How to rewrite the following functions using only numpy array operations (universal functions, aggregations, boolean indexing) without using loops
def mean_squared_error (v , p ):
n = len ( v [0])
result = 0
for i in range ( n ):
result += ( v [0][ i ] - p [0])**2 + ( v [1][ i ] - p [1])**2
result = result / n
return result
I tried doing it like this but nothing printed out
def mean_squared_error2 (v, p ):
v=np.array(v)
n = v.shape[1]
result = np.sum((v[0,:]- p[0])**2 + (v[1,:] - p[1])**2)
print(result)
return result/n
How to rewrite the following functions using only numpy array operations (universal functions, aggregations, boolean indexing)...
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