def compute_distances(matrix1, matrix2):
nmatrix1 = normalize(matrix1)
nmatrix2 = normalize(matrix2)
distances = np.arccos(np.maximum(-1, np.minimum(1, dot(nmatrix1, nmatrix2.T))))
assert isinstance(distances, DenseMatrix)
assert distances.same_row_labels_as(nmatrix1)
return multiply(distances, distances)
def compute_distances(matrix1, matrix2):
nmatrix1 = normalize(matrix1)
nmatrix2 = normalize(matrix2)
distances = np.arccos(
np.maximum(-1, np.minimum(1, dot(nmatrix1, nmatrix2.T))))
assert isinstance(distances, DenseMatrix)
assert distances.same_row_labels_as(nmatrix1)
return multiply(distances, distances)
def project(self, vector):
# vector can in fact be a matrix of many vectors
# Dimensions:
# vector = (m x ndim) or possibly just (ndim),
# with ndim = K from the SVD
# dist = (m x N)
# means = (N)
# mdsarray_sharp = (N x k)
dist = (compute_distances(vector, self.landmarks) - self.means) / 2
return dot(dist, -self.mdsarray_sharp)
def project(self, vector):
# vector can in fact be a matrix of many vectors
# Dimensions:
# vector = (m x ndim) or possibly just (ndim),
# with ndim = K from the SVD
# dist = (m x N)
# means = (N)
# mdsarray_sharp = (N x k)
dist = (compute_distances(vector, self.landmarks) - self.means) / 2
return dot(dist, -self.mdsarray_sharp)