def PCA_components(x):
    """
    Principal Component Analysis helper to check out eigenvalues of components.

    **Args:**

    * `x` : input matrix (2d array), every row represents new sample

    **Returns:**
    
    * `components`: sorted array of principal components eigenvalues 
        
    """ 
    # validate inputs
    try:    
        x = np.array(x)
    except:
        raise ValueError('Impossible to convert x to a numpy array.')
    # eigen values and eigen vectors of data covariance matrix
    eigen_values, eigen_vectors = np.linalg.eig(np.cov(x.T))
    # sort eigen vectors according biggest eigen value
    eigen_order = eigen_vectors.T[(-eigen_values).argsort()]
    # form output - order the eigenvalues
    return eigen_values[(-eigen_values).argsort()]
def PCA_components ( x ) : try : x = np . array ( x ) except : raise ValueError ( 'Impossible to convert x to a numpy array.' ) eigen_values , eigen_vectors = np . linalg . eig ( np . cov ( x . T ) ) eigen_order = eigen_vectors . T [ ( - eigen_values ) . argsort ( ) ] return eigen_values [ ( - eigen_values ) . argsort ( ) ]
Principal Component Analysis helper to check out eigenvalues of components.

    **Args:**

    * `x` : input matrix (2d array), every row represents new sample

    **Returns:**
    
    * `components`: sorted array of principal components eigenvalues