LSH/ANN.py
def train(self, trainDat, trainLabs):
        '''
        Nearest neighbor classifiers have the advantage that they have virtually
        no training phase, other than storing the training data.
        @param trainDat 2D numpy array containing the training data
        @param trainLabs 1D numpy array containing the training data labels
        @return the model itself
        '''
        
        self.trainDat = trainDat
        self.trainLabs = trainLabs
        return self

        Nearest neighbor classifiers have the advantage that they have virtually
        no training phase, other than storing the training data.
        @param trainDat 2D numpy array containing the training data
        @param trainLabs 1D numpy array containing the training data labels
        @return the model itself
        
Nearest neighbor classifiers have the advantage that they have virtually
no training phase, other than storing the training data.
@param trainDat 2D numpy array containing the training data
@param trainLabs 1D numpy array containing the training data labels
@return the model itself
Nearest neighbor classifiers have the advantage that they have virtually no training phase other than storing the training data . @param trainDat 2D numpy array containing the training data @param trainLabs 1D numpy array containing the training data labels @return the model itself
def train(self, trainDat, trainLabs):
        self.trainDat = trainDat
        self.trainLabs = trainLabs
        return self
def train ( self , trainDat , trainLabs ) : self . trainDat = trainDat self . trainLabs = trainLabs return self
Nearest neighbor classifiers have the advantage that they have virtually
no training phase, other than storing the training data.
Nearest neighbor classifiers have the advantage that they have virtually no training phase other than storing the training data .
'''
        Nearest neighbor classifiers have the advantage that they have virtually
        no training phase, other than storing the training data.
        @param trainDat 2D numpy array containing the training data
        @param trainLabs 1D numpy array containing the training data labels
        @return the model itself
        '''
self
trainDat
trainLabs