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/tests/test_clean_functions.ipynb

https://gitlab.com/ReturnDensityEstimation/LongMemoryModel
Jupyter | 393 lines | 393 code | 0 blank | 0 comment | 0 complexity | 9dc3850fb6b31c39e8a3566acd7c8ed1 MD5 | raw file
  1. {
  2. "cells": [
  3. {
  4. "cell_type": "markdown",
  5. "metadata": {},
  6. "source": [
  7. "## Code for testing the cleaning functions ##"
  8. ]
  9. },
  10. {
  11. "cell_type": "markdown",
  12. "metadata": {},
  13. "source": [
  14. "This notebook contains code to testing the cleaning code. "
  15. ]
  16. },
  17. {
  18. "cell_type": "code",
  19. "execution_count": 1,
  20. "metadata": {
  21. "collapsed": false
  22. },
  23. "outputs": [],
  24. "source": [
  25. "import data_cleaning as clean\n",
  26. "import pandas as pd\n",
  27. "import numpy as np\n",
  28. "from BayesianKalman import kalmanfilter as kf"
  29. ]
  30. },
  31. {
  32. "cell_type": "markdown",
  33. "metadata": {},
  34. "source": [
  35. "### Setup the parameters ###"
  36. ]
  37. },
  38. {
  39. "cell_type": "code",
  40. "execution_count": 2,
  41. "metadata": {
  42. "collapsed": false
  43. },
  44. "outputs": [],
  45. "source": [
  46. "data_mean = 0\n",
  47. "data_var = .5\n",
  48. "state_var = .1\n",
  49. "state_trans = np.array([[0, 1], [0,0]])\n",
  50. "data_loadings = np.array([1,-1])\n",
  51. "state_innov_var = np.array([[0,0], [0,state_var]])"
  52. ]
  53. },
  54. {
  55. "cell_type": "code",
  56. "execution_count": 3,
  57. "metadata": {
  58. "collapsed": true
  59. },
  60. "outputs": [],
  61. "source": [
  62. "data_dimension = 10"
  63. ]
  64. },
  65. {
  66. "cell_type": "code",
  67. "execution_count": 4,
  68. "metadata": {
  69. "collapsed": false
  70. },
  71. "outputs": [],
  72. "source": [
  73. "simulated_model = kf.simulate_model(\n",
  74. " data_mean=data_mean,\n",
  75. " state_trans=state_trans,\n",
  76. " data_loadings=data_loadings,\n",
  77. " state_innov_var=state_innov_var,\n",
  78. " data_innov_var=data_var,\n",
  79. " num_periods=250\n",
  80. ")"
  81. ]
  82. },
  83. {
  84. "cell_type": "code",
  85. "execution_count": 5,
  86. "metadata": {
  87. "collapsed": false
  88. },
  89. "outputs": [],
  90. "source": [
  91. "cleaned_data = simulated_model.data - np.array(simulated_model.states @ data_loadings.T).reshape(simulated_model.data.size, 1)"
  92. ]
  93. },
  94. {
  95. "cell_type": "code",
  96. "execution_count": 14,
  97. "metadata": {
  98. "collapsed": false
  99. },
  100. "outputs": [
  101. {
  102. "name": "stdout",
  103. "output_type": "stream",
  104. "text": [
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  111. " [ 1.37795756]\n",
  112. " [-0.96880288]\n",
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  115. " [ 0.74519605]\n",
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  117. " [-0.22572885]\n",
  118. " [-1.32483154]\n",
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  120. " [-0.55723926]\n",
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  356. }
  357. ],
  358. "source": [
  359. "print(cleaned_data)"
  360. ]
  361. },
  362. {
  363. "cell_type": "code",
  364. "execution_count": null,
  365. "metadata": {
  366. "collapsed": true
  367. },
  368. "outputs": [],
  369. "source": []
  370. }
  371. ],
  372. "metadata": {
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  375. "language": "python",
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