def __init__(self, trainingSet, anomalyMethod = "KNN", h = None ): self.method = anomalyMethod if self.method == "online": self.h = h if self.method == "centroid": self.h = Util.centroid( trainingSet ) if self.method == "medoid": self.h = Util.medoid( trainingSet ) if self.method == "IGNG": self.h = IGNG( radius = PARAMS["R"] ) # IGNG.estimate_radius( trainingSet ) self.h.train( trainingSet ) # print len( self.h.get_nodes_positions() ), len(trainingSet) if self.method == "GNG": self.h = GNG(period = 50) self.h.train( trainingSet ) if self.method == "KNN": self.h = NearestNeighbors(algorithm='ball_tree', metric='euclidean').fit(trainingSet) if self.method == "RNN": self.h = NearestNeighbors(algorithm='ball_tree', metric='euclidean').fit(trainingSet) if self.method == "SVM": self.h = svm.OneClassSVM(nu=PARAMS["NU"], kernel="rbf", gamma=PARAMS["GAMMA"]).fit(trainingSet)
def query_sufficient_distance(self): ids, _ = self.query_margin() scores = [] for ix, x in enumerate(self.Ux): if ix in ids[:self.optimize]: y1, y2, p1, p2 = self.clf.getMarginInfo(x) C = [dp for idp, dp in enumerate(self.Lx) if self.Ly[idp] == y2 ] CDx = [Util.dist(dp, x) for idp, dp in enumerate(self.Lx) if self.Ly[idp] == y2 ] idsC = (np.array(CDx)).argsort(); xx = Util.medoid( [ C[idp] for idp in idsC[:1] ] ) step = 0.01; lower = 0.; upper = 1. while (upper - lower > step): w = (upper + lower) / 2. px = np.array(x) + w * ( np.array(xx) - np.array(x) ) if self.clf.predict_label(px) != y1: upper = w else: lower = w informativeness = 1. - w else: informativeness = 0. scores.append( informativeness ) return self.sort_scores(scores)
def get_templates(self): # maxval = max([ max(self.edgeHist(e)) for e in self.graph.edges ] )*1. # minval = min([ min(self.edgeHist(e)) for e in self.graph.edges ] )*1. maxval = 1. minval = 0. bins = len(self.graph.edges[0].data.hisv2) hs = [ self.edgeHist(e) for e in self.graph.edges ] H_centroid = Util.centroid( hs ) H_medoid = Util.medoid( hs ) H_stable1 = [ maxval for v in range(bins) ] H_stable2 = [ maxval/2. for v in range(bins) ] H_pyramid = self.inc_vals( bins/2, maxval, minval, 1 ) + self.dec_vals( bins/2, maxval, minval, 1 ) H_gap = self.dec_vals( bins/2, maxval, minval, 1 ) + self.inc_vals( bins/2, maxval, minval, 1 ) H_gap2 = self.dec_vals( bins/2, maxval, minval, 2 ) + self.inc_vals( bins/2, maxval, minval, 2 ) H_half_dec = self.dec_vals( bins/2, maxval, minval, 1 ) + [0.]*(bins/2) H_half_dec2 = self.dec_vals( bins/2, maxval, minval, 2 ) + [0.]*(bins/2) H_half_inc = [0.]*(bins/2) + self.inc_vals( bins/2, maxval, minval, 1 ) H_half_inc2 = [0.]*(bins/2) + self.inc_vals( bins/2, maxval, minval, 2 ) H_dec = self.dec_vals( bins/4, maxval, minval, 1 ) + [0.]*(bins-bins/4) H_dec2 = self.dec_vals( bins/4, maxval, minval, 2 ) + [0.]*(bins-bins/4) H_inc = [0.]*(bins-bins/4) + self.inc_vals( bins/4, maxval, minval, 1 ) H_inc2 = [0.]*(bins-bins/4) + self.inc_vals( bins/4, maxval, minval, 2 ) H = [ H_centroid, H_medoid, H_pyramid, H_stable1, H_stable2, H_gap, H_gap2, H_half_dec, H_half_dec2, H_half_inc, H_half_inc2, H_dec, H_dec2, H_inc, H_inc2 ]; H = [ h[:bins] for h in H ] C = ['r', 'r', 'r', 'r', 'r', 'y', 'y', 'g', 'g', 'g', 'g', 'b', 'b', 'b', 'b'] return H, C
#-----------------------------------
data = Data("datasets\\data_MSL.mat", "array_slip_ratio", randomize = False)
# data.rescale()
# data.standardize()
print "nb points in data :", len(data.X)
print "nb features in data:", data.nb_features
# Util.pickleSave("MIT_hists.txt", data.X)
#-----------------------------------
viz = Visualize()
# med = Util.centroid(data.X)
med = Util.medoid(data.X)
clrs_true = [ 1. if v > 60 else 0. for v in data.Y ]
viz.PCA_Plot(data.X_transpose, dim = 3, fig = "_PCA_true.png", color = clrs_true)
dists = [ Util.dist(x, med) for x in data.X ]; mean = np.mean(dists); std = np.std(dists)
for alpha in np.arange(0, 2, 0.1):
clrs = [ 1. if v - mean > alpha*std else 0. for v in dists ]
print "alpha", alpha, "Acc", 100.*len([1. for i, j in zip(clrs, clrs_true) if i == j]) / len(data.Y)
viz.PCA_Plot(data.X_transpose, dim = 3, fig = "_PCA_"+str(alpha)+".png", color = clrs)
# viz.plot(data.X_transpose[:3], data.features_name[:3], fig = "vizu.png", color = data.X_transpose[0])
#-----------------------------------
# gng = GNG(period = 50)
# gng.train(data.X, step = 1000, directory = "graph_plots_GNG\\50\\")