hierarchy_test_data.py 6.7 KB

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  1. from numpy import array
  2. Q_X = array([[5.26563660e-01, 3.14160190e-01, 8.00656370e-02],
  3. [7.50205180e-01, 4.60299830e-01, 8.98696460e-01],
  4. [6.65461230e-01, 6.94011420e-01, 9.10465700e-01],
  5. [9.64047590e-01, 1.43082200e-03, 7.39874220e-01],
  6. [1.08159060e-01, 5.53028790e-01, 6.63804780e-02],
  7. [9.31359130e-01, 8.25424910e-01, 9.52315440e-01],
  8. [6.78086960e-01, 3.41903970e-01, 5.61481950e-01],
  9. [9.82730940e-01, 7.04605210e-01, 8.70978630e-02],
  10. [6.14691610e-01, 4.69989230e-02, 6.02406450e-01],
  11. [5.80161260e-01, 9.17354970e-01, 5.88163850e-01],
  12. [1.38246310e+00, 1.96358160e+00, 1.94437880e+00],
  13. [2.10675860e+00, 1.67148730e+00, 1.34854480e+00],
  14. [1.39880070e+00, 1.66142050e+00, 1.32224550e+00],
  15. [1.71410460e+00, 1.49176380e+00, 1.45432170e+00],
  16. [1.54102340e+00, 1.84374950e+00, 1.64658950e+00],
  17. [2.08512480e+00, 1.84524350e+00, 2.17340850e+00],
  18. [1.30748740e+00, 1.53801650e+00, 2.16007740e+00],
  19. [1.41447700e+00, 1.99329070e+00, 1.99107420e+00],
  20. [1.61943490e+00, 1.47703280e+00, 1.89788160e+00],
  21. [1.59880600e+00, 1.54988980e+00, 1.57563350e+00],
  22. [3.37247380e+00, 2.69635310e+00, 3.39981700e+00],
  23. [3.13705120e+00, 3.36528090e+00, 3.06089070e+00],
  24. [3.29413250e+00, 3.19619500e+00, 2.90700170e+00],
  25. [2.65510510e+00, 3.06785900e+00, 2.97198540e+00],
  26. [3.30941040e+00, 2.59283970e+00, 2.57714110e+00],
  27. [2.59557220e+00, 3.33477370e+00, 3.08793190e+00],
  28. [2.58206180e+00, 3.41615670e+00, 3.26441990e+00],
  29. [2.71127000e+00, 2.77032450e+00, 2.63466500e+00],
  30. [2.79617850e+00, 3.25473720e+00, 3.41801560e+00],
  31. [2.64741750e+00, 2.54538040e+00, 3.25354110e+00]])
  32. ytdist = array([662., 877., 255., 412., 996., 295., 468., 268., 400., 754.,
  33. 564., 138., 219., 869., 669.])
  34. linkage_ytdist_single = array([[2., 5., 138., 2.],
  35. [3., 4., 219., 2.],
  36. [0., 7., 255., 3.],
  37. [1., 8., 268., 4.],
  38. [6., 9., 295., 6.]])
  39. linkage_ytdist_complete = array([[2., 5., 138., 2.],
  40. [3., 4., 219., 2.],
  41. [1., 6., 400., 3.],
  42. [0., 7., 412., 3.],
  43. [8., 9., 996., 6.]])
  44. linkage_ytdist_average = array([[2., 5., 138., 2.],
  45. [3., 4., 219., 2.],
  46. [0., 7., 333.5, 3.],
  47. [1., 6., 347.5, 3.],
  48. [8., 9., 680.77777778, 6.]])
  49. linkage_ytdist_weighted = array([[2., 5., 138., 2.],
  50. [3., 4., 219., 2.],
  51. [0., 7., 333.5, 3.],
  52. [1., 6., 347.5, 3.],
  53. [8., 9., 670.125, 6.]])
  54. # the optimal leaf ordering of linkage_ytdist_single
  55. linkage_ytdist_single_olo = array([[5., 2., 138., 2.],
  56. [4., 3., 219., 2.],
  57. [7., 0., 255., 3.],
  58. [1., 8., 268., 4.],
  59. [6., 9., 295., 6.]])
  60. X = array([[1.43054825, -7.5693489],
  61. [6.95887839, 6.82293382],
  62. [2.87137846, -9.68248579],
  63. [7.87974764, -6.05485803],
  64. [8.24018364, -6.09495602],
  65. [7.39020262, 8.54004355]])
  66. linkage_X_centroid = array([[3., 4., 0.36265956, 2.],
  67. [1., 5., 1.77045373, 2.],
  68. [0., 2., 2.55760419, 2.],
  69. [6., 8., 6.43614494, 4.],
  70. [7., 9., 15.17363237, 6.]])
  71. linkage_X_median = array([[3., 4., 0.36265956, 2.],
  72. [1., 5., 1.77045373, 2.],
  73. [0., 2., 2.55760419, 2.],
  74. [6., 8., 6.43614494, 4.],
  75. [7., 9., 15.17363237, 6.]])
  76. linkage_X_ward = array([[3., 4., 0.36265956, 2.],
  77. [1., 5., 1.77045373, 2.],
  78. [0., 2., 2.55760419, 2.],
  79. [6., 8., 9.10208346, 4.],
  80. [7., 9., 24.7784379, 6.]])
  81. # the optimal leaf ordering of linkage_X_ward
  82. linkage_X_ward_olo = array([[4., 3., 0.36265956, 2.],
  83. [5., 1., 1.77045373, 2.],
  84. [2., 0., 2.55760419, 2.],
  85. [6., 8., 9.10208346, 4.],
  86. [7., 9., 24.7784379, 6.]])
  87. inconsistent_ytdist = {
  88. 1: array([[138., 0., 1., 0.],
  89. [219., 0., 1., 0.],
  90. [255., 0., 1., 0.],
  91. [268., 0., 1., 0.],
  92. [295., 0., 1., 0.]]),
  93. 2: array([[138., 0., 1., 0.],
  94. [219., 0., 1., 0.],
  95. [237., 25.45584412, 2., 0.70710678],
  96. [261.5, 9.19238816, 2., 0.70710678],
  97. [233.66666667, 83.9424406, 3., 0.7306594]]),
  98. 3: array([[138., 0., 1., 0.],
  99. [219., 0., 1., 0.],
  100. [237., 25.45584412, 2., 0.70710678],
  101. [247.33333333, 25.38372182, 3., 0.81417007],
  102. [239., 69.36377537, 4., 0.80733783]]),
  103. 4: array([[138., 0., 1., 0.],
  104. [219., 0., 1., 0.],
  105. [237., 25.45584412, 2., 0.70710678],
  106. [247.33333333, 25.38372182, 3., 0.81417007],
  107. [235., 60.73302232, 5., 0.98793042]])}
  108. fcluster_inconsistent = {
  109. 0.8: array([6, 2, 2, 4, 6, 2, 3, 7, 3, 5, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1,
  110. 1, 1, 1, 1, 1, 1, 1, 1, 1]),
  111. 1.0: array([6, 2, 2, 4, 6, 2, 3, 7, 3, 5, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1,
  112. 1, 1, 1, 1, 1, 1, 1, 1, 1]),
  113. 2.0: array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
  114. 1, 1, 1, 1, 1, 1, 1, 1, 1])}
  115. fcluster_distance = {
  116. 0.6: array([4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 6, 6, 6, 6, 6, 7, 6, 6, 6, 6, 3,
  117. 1, 1, 1, 2, 1, 1, 1, 1, 1]),
  118. 1.0: array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1,
  119. 1, 1, 1, 1, 1, 1, 1, 1, 1]),
  120. 2.0: array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
  121. 1, 1, 1, 1, 1, 1, 1, 1, 1])}
  122. fcluster_maxclust = {
  123. 8.0: array([5, 5, 5, 5, 5, 5, 5, 6, 5, 5, 7, 7, 7, 7, 7, 8, 7, 7, 7, 7, 4,
  124. 1, 1, 1, 3, 1, 1, 1, 1, 2]),
  125. 4.0: array([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2,
  126. 1, 1, 1, 1, 1, 1, 1, 1, 1]),
  127. 1.0: array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
  128. 1, 1, 1, 1, 1, 1, 1, 1, 1])}