def learnNew(self, new_attribute_dict, label):
#mean_new=example_new/(label*example_new*example_new)
new_attribute_vector = misc.dictToNumpyArray(new_attribute_dict)
new_partial_mean, new_covariance_dict = self.updateNewPart(
new_attribute_vector, label, new_attribute_dict)
new_mean_dict = misc.numpyArrayToDict(new_partial_mean,
list(new_attribute_dict.keys()))
return new_mean_dict, new_covariance_dict
def set_classifier(self):
self.classifier = clone(self.classifier)
self.classifier.alpha = 1
self.classifier.eta0 = 1
self.classifier.partial_fit(self.init_classifier, [self.y[0]],
np.unique(self.y))
self.weight_dict = misc.numpyArrayToDict(self.classifier.coef_[0],
list(self.X[0].keys()))
def learnCommon(self, mean_dict, covariance_dict, row_dict, label,
full_covariance_dict):
#order of the values in two dictionaries are the same
#transform dicts to matrix, vector for math operations
full_cov_vector = misc.dictToNumpyArray(full_covariance_dict)
mean_vector = misc.dictToNumpyArray(mean_dict)
row_vector = misc.dictToNumpyArray(row_dict)
covariance_matrix = misc.dictToUnitNumpyMatrix(covariance_dict)
if self.classify_train(row_vector, mean_vector, label):
return mean_dict, covariance_dict, 1 #large margin classified
else:
#recalculate mean and covariance
mean_vector, covariance_matrix = self.update(
mean_vector, covariance_matrix, row_vector, label,
full_cov_vector)
#transform everything back to labeled dictionary
new_mean_dict = misc.numpyArrayToDict(mean_vector,
list(mean_dict.keys()))
new_covariance_dict = misc.numpyMatrixToDict(
covariance_matrix, list(mean_dict.keys()))
return new_mean_dict, new_covariance_dict, 0
def learnCommon(self, mean_dict, covariance_dict, row_dict,
label): #return 1 if correctly classified
mean_vector = misc.dictToNumpyArray(mean_dict)
row_vector = misc.dictToNumpyArray(row_dict)
covariance_matrix = misc.dictToUnitNumpyMatrix(covariance_dict)
covsum = np.sum(covariance_matrix)
#
V = np.dot(np.dot(row_vector, covariance_matrix), row_vector)
phi = global_phi / (V / covsum)
M = (row_vector.dot(mean_vector)) * label
part1 = -(1 + 2 * phi * M)
#print("Part 1:"+str(M))
#print("row vector: "+str(row_vector))
#print("covariance matrix: "+ str(covariance_matrix))
#print("v"+str(V))
part2 = np.sqrt(np.square(part1) - 8 * phi * (M - phi * V))
#print("part2: "+str(part2))
part3 = 4 * phi * V
gamma = (part1 + part2) / part3
#print("gamma: "+str(gamma))
alpha = np.max(gamma, 0)
mean_vector = mean_vector + np.dot(
np.multiply(alpha * label, covariance_matrix), row_vector)
#print(mean_vector)
covariance_matrix = covariance_matrix + np.multiply(
2 * alpha * phi, np.diag(row_vector))
#transform everything back to labeled dictionary
new_mean_dict = misc.numpyArrayToDict(mean_vector,
list(mean_dict.keys()))
new_covariance_dict = misc.numpyMatrixToDict(covariance_matrix,
list(mean_dict.keys()))
return new_mean_dict, new_covariance_dict, 0
def learnNew(self, row_dict, label, tao):
row_vector=misc.dictToNumpyArray(row_dict)
new_weight_vector= np.multiply(tao*label, row_vector)
new_weight_dict=misc.numpyArrayToDict(new_weight_vector, list(row_dict.keys()))
return new_weight_dict
def learnCommon(self, weight_dict, row_dict, label, tao):
weight_vector=misc.dictToNumpyArray(weight_dict)
row_vector=misc.dictToNumpyArray(row_dict)
new_weight_vector= weight_vector+ np.multiply(tao*label, row_vector)
new_weight_dict=misc.numpyArrayToDict(new_weight_vector, list(weight_dict.keys()))
return new_weight_dict