Mercurial Hosting > traffic-intelligence
changeset 735:0e875a7f5759 dev
modified prototypeCluster algorithm to enforce similarity when re-assigning and to compute only the necessary similarities
| author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
|---|---|
| date | Wed, 12 Aug 2015 00:24:06 -0400 |
| parents | 1d4dcb5c8708 |
| children | 967d244968a4 |
| files | python/events.py python/ml.py scripts/learn-motion-patterns.py |
| diffstat | 3 files changed, 38 insertions(+), 18 deletions(-) [+] |
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--- a/python/events.py Tue Aug 11 12:55:09 2015 -0400 +++ b/python/events.py Wed Aug 12 00:24:06 2015 -0400 @@ -295,8 +295,8 @@ print('unknown type of point: '+pointType) return allPoints -def prototypeCluster(interactions, similarityMatrix, indicatorName, minSimilarity, minClusterSize = None, randomInitialization = False): - return ml.prototypeCluster([inter.getIndicator(indicatorName) for inter in interactions], similarityMatrix, minSimilarity, minClusterSize, randomInitialization) +def prototypeCluster(interactions, similarities, indicatorName, minSimilarity, similarityFunc = None, minClusterSize = None, randomInitialization = False): + return ml.prototypeCluster([inter.getIndicator(indicatorName) for inter in interactions], similarities, minSimilarity, similarityFunc, minClusterSize, randomInitialization) class Crossing(moving.STObject): '''Class for the event of a street crossing
--- a/python/ml.py Tue Aug 11 12:55:09 2015 -0400 +++ b/python/ml.py Wed Aug 12 00:24:06 2015 -0400 @@ -111,12 +111,14 @@ code,distance = vq(features,centroids) # code starting from 0 (represent first cluster) to k-1 (last cluster) return code,sigma -def prototypeCluster(instances, similarityMatrix, minSimilarity, minClusterSize = None, randomInitialization = False): +def prototypeCluster(instances, similarities, minSimilarity, similarityFunc = None, minClusterSize = None, randomInitialization = False): '''Finds exemplar (prototype) instance that represent each cluster Returns the prototype indices (in the instances list) and the cluster label of each instance the elements in the instances list must have a length (method __len__), or one can use the random initialization - the positions in the instances list corresponds to the similarityMatrix + the positions in the instances list corresponds to the similarities + if similarityFunc is provided, the similarities are calculated as needed (this is faster) if not in similarities (negative if not computed) + similarities must still be allocated with the right size if an instance is different enough (<minSimilarity), it will become a new prototype. @@ -125,7 +127,7 @@ and reassigning all elements in the cluster until no cluster is smaller than minClusterSize''' # sort instances based on length - indices = range(similarityMatrix.shape[0]) + indices = range(len(instances)) if randomInitialization: indices = np.random.permutation(indices) else: @@ -140,21 +142,34 @@ # go through all instances prototypeIndices = [indices[0]] for i in indices[1:]: - if similarityMatrix[i][prototypeIndices].max() < minSimilarity: + if similarityFunc is not None: + for j in prototypeIndices: + if similarities[i][j] < 0: + similarities[i][j] = similarityFunc(instances[i], instances[j]) + similarities[j][i] = similarities[i][j] + if similarities[i][prototypeIndices].max() < minSimilarity: prototypeIndices.append(i) # assignment - indices = [i for i in range(similarityMatrix.shape[0]) if i not in prototypeIndices] + indices = [i for i in range(similarities.shape[0]) if i not in prototypeIndices] assign = True while assign: - labels = [-1]*similarityMatrix.shape[0] + labels = [-1]*similarities.shape[0] for i in prototypeIndices: labels[i] = i for i in indices: - prototypeIndex = similarityMatrix[i][prototypeIndices].argmax() - labels[i] = prototypeIndices[prototypeIndex] + if similarityFunc is not None: + for j in prototypeIndices: + if similarities[i][j] < 0: + similarities[i][j] = similarityFunc(instances[i], instances[j]) + similarities[j][i] = similarities[i][j] + prototypeIdx = similarities[i][prototypeIndices].argmax() + if similarities[i][prototypeIndices[prototypeIdx]] >= minSimilarity: + labels[i] = prototypeIndices[prototypeIdx] + else: + labels[i] = -1 # outlier clusterSizes = {i: sum(np.array(labels) == i) for i in prototypeIndices} - smallestClusterIndex = min(clusterSizes, key = clusterSizes.get) + smallestClusterIndex = min(clusterSizes, key = clusterSizes.get) assign = (clusterSizes[smallestClusterIndex] < minClusterSize) if assign: prototypeIndices.remove(smallestClusterIndex)
--- a/scripts/learn-motion-patterns.py Tue Aug 11 12:55:09 2015 -0400 +++ b/scripts/learn-motion-patterns.py Wed Aug 12 00:24:06 2015 -0400 @@ -42,18 +42,23 @@ lcss = utils.LCSS(metric = args.metric, epsilon = args.epsilon) nTrajectories = len(trajectories) -similarities = np.zeros((nTrajectories, nTrajectories)) -for i in xrange(nTrajectories): - for j in xrange(i): - similarities[i,j] = lcss.computeNormalized(trajectories[i], trajectories[j]) - similarities[j,i] = similarities[i,j] +similarities = -np.ones((nTrajectories, nTrajectories)) +# for i in xrange(nTrajectories): +# for j in xrange(i): +# similarities[i,j] = lcss.computeNormalized(trajectories[i], trajectories[j]) +# similarities[j,i] = similarities[i,j] -prototypeIndices, labels = ml.prototypeCluster(trajectories, similarities, args.minSimilarity, args.minClusterSize) +prototypeIndices, labels = ml.prototypeCluster(trajectories, similarities, args.minSimilarity, lambda x,y : lcss.computeNormalized(x, y), args.minClusterSize) # this line can be called again without reinitializing similarities if args.display: + from matplotlib.pyplot import figure + figure() for i,o in enumerate(objects): if i not in prototypeIndices: - o.plot(utils.colors[labels[i]]) + if labels[i] < 0: + o.plot('kx') + else: + o.plot(utils.colors[labels[i]]) for i in prototypeIndices: objects[i].plot(utils.colors[i]+'o')