# Demo entry 6785156

**Дмитрий Прогер**

Submitted by **Hi**
on Mar 13, 2019 at 16:50

Language: Python. Code size: 1.0 kB.

import numpy as np class NearestNeighbor(object): def __init__(self): pass def train(self, X, y): """ X is N x D where each row is an example. Y is 1-dimension of size N """ # the nearest neighbor classifier simply remembers all the training data self.Xtr = X self.ytr = y def predict(self, X): """ X is N x D where each row is an example we wish to predict label for """ num_test = X.shape[0] # lets make sure that the output type matches the input type Ypred = np.zeros(num_test, dtype = self.ytr.dtype) # loop over all test rows for i in range(num_test): # find the nearest training image to the i'th test image # using the L1 distance (sum of absolute value differences) distances = np.sum(np.abs(self.Xtr - X[i,:]), axis = 1) min_index = np.argmin(distances) # get the index with smallest distance Ypred[i] = self.ytr[min_index] # predict the label of the nearest example return Ypred

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