コード例 #1
0
def lapeig_linear(X=None, W=None, L=None, num_vecs=None, k=None, eball=None):
    if L is None:
        if W is None:
            W = neighbor_graph(X, k=k, epsilon=eball)
        L = laplacian(W)
    u, s, _ = np.linalg.svd(np.dot(X.T, X))
    Fplus = np.linalg.pinv(np.dot(u, np.diag(np.sqrt(s))))
    T = reduce(np.dot, (Fplus, X.T, L, X, Fplus.T))
    L = 0.5 * (T + T.T)
    return lapeig(L=L, num_vecs=num_vecs)
コード例 #2
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def lapeig_linear(X=None, W=None, L=None, num_vecs=None, k=None, eball=None):
    if L is None:
        if W is None:
            W = neighbor_graph(X, k=k, epsilon=eball)
        L = laplacian(W)
    u, s, _ = np.linalg.svd(np.dot(X.T, X))
    Fplus = np.linalg.pinv(np.dot(u, np.diag(np.sqrt(s))))
    T = reduce(np.dot, (Fplus, X.T, L, X, Fplus.T))
    L = 0.5 * (T + T.T)
    return lapeig(L=L, num_vecs=num_vecs)
コード例 #3
0
ファイル: embedding.py プロジェクト: Jiawei98/deepalignomics
def lapeig(W=None, L=None, num_vecs=None, return_vals=False):
  tmp_L = (L is None)  # we can overwrite L if it's a tmp variable
  if L is None:
    L = laplacian(W, normed=True)
  vals,vecs = sp.linalg.eigh(L, overwrite_a=tmp_L)  # assumes L is symmetric!
  # not guaranteed to be in sorted order
  idx = np.argsort(vals)
  vecs = vecs.real[:,idx]
  vals = vals.real[idx]
  # discard any with really small eigenvalues
  for i in range(vals.shape[0]):
    if vals[i] >= 1e-8:
      break
  if num_vecs is None:
    # take all of them
    num_vecs = vals.shape[0] - i
  embedding = vecs[:,i:i+num_vecs]
  if return_vals:
    return embedding, vals[i:i+num_vecs]
  return embedding
コード例 #4
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def lapeig(W=None, L=None, num_vecs=None, return_vals=False):
    tmp_L = L is None  # we can overwrite L if it's a tmp variable
    if L is None:
        L = laplacian(W, normed=True)
    vals, vecs = sp.linalg.eigh(L, overwrite_a=tmp_L)  # assumes L is symmetric!
    # not guaranteed to be in sorted order
    idx = np.argsort(vals)
    vecs = vecs.real[:, idx]
    vals = vals.real[idx]
    # discard any with really small eigenvalues
    for i in xrange(vals.shape[0]):
        if vals[i] >= 1e-8:
            break
    if num_vecs is None:
        # take all of them
        num_vecs = vals.shape[0] - i
    embedding = vecs[:, i : i + num_vecs]
    if return_vals:
        return embedding, vals[i : i + num_vecs]
    return embedding
コード例 #5
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 def __init__(self, X, Y, corr, num_dims, eps=1e-8):
     Wxy = corr.matrix()
     L = laplacian(block_antidiag(Wxy, Wxy.T))
     self.pX, self.pY = _linear_decompose(X, Y, L, num_dims, eps)
コード例 #6
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def _manifold_setup(Wx, Wy, Wxy, mu):
    Wxy = mu * (Wx.sum() + Wy.sum()) / (2 * Wxy.sum()) * Wxy
    W = np.asarray(np.bmat(((Wx, Wxy), (Wxy.T, Wy))))
    return laplacian(W)
コード例 #7
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 def __init__(self,X,Y,corr,num_dims,eps=1e-8):
   Wxy = corr.matrix()
   L = laplacian(block_antidiag(Wxy,Wxy.T))
   self.pX, self.pY = _linear_decompose(X,Y,L,num_dims,eps)
コード例 #8
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def _manifold_setup(Wx,Wy,Wxy,mu):
  Wxy = mu * (Wx.sum() + Wy.sum()) / (2 * Wxy.sum()) * Wxy
  W = np.asarray(np.bmat(((Wx,Wxy),(Wxy.T,Wy))))
  return laplacian(W)