VideoMatching_PCA_SVD.py 文件源码

def PCA(self,data):
        """
        returns: data transformed in 2 dims/columns + regenerated original data
        pass in: data as 2D NumPy array
        """
        dims_rescaled_data=2
        m, n = data.shape

        data -= data.mean(axis=0)

        # calculate the covariance matrix
        R = np.cov(data, rowvar=False)
        # calculate eigenvectors & eigenvalues of the covariance matrix
        # use 'eigh' rather than 'eig' since R is symmetric,
        # the performance gain is substantial
        evals, evecs = LA.eig(R)
        # sort eigenvalue in decreasing order
        idx = np.argsort(evals)[::-1]
        evecs = evecs[:,idx]
        # sort eigenvectors according to same index
        evals = evals[idx]
        # select the first n eigenvectors (n is desired dimension
        # of rescaled data array, or dims_rescaled_data)
        evecs = evecs[:, :dims_rescaled_data]
        # carry out the transformation on the data using eigenvectors
        # and return the re-scaled data, eigenvalues, and eigenvectors
        return np.dot(evecs.T, data.T).T, evals, evecs