I have a list of vectors as a numpy array.
[[ 1., 0., 0.],
[ 0., 1., 2.] ...]
They all have the same dimension. How do I find out that in the vector space which vector is the closest to all the other vectors in the array? Is there scipy or sklearn function that calculates this?
Update
:
By "closest", I meant the cosine and the Euclidean distance.
Update 2
:
Let's say I have 4 vectors (a,b,c,d), and the Cosine distance between the vectors are:
a,b = 0.2
a,c = 0.9
a,d = 0.7
b,c = 0.5
b,d = 0.75
c,d = 0.8
So for each, vector a,b,c,d I get :
{
'a': [1,0.2,0.9,0.7],
'b': [0.2,1,0.5,0.75],
'c' : [0.9,0.5,1,0.75],
'd' : [0.7,0.75,0.8,1]
}
Is there a way of saying let's say vector d is the one that is the most similar to a,b,c ?
You could brute force it something like this. Note that this is O(n^2), and will get slow for large n.
import numpy as np
def cost_function(v1, v2):
"""Returns the square of the distance between vectors v1 and v2."""
diff = np.subtract(v1, v2)
# You may want to take the square root here
return np.dot(diff, diff)
n_vectors = 5
vectors = np.random.rand(n_vectors,3)
min_i = -1
min_cost = 0
for i in range (0, n_vectors):
sum_cost = 0.0
for j in range(0, n_vectors):
sum_cost = sum_cost + cost_function(vectors[i,:],vectors[j,:])
if min_i < 0 or min_cost > sum_cost:
min_i = i
min_cost = sum_cost
print('{} at {}: {:.3f}'.format(i, vectors[i,:], sum_cost))
print('Lowest cost point is {} at {}: {:.3f}'.format(min_i, vectors[min_i,:], min_cost))
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