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auxilin-prediction
mirror of https://github.com/Yu-Group/auxilin-prediction

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  1. <!doctype html>
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  6. <meta name="generator" content="pdoc 0.7.2" />
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  7. <title>src.trend_filtering API documentation</title>
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    14. 

  14. <style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
    15. 

  15. </head>
    16. 

  16. <body>
    17. 

  17. <main>
    18. 

  18. <article id="content">
    19. 

  19. <header>
    20. 

  20. <h1 class="title">Module <code>src.trend_filtering</code></h1>
    21. 

  21. </header>
    22. 

  22. <section id="section-intro">
    23. 

  23. <details class="source">
    24. 

  24. <summary>
    25. 

  25. <span>Expand source code</span>
    26. 

  26. </summary>
    27. 

  27. <pre><code class="python">import numpy as np
    28. 

  28. import cvxpy as cp
    29. 

  29. import scipy as scipy
    30. 

  30. 

  31. 

  32. def trend_filtering(y, vlambda, order=1):
    33. 

  33.     n = len(y)
    34. 

  34.     e = np.ones((1, n))
    35. 

  35.     if order == 1:
    36. 

  36.         D = scipy.sparse.spdiags(np.vstack((e, -2 * e, e)), range(3), n - 2, n)
    37. 

  37.     elif order == 2:
    38. 

  38.         D = scipy.sparse.spdiags(np.vstack((e, -3 * e, 3 * e, -e)), range(4), n - 3, n)
    39. 

  39.     elif order == 3:
    40. 

  40.         D = scipy.sparse.spdiags(np.vstack((e, -4 * e, 6 * e, -4 * e, e)), range(5), n - 4, n)
    41. 

  41.     else:
    42. 

  42.         raise Exception(&#34;order can only be 1-3&#34;)
    43. 

  43. 

  44.     x = cp.Variable(shape=n)
    45. 

  45.     obj = cp.Minimize(0.5 * cp.sum_squares(y - x)
    46. 

  46.                       + vlambda * cp.norm(D * x, 1))
    47. 

  47.     prob = cp.Problem(obj)
    48. 

  48. 

  49.     prob.solve(solver=cp.OSQP)
    50. 

  50. 

  51.     return x.value</code></pre>
    52. 

  52. </details>
    53. 

  53. </section>
    54. 

  54. <section>
    55. 

  55. </section>
    56. 

  56. <section>
    57. 

  57. </section>
    58. 

  58. <section>
    59. 

  59. <h2 class="section-title" id="header-functions">Functions</h2>
    60. 

  60. <dl>
    61. 

  61. <dt id="src.trend_filtering.trend_filtering"><code class="name flex">
    62. 

  62. <span>def <span class="ident">trend_filtering</span></span>(<span>y, vlambda, order=1)</span>
    63. 

  63. </code></dt>
    64. 

  64. <dd>
    65. 

  65. <section class="desc"></section>
    66. 

  66. <details class="source">
    67. 

  67. <summary>
    68. 

  68. <span>Expand source code</span>
    69. 

  69. </summary>
    70. 

  70. <pre><code class="python">def trend_filtering(y, vlambda, order=1):
    71. 

  71.     n = len(y)
    72. 

  72.     e = np.ones((1, n))
    73. 

  73.     if order == 1:
    74. 

  74.         D = scipy.sparse.spdiags(np.vstack((e, -2 * e, e)), range(3), n - 2, n)
    75. 

  75.     elif order == 2:
    76. 

  76.         D = scipy.sparse.spdiags(np.vstack((e, -3 * e, 3 * e, -e)), range(4), n - 3, n)
    77. 

  77.     elif order == 3:
    78. 

  78.         D = scipy.sparse.spdiags(np.vstack((e, -4 * e, 6 * e, -4 * e, e)), range(5), n - 4, n)
    79. 

  79.     else:
    80. 

  80.         raise Exception(&#34;order can only be 1-3&#34;)
    81. 

  81. 

  82.     x = cp.Variable(shape=n)
    83. 

  83.     obj = cp.Minimize(0.5 * cp.sum_squares(y - x)
    84. 

  84.                       + vlambda * cp.norm(D * x, 1))
    85. 

  85.     prob = cp.Problem(obj)
    86. 

  86. 

  87.     prob.solve(solver=cp.OSQP)
    88. 

  88. 

  89.     return x.value</code></pre>
    90. 

  90. </details>
    91. 

  91. </dd>
    92. 

  92. </dl>
    93. 

  93. </section>
    94. 

  94. <section>
    95. 

  95. </section>
    96. 

  96. </article>
    97. 

  97. <nav id="sidebar">
    98. 

  98. <h1>Index</h1>
    99. 

  99. <div class="toc">
    100. 

  100. <ul></ul>
    101. 

  101. </div>
    102. 

  102. <ul id="index">
    103. 

  103. <li><h3>Super-module</h3>
    104. 

  104. <ul>
    105. 

  105. <li><code><a title="src" href="index.html">src</a></code></li>
    106. 

  106. </ul>
    107. 

  107. </li>
    108. 

  108. <li><h3><a href="#header-functions">Functions</a></h3>
    109. 

  109. <ul class="">
    110. 

  110. <li><code><a title="src.trend_filtering.trend_filtering" href="#src.trend_filtering.trend_filtering">trend_filtering</a></code></li>
    111. 

  111. </ul>
    112. 

  112. </li>
    113. 

  113. </ul>
    114. 

  114. </nav>
    115. 

  115. </main>
    116. 

  116. <footer id="footer">
    117. 

  117. <p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.2</a>.</p>
    118. 

  118. </footer>
    119. 

  119. <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script>
    120. 

  120. <script>hljs.initHighlightingOnLoad()</script>
    121. 

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    122. 

  122. </html>
    123.
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