I want to code my own kNN algorithm from scratch, the reason is that I need to weight the features. The problem is that my program is still really slow despite removing for loops and using built in numpy functionality. Can anyone suggest a way to speed this up? I don't use np.sqrt for the L2 distance because it's unnecessary and actually slows it all up quite a bit. class GlobalWeightedKNN: """ A k-NN classifier with feature weights Returns: predictions of k-NN. """ def __init__(self): self.X_train = None self.y_train = None self.k = None self.weights = None self.predictions = list() def fit(self, X_train, y_train, k, weights): self.X_train = X_train self.y_train = y_train self.k = k self.weights = weights def predict(self, testing_data): """ Takes a 2d array of query cases. Returns a list of predictions for k-NN classifier """ np.fromiter((self.__helper(qc) for qc in testing_data), float) return self.predictions def __helper(self, qc): neighbours = np.fromiter((self.__weighted_euclidean(qc, x) for x in self.X_train), float) neighbours = np.array([neighbours]).T indexes = np.array([range(len(self.X_train))]).T neighbours = np.append(indexes, neighbours, axis=1) # Sort by second column - distances neighbours = neighbours[neighbours[:,1].argsort()] k_cases = neighbours[ :self.k] indexes = [x[0] for x in k_cases] y_answers = [self.y_train[int(x)] for x in indexes] answer = max(set(y_answers), key=y_answers.count) # get most common value self.predictions.append(answer) def __weighted_euclidean(self, qc, other): """ Custom weighted euclidean distance returns: floating point number """ return np.sum( ((qc - other)**2) * self.weights ) Continue reading...
