146 lines
6.7 KiB
Python
146 lines
6.7 KiB
Python
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from numpy import array
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Q_X = array([[5.26563660e-01, 3.14160190e-01, 8.00656370e-02],
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[7.50205180e-01, 4.60299830e-01, 8.98696460e-01],
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[6.65461230e-01, 6.94011420e-01, 9.10465700e-01],
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[9.64047590e-01, 1.43082200e-03, 7.39874220e-01],
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[1.08159060e-01, 5.53028790e-01, 6.63804780e-02],
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[9.31359130e-01, 8.25424910e-01, 9.52315440e-01],
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[6.78086960e-01, 3.41903970e-01, 5.61481950e-01],
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[9.82730940e-01, 7.04605210e-01, 8.70978630e-02],
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[6.14691610e-01, 4.69989230e-02, 6.02406450e-01],
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[5.80161260e-01, 9.17354970e-01, 5.88163850e-01],
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[1.38246310e+00, 1.96358160e+00, 1.94437880e+00],
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[2.10675860e+00, 1.67148730e+00, 1.34854480e+00],
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[1.39880070e+00, 1.66142050e+00, 1.32224550e+00],
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[1.71410460e+00, 1.49176380e+00, 1.45432170e+00],
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[1.54102340e+00, 1.84374950e+00, 1.64658950e+00],
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[2.08512480e+00, 1.84524350e+00, 2.17340850e+00],
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[1.30748740e+00, 1.53801650e+00, 2.16007740e+00],
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[1.41447700e+00, 1.99329070e+00, 1.99107420e+00],
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[1.61943490e+00, 1.47703280e+00, 1.89788160e+00],
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[1.59880600e+00, 1.54988980e+00, 1.57563350e+00],
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[3.37247380e+00, 2.69635310e+00, 3.39981700e+00],
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[3.13705120e+00, 3.36528090e+00, 3.06089070e+00],
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[3.29413250e+00, 3.19619500e+00, 2.90700170e+00],
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[2.65510510e+00, 3.06785900e+00, 2.97198540e+00],
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[3.30941040e+00, 2.59283970e+00, 2.57714110e+00],
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[2.59557220e+00, 3.33477370e+00, 3.08793190e+00],
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[2.58206180e+00, 3.41615670e+00, 3.26441990e+00],
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[2.71127000e+00, 2.77032450e+00, 2.63466500e+00],
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[2.79617850e+00, 3.25473720e+00, 3.41801560e+00],
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[2.64741750e+00, 2.54538040e+00, 3.25354110e+00]])
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ytdist = array([662., 877., 255., 412., 996., 295., 468., 268., 400., 754.,
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564., 138., 219., 869., 669.])
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linkage_ytdist_single = array([[2., 5., 138., 2.],
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[3., 4., 219., 2.],
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[0., 7., 255., 3.],
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[1., 8., 268., 4.],
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[6., 9., 295., 6.]])
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linkage_ytdist_complete = array([[2., 5., 138., 2.],
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[3., 4., 219., 2.],
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[1., 6., 400., 3.],
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[0., 7., 412., 3.],
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[8., 9., 996., 6.]])
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linkage_ytdist_average = array([[2., 5., 138., 2.],
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[3., 4., 219., 2.],
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[0., 7., 333.5, 3.],
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[1., 6., 347.5, 3.],
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[8., 9., 680.77777778, 6.]])
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linkage_ytdist_weighted = array([[2., 5., 138., 2.],
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[3., 4., 219., 2.],
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[0., 7., 333.5, 3.],
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[1., 6., 347.5, 3.],
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[8., 9., 670.125, 6.]])
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# the optimal leaf ordering of linkage_ytdist_single
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linkage_ytdist_single_olo = array([[5., 2., 138., 2.],
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[4., 3., 219., 2.],
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[7., 0., 255., 3.],
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[1., 8., 268., 4.],
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[6., 9., 295., 6.]])
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X = array([[1.43054825, -7.5693489],
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[6.95887839, 6.82293382],
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[2.87137846, -9.68248579],
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[7.87974764, -6.05485803],
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[8.24018364, -6.09495602],
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[7.39020262, 8.54004355]])
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linkage_X_centroid = array([[3., 4., 0.36265956, 2.],
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[1., 5., 1.77045373, 2.],
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[0., 2., 2.55760419, 2.],
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[6., 8., 6.43614494, 4.],
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[7., 9., 15.17363237, 6.]])
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linkage_X_median = array([[3., 4., 0.36265956, 2.],
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[1., 5., 1.77045373, 2.],
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[0., 2., 2.55760419, 2.],
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[6., 8., 6.43614494, 4.],
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[7., 9., 15.17363237, 6.]])
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linkage_X_ward = array([[3., 4., 0.36265956, 2.],
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[1., 5., 1.77045373, 2.],
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[0., 2., 2.55760419, 2.],
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[6., 8., 9.10208346, 4.],
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[7., 9., 24.7784379, 6.]])
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# the optimal leaf ordering of linkage_X_ward
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linkage_X_ward_olo = array([[4., 3., 0.36265956, 2.],
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[5., 1., 1.77045373, 2.],
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[2., 0., 2.55760419, 2.],
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[6., 8., 9.10208346, 4.],
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[7., 9., 24.7784379, 6.]])
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inconsistent_ytdist = {
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1: array([[138., 0., 1., 0.],
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[219., 0., 1., 0.],
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[255., 0., 1., 0.],
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[268., 0., 1., 0.],
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[295., 0., 1., 0.]]),
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2: array([[138., 0., 1., 0.],
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[219., 0., 1., 0.],
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[237., 25.45584412, 2., 0.70710678],
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[261.5, 9.19238816, 2., 0.70710678],
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[233.66666667, 83.9424406, 3., 0.7306594]]),
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3: array([[138., 0., 1., 0.],
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[219., 0., 1., 0.],
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[237., 25.45584412, 2., 0.70710678],
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[247.33333333, 25.38372182, 3., 0.81417007],
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[239., 69.36377537, 4., 0.80733783]]),
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4: array([[138., 0., 1., 0.],
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[219., 0., 1., 0.],
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[237., 25.45584412, 2., 0.70710678],
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[247.33333333, 25.38372182, 3., 0.81417007],
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[235., 60.73302232, 5., 0.98793042]])}
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fcluster_inconsistent = {
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0.8: array([6, 2, 2, 4, 6, 2, 3, 7, 3, 5, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1,
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1, 1, 1, 1, 1, 1, 1, 1, 1]),
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1.0: array([6, 2, 2, 4, 6, 2, 3, 7, 3, 5, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 1,
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1, 1, 1, 1, 1, 1, 1, 1, 1]),
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2.0: array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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1, 1, 1, 1, 1, 1, 1, 1, 1])}
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fcluster_distance = {
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0.6: array([4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 6, 6, 6, 6, 6, 7, 6, 6, 6, 6, 3,
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1, 1, 1, 2, 1, 1, 1, 1, 1]),
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1.0: array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1,
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1, 1, 1, 1, 1, 1, 1, 1, 1]),
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2.0: array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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1, 1, 1, 1, 1, 1, 1, 1, 1])}
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fcluster_maxclust = {
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8.0: array([5, 5, 5, 5, 5, 5, 5, 6, 5, 5, 7, 7, 7, 7, 7, 8, 7, 7, 7, 7, 4,
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1, 1, 1, 3, 1, 1, 1, 1, 2]),
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4.0: array([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2,
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1, 1, 1, 1, 1, 1, 1, 1, 1]),
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1.0: array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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1, 1, 1, 1, 1, 1, 1, 1, 1])}
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