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def predict(self, x): """Predict and return labels for each feature vector from x x -- feature vectors (N x D) """ predictions = [] # placeholder for N labels # loop over all test samples for x_test in x: # array of distances between current test and all training samples distances = np.sum(self.distance(self.x_train - x_test), axis=1) # get the closest one min_index = np.argmin(distances) # add corresponding label predictions.append(self.y_train[min_index]) return predictions
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