Mercurial > hg > nsaunier > traffic-intelligence

annotate python/tests/utils.txt @ 728:4e89341edd29 dev

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added the capability to enforce a minimum cluster size and re-assign the indicators to other clusters
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Mon, 10 Aug 2015 17:51:49 -0400
parents 9990ef119bce
children c35e4a4b199d
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
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1 >>> from utils import *
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2 >>> from moving import Point
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3
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4 >>> computeChi2([],[])
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5 0.0
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6 >>> computeChi2(range(1,10),range(1,10))
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7 0.0
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8 >>> computeChi2(range(1,9),range(1,10))
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9 0.0
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10
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11 >>> ceilDecimals(1.23, 0)
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12 2.0
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13 >>> ceilDecimals(1.23, 1)
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14 1.3
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15
152
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16 >>> inBetween(1,2,1.5)
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17 True
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18 >>> inBetween(2.1,1,1.5)
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19 True
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20 >>> inBetween(1,2,0)
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21 False
31
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22
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23 >>> removeExtension('test-adfasdf.asdfa.txt')
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24 'test-adfasdf.asdfa'
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25 >>> removeExtension('test-adfasdf')
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26 'test-adfasdf'
42
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27
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28 >>> values = line2Ints('1 2 3 5 6')
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29 >>> values[0]
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30 1
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31 >>> values[-1]
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32 6
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33 >>> values = line2Floats('1.3 2.45 7.158e+01 5 6')
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34 >>> values[0]
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35 1.3
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36 >>> values[2] #doctest: +ELLIPSIS
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37 71.5...
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38 >>> values[-1]
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39 6.0
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40
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41 >>> stepPlot([3, 5, 7, 8], 1, 10, 0)
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42 ([1, 3, 3, 5, 5, 7, 7, 8, 8, 10], [0, 0, 1, 1, 2, 2, 3, 3, 4, 4])
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43
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44 >>> mostCommon(['a','b','c','b'])
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45 'b'
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46 >>> mostCommon(['a','b','c','b', 'c'])
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47 'b'
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48 >>> mostCommon(range(10)+[1])
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49 1
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50 >>> mostCommon([range(2), range(4), range(2)])
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51 [0, 1]
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52
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53 >>> lcss = LCSS(similarityFunc = lambda x,y: abs(x-y) <= 0.1)
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54 >>> lcss.compute(range(5), range(5))
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55 5
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56 >>> lcss.compute(range(1,5), range(5))
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57 4
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58 >>> lcss.compute(range(5,10), range(5))
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59 0
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60 >>> lcss.compute(range(5), range(10))
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61 5
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62 >>> lcss.similarityFunc = lambda x,y: x == y
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63 >>> lcss.compute(['a','b','c'], ['a','b','c', 'd'])
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64 3
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65 >>> lcss.computeNormalized(['a','b','c'], ['a','b','c', 'd']) #doctest: +ELLIPSIS
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66 1.0
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67 >>> lcss.computeNormalized(['a','b','c','x'], ['a','b','c', 'd']) #doctest: +ELLIPSIS
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68 0.75
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69 >>> lcss.compute(['a','b','c'], ['a','b','c', 'd'])
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70 3
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71 >>> lcss.compute(['a','x','b','c'], ['a','b','c','d','x'])
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72 3
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73 >>> lcss.compute(['a','b','c','x','d'], ['a','b','c','d','x'])
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74 4
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75 >>> lcss.delta = 1
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76 >>> lcss.compute(['a','b','c'], ['a','b','x','x','c'])
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77 2
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78
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79 >>> lcss.delta = float('inf')
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80 >>> lcss.compute(['a','b','c'], ['a','b','c', 'd'], computeSubSequence = True)
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81 3
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82 >>> lcss.subSequenceIndices
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83 [(0, 0), (1, 1), (2, 2)]
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84 >>> lcss.compute(['a','b','c'], ['x','a','b','c'], computeSubSequence = True)
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85 3
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86 >>> lcss.subSequenceIndices
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87 [(0, 1), (1, 2), (2, 3)]
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88 >>> lcss.compute(['a','g','b','c'], ['a','b','c', 'd'], computeSubSequence = True)
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89 3
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90 >>> lcss.subSequenceIndices
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91 [(0, 0), (2, 1), (3, 2)]
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92
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93 >>> alignedLcss = LCSS(lambda x,y:(abs(x-y) <= 0.1), delta = 2, aligned = True)
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94 >>> alignedLcss.compute(range(5), range(5))
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95 5
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96 >>> alignedLcss.compute(range(1,5), range(5))
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97 4
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98
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99 >>> alignedLcss.compute(range(5,10), range(10))
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100 5
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101
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102 >>> lcss.delta = 2
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103 >>> lcss.compute(range(5,10), range(10))
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104 0
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105 >>> alignedLcss.delta = 6
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106 >>> alignedLcss.compute(range(5), range(5))
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107 5
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108 >>> alignedLcss.compute(range(5), range(6))
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109 5
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110 >>> lcss.delta = 10
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111 >>> alignedLcss.compute(range(1,7), range(6))
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112 5
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113 >>> lcss = LCSS(lambda x,y: x == y, delta = 2, aligned = True)
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114 >>> lcss.compute(range(20), [2,4,6,7,8,9,11,13], True)
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115 8
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116 >>> lcss.subSequenceIndices
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117 [(2, 0), (4, 1), (6, 2), (7, 3), (8, 4), (9, 5), (11, 6), (13, 7)]
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118
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119 >>> lcss = LCSS(metric = 'cityblock', epsilon = 0.1)
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120 >>> lcss.compute([[i] for i in range(5)], [[i] for i in range(5)])
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121 5
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122 >>> lcss.compute([[i] for i in range(1,5)], [[i] for i in range(5)])
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123 4
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124 >>> lcss.compute([[i] for i in range(5,10)], [[i] for i in range(5)])
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125 0
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126 >>> lcss.compute([[i] for i in range(5)], [[i] for i in range(10)])
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127 5
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128