CostFunctions.py 文件源码

def __init__(self,
                 MMDLayer,
                 MMDTargetTrain,
                 MMDTargetValidation_split=0.1,
                 MMDTargetSampleSize=1000,
                 n_neighbors = 25,
                 scales = None,
                 weights = None):
        if scales == None:
            print("setting scales using KNN")
            med = np.zeros(20)
            for ii in range(1,20):
                sample = MMDTargetTrain[np.random.randint(MMDTargetTrain.shape[0], size=MMDTargetSampleSize),:]
                nbrs = NearestNeighbors(n_neighbors=n_neighbors).fit(sample)
                distances,dummy = nbrs.kneighbors(sample)
                #nearest neighbor is the point so we need to exclude it
                med[ii]=np.median(distances[:,1:n_neighbors])
            med = np.median(med)  
            scales = [med/2, med, med*2] # CyTOF    
            print(scales)
        scales = K.variable(value=np.asarray(scales))
        if weights == None:
            print("setting all scale weights to 1")
            weights = K.eval(K.shape(scales)[0])
        weights = K.variable(value=np.asarray(weights))
        self.MMDLayer =  MMDLayer
        MMDTargetTrain, MMDTargetValidation = train_test_split(MMDTargetTrain, test_size=MMDTargetValidation_split, random_state=42)
        self.MMDTargetTrain = K.variable(value=MMDTargetTrain)
        self.MMDTargetTrainSize = K.eval(K.shape(self.MMDTargetTrain)[0])
        self.MMDTargetValidation = K.variable(value=MMDTargetValidation)
        self.MMDTargetValidationSize = K.eval(K.shape(self.MMDTargetValidation)[0])
        self.MMDTargetSampleSize = MMDTargetSampleSize
        self.kernel = self.RaphyKernel
        self.scales = scales
        self.weights = weights


    #calculate the raphy kernel applied to all entries in a pairwise distance matrix