Mercurial > hg > nsaunier > traffic-intelligence
comparison trafficintelligence/tests/utils.txt @ 1028:cc5cb04b04b0
major update using the trafficintelligence package name and install through pip
| author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
|---|---|
| date | Fri, 15 Jun 2018 11:19:10 -0400 |
| parents | python/tests/utils.txt@4f3387a242a1 |
| children | aafbc0bab925 |
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| 1027:6129296848d3 | 1028:cc5cb04b04b0 |
|---|---|
| 1 >>> from utils import * | |
| 2 >>> from moving import Point | |
| 3 | |
| 4 >>> upperCaseFirstLetter('mmmm... donuts') | |
| 5 'Mmmm... Donuts' | |
| 6 >>> s = upperCaseFirstLetter('much ado about nothing') | |
| 7 >>> s == 'Much Ado About Nothing' | |
| 8 True | |
| 9 >>> upperCaseFirstLetter(s) == s | |
| 10 True | |
| 11 | |
| 12 >>> computeChi2([],[]) | |
| 13 0 | |
| 14 >>> computeChi2(list(range(1,10)),list(range(1,10))) | |
| 15 0.0 | |
| 16 >>> computeChi2(list(range(1,9)),list(range(1,10))) | |
| 17 0.0 | |
| 18 | |
| 19 >>> ceilDecimals(1.23, 0) | |
| 20 2.0 | |
| 21 >>> ceilDecimals(1.23, 1) | |
| 22 1.3 | |
| 23 | |
| 24 >>> inBetween(1,2,1.5) | |
| 25 True | |
| 26 >>> inBetween(2.1,1,1.5) | |
| 27 True | |
| 28 >>> inBetween(1,2,0) | |
| 29 False | |
| 30 | |
| 31 >>> removeExtension('test-adfasdf.asdfa.txt') | |
| 32 'test-adfasdf.asdfa' | |
| 33 >>> removeExtension('test-adfasdf') | |
| 34 'test-adfasdf' | |
| 35 | |
| 36 >>> values = line2Ints('1 2 3 5 6') | |
| 37 >>> values[0] | |
| 38 1 | |
| 39 >>> values[-1] | |
| 40 6 | |
| 41 >>> values = line2Floats('1.3 2.45 7.158e+01 5 6') | |
| 42 >>> values[0] | |
| 43 1.3 | |
| 44 >>> values[2] #doctest: +ELLIPSIS | |
| 45 71.5... | |
| 46 >>> values[-1] | |
| 47 6.0 | |
| 48 | |
| 49 >>> stepPlot([3, 5, 7, 8], 1, 10, 0) | |
| 50 ([1, 3, 3, 5, 5, 7, 7, 8, 8, 10], [0, 0, 1, 1, 2, 2, 3, 3, 4, 4]) | |
| 51 | |
| 52 >>> mostCommon(['a','b','c','b']) | |
| 53 'b' | |
| 54 >>> mostCommon(['a','b','c','b', 'c']) | |
| 55 'b' | |
| 56 >>> mostCommon(list(range(10))+[1]) | |
| 57 1 | |
| 58 >>> mostCommon([list(range(2)), list(range(4)), list(range(2))]) | |
| 59 [0, 1] | |
| 60 | |
| 61 >>> res = sortByLength([list(range(3)), list(range(4)), list(range(1))]) | |
| 62 >>> [len(r) for r in res] | |
| 63 [1, 3, 4] | |
| 64 >>> res = sortByLength([list(range(3)), list(range(4)), list(range(1)), list(range(5))], reverse = True) | |
| 65 >>> [len(r) for r in res] | |
| 66 [5, 4, 3, 1] | |
| 67 | |
| 68 >>> lcss = LCSS(similarityFunc = lambda x,y: abs(x-y) <= 0.1) | |
| 69 >>> lcss.compute(list(range(5)), list(range(5))) | |
| 70 5 | |
| 71 >>> lcss.compute(list(range(1,5)), list(range(5))) | |
| 72 4 | |
| 73 >>> lcss.compute(list(range(5,10)), list(range(5))) | |
| 74 0 | |
| 75 >>> lcss.compute(list(range(5)), list(range(10))) | |
| 76 5 | |
| 77 >>> lcss.similarityFunc = lambda x,y: x == y | |
| 78 >>> lcss.compute(['a','b','c'], ['a','b','c', 'd']) | |
| 79 3 | |
| 80 >>> lcss.computeNormalized(['a','b','c'], ['a','b','c', 'd']) #doctest: +ELLIPSIS | |
| 81 1.0 | |
| 82 >>> lcss.computeNormalized(['a','b','c','x'], ['a','b','c', 'd']) #doctest: +ELLIPSIS | |
| 83 0.75 | |
| 84 >>> lcss.compute(['a','b','c'], ['a','b','c', 'd']) | |
| 85 3 | |
| 86 >>> lcss.compute(['a','x','b','c'], ['a','b','c','d','x']) | |
| 87 3 | |
| 88 >>> lcss.compute(['a','b','c','x','d'], ['a','b','c','d','x']) | |
| 89 4 | |
| 90 >>> lcss.delta = 1 | |
| 91 >>> lcss.compute(['a','b','c'], ['a','b','x','x','c']) | |
| 92 2 | |
| 93 | |
| 94 >>> lcss.delta = float('inf') | |
| 95 >>> lcss.compute(['a','b','c'], ['a','b','c', 'd'], computeSubSequence = True) | |
| 96 3 | |
| 97 >>> lcss.subSequenceIndices | |
| 98 [(0, 0), (1, 1), (2, 2)] | |
| 99 >>> lcss.compute(['a','b','c'], ['x','a','b','c'], computeSubSequence = True) | |
| 100 3 | |
| 101 >>> lcss.subSequenceIndices | |
| 102 [(0, 1), (1, 2), (2, 3)] | |
| 103 >>> lcss.compute(['a','g','b','c'], ['a','b','c', 'd'], computeSubSequence = True) | |
| 104 3 | |
| 105 >>> lcss.subSequenceIndices | |
| 106 [(0, 0), (2, 1), (3, 2)] | |
| 107 | |
| 108 >>> alignedLcss = LCSS(lambda x,y:(abs(x-y) <= 0.1), delta = 2, aligned = True) | |
| 109 >>> alignedLcss.compute(list(range(5)), list(range(5))) | |
| 110 5 | |
| 111 >>> alignedLcss.compute(list(range(1,5)), list(range(5))) | |
| 112 4 | |
| 113 | |
| 114 >>> alignedLcss.compute(list(range(5,10)), list(range(10))) | |
| 115 5 | |
| 116 | |
| 117 >>> lcss.delta = 2 | |
| 118 >>> lcss.compute(list(range(5,10)), list(range(10))) | |
| 119 0 | |
| 120 >>> alignedLcss.delta = 6 | |
| 121 >>> alignedLcss.compute(list(range(5)), list(range(5))) | |
| 122 5 | |
| 123 >>> alignedLcss.compute(list(range(5)), list(range(6))) | |
| 124 5 | |
| 125 >>> lcss.delta = 10 | |
| 126 >>> alignedLcss.compute(list(range(1,7)), list(range(6))) | |
| 127 5 | |
| 128 >>> lcss = LCSS(lambda x,y: x == y, delta = 2, aligned = True) | |
| 129 >>> lcss.compute(list(range(20)), [2,4,6,7,8,9,11,13], True) | |
| 130 8 | |
| 131 >>> lcss.subSequenceIndices | |
| 132 [(2, 0), (4, 1), (6, 2), (7, 3), (8, 4), (9, 5), (11, 6), (13, 7)] | |
| 133 | |
| 134 >>> lcss = LCSS(metric = 'cityblock', epsilon = 0.1) | |
| 135 >>> lcss.compute([[i] for i in range(5)], [[i] for i in range(5)]) | |
| 136 5 | |
| 137 >>> lcss.compute([[i] for i in range(1,5)], [[i] for i in range(5)]) | |
| 138 4 | |
| 139 >>> lcss.compute([[i] for i in range(5,10)], [[i] for i in range(5)]) | |
| 140 0 | |
| 141 >>> lcss.compute([[i] for i in range(5)], [[i] for i in range(10)]) | |
| 142 5 | |
| 143 | |
| 144 |
