import numpy as np


class PCA():
    def __init__(self):
        self.k = 2

    def mean_zero(self, data):
        mean = np.mean(data, axis=0)
        new_data = data - mean
        return new_data, mean

    def pca(self, data, mean):
        cov = np.cov(data, rowvar=0)
        eigvals, eigvectors = np.linalg.eig(np.mat(cov))
        sort = np.argsort(eigvals)
        eigval = sort[-1:-(k + 1):-1]
        eigvector = eigvectors[:, eigval]
        low_dimension_data = data * eigvector
        rec_data = (low_dimension_data * eigvector.T) + mean
        return low_dimension_data, rec_data


if __name__ == '__main__':
   pca = PCA()