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Commit 2d7834ad authored by bbauvin's avatar bbauvin
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Mumbo classifier

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.idea/workspace.xml
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− 15
View file @ 2d7834ad
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......@@ -35,8 +37,27 @@
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......@@ -44,6 +65,7 @@
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\ No newline at end of file
Code/MultiView/Mumbo/Classifiers/DecisionTree.py 0 → 100644
+ 7
− 0
View file @ 2d7834ad
from sklearn import tree
from sklearn.multiclass import OneVsRestClassifier
def decisionTree(data, labels, arg):
classifier = OneVsRestClassifier(tree.DecisionTreeClassifier(max_depth=arg))
classifier.fit(data, labels)
return classifier, classifier.predict(data)
\ No newline at end of file
Code/MultiView/Mumbo/Mumbo.py
+ 15
− 6
View file @ 2d7834ad
......@@ -9,7 +9,7 @@ def initialize(NB_CLASS, NB_VIEW, NB_ITER, DATASET_LENGTH, CLASS_LABELS):
else -(NB_CLASS-1)
for classe in range(NB_CLASS)
]) for exampleIndice in range(DATASET_LENGTH)
]) for viewIndice in range(NB_VIEW)]) for iteration in range(NB_ITER+1)
]) for viewIndice in range(NB_VIEW)]) if iteration==0 else np.zeros((NB_VIEW, DATASET_LENGTH, NB_CLASS)) for iteration in range(NB_ITER+1)
])
generalCostMatrix = np.array([
np.array([
......@@ -85,10 +85,10 @@ def chooseView(predictions, generalCostMatrix, iterIndice, NB_VIEW):
return bestView, edges[bestView]
def updateGeneralFs(generalFs, iterIndice, predictions, alphas, DATASET_LENGTH, NB_CLASS, bestView, generalAlpha, CLASS_LABELS):
def updateGeneralFs(generalFs, iterIndice, predictions, alphas, DATASET_LENGTH, NB_CLASS, bestView, generalAlphas, CLASS_LABELS):
for exampleIndice in range(DATASET_LENGTH):
for classe in range(NB_CLASS):
generalFs[iterIndice, exampleIndice, classe]=np.sum(np.array([generalAlpha[pastIterIndice] if predictions[pastIterIndice, bestView, exampleIndice]==CLASS_LABELS[exampleIndice] else 0 for pastIterIndice in range(iterIndice)]))
generalFs[iterIndice, exampleIndice, classe]=np.sum(np.array([generalAlphas[pastIterIndice] if predictions[pastIterIndice, bestView, exampleIndice]==CLASS_LABELS[exampleIndice] else 0 for pastIterIndice in range(iterIndice)]))
return generalFs
......@@ -101,9 +101,18 @@ def updateGeneralCostMatrix(generalCostMatrix, generalFs, iterIndice, DATASET_LE
generalCostMatrix[iterIndice, exampleIndice, classe]= -1 * np.sum(np.exp(generalFs[iterIndice, exampleIndice] - generalFs[iterIndice, exampleIndice, classe]))
return generalCostMatrix
def mumbo(DATASET, CLASS_LABELS, NB_CLASS, NB_VIEW, NB_ITER, DATASET_LENGTH):
def mumbo(DATASET, CLASS_LABELS, NB_CLASS, NB_VIEW, NB_ITER, DATASET_LENGTH, classifier):
costMatrices, generalCostMatrix, fs, ds, edges, alphas, predictions, generalAlphas, generalFs = initialize(NB_CLASS, NB_VIEW, NB_ITER, DATASET_LENGTH, CLASS_LABELS)
for iterIndice in range(NB_ITER):
poulet = trainWeakClassifers(classifer, costMatrices)
poulet = trainWeakClassifers(classifier, costMatrices)
for viewIndice in range(NB_VIEW):
edges[iterIndice, viewIndice] = computeEdge(predictions[iterIndice, viewIndice], costMatrices[])
\ No newline at end of file
edges[iterIndice, viewIndice] = computeEdge(predictions[iterIndice, viewIndice], costMatrices[iterIndice+1, viewIndice])
alphas[iterIndice, viewIndice] = computeAlpha(edges[iterIndice, viewIndice])
ds = updateDs(ds, predictions, CLASS_LABELS, NB_VIEW, DATASET_LENGTH, NB_CLASS, iterIndice)
fs = updateFs(predictions, ds, alphas, fs, iterIndice, NB_VIEW, DATASET_LENGTH, NB_CLASS, CLASS_LABELS)
costMatrices = updateCostmatrices(costMatrices, fs, iterIndice, NB_VIEW, DATASET_LENGTH, NB_CLASS, CLASS_LABELS)
bestView, edge = chooseView(predictions, generalCostMatrix, iterIndice, NB_VIEW)
generalAlphas[iterIndice] = computeAlpha(edge)
generalFs = updateGeneralFs(generalFs, iterIndice, predictions, alphas, DATASET_LENGTH, NB_CLASS, bestView, generalAlphas, CLASS_LABELS)
generalCostMatrix = updateGeneralCostMatrix(generalCostMatrix, generalFs, iterIndice, DATASET_LENGTH, NB_CLASS, CLASS_LABELS)
return
\ No newline at end of file
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