KNN compute_distances_one_loop
def compute_distances_one_loop(self, X):
"""
Compute the distance between each test point in X and each training point
in self.X_train using a single loop over the test data.
Input / Output: Same as compute_distances_two_loops
"""
num_test = X.shape[0]
num_train = self.X_train.shape[0]
dists = np.zeros((num_test, num_train))
for i in range(num_test):
# Note axis=1 computes norm along rows
dists[i] = np.linalg.norm(X[i]-self.X_train, axis=1)
return dists
Difference was: 0.000000
Good! The distance matrices are the same
Grieving Gazelle