working on graphs.

Author: parrt

python/src/antlr_one_file_capture.py

@@ -1,6 +1,6 @@
 #
 # AUTO-GENERATED FILE. DO NOT EDIT
-# CodeBuff 1.4.14 'Sat May 14 16:12:13 PDT 2016'
+# CodeBuff 1.4.19 'Sat Jun 18 16:50:03 PDT 2016'
 #
 import matplotlib.pyplot as plt
 import numpy as np
@@ -11,8 +11,8 @@
 N = len(labels)
 
 featureIndexes = range(0,N)
-antlr_self = [0.019966016, 0.09315267, 0.028032573, 0.006559583, 0.011904762, 0.008215752, 0.031296574, 0.08862314, 0.05, 0.08841249, 0.043825977, 0.006999533]
-antlr_corpus = [0.027665675, 0.0892776, 0.057796706, 0.022554493, 0.0852459, 0.048490804, 0.11367837, 0.12763733, 0.37931034, 0.11046658, 0.0678183, 0.024601366]
+antlr_self = [0.019541206, 0.094120495, 0.028079372, 0.006559583, 0.011904762, 0.008215752, 0.031296574, 0.08848672, 0.05, 0.08849395, 0.043825977, 0.006999533]
+antlr_corpus = [0.027665675, 0.0892776, 0.05877948, 0.014419165, 0.0852459, 0.048490804, 0.11419325, 0.12763733, 0.37931034, 0.11046449, 0.0678183, 0.024601366]
 antlr_diff = np.abs(np.subtract(antlr_self, antlr_corpus))
 
 all = zip(antlr_self, antlr_corpus, antlr_diff, labels)

python/src/images/stability.pdf

@@ -1,6 +1,6 @@
 #
 # AUTO-GENERATED FILE. DO NOT EDIT
-# CodeBuff 1.4.14 'Sat May 14 15:55:51 PDT 2016'
+# CodeBuff 1.4.19 'Sat Jun 18 16:47:47 PDT 2016'
 #
 import matplotlib.pyplot as plt
 import numpy as np
@@ -11,8 +11,8 @@
 N = len(labels)
 
 featureIndexes = range(0,N)
-quorum_self = [0.0073099416, 0.0059931506, 0.004962779, 0.008215962, 0.003688604, 0.0038986355, 0.0055555557, 0.016325798, 0.005610098, 0.003968254, 0.0054274085, 0.015222161, 0.0057471264, 0.0028248588, 0.005, 0.0047959182, 0.0032733225, 0.0052083335, 0.0074608787, 0.0062421975, 0.0056917686, 0.008695652, 0.0056306305, 0.0041695624, 0.0064020487, 0.01010101, 0.009377664, 0.006305638, 0.0062176166, 0.011499337, 0.0055599683, 0.0034642033, 0.006535948, 0.003220612, 0.026105117, 0.007317583, 0.003965228, 0.0044288333, 0.0033964096, 0.03587444, 0.6544432, 0.039647575, 0.039187226, 0.0076465593, 0.0034078807, 0.0057077627, 0.0038704684, 0.0041935, 0.009199632, 0.010670732, 0.010670732, 0.003618176, 0.0073891627, 0.010332434, 0.014693417, 0.009259259, 0.008708273, 0.008828073, 0.00814664, 0.011797578, 0.0074487897, 0.005622688, 0.007013391, 0.005635333, 0.006359895, 0.0035540916, 0.0018214936, 0.0033557047, 0.0023474179, 0.0037556335, 0.0029895366, 0.0021482278, 0.0028612304, 0.0023584906, 0.007226739, 0.0028985508, 0.002444988, 0.0016025641, 0.0031152647, 0.0031069685, 0.002762431, 0.0051223678, 0.0057242992, 0.0060038073, 0.0070229797, 0.0033198218, 0.0076595745, 0.00385208, 0.006593163, 0.00885878, 0.0068649887, 0.005846774, 0.0048687183, 0.00907544, 0.020926757, 0.027496383, 0.027777778, 0.005990783, 0.0046813106, 0.0061018225, 0.0048613413, 0.0058425297, 0.004263693, 0.009960718, 0.0050929463, 0.00414823, 0.005701254]
-quorum_corpus = [0.009502924, 0.008354756, 0.0049663, 0.008215962, 0.0038842494, 0.001953125, 0.0041724616, 0.025296018, 0.004213483, 0.003968254, 0.004076087, 0.020844761, 0.004316547, 0.0028248588, 0.0037546933, 0.013663968, 0.0032733225, 0.003911343, 0.010111761, 0.0068621333, 0.0056917686, 0.014534884, 0.0056306305, 0.006978367, 0.006828852, 0.0115440115, 0.010238908, 0.0066790353, 0.008324662, 0.011057055, 0.0063593006, 0.00461361, 0.006546645, 0.004022526, 0.027298493, 0.17013241, 0.08595828, 0.0047357166, 0.10442555, 0.12340426, 0.05612553, 0.041116007, 0.040638607, 0.031936955, 0.16104734, 0.076079264, 0.006474573, 0.0052473764, 0.05292479, 0.010670732, 0.010670732, 0.0038310098, 0.00863132, 0.012578616, 0.005141388, 0.010819165, 0.010174419, 0.009931582, 0.010204081, 0.005319149, 0.008387698, 0.0068655536, 0.00789669, 0.07775894, 0.008230452, 0.0029506206, 0.0036363637, 0.0050251256, 0.0023474179, 0.0037556335, 0.0029895366, 0.0021482278, 0.0028612304, 0.0023584906, 0.007226739, 0.0028985508, 0.002444988, 0.0032, 0.0031152647, 0.004436557, 0.0068775793, 0.005701254, 0.008393565, 0.006447831, 0.007333196, 0.0037424327, 0.0072340425, 0.003468208, 0.014850937, 0.0093847755, 0.0038255546, 0.005846774, 0.0053875563, 0.00907544, 0.021674141, 0.027496383, 0.027777778, 0.0064545874, 0.004860486, 0.021181656, 0.0048613413, 0.0059352685, 0.0041543674, 0.010241302, 0.0048370673, 0.004426003, 0.005323194]
+quorum_self = [0.0073099416, 0.0057061343, 0.004962779, 0.008215962, 0.003688604, 0.0038986355, 0.0055555557, 0.016325798, 0.005610098, 0.003968254, 0.0054274085, 0.015222161, 0.0057471264, 0.0028248588, 0.005, 0.0047959182, 0.0032733225, 0.0052083335, 0.0074608787, 0.0062421975, 0.0056917686, 0.008695652, 0.0056306305, 0.0041695624, 0.0064020487, 0.01010101, 0.009377664, 0.006305638, 0.0062176166, 0.011499337, 0.0055599683, 0.0034642033, 0.006535948, 0.003220612, 0.020187957, 0.004227557, 0.003965228, 0.0044288333, 0.0033964096, 0.03587444, 0.16777308, 0.039647575, 0.039187226, 0.0076465593, 0.0034078807, 0.0057077627, 0.0038704684, 0.0041935, 0.009199632, 0.010670732, 0.010670732, 0.003618176, 0.0073891627, 0.010332434, 0.013578501, 0.009259259, 0.008708273, 0.008828073, 0.00814664, 0.011797578, 0.0074487897, 0.005622688, 0.007048723, 0.005635333, 0.006359895, 0.0032948928, 0.0018214936, 0.0033557047, 0.0023474179, 0.0037556335, 0.0029895366, 0.0021482278, 0.0028612304, 0.0023584906, 0.007226739, 0.0028985508, 0.002444988, 0.0016025641, 0.0031152647, 0.0031069685, 0.002762431, 0.0051223678, 0.0057242992, 0.0060038073, 0.0070229797, 0.0033198218, 0.0076595745, 0.00385208, 0.0065352237, 0.00885878, 0.0068649887, 0.005846774, 0.0048687183, 0.00907544, 0.020926757, 0.027496383, 0.027777778, 0.005990783, 0.0046813106, 0.0061018225, 0.0048613413, 0.0058425297, 0.004263693, 0.009960718, 0.0050929463, 0.00414823, 0.005701254]
+quorum_corpus = [0.009502924, 0.007890603, 0.0049663, 0.008215962, 0.0038842494, 0.001953125, 0.0041724616, 0.025296018, 0.004213483, 0.003968254, 0.004076087, 0.020844761, 0.004316547, 0.0028248588, 0.0037546933, 0.013663968, 0.0032733225, 0.003911343, 0.010111761, 0.0068621333, 0.0061269146, 0.014534884, 0.0056306305, 0.007670851, 0.006828852, 0.0115440115, 0.010238908, 0.0066790353, 0.008324662, 0.011057055, 0.007148531, 0.00461361, 0.0073589534, 0.004022526, 0.027298493, 0.17020446, 0.08595828, 0.0047357166, 0.10442555, 0.12340426, 0.05612553, 0.041116007, 0.040638607, 0.031936955, 0.16104734, 0.076079264, 0.006474573, 0.0052473764, 0.05292479, 0.010670732, 0.010670732, 0.003724593, 0.00863132, 0.01212938, 0.006843456, 0.010819165, 0.010174419, 0.009710881, 0.010204081, 0.005941213, 0.008387698, 0.0068655536, 0.00789669, 0.07775894, 0.008230452, 0.0029506206, 0.0036363637, 0.0050251256, 0.0023474179, 0.0037556335, 0.0029895366, 0.0021482278, 0.0028612304, 0.0023584906, 0.007226739, 0.0028985508, 0.002444988, 0.0032, 0.0031152647, 0.004436557, 0.009602195, 0.005701254, 0.008393565, 0.006447831, 0.007333196, 0.0037424327, 0.0072340425, 0.003468208, 0.014850937, 0.0093847755, 0.0038255546, 0.005846774, 0.0053875563, 0.00907544, 0.021674141, 0.027496383, 0.027777778, 0.0064545874, 0.004860486, 0.021181656, 0.0048613413, 0.0059352685, 0.0041543674, 0.010241302, 0.0053435112, 0.004426003, 0.005323194]
 quorum_diff = np.abs(np.subtract(quorum_self, quorum_corpus))
 
 all = zip(quorum_self, quorum_corpus, quorum_diff, labels)

python/src/images/vary_k.pdf

@@ -1,6 +1,6 @@
 #
 # AUTO-GENERATED FILE. DO NOT EDIT
-# CodeBuff 1.4.14 'Sat May 14 16:12:21 PDT 2016'
+# CodeBuff 1.4.19 'Sat Jun 18 16:50:10 PDT 2016'
 #
 import matplotlib.pyplot as plt
 import numpy as np
@@ -11,8 +11,8 @@
 N = len(labels)
 
 featureIndexes = range(0,N)
-sqlite_noisy_self = [0.4841343, 0.07627565, 0.10773481, 0.02, 0.011173184, 0.17874396, 0.010526316, 0.038737066, 0.05302551, 0.15537849, 0.072855465, 0.045490824, 0.00790798, 0.03898051, 0.13157895, 0.20668058, 0.03359684, 0.10793651, 0.055353243, 0.067047216, 0.07061069, 0.0982958, 0.13513513, 0.082965575, 0.04957265, 0.009433962, 0.029060716, 0.01033295, 0.112651646, 0.11394713, 0.071287125, 0.07747148, 0.035532996, 0.03195311, 0.24308406, 0.024079321]
-sqlite_noisy_corpus = [0.14053689, 0.15307733, 0.12960406, 0.092651755, 0.061452515, 0.17874396, 0.3195652, 0.053085774, 0.052920092, 0.09471613, 0.060961314, 0.29099157, 0.0395399, 0.12968516, 0.2882736, 0.19311064, 0.10301837, 0.08460532, 0.059966013, 0.17651702, 0.099236645, 0.06908095, 0.11824324, 0.17833333, 0.19546157, 0.043396227, 0.06953814, 0.012629162, 0.17157713, 0.19234276, 0.07590759, 0.14591254, 0.08095111, 0.06200378, 0.3255976, 0.05077574]
+sqlite_noisy_self = [0.042000484, 0.089952655, 0.10313076, 0.02, 0.011173184, 0.17874396, 0.010526316, 0.03900239, 0.053552605, 0.1551179, 0.071680374, 0.045490824, 0.00790798, 0.036731634, 0.13157895, 0.151357, 0.03359684, 0.08329699, 0.06020879, 0.06575026, 0.07061069, 0.0982958, 0.13344595, 0.082965575, 0.04957265, 0.009433962, 0.029060716, 0.01033295, 0.112651646, 0.111212395, 0.050165016, 0.0769962, 0.033333335, 0.031764038, 0.30019632, 0.024079321]
+sqlite_noisy_corpus = [0.14073072, 0.22530864, 0.11832412, 0.092651755, 0.061452515, 0.17874396, 0.3195652, 0.055469558, 0.05386886, 0.09396665, 0.059859157, 0.2923674, 0.038102087, 0.12968516, 0.3110749, 0.2453027, 0.10737813, 0.07784562, 0.059480455, 0.16615689, 0.122137405, 0.069689594, 0.11402027, 0.21098725, 0.19670443, 0.050943397, 0.06953814, 0.012629162, 0.168447, 0.17319964, 0.06160616, 0.14624882, 0.08068395, 0.058990356, 0.33832902, 0.06853147]
 sqlite_noisy_diff = np.abs(np.subtract(sqlite_noisy_self, sqlite_noisy_corpus))
 
 all = zip(sqlite_noisy_self, sqlite_noisy_corpus, sqlite_noisy_diff, labels)

python/src/quorum_one_file_capture.py

@@ -1,6 +1,6 @@
 #
 # AUTO-GENERATED FILE. DO NOT EDIT
-# CodeBuff 1.4.14 'Sat May 14 16:12:28 PDT 2016'
+# CodeBuff 1.4.19 'Sat Jun 18 16:50:16 PDT 2016'
 #
 import matplotlib.pyplot as plt
 import numpy as np
@@ -11,8 +11,8 @@
 N = len(labels)
 
 featureIndexes = range(0,N)
-sqlite_self = [0.1157388, 0.038131554, 0.042547897, 0.0011862396, 0.00591716, 0.06322795, 0.018469658, 0.044878565, 0.04975907, 0.07500838, 0.036486488, 0.08414985, 0.014715719, 0.029032258, 0.04863813, 0.011639185, 0.057683643, 0.044763334, 0.03748198, 0.018883733, 0.11345219, 0.10630111, 0.008488964, 0.05945946, 0.05871212, 0.01718213, 0.01963672, 0.012269938, 0.06518283, 0.1204025, 0.060365368, 0.064244576, 0.0304414, 0.041209284, 0.22858973, 0.08276088]
-sqlite_corpus = [0.14190038, 0.11939571, 0.042558614, 0.009422851, 0.00591716, 0.05059022, 0.24899599, 0.053326294, 0.06116629, 0.09302715, 0.1627095, 0.22939481, 0.07504938, 0.058064517, 0.06614786, 0.16197866, 0.14601171, 0.10475988, 0.05069678, 0.060582623, 0.1458671, 0.14694564, 0.008488964, 0.16447876, 0.19412878, 0.0395189, 0.08070678, 0.012269938, 0.0453125, 0.2297016, 0.10606061, 0.05344523, 0.026778882, 0.05708208, 0.13093337, 0.1261468]
+sqlite_self = [0.11657597, 0.014002897, 0.040142275, 0.0011862396, 0.00591716, 0.06322795, 0.018469658, 0.042454123, 0.04212904, 0.07337002, 0.036486488, 0.08184438, 0.014715719, 0.02764613, 0.04780115, 0.011639185, 0.043615106, 0.0497807, 0.04228736, 0.021525823, 0.12567325, 0.10623229, 0.008488964, 0.05945946, 0.056818184, 0.01718213, 0.01963672, 0.012269938, 0.0015898251, 0.14559124, 0.060365368, 0.018165708, 0.024222335, 0.041209284, 0.22858973, 0.061799552]
+sqlite_corpus = [0.14064462, 0.06335282, 0.05606524, 0.0059101656, 0.00591716, 0.07419899, 0.27021697, 0.046288688, 0.059609152, 0.10405203, 0.32402235, 0.1371758, 0.05450875, 0.076619275, 0.04206501, 0.10087294, 0.20684993, 0.110826395, 0.05069678, 0.11356203, 0.14362657, 0.14140698, 0.021026073, 0.12509653, 0.12394761, 0.0395189, 0.050314464, 0.024242423, 0.0190779, 0.20027341, 0.110175975, 0.01683651, 0.031624585, 0.031317107, 0.07699696, 0.11760884]
 sqlite_diff = np.abs(np.subtract(sqlite_self, sqlite_corpus))
 
 all = zip(sqlite_self, sqlite_corpus, sqlite_diff, labels)

python/src/sqlite_noisy_one_file_capture.py

@@ -1,36 +1,22 @@
 #
 # AUTO-GENERATED FILE. DO NOT EDIT
-# CodeBuff 1.4.15 'Wed May 18 15:45:53 PDT 2016'
+# CodeBuff 1.4.19 'Sat Jun 18 17:27:26 PDT 2016'
 #
 import matplotlib
 import matplotlib.pyplot as plt
 fig = plt.figure()
 ax = plt.subplot(111)
-N = 11
+N = 6
 sizes = range(0,N)
-tsql_noisy = [0.1827957,0.12292359,0.035439137,0.031007752,0.031007752,0.04037267,0.025188917,0.02293578,0.023728814,0.022727273,0.02020202]
-ax.plot(sizes, tsql_noisy, label="tsql_noisy", marker='o')
-sqlite_noisy = [0.17948718,0.12468828,0.046875,0.029985007,0.050691243,0.0375817,0.03137255,0.028037382,0.025974026,0.025974026,0.025974026]
-ax.plot(sizes, sqlite_noisy, label="sqlite_noisy", marker='o')
-java = [0.05303761,0.018867925,0.013100437,0.010752688,0.011130137,0.012048192,0.011463845,0.011130137,0.012658228,0.012607626,0.013100437]
-ax.plot(sizes, java, label="java", marker='o')
-sqlite = [0.10973085,0.12468828,0.046875,0.029985007,0.029007634,0.036585364,0.036923077,0.035276074,0.030927835,0.029023746,0.028455285]
-ax.plot(sizes, sqlite, label="sqlite", marker='o')
-java8 = [0.047131147,0.018348623,0.012048192,0.009417809,0.009259259,0.009345794,0.010784314,0.009427121,0.0091743115,0.010989011,0.009417809]
-ax.plot(sizes, java8, label="java8", marker='o')
-quorum = [0.041800644,0.023255814,0.01631964,0.015075377,0.013040494,0.011494253,0.012861736,0.011494253,0.012861736,0.013333334,0.012861736]
+quorum = [0.041800644,0.8050103,0.8150558,0.80688065,0.8070041,0.8075207]
 ax.plot(sizes, quorum, label="quorum", marker='o')
-antlr = [0.16694611,0.04863722,0.025252525,0.03448276,0.022988506,0.03539823,0.044247787,0.028735632,0.022988506,0.023362696,0.023745691]
-ax.plot(sizes, antlr, label="antlr", marker='o')
-tsql = [0.09504132,0.12292359,0.035439137,0.031007752,0.022421524,0.02739726,0.02739726,0.018376723,0.02008032,0.023655914,0.018691588]
-ax.plot(sizes, tsql, label="tsql", marker='o')
 
 ax.yaxis.grid(True, linestyle='-', which='major', color='lightgrey', alpha=0.5)
 xa = ax.get_xaxis()
 xa.set_major_locator(matplotlib.ticker.MaxNLocator(integer=True))
 ax.set_xlabel("Formatting Stage; stage 0 is first formatting pass")
 ax.set_ylabel("Median Leave-one-out Validation Error Rate")
-ax.set_title("11-Stage Formatting Stability\nStage $n$ trained on $n-1$")
+ax.set_title("6-Stage Formatting Stability\nStage $n$ is formatted output of stage $n-1$")
 plt.legend()
 plt.tight_layout()
 fig.savefig('images/stability.pdf', format='pdf')

python/src/sqlite_one_file_capture.py

@@ -1,6 +1,6 @@
 #
 # AUTO-GENERATED FILE. DO NOT EDIT
-# CodeBuff 1.4.14 'Sat May 14 16:12:34 PDT 2016'
+# CodeBuff 1.4.19 'Sat Jun 18 16:50:22 PDT 2016'
 #
 import matplotlib.pyplot as plt
 import numpy as np
@@ -11,8 +11,8 @@
 N = len(labels)
 
 featureIndexes = range(0,N)
-tsql_noisy_self = [0.076227985, 0.050177395, 0.113950275, 0.02, 0.011173184, 0.17552336, 0.010526316, 0.04139029, 0.050495468, 0.09078134, 0.05640423, 0.045490824, 0.00790798, 0.03748126, 0.13157895, 0.1565762, 0.032279316, 0.09180114, 0.058752123, 0.026165765, 0.07442748, 0.105903834, 0.1258446, 0.089143865, 0.05014245, 0.01509434, 0.031136481, 0.03258427, 0.112651646, 0.09708295, 0.07238724, 0.09553232, 0.03537234, 0.020797882, 0.28449047, 0.04287046]
-tsql_noisy_corpus = [0.13265073, 0.15465544, 0.12131676, 0.022033898, 0.061452515, 0.18196458, 0.31681034, 0.06453278, 0.06029939, 0.16860062, 0.059929494, 0.2625387, 0.05362117, 0.13868067, 0.257329, 0.26409185, 0.09486166, 0.115821026, 0.06457878, 0.058658116, 0.122137405, 0.056603774, 0.17483108, 0.17901748, 0.22862454, 0.035849057, 0.088455774, 0.030998852, 0.19876733, 0.17411122, 0.15128152, 0.17702703, 0.14312367, 0.08527273, 0.30501518, 0.08877053]
+tsql_noisy_self = [0.076227985, 0.050177395, 0.11878453, 0.02, 0.011173184, 0.17552336, 0.010526316, 0.028654816, 0.050495468, 0.09415402, 0.05640423, 0.045490824, 0.00790798, 0.03748126, 0.13157895, 0.1565762, 0.032279316, 0.07828173, 0.059966013, 0.026165765, 0.07442748, 0.10529519, 0.12922297, 0.089143865, 0.05014245, 0.01509434, 0.022739017, 0.03258427, 0.112651646, 0.09617138, 0.07216722, 0.09363118, 0.03537234, 0.012289658, 0.28449047, 0.04287046]
+tsql_noisy_corpus = [0.08400097, 0.105207786, 0.12039595, 0.016949153, 0.06703911, 0.18035427, 0.31681034, 0.06707472, 0.05935062, 0.1066142, 0.055229142, 0.10428016, 0.023005033, 0.13868067, 0.257329, 0.2526096, 0.09486166, 0.1001306, 0.06457878, 0.058658116, 0.114503816, 0.057212416, 0.1714527, 0.18038237, 0.09441341, 0.035849057, 0.08885616, 0.030998852, 0.28156027, 0.18732908, 0.09658966, 0.25, 0.14528553, 0.078667864, 0.3151883, 0.08043956]
 tsql_noisy_diff = np.abs(np.subtract(tsql_noisy_self, tsql_noisy_corpus))
 
 all = zip(tsql_noisy_self, tsql_noisy_corpus, tsql_noisy_diff, labels)

python/src/stability.py

@@ -1,6 +1,6 @@
 #
 # AUTO-GENERATED FILE. DO NOT EDIT
-# CodeBuff 1.4.14 'Sat May 14 16:12:39 PDT 2016'
+# CodeBuff 1.4.19 'Sat Jun 18 16:50:27 PDT 2016'
 #
 import matplotlib.pyplot as plt
 import numpy as np
@@ -11,8 +11,8 @@
 N = len(labels)
 
 featureIndexes = range(0,N)
-tsql_self = [0.1358309, 0.024554646, 0.03567788, 0.0011862396, 0.00591716, 0.0539629, 0.018469658, 0.015839493, 0.09705969, 0.10122021, 0.11033519, 0.08414985, 0.014715719, 0.024193548, 0.04863813, 0.04946654, 0.10274898, 0.05896484, 0.03940413, 0.015919052, 0.10372771, 0.19480519, 0.0025445293, 0.05945946, 0.066287875, 0.02233677, 0.013705336, 0.02145046, 0.06518283, 0.16099931, 0.05401112, 0.056269385, 0.04565162, 0.016555695, 0.22154634, 0.13278419]
-tsql_corpus = [0.14231896, 0.06335282, 0.04332314, 0.0011862396, 0.00591716, 0.09780776, 0.19739696, 0.031414993, 0.06264136, 0.12286289, 0.3784916, 0.129683, 0.041132838, 0.052419353, 0.1119403, 0.14354995, 0.24740875, 0.07300216, 0.041086018, 0.055942252, 0.13614263, 0.09235209, 0.006756757, 0.16412213, 0.14259173, 0.04639175, 0.017569546, 0.012269938, 0.00953895, 0.19361554, 0.08101668, 0.024637043, 0.05228258, 0.017182745, 0.17979583, 0.14332785]
+tsql_self = [0.1358309, 0.024554646, 0.03950051, 0.0011862396, 0.00591716, 0.0539629, 0.018469658, 0.016433854, 0.09162329, 0.09557091, 0.108938545, 0.08414985, 0.014715719, 0.023696683, 0.04780115, 0.04946654, 0.10274898, 0.04357251, 0.040605478, 0.018026926, 0.11490126, 0.19121814, 0.0025445293, 0.05945946, 0.066287875, 0.02233677, 0.013705336, 0.02145046, 0.0015898251, 0.14969242, 0.05381255, 0.010190519, 0.05529499, 0.016555695, 0.22154634, 0.12573099]
+tsql_corpus = [0.12180829, 0.04191033, 0.05988787, 0.0011862396, 0.00591716, 0.09780776, 0.19739696, 0.028211448, 0.060983993, 0.12239453, 0.37918994, 0.08299712, 0.06827048, 0.048183255, 0.04206501, 0.12027158, 0.273096, 0.063057326, 0.046131667, 0.018559366, 0.13105924, 0.11024551, 0.013422819, 0.13280116, 0.16266094, 0.04639175, 0.06626506, 0.01629328, 0.011923688, 0.17771702, 0.08141382, 0.03809524, 0.058913544, 0.0395174, 0.17325458, 0.12248213]
 tsql_diff = np.abs(np.subtract(tsql_self, tsql_corpus))
 
 all = zip(tsql_self, tsql_corpus, tsql_diff, labels)