t3toolbox.tucker_tensor_train.t3_probe

t3toolbox.tucker_tensor_train.t3_probe(ww: t3toolbox.backend.common.typ.Sequence[t3toolbox.backend.common.NDArray], x: TuckerTensorTrain, use_jax: bool = False) → t3toolbox.backend.common.typ.Sequence[t3toolbox.backend.common.NDArray]

Probe a TuckerTensorTrain.

Examples

>>> import numpy as np
>>> import t3toolbox.tucker_tensor_train as t3
>>> import t3toolbox.backend.probing as probing
>>> x = t3.t3_corewise_randn((10,11,12),(5,6,4),(2,3,4,2))
>>> ww = (np.random.randn(10), np.random.randn(11), np.random.randn(12))
>>> zz = t3.t3_probe(ww, x)
>>> x_dense = x.to_dense()
>>> zz2 = probing.probe_dense(ww, x_dense)
>>> print([np.linalg.norm(z - z2) for z, z2 in zip(zz, zz2)])
[1.0259410400851746e-12, 1.0909087370186656e-12, 3.620283224238675e-13]

Vectorize over probes:

>>> import numpy as np
>>> import t3toolbox.tucker_tensor_train as t3
>>> import t3toolbox.backend.probing as probing
>>> x = t3.t3_corewise_randn((10,11,12),(5,6,4),(2,3,4,2))
>>> ww = (np.random.randn(2,3, 10), np.random.randn(2,3, 11), np.random.randn(2,3, 12))
>>> zz = t3.t3_probe(ww, x)
>>> x_dense = x.to_dense()
>>> zz2 = probing.probe_dense(ww, x_dense)
>>> print([np.linalg.norm(z - z2) for z, z2 in zip(zz, zz2)])
[2.9290244450205316e-12, 2.0347746956505754e-12, 1.7784156096697445e-12]

Vectorize over probes and T3s:

>>> import numpy as np
>>> import t3toolbox.tucker_tensor_train as t3
>>> import t3toolbox.backend.probing as probing
>>> x = t3.t3_corewise_randn((10,11,12),(5,6,4),(2,3,4,2), stack_shape=(4,5))
>>> ww = (np.random.randn(2,3, 10), np.random.randn(2,3, 11), np.random.randn(2,3, 12))
>>> zz = t3.t3_probe(ww, x)
>>> x_dense = x.to_dense()
>>> zz2 = probing.probe_dense(ww, x_dense)
>>> print([np.linalg.norm(z - z2) for z, z2 in zip(zz, zz2)])
[1.4471391818397927e-11, 1.0485601346346092e-11, 1.437623640611662e-11]