Exemple #1
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 def kmeansselect(self):
         kmeans_mdl = Kmeans(self.data, num_bases=self._nsub)
         kmeans_mdl.initialization()
         kmeans_mdl.factorize()
         
         # pick data samples closest to the centres
         idx = dist.vq(kmeans_mdl.data, kmeans_mdl.W)            
         return idx
Exemple #2
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    def kmeansselect(self):
        kmeans_mdl = Kmeans(self.data, num_bases=self._nsub)
        kmeans_mdl.initialization()
        kmeans_mdl.factorize()

        # pick data samples closest to the centres
        idx = dist.vq(kmeans_mdl.data, kmeans_mdl.W)
        return idx
Exemple #3
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    def _map_w_to_data(self):
        """ Return data points that are most similar to basis vectors W
        """

        # assign W to the next best data sample
        self._Wmapped_index = vq(self.data, self.W)
        self.Wmapped = np.zeros(self.W.shape)

        # do not directly assign, i.e. Wdist = self.data[:,sel]
        # as self might be unsorted (in non ascending order)
        # -> sorting sel would screw the matching to W if
        # self.data is stored as a hdf5 table (see h5py)
        for i, s in enumerate(self._Wmapped_index):
            self.Wmapped[:,i] = self.data[:,s]
Exemple #4
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    def _map_w_to_data(self):
        """ Return data points that are most similar to basis vectors W
        """

        # assign W to the next best data sample
        self._Wmapped_index = vq(self.data, self.W)
        self.Wmapped = np.zeros(self.W.shape)

        # do not directly assign, i.e. Wdist = self.data[:,sel]
        # as self might be unsorted (in non ascending order)
        # -> sorting sel would screw the matching to W if
        # self.data is stored as a hdf5 table (see h5py)
        for i, s in enumerate(self._Wmapped_index):
            self.Wmapped[:, i] = self.data[:, s]
Exemple #5
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        def select_hull_points(data, n=3):
            """ select data points for pairwise projections of the first n
            dimensions """
    
            # iterate over all projections and select data points
            idx = np.array([])

            # iterate over some pairwise combinations of dimensions
            for i in combinations(range(n), 2):
                # sample convex hull points in 2D projection                    
                convex_hull_d = quickhull(data[i, :].T)
            
                # get indices for convex hull data points
                idx = np.append(idx, vq(data[i, :], convex_hull_d.T))
                idx = np.unique(idx)
                
            return np.int32(idx)
Exemple #6
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        def select_hull_points(data, n=3):
            """ select data points for pairwise projections of the first n
            dimensions """

            # iterate over all projections and select data points
            idx = np.array([])

            # iterate over some pairwise combinations of dimensions
            for i in combinations(range(n), 2):
                # sample convex hull points in 2D projection
                convex_hull_d = quickhull(data[i, :].T)

                # get indices for convex hull data points
                idx = np.append(idx, vq(data[i, :], convex_hull_d.T))
                idx = np.unique(idx)

            return np.int32(idx)
Exemple #7
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	def updateW(self):
		
		idx = self._subfunc()	
		idx = np.sort(np.int32(idx))

		
		mdl_small = self._mfmethod(self.data[:, idx], 
								num_bases=self._num_bases, 
								niter=self._niter, 
								show_progress=self._show_progress, 
								compW=True)

		# initialize W, H, and beta
		mdl_small.initialization()

		# determine W
		mdl_small.factorize()
		
		
		self.mdl = self._mfmethod(self.data[:, :], 
									num_bases=self._num_bases , 
									niter=self._niterH, 
									show_progress=self._show_progress, 
									compW=False)


		self.mdl.initialization()
		
		if self._mapW:
			# compute pairwise distances
			#distance = vq(self.data, self.W)
			_Wmapped_index = dist.vq(self.mdl.data, mdl_small.W)			
			
			# do not directly assign, i.e. Wdist = self.data[:,sel]
			# as self might be unsorted (in non ascending order)
			# -> sorting sel would screw the matching to W if
			# self.data is stored as a hdf5 table (see h5py)
			for i,s in enumerate(_Wmapped_index):
				self.mdl.W[:,i] = self.mdl.data[:,s]
		else:
			self.mdl.W = np.copy(mdl_small.W)
Exemple #8
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    def update_w(self):
        
        idx = self._subfunc()    
        idx = np.sort(np.int32(idx))

        
        mdl_small = self._mfmethod(self.data[:, idx], 
                                num_bases=self._num_bases,                                
                                show_progress=self._show_progress, 
                                compute_w=True)

        # initialize W, H, and beta
        mdl_small.initialization()

        # determine W
        mdl_small.factorize()
        
        
        self.mdl = self._mfmethod(self.data[:, :], 
                                    num_bases=self._num_bases ,                                    
                                    show_progress=self._show_progress, 
                                    compute_w=False)


        self.mdl.initialization()
        
        if self._mapW:
            # compute pairwise distances
            #distance = vq(self.data, self.W)
            _Wmapped_index = dist.vq(self.mdl.data, mdl_small.W)            
            
            # do not directly assign, i.e. Wdist = self.data[:,sel]
            # as self might be unsorted (in non ascending order)
            # -> sorting sel would screw the matching to W if
            # self.data is stored as a hdf5 table (see h5py)
            for i,s in enumerate(_Wmapped_index):
                self.mdl.W[:,i] = self.mdl.data[:,s]
        else:
            self.mdl.W = np.copy(mdl_small.W)
Exemple #9
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 def update_h(self):                    
     # and assign samples to the best matching centers
     self.assigned = dist.vq(self.W, self.data)
     self.H = np.zeros(self.H.shape)
     self.H[self.assigned, range(self._num_samples)] = 1.0
Exemple #10
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 def update_h(self):
     # and assign samples to the best matching centers
     self.assigned = dist.vq(self.W, self.data)
     self.H = np.zeros(self.H.shape)
     self.H[self.assigned, range(self._num_samples)] = 1.0