PCA Covariance

Compute the sample covariance matrix of a data matrix X with $nn$ rows (samples) and $dd$ columns (features), dividing by $n-1n-1$.

Implement solve(X).

Returns. Return a symmetric $d\times dd\backslash times\; d$ float NumPy array. At least two rows are needed; a single row has no sample covariance (the result is nan).

Examples

Example 1

Input:

solve([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])

Output:

[[4.0, 4.0], [4.0, 4.0]]

Example 2

Input:

solve([[1.0, 0.0], [2.0, 0.0], [3.0, 0.0]])

Output:

[[1.0, 0.0], [0.0, 0.0]]