm-1 3a1 1 0 1 0 0-2 1 1 0 0 0 0 2Z"></path>
</svg>
</span>
<span></span>
</div>
</div>
<div data-target="query-builder.screenReaderFeedback" aria-live="polite" aria-atomic="true" class="sr-only"></div>
</query-builder>
</form>POST /search/feedback
<form id="code-search-feedback-form" data-turbo="false" action="/search/feedback" accept-charset="UTF-8" method="post"><input type="hidden" data-csrf="true" name="authenticity_token"
value="mkQaI+NTT1C/dZNTNt+AZlVtXU/EdWOioty7FxPzuXIN5d8RTo1MK3oDBX0WIfcLBPGcIKnE/qsS9h1wbneOJQ==">
<p>We read every piece of feedback, and take your input very seriously.</p>
<textarea name="feedback" class="form-control width-full mb-2" style="height: 120px" id="feedback"></textarea>
<input name="include_email" id="include_email" aria-label="Include my email address so I can be contacted" class="form-control mr-2" type="checkbox">
<label for="include_email" style="font-weight: normal">Include my email address so I can be contacted</label>
</form>POST /search/custom_scopes
<form id="custom-scopes-dialog-form" data-turbo="false" action="/search/custom_scopes" accept-charset="UTF-8" method="post"><input type="hidden" data-csrf="true" name="authenticity_token"
value="dYg5OmtGWjJA/syiypIAsYNsDAYbL9XYE9ZqfohzKX5ZxmRypxHMio78opUS6FulJa5AaEjPKbWl5Y8oyjCsvA==">
<div data-target="custom-scopes.customScopesModalDialogFlash"></div>
<input type="hidden" id="custom_scope_id" name="custom_scope_id" data-target="custom-scopes.customScopesIdField">
<div class="form-group">
<label for="custom_scope_name">Name</label>
<auto-check src="/search/custom_scopes/check_name" required="">
<input type="text" name="custom_scope_name" id="custom_scope_name" data-target="custom-scopes.customScopesNameField" class="form-control" autocomplete="off" placeholder="github-ruby" required="" maxlength="50" spellcheck="false">
<input type="hidden" data-csrf="true" value="xZn1A5P/Pq8wI+KQLeB+ILke9wFBLF7Q06w8Ss+7vraQcgDj5y1uP5zaDPeL/ToQfFgGyjER+zAhuFPxLdf/Xw==">
</auto-check>
</div>
<div class="form-group">
<label for="custom_scope_query">Query</label>
<input type="text" name="custom_scope_query" id="custom_scope_query" data-target="custom-scopes.customScopesQueryField" class="form-control" autocomplete="off" placeholder="(repo:mona/a OR repo:mona/b) AND lang:python" required=""
maxlength="500">
</div>
<p class="text-small color-fg-muted"> To see all available qualifiers, see our <a class="Link--inTextBlock" href="https://docs.github.com/search-github/github-code-search/understanding-github-code-search-syntax">documentation</a>. </p>
</form>Text Content
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REPOSITORY FILES NAVIGATION
* README
* Code of conduct
* License
* Security
SANDWOOD
Sandwood is a language, compiler, and runtime for JVM based probabilistic
models. It is designed to allow models to be written in a language that is
familiar to Java developers. The resulting models take the form of Java objects
allowing them to be well abstracted components of an encompassing system.
WHAT IS PROBABILISTIC PROGRAMMING?
With a traditional Bayesian Model the user has to design the model, and then
implement inference code for any operation they wish to perform on the model.
This creates a number of issues:
1. Constructing inference code is technically challenging to do, and time
consuming. This step presents an opportunity for subtle bugs to be
introduced.
2. If the model is modified, then the inference code will have to be updated.
This is also time consuming and technically challenging, leading to the
following problems:
* It acts as a deterrent to modifying models.
* It is possible for different inference operations to get out of step so some
work on the old model and some work on the new model.
* It presents another opportunity for bugs to get in the inference algorithm as
users try to make subtle adjustments to existing code.
3. Looking at the code it is hard to see what the model is. This harms
maintainability.
Probabilistic programming overcomes these issues by allowing models to be
described using either an API, or a domain specific language (DSL) as is the
case with Sandwood. The Sandwood DSL is compiled to produce Java classes that
represent the model and implement all the required inference operations. This
has a number of advantages:
* Users are no longer required to handle the technical complexity of
constructing inference code.
* Updating the model is now just requires changes to the high-level language
and then recompilation to generate the new inference code.
* Models described in the high-level language are much more understandable
improving code maintainability.
* All the inference operations are guaranteed to be for the same model.
* User bugs are confined to the model, with the inference code coming from the
compiler.
INSTALLATION
Sandwood consists of 3 components each in their corresponding directory:
* The compiler and runtime (Sandwood)
* The plugin for Maven (SandwoodMavenPlugin)
* A set of example models (SandwoodExamples)
Each piece is dependent on the preceding pieces. Each component directory
contains a Maven POM file to construct the component. For the compiler and the
plugin these will need to be called with install to make them available for
later stages, i.e. mvn clean install. The examples should only be built as mvn
clean package.
Having installed Sandwood there are currently 3 ways of compiling a model:
1. A command line tool.
2. A Java library call.
3. A Maven plugin.
COMMAND LINE TOOL
To use Sandwood from the command line once the compiler and runtime have been
built command line scripts that have similar functionality to javac can be found
in commandline/SandwoodC/bin. To use this the user would typically add the bin
directory to the path, then call sandwoodc.sh HMM.sandwood to compile the HMM
model. sandwoodc.sh -h or sandwoodc.bat -h will result in a description of the
usage and available options being printed out.
JAVA LIBRARY CALL
All the functionality of SandwoodC can be reached by calling the method compile
in org.sandwood.compilation.SandwoodC and passing an array containing the
arguments that would have been passed to the command line.
MAVEN PLUGIN
The Maven plugin can be used to automatically trigger compilation of sandwood
files when the dependent project is built. To use the plugin you need to add the
sandwood runtime as a dependency, and add the plugin to the build. This is
achieved with the following additions to the POM file:
<dependencies>
<dependency>
<groupId>org.sandwood</groupId>
<artifactId>sandwood-runtime</artifactId>
<version>0.3.0</version>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<groupId>org.sandwood</groupId>
<artifactId>sandwoodc-maven-plugin</artifactId>
<version>0.3-SNAPSHOT</version>
<executions>
<execution>
<configuration>
<partialInferenceWarning>true</partialInferenceWarning>
<sourceDirectory>${basedir}/src/main/java</sourceDirectory>
</configuration>
<goals>
<goal>sandwoodc</goal>
</goals>
</execution>
</executions>
</plugin>
</plugins>
</build>`
The inclusion of the element
<sourceDirectory>${basedir}/src/main/java</sourceDirectory> instructs the plugin
which directory to look in for models. Other useful flags include:
* debug This option is used to obtain debugging information from SandwoodC.
Setting this option to true causes Sandwood to generate a trace of its
actions. The default value is false. Note this flag is for debugging errors
with the compiler configuration/compiler, not with the model being compiled.
Errors and warnings in the sandwood model files will always be returned by
the compiler.
* partialInferenceWarning This option is used to stop SandwoodC from failing
when some inference steps cannot be constructed. Setting this option to true
causes Sandwood to just generate warnings on missing steps. Default value is
false.
* sourceDirectory This parameter sets which directory to look in for model
files. Within this directory the models can be located in different packages.
* outputDirectory This parameter sets which directory the Java source code for
the models should be placed into. The default value is
${project.build.directory}/generated-sources/sandwood.
* calculateIndividualProbabilities This parameter specifies if the
probabilities for each random variable constructed in a loop should be
calculated instead of a single value for all instances. The default value is
false.
* javadoc This parameter instructs the compiler to generate JavaDoc to
compliment the model. The default value is false.
* javadocDirectory This parameter specifies the location that the generated
should be placed.
* executable This parameter allow for an alternative JVM to be specified to run
the Sandwood compiler with.
DOCUMENTATION
What follows is an introduction to how to write Sandwood models and how to use
the resultant classes that implement the models.
An outline of the steps a model goes through can be seen in this diagram. Models
start as a .sandwood file that is compiled to a set of class files. These can be
instantiated multiple times to generate multiple instances of the model with
different configurations.
EXAMPLE MODEL
As a running example we will use a Hidden Markov Model (HMM). This model is
written here in Sandwood. This model should be saved in a file called
HMM.sandwood in a package directory org/sandwood/examples/hmm. A fuller
description of the language can be found here.
package org.sandwood.examples.hmm;
model HMM(int[] eventsMeasured, int numStates, int numEvents) {
//Construct a transition matrix m.
double[] v = new double[numStates] <~ 0.1;
double[][] m = dirichlet(v).sample(numStates);
//Construct weighting for which state to start in.
double[] initialState = new Dirichlet(v).sample();
//Construct weighting for each event in each state.
double[] w = new double[numEvents] <~ 0.1;
double[][] bias = dirichlet(w).sample(numStates);
//Allocate space to record the sequence of states.
int sequenceLength = eventsMeasured.length;
int[] st = new int[sequenceLength];
//Calculate the movements between states.
st[0] = categorical(initialState).sampleDistribution();
for (int i: [1..sequenceLength) )
st[i] = categorical(m[st[i - 1]]).sampleDistribution();
//Emit the events for each state.
int[] events = new int[sequenceLength];
for (int j = 0; j < sequenceLength; j++)
events[j] = new Categorical(bias[st[j]]).sample();
//Assert that the events match the eventsMeasured data.
events.observe(eventsMeasured);
}
In addition to the documentation of the Sandwood language and the JavaDoc
comment that can be generated for a model there are a number of examples in the
Sandwood Examples directory and we suggest new users start by examining and
modifying these.
SANDWOOD LANGUAGE
A description of the language used to describe Sandwood models can be found
here. The language is constructed with the intent of being familiar to Java
developers, but does not contain the ability to construct Objects. We plan to
add support for record types in the future to make the import and export of data
to and from models simpler.
COMPILED MODELS
When a model is compiled a number of class files are generated in the same
package that the model is defined in. One of these classes will have the same
name as the name provided to the model, so in this case HMM.class , and this is
the class that the user should instantiate in order to have an instance of the
model. Each publicly visible variable in the model corresponds to a field in the
generated class. The example HMM can be seen below.
By running the compiler with the javadoc flag set JavaDoc will be created for
each public method and class in the generated model file.
USING COMPILED MODELS
Once the model has been compiled we need to instantiate instances of it. These
instances are independent and the user can create as many different copies of
the model as they wish.
CONSTRUCTING A MODEL
Instances of the model object are constructed via the class constructor. As
described earlier there are typically 3 constructors for the model. The only
case when there will be fewer is when the different variants of the constructor
map to the same signature, in which case one constructor will apply to more than
one of these scenarios.
* Full constructor - This constructor takes all the arguments that appear in
the model signature and sets them. This constructor is used for the infer
values and infer probabilities operations.
* Empty constructor - This constructor takes no arguments, leaving the
parameters for the user to set later.
* Execution Constructor - This constructor removes arguments that are only
observed, and for observed arguments whose dimensions are used as inputs to
the code, takes those dimensions instead of the full parameters. So in the
HMM example the eventsMeasured parameter will become an integer describing
the length of the sequence.
These code samples demonstrate how to make calls to the compiled models.
INTERACTING WITH A MODEL
Interactions with a model via the model object takes two forms:
* Calls to model object methods for global operations such as setting default
retention policies, checking if the model is ready for inference, and
starting inference steps, etc.
* Calls to model parameter objects. Each named public variable in the model is
represented by a corresponding field in the model object. Variables are
public if they are declared in the outermost scope of the model and not
labelled private, or are declared in an inner scope and are not labelled
public. If a field is declared public in an inner iterative scope, for
example the body of a for loop, the value from each iteration will be stored.
The type of the object will depend on the variable. These can be split into 3
categories:
1. Observed variables: These are scalar or array variables that are set by
the user.
2. Inferred variables: These are scalar or array variables inferred by the
model.
3. Random variables: These Random Variables declared as named fields in the
model. These can only have their probability queried.
Each of these fields references an object with a set of methods that allow
the user to set and read values and properties from the parameter. Properties
that can be set and read include the probability of the parameter, the
retention policy of the parameter, and if the parameter should be fixed at
its current value.
Some of the more important methods of the parameter object when performing model
inference are:
* getSamples to return sampled values.
* getMAP to return the Maximum A Posteriori value.
* setValue to allow a value to be set to a specific value.
* setFixed which takes a boolean to mark the value as fixed, and therefore not
to be updated during inference. It is important to set the value of the
parameter before fixing it.
* getLogProbability which gets the log probability of the variable after
inferring probabilities.
There are more methods, and we recommend consulting the JavaDoc to familiarise
yourself with them.
MODEL OPERATIONS
There are 3 basic types of operation that can be performed on a model:
1. Infer Values Having set the value of some of the variables of a model, infer
values for the remaining variables. For the results of this method to be of
interest some of the inferred values will need to be recorded. This can be
done by recording the inferred values for every unset variable in the model
model, or just a subset of the variables. The retention policy can be set
for the entire model by a call to the method setRentionPolicy in the model
class. Optionally individual variables can then have their retention policy
set by calls to the corresponding setRetentionPolicy method in each variable
object.
There are 3 sampling policies:
* NONE records no values. This is particularly useful if one of the variables
is large so taking the time and space to store it would be wasteful.
* SAMPLE records the value from every iteration of the inference algorithm, so
if 1000 iterations are performed 1000 values will be sampled from the each
variable set to this retention policy. This is useful for calculating the
variance as well as the average value. There is a weakness to this though, if
the positions of values within the model can move during the inference then
the values cannot be averaged over. For example with a topic model topics 2
and 3 may swap places during the inference, so averaging all the values for
topic 2 with produce a mixture of topic 2 and topic 3. To overcome this
Maximum A Posteriori (MAP) is also provided as a retention policy.
* MAP or Maximum A Posteriori (MAP) records the values of the variables when
the model is in its most probable state. This overcomes the issue with
transient value positions meaning values cannot be averaged, but at the
expense of being able to calculate bounds. This option also has space
advantages if some of the variables are large.
Configuration: Additional method calls on the model object allow the user to set
properties such as burnin and thinning when performing this inference step.
Burnin ignores the values from the first n iterations allowing the model to move
away from a low probability starting point before starting to sample. Thinning
reduces the autocorrelation induced by the MCMC procedure by only considering
the values from every n th iteration.
2. Infer Probabilities Having set the values of some or all the parameters in
the model calculate the probability of generating those values. This can be
calculated for each variable in the model and for the model as a whole.
3. Execute Model Run the model as if it was regular code generating new values
for any parameters that are not fixed by the user. An example of when this
behaviour would be used is for a linear regression model. In this case the
model coefficients would first be inferred using training data. Once they
have been inferred they would be fixed and new input data set. The model
would then be executed to generate the corresponding predictions for this
new input data. This form of execution could also be used to generate
representative synthetic data from a trained model.
Construct and train a model
//Load inputs
int nStates = 25;
int[] actions = loadActions(....);
int nActions = maxActions(....);
//Construct the model
HMM model = new HMM(actions, nActions, nStates);
//Set the retention policies
model.setDefaultRetentionPolicy(RetentionPolicy.MAP);
model.st.setRetentionPolicy(RetentionPolicy.NONE);
//Pick a random number generator. The ones introduced in Java 17 are faster and better quality.
model.setRNGType(RandomType.L64X1024MixRandom);
//Instruct the model to use the ForkJoin framework for parallel execution.
model.setExecutionTarget(ExecutionTarget.forkJoin);
//Run 2000 inference steps to infer model values
model.inferValues(2000);
//Gather the results.
double[] initialState = model.initialState.getMAP();
double[][] bias = model.bias.getMAP();
double[][] transitions = model.m.getMAP();
Construct model and infer probabilities
//Load inputs
int nStates = 25;
int[] actions = loadActions(....);
int nActions = maxActions(....);
//Load model parameters
double[][] bias = model.bias.getMAP();
double[][] transitions = model.m.getMAP();
//Construct the model
HMM model = new HMM(actions, nActions, nStates);
//Set and fix trained values
model.bias.setValue(bias);
Model.m.setValue(transitions);
//Run 2000 inference steps to infer probabilities
model.inferProbabilities(2000);
//Recover the probabilities of the model parameter actions.
double actionsProbability = model.actions.getProbability();
//Recover the probability of the model as a whole
double modelProbability = model.getProbability()
Train and execute a Liner Regression model
//Load parameters
double[] xs = loadXs(....);
double[] ys = loadYs(....);
//Construct the model
LinearRegression model = new LinearRegression(xs, ys);
…
//Run 2000 inference steps to c, m, and sigma.
model.inferValues(2000);
//Fix the inferred values
model.c.setFixed(true);
model.m.setFixed(true);
model.sigma.setFixed(true);
//Set new input values
double[] new_xs = loadXs(....);
model.xs.setValue(new_xs);
//Run the model to generate sequences of actions using the inferred values
model.execute(1000);
//Recover the generated values
double[][] new_ys = model.ys.getSamples();
HELP
For help with Sandwood please start or join a discussion on the discussions
page.
CONTRIBUTING
This project welcomes contributions from the community. Before submitting a pull
request, please review our contribution guide.
SECURITY
Please consult the security guide for our responsible security vulnerability
disclosure process.
LICENSE
Copyright (c) 2019-2024 Oracle and/or its affiliates.
Released under the Universal Permissive License v1.0 as shown at
https://oss.oracle.com/licenses/upl/.
ABOUT
Sandwood - A JVM based Java like Probabilistic Programming Language
TOPICS
java machine-learning compiler runtime ml probabilistic-programming
probabilistic-models
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* Daniel-Goodman Daniel Goodman
* spavlusieva Oleksandra Pavlusieva
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