d3620bbb13620338cc8aaf39d78cead58ac5d410,metric_learn/rca.py,RCA,fit,#RCA#Any#Any#,90

Before Change

    chunks = np.asanyarray(chunks, dtype=int)
    chunk_mask, chunked_data = _chunk_mean_centering(X_t, chunks)

    inner_cov = np.cov(chunked_data, rowvar=0, bias=1)
    dim = self._check_dimension(np.linalg.matrix_rank(inner_cov), X_t)

    // Fisher Linear Discriminant projection
    if dim < X_t.shape[1]:
      total_cov = np.cov(X_t[chunk_mask], rowvar=0)
      tmp = np.linalg.lstsq(total_cov, inner_cov)[0]
      vals, vecs = np.linalg.eig(tmp)
      inds = np.argsort(vals)[:dim]
      A = vecs[:, inds]
      inner_cov = A.T.dot(inner_cov).dot(A)
      self.transformer_ = _inv_sqrtm(inner_cov).dot(A.T)
    else:
      self.transformer_ = _inv_sqrtm(inner_cov).T

    if M_pca is not None:
        self.transformer_ = self.transformer_.dot(M_pca)

    return self

After Change

    chunks = np.asanyarray(chunks, dtype=int)
    chunk_mask, chunked_data = _chunk_mean_centering(X_t, chunks)

    inner_cov = np.atleast_2d(np.cov(chunked_data, rowvar=0, bias=1))
    dim = self._check_dimension(np.linalg.matrix_rank(inner_cov), X_t)

    // Fisher Linear Discriminant projection
    if dim < X_t.shape[1]:
      total_cov = np.cov(X_t[chunk_mask], rowvar=0)
      tmp = np.linalg.lstsq(total_cov, inner_cov)[0]
      vals, vecs = np.linalg.eig(tmp)
      inds = np.argsort(vals)[:dim]
      A = vecs[:, inds]
      inner_cov = np.atleast_2d(A.T.dot(inner_cov).dot(A))
      self.transformer_ = _inv_sqrtm(inner_cov).dot(A.T)
    else:
      self.transformer_ = _inv_sqrtm(inner_cov).T

    if M_pca is not None:
        self.transformer_ = np.atleast_2d(self.transformer_.dot(M_pca))

    return self

Instances