def __init__(self, kernel, adopt_thresh, state, target, maxsize, adaptive=True, forget_rate=0.0): """ kernel can be an instance of i2maps.contrib.krls.Gaussian, or a custom Python function taking two matrices as arguments. """ self.dp = KernelDict(kernel, adopt_thresh, state, target, maxsize, adaptive, forget_rate) self.P = [[1]] self.Alpha = np.dot(self.dp.Kinv, target)
class KRLS: """ Kernel Recursive Least Squares Initialization parameters: kernel The kernel to be used. Can be an instance of i2maps.contrib.krls.Gaussian, or any user-defined function taking two matrices as arguments and returning a numpy array as result. adopt_thresh approximate linear dependence threshold, [0, 1] state a data sample structure (array) target a target value (float) maxsize maximum size of the dictionary adaptive indicator if elimination is data-adaptive, True/False forget_rate frequency rate for forced elimination of the oldest entry, [0, 1] """ def __init__(self, kernel, adopt_thresh, state, target, maxsize, adaptive=True, forget_rate=0.0): """ kernel can be an instance of i2maps.contrib.krls.Gaussian, or a custom Python function taking two matrices as arguments. """ self.dp = KernelDict(kernel, adopt_thresh, state, target, maxsize, adaptive, forget_rate) self.P = [[1]] self.Alpha = np.dot(self.dp.Kinv, target) def update(self, state, target): """ Updates the model. """ # dictionary update preceeds weights update. They are not independent: # dictionary needs to know weights Alpha to enable adaptive elimination self.dp.update( state, target, self.Alpha ) # now we update the weights using values precomputed in dicionary # to enable recursive updates here at = self.dp.at dt = self.dp.dt ktwid = self.dp.ktwid if self.dp.addedFlag: # if a new entry was added to the dictionary if self.dp.eliminatedFlag: # and an older/unrelevant entry was eliminated to make some room # I suspect the smart tricks got partly waisted here by matrix multiply # cause weights only become dependent on dictionary and not on all incoming samples. # Still need to figure out what's happening. self.Alpha = np.dot(self.dp.Kinv, self.dp.Targ) else: # was enough room, so update as per original paper self.P = np.vstack( [np.hstack([self.P, np.zeros((self.dp.numel-1,1))]), np.hstack( [np.zeros((1,self.dp.numel-1)), [[1]]] )] ) inno = ( target - np.dot(ktwid.T,self.Alpha) )/dt self.Alpha = np.vstack([self.Alpha - np.dot(at,inno), inno]) self.Alpha = np.dot(self.dp.Kinv, self.dp.Targ) self.addedFlag = 1; else: # we don't add an entry but update weights not to waste the sample # kinda smart incremental reduced rank regression. tmp = np.dot(self.P, at) qt = tmp / ( 1 + np.dot(at.T,tmp) ) self.P = self.P - np.dot(qt,tmp.T) self.Alpha = self.Alpha + np.dot(self.dp.Kinv, qt*( target - np.dot(ktwid.T,self.Alpha) )) self.addedFlag = 0 def query(self, sample): """ Queries the model to make prediction based on the provided sample. """ # compute the kernel of the input with the dictionary kernvals = self.dp.query(sample) # compute the weighted sum return np.dot(kernvals, self.Alpha)
