pca.py 文件源码

def PCA_values(data, centered=True):
    n_samples, n_features = data.shape
    #By default, the data are centered
    if (centered == True):
        data_centered = data - mean(data, axis=0)
    else:
        data_centered = data

    #apply the Single Vector Decomposition 
    U, S, V = linalg.svd(data_centered, full_matrices=False)
    # flip eigenvectors' sign to enforce deterministic output
    U, V = svd_flip(U, V)

    #components
    components_ = V

    #variance explained by PCs
    explained_variance_ratio_ = varianceExplained(S, n_samples)

    return(components_, explained_variance_ratio_)