协方差矩阵定义了数据的形状；对角线是方差，非对角线是协方差
Two-dimensional normally distributed data is explained completely by its mean and its covariance matrix(二维正态分布数据通过其均值和协方差矩阵得到了完全解释)
covariance matrix defines the shape of the data. Diagonal spread is captured by the covariance, while axis-aligned spread is captured by the variance.
the covariance matrix defines both the spread (variance), and the orientation (covariance) of our data.(描述了数据的分布和方向)
