Merge pull request #2641 from SpecLad:merge-2.4
This commit is contained in:
commit
4211d8fbd9
@ -777,6 +777,66 @@ struct ZeroIterator
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};
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/*
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* Depending on processed distances, some of them are already squared (e.g. L2)
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* and some are not (e.g.Hamming). In KMeans++ for instance we want to be sure
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* we are working on ^2 distances, thus following templates to ensure that.
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*/
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template <typename Distance, typename ElementType>
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struct squareDistance
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{
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typedef typename Distance::ResultType ResultType;
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ResultType operator()( ResultType dist ) { return dist*dist; }
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};
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template <typename ElementType>
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struct squareDistance<L2_Simple<ElementType>, ElementType>
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{
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typedef typename L2_Simple<ElementType>::ResultType ResultType;
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ResultType operator()( ResultType dist ) { return dist; }
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};
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template <typename ElementType>
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struct squareDistance<L2<ElementType>, ElementType>
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{
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typedef typename L2<ElementType>::ResultType ResultType;
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ResultType operator()( ResultType dist ) { return dist; }
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};
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template <typename ElementType>
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struct squareDistance<MinkowskiDistance<ElementType>, ElementType>
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{
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typedef typename MinkowskiDistance<ElementType>::ResultType ResultType;
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ResultType operator()( ResultType dist ) { return dist; }
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};
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template <typename ElementType>
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struct squareDistance<HellingerDistance<ElementType>, ElementType>
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{
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typedef typename HellingerDistance<ElementType>::ResultType ResultType;
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ResultType operator()( ResultType dist ) { return dist; }
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};
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template <typename ElementType>
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struct squareDistance<ChiSquareDistance<ElementType>, ElementType>
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{
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typedef typename ChiSquareDistance<ElementType>::ResultType ResultType;
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ResultType operator()( ResultType dist ) { return dist; }
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};
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template <typename Distance>
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typename Distance::ResultType ensureSquareDistance( typename Distance::ResultType dist )
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{
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typedef typename Distance::ElementType ElementType;
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squareDistance<Distance, ElementType> dummy;
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return dummy( dist );
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}
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}
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#endif //OPENCV_FLANN_DIST_H_
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@ -209,8 +209,11 @@ private:
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assert(index >=0 && index < n);
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centers[0] = dsindices[index];
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// Computing distance^2 will have the advantage of even higher probability further to pick new centers
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// far from previous centers (and this complies to "k-means++: the advantages of careful seeding" article)
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for (int i = 0; i < n; i++) {
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closestDistSq[i] = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols);
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closestDistSq[i] = ensureSquareDistance<Distance>( closestDistSq[i] );
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currentPot += closestDistSq[i];
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}
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@ -236,7 +239,10 @@ private:
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// Compute the new potential
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double newPot = 0;
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for (int i = 0; i < n; i++) newPot += std::min( distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols), closestDistSq[i] );
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for (int i = 0; i < n; i++) {
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DistanceType dist = distance(dataset[dsindices[i]], dataset[dsindices[index]], dataset.cols);
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newPot += std::min( ensureSquareDistance<Distance>(dist), closestDistSq[i] );
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}
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// Store the best result
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if ((bestNewPot < 0)||(newPot < bestNewPot)) {
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@ -248,7 +254,10 @@ private:
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// Add the appropriate center
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centers[centerCount] = dsindices[bestNewIndex];
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currentPot = bestNewPot;
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for (int i = 0; i < n; i++) closestDistSq[i] = std::min( distance(dataset[dsindices[i]], dataset[dsindices[bestNewIndex]], dataset.cols), closestDistSq[i] );
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for (int i = 0; i < n; i++) {
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DistanceType dist = distance(dataset[dsindices[i]], dataset[dsindices[bestNewIndex]], dataset.cols);
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closestDistSq[i] = std::min( ensureSquareDistance<Distance>(dist), closestDistSq[i] );
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}
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}
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centers_length = centerCount;
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@ -210,6 +210,7 @@ public:
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for (int i = 0; i < n; i++) {
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closestDistSq[i] = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols);
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closestDistSq[i] = ensureSquareDistance<Distance>( closestDistSq[i] );
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currentPot += closestDistSq[i];
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}
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@ -235,7 +236,10 @@ public:
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// Compute the new potential
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double newPot = 0;
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for (int i = 0; i < n; i++) newPot += std::min( distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols), closestDistSq[i] );
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for (int i = 0; i < n; i++) {
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DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[index]], dataset_.cols);
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newPot += std::min( ensureSquareDistance<Distance>(dist), closestDistSq[i] );
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}
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// Store the best result
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if ((bestNewPot < 0)||(newPot < bestNewPot)) {
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@ -247,7 +251,10 @@ public:
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// Add the appropriate center
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centers[centerCount] = indices[bestNewIndex];
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currentPot = bestNewPot;
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for (int i = 0; i < n; i++) closestDistSq[i] = std::min( distance_(dataset_[indices[i]], dataset_[indices[bestNewIndex]], dataset_.cols), closestDistSq[i] );
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for (int i = 0; i < n; i++) {
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DistanceType dist = distance_(dataset_[indices[i]], dataset_[indices[bestNewIndex]], dataset_.cols);
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closestDistSq[i] = std::min( ensureSquareDistance<Distance>(dist), closestDistSq[i] );
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}
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}
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centers_length = centerCount;
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@ -40,6 +40,7 @@ if __name__ == "__main__":
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parser.add_option("", "--with-cycles-reduction", action="store_true", dest="calc_cr", default=False, help="output cycle reduction percentages")
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parser.add_option("", "--with-score", action="store_true", dest="calc_score", default=False, help="output automatic classification of speedups")
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parser.add_option("", "--progress", action="store_true", dest="progress_mode", default=False, help="enable progress mode")
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parser.add_option("", "--regressions", dest="regressions", default=None, metavar="LIST", help="comma-separated custom regressions map: \"[r][c]#current-#reference\" (indexes of columns are 0-based, \"r\" - reverse flag, \"c\" - color flag for base data)")
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parser.add_option("", "--show-all", action="store_true", dest="showall", default=False, help="also include empty and \"notrun\" lines")
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parser.add_option("", "--match", dest="match", default=None)
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parser.add_option("", "--match-replace", dest="match_replace", default="")
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@ -56,6 +57,24 @@ if __name__ == "__main__":
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if options.columns:
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options.columns = [s.strip().replace("\\n", "\n") for s in options.columns.split(",")]
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if options.regressions:
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assert not options.progress_mode, 'unsupported mode'
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def parseRegressionColumn(s):
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""" Format: '[r][c]<uint>-<uint>' """
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reverse = s.startswith('r')
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if reverse:
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s = s[1:]
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addColor = s.startswith('c')
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if addColor:
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s = s[1:]
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parts = s.split('-', 1)
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link = (int(parts[0]), int(parts[1]), reverse, addColor)
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assert link[0] != link[1]
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return link
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options.regressions = [parseRegressionColumn(s) for s in options.regressions.split(',')]
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# expand wildcards and filter duplicates
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files = []
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seen = set()
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@ -90,8 +109,18 @@ if __name__ == "__main__":
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sys.stderr.write("Error: no test data found" + os.linesep)
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quit()
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# find matches
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setsCount = len(test_sets)
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if options.regressions is None:
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reference = -1 if options.progress_mode else 0
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options.regressions = [(i, reference, False, True) for i in range(1, len(test_sets))]
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for link in options.regressions:
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(i, ref, reverse, addColor) = link
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assert i >= 0 and i < setsCount
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assert ref < setsCount
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# find matches
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test_cases = {}
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name_extractor = lambda name: str(name)
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@ -117,29 +146,29 @@ if __name__ == "__main__":
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# header
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tbl.newColumn("name", "Name of Test", align = "left", cssclass = "col_name")
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i = 0
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for set in test_sets:
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tbl.newColumn(str(i), getSetName(set, i, options.columns, False), align = "center")
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i += 1
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metric_sets = test_sets[1:]
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for i in range(setsCount):
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tbl.newColumn(str(i), getSetName(test_sets[i], i, options.columns, False), align = "center")
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def addHeaderColumns(suffix, description, cssclass):
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for link in options.regressions:
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(i, ref, reverse, addColor) = link
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if reverse:
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i, ref = ref, i
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current_set = test_sets[i]
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current = getSetName(current_set, i, options.columns)
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if ref >= 0:
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reference_set = test_sets[ref]
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reference = getSetName(reference_set, ref, options.columns)
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else:
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reference = 'previous'
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tbl.newColumn(str(i) + '-' + str(ref) + suffix, '%s\nvs\n%s\n(%s)' % (current, reference, description), align='center', cssclass=cssclass)
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if options.calc_cr:
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i = 1
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for set in metric_sets:
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reference = getSetName(test_sets[0], 0, options.columns) if not options.progress_mode else 'previous'
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tbl.newColumn(str(i) + "$", getSetName(set, i, options.columns) + "\nvs\n" + reference + "\n(cycles reduction)", align = "center", cssclass = "col_cr")
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i += 1
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addHeaderColumns(suffix='$', description='cycles reduction', cssclass='col_cr')
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if options.calc_relatives:
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i = 1
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for set in metric_sets:
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reference = getSetName(test_sets[0], 0, options.columns) if not options.progress_mode else 'previous'
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tbl.newColumn(str(i) + "%", getSetName(set, i, options.columns) + "\nvs\n" + reference + "\n(x-factor)", align = "center", cssclass = "col_rel")
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i += 1
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addHeaderColumns(suffix='%', description='x-factor', cssclass='col_rel')
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if options.calc_score:
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i = 1
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for set in metric_sets:
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reference = getSetName(test_sets[0], 0, options.columns) if not options.progress_mode else 'previous'
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tbl.newColumn(str(i) + "S", getSetName(set, i, options.columns) + "\nvs\n" + reference + "\n(score)", align = "center", cssclass = "col_name")
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i += 1
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addHeaderColumns(suffix='S', description='score', cssclass='col_name')
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# rows
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prevGroupName = None
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@ -166,68 +195,87 @@ if __name__ == "__main__":
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if options.intersect_logs:
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needNewRow = False
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break
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tbl.newCell(str(i), "-")
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if options.calc_relatives and i > 0:
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tbl.newCell(str(i) + "%", "-")
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if options.calc_cr and i > 0:
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tbl.newCell(str(i) + "$", "-")
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if options.calc_score and i > 0:
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tbl.newCell(str(i) + "$", "-")
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else:
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status = case.get("status")
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if status != "run":
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tbl.newCell(str(i), status, color = "red")
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if status != "notrun":
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needNewRow = True
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if options.calc_relatives and i > 0:
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tbl.newCell(str(i) + "%", "-", color = "red")
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if options.calc_cr and i > 0:
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tbl.newCell(str(i) + "$", "-", color = "red")
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if options.calc_score and i > 0:
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tbl.newCell(str(i) + "S", "-", color = "red")
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tbl.newCell(str(i), status, color="red")
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else:
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val = getter(case, cases[0], options.units)
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def getter_fn(fn):
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if fn and i > 0 and val:
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for j in reversed(range(i)) if options.progress_mode else [0]:
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r = cases[j]
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if r is not None and r.get("status") == 'run':
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return fn(case, r, options.units)
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return None
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valp = getter_fn(getter_p) if options.calc_relatives or options.progress_mode else None
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valcr = getter_fn(getter_cr) if options.calc_cr else None
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val_score = getter_fn(getter_score) if options.calc_score else None
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if not valp or i == 0:
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color = None
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elif valp > 1.05:
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color = "green"
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elif valp < 0.95:
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color = "red"
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else:
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color = None
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if val:
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needNewRow = True
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tbl.newCell(str(i), formatValue(val, options.metric, options.units), val, color = color)
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if options.calc_relatives and i > 0:
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tbl.newCell(str(i) + "%", formatValue(valp, "%"), valp, color = color, bold = color)
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if options.calc_cr and i > 0:
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tbl.newCell(str(i) + "$", formatValue(valcr, "$"), valcr, color = color, bold = color)
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if options.calc_score and i > 0:
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tbl.newCell(str(i) + "S", formatValue(val_score, "S"), val_score, color = color, bold = color)
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tbl.newCell(str(i), formatValue(val, options.metric, options.units), val)
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if needNewRow:
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for link in options.regressions:
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(i, reference, reverse, addColor) = link
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if reverse:
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i, reference = reference, i
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tblCellID = str(i) + '-' + str(reference)
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case = cases[i]
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if case is None:
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if options.calc_relatives:
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tbl.newCell(tblCellID + "%", "-")
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if options.calc_cr:
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tbl.newCell(tblCellID + "$", "-")
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if options.calc_score:
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tbl.newCell(tblCellID + "$", "-")
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else:
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status = case.get("status")
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if status != "run":
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tbl.newCell(str(i), status, color="red")
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if status != "notrun":
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needNewRow = True
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if options.calc_relatives:
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tbl.newCell(tblCellID + "%", "-", color="red")
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if options.calc_cr:
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tbl.newCell(tblCellID + "$", "-", color="red")
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if options.calc_score:
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tbl.newCell(tblCellID + "S", "-", color="red")
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else:
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val = getter(case, cases[0], options.units)
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def getRegression(fn):
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if fn and val:
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for j in reversed(range(i)) if reference < 0 else [reference]:
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r = cases[j]
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if r is not None and r.get("status") == 'run':
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return fn(case, r, options.units)
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valp = getRegression(getter_p) if options.calc_relatives or options.progress_mode else None
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valcr = getRegression(getter_cr) if options.calc_cr else None
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val_score = getRegression(getter_score) if options.calc_score else None
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if not valp:
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color = None
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elif valp > 1.05:
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color = 'green'
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elif valp < 0.95:
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color = 'red'
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else:
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color = None
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if addColor:
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if not reverse:
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tbl.newCell(str(i), formatValue(val, options.metric, options.units), val, color=color)
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else:
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r = cases[reference]
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if r is not None and r.get("status") == 'run':
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val = getter(r, cases[0], options.units)
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tbl.newCell(str(reference), formatValue(val, options.metric, options.units), val, color=color)
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if options.calc_relatives:
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tbl.newCell(tblCellID + "%", formatValue(valp, "%"), valp, color=color, bold=color)
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if options.calc_cr:
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tbl.newCell(tblCellID + "$", formatValue(valcr, "$"), valcr, color=color, bold=color)
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if options.calc_score:
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tbl.newCell(tblCellID + "S", formatValue(val_score, "S"), val_score, color = color, bold = color)
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if not needNewRow:
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tbl.trimLastRow()
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if options.regressionsOnly:
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for r in reversed(range(len(tbl.rows))):
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delete = True
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i = 1
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for set in metric_sets:
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val = tbl.rows[r].cells[len(tbl.rows[r].cells)-i].value
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for i in range(1, len(options.regressions) + 1):
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val = tbl.rows[r].cells[len(tbl.rows[r].cells) - i].value
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if val is not None and val < float(options.regressionsOnly):
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delete = False
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i += 1
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if (delete):
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break
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else:
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tbl.rows.pop(r)
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# output table
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