Mercurial Hosting > traffic-intelligence
comparison python/events.py @ 728:4e89341edd29 dev
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 | e395bffe1412 |
| children | b02431a8234c |
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| 716:85af65b6d531 | 728:4e89341edd29 |
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| 293 allPoints += points | 293 allPoints += points |
| 294 else: | 294 else: |
| 295 print('unknown type of point: '+pointType) | 295 print('unknown type of point: '+pointType) |
| 296 return allPoints | 296 return allPoints |
| 297 | 297 |
| 298 def prototypeCluster(interactions, similarityMatrix, alignmentMatrix, indicatorName, minSimilarity): | 298 def prototypeCluster(interactions, similarityMatrix, alignmentMatrix, indicatorName, minSimilarity, minClusterSize = None): |
| 299 '''Finds exemplar indicator time series for all interactions | 299 '''Finds exemplar indicator time series for all interactions |
| 300 Returns the prototype indices (in the interaction list) and the label of each indicator (interaction) | 300 Returns the prototype indices (in the interaction list) and the label of each indicator (interaction) |
| 301 | 301 |
| 302 if an indicator profile (time series) is different enough (<minSimilarity), | 302 if an indicator profile (time series) is different enough (<minSimilarity), |
| 303 it will become a new prototype. | 303 it will become a new prototype. |
| 304 Non-prototype interactions will be assigned to an existing prototype''' | 304 Non-prototype interactions will be assigned to an existing prototype |
| 305 if minClusterSize is not None, the clusters will be refined by removing iteratively the smallest clusters | |
| 306 and reassigning all elements in the cluster until no cluster is smaller than minClusterSize''' | |
| 305 | 307 |
| 306 # sort indicators based on length | 308 # sort indicators based on length |
| 307 indices = range(similarityMatrix.shape[0]) | 309 indices = range(similarityMatrix.shape[0]) |
| 308 def compare(i, j): | 310 def compare(i, j): |
| 309 if len(interactions[i].getIndicator(indicatorName)) > len(interactions[j].getIndicator(indicatorName)): | 311 if len(interactions[i].getIndicator(indicatorName)) > len(interactions[j].getIndicator(indicatorName)): |
| 318 for i in indices[1:]: | 320 for i in indices[1:]: |
| 319 if similarityMatrix[i][prototypeIndices].max() < minSimilarity: | 321 if similarityMatrix[i][prototypeIndices].max() < minSimilarity: |
| 320 prototypeIndices.append(i) | 322 prototypeIndices.append(i) |
| 321 | 323 |
| 322 # assignment | 324 # assignment |
| 323 labels = [-1]*similarityMatrix.shape[0] | |
| 324 indices = [i for i in range(similarityMatrix.shape[0]) if i not in prototypeIndices] | 325 indices = [i for i in range(similarityMatrix.shape[0]) if i not in prototypeIndices] |
| 325 for i in prototypeIndices: | 326 assign = True |
| 326 labels[i] = i | 327 while assign: |
| 327 for i in indices: | 328 labels = [-1]*similarityMatrix.shape[0] |
| 328 prototypeIndex = similarityMatrix[i][prototypeIndices].argmax() | 329 for i in prototypeIndices: |
| 329 labels[i] = prototypeIndices[prototypeIndex] | 330 labels[i] = i |
| 331 for i in indices: | |
| 332 prototypeIndex = similarityMatrix[i][prototypeIndices].argmax() | |
| 333 labels[i] = prototypeIndices[prototypeIndex] | |
| 334 clusterSizes = {i: sum(np.array(labels) == i) for i in prototypeIndices} | |
| 335 smallestClusterIndex = min(clusterSizes, key = clusterSizes.get) | |
| 336 assign = (clusterSizes[smallestClusterIndex] < minClusterSize) | |
| 337 print prototypeIndices, smallestClusterIndex, clusterSizes[smallestClusterIndex] | |
| 338 if assign: | |
| 339 prototypeIndices.remove(smallestClusterIndex) | |
| 340 indices.append(smallestClusterIndex) | |
| 330 | 341 |
| 331 return prototypeIndices, labels | 342 return prototypeIndices, labels |
| 332 | 343 |
| 333 def prototypeMultivariateCluster(interactions, similarityMatrics, indicatorNames, minSimilarities, minClusterSize): | 344 def prototypeMultivariateCluster(interactions, similarityMatrics, indicatorNames, minSimilarities, minClusterSize): |
| 334 '''Finds exmaple indicator time series (several indicators) for all interactions | 345 '''Finds exmaple indicator time series (several indicators) for all interactions |