I'm trying implementing k-NN with Mahalanobis's distance in python with numpy. However, the code below works very slowly when I use broadcasting. Please teach me how can I improve numpy speed or implement this better.
from __future__ import division
from sklearn.utils import shuffle
from sklearn.metrics import f1_score
from sklearn.datasets import fetch_mldata
from sklearn.cross_validation import train_test_split
import numpy as np
import matplotlib.pyplot as plt
mnist = fetch_mldata('MNIST original')
mnist_X, mnist_y = shuffle(mnist.data, mnist.target.astype('int32'))
mnist_X = mnist_X/255.0
train_X, test_X, train_y, test_y = train_test_split(mnist_X, mnist_y, test_size=0.2)
k = 2
def data_gen(n):
return train_X[train_y == n]
train_X_num = [data_gen(i) for i in range(10)]
inv_cov = [np.linalg.inv(np.cov(train_X_num[i], rowvar=0)+np.eye(784)*0.00001) for i in range(10)] # Making Inverse covariance matrices
for i in range(10):
ivec = train_X_num[i] # ivec size is (number of 'i' data, 784)
ivec = ivec - test_X[:, np.newaxis, :] # This code is too much slowly, and using huge memory
iinv_cov = inv_cov[i]
d[i] = np.add.reduce(np.dot(ivec, iinv_cov)*ivec, axis=2).sort(1)[:, :k+1] # Calculate x.T inverse(sigma) x, and extract k-minimal distance
