# Source code for strawberryfields.apps.train.embed

# Copyright 2020 Xanadu Quantum Technologies Inc.

# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

# Unless required by applicable law or agreed to in writing, software
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
r"""
Submodule for embedding trainable parameters into the GBS distribution.
"""

import numpy as np

[docs]class ExpFeatures:
r"""Exponential embedding with feature vectors.

Weights of the :math:W matrix in the :math:WAW parametrization are expressed as an exponential
of the inner product between user-specified feature vectors and trainable parameters:
:math:w_i = \exp(-f^{(i)}\cdot\theta). The Jacobian, which encapsulates the derivatives of
the weights with respect to the parameters can be computed straightforwardly as:
:math:\frac{d w_i}{d\theta_k} = -f^{(i)}_k w_i.

**Example usage:**

>>> features = np.array([[0.1, 0.1, 0.1], [0.2, 0.2, 0.2], [0.3, 0.3, 0.3]])
>>> embedding = ExpFeatures(features)
>>> parameters = np.array([0.1, 0.2, 0.3])
>>> embedding(parameters)
[0.94176453 0.88692044 0.83527021]

Args:
features (np.array): Matrix of feature vectors where the i-th row is the i-th feature vector
"""

def __init__(self, features):
"""Initializes the class for an input matrix whose rows are feature vectors"""
self.features = features
self.m, self.d = np.shape(features)

def __call__(self, params):
return self.weights(params)

[docs]    def weights(self, params):
r"""Computes weights as a function of input parameters.

Args:
params (np.array): trainable parameters

Returns:
np.array: weights
"""
if self.d != len(params):
raise ValueError(
"Dimension of parameter vector must be equal to dimension of feature vectors"
)
return np.exp(-self.features @ params)

[docs]    def jacobian(self, params):
r"""Computes the Jacobian matrix of weights with respect to input parameters :math:J_{
ij} = \frac{\partial w_i}{\partial \theta_j}.

Args:
params (np.array): trainable parameters

Returns:
np.array: Jacobian matrix of weights with respect to parameters
"""
if self.d != len(params):
raise ValueError(
"Dimension of parameter vector must be equal to dimension of feature vectors"
)
w = self.weights(params)
return -self.features * w[:, np.newaxis]

[docs]class Exp(ExpFeatures):
r"""Simple exponential embedding.

Weights of the :math:W matrix in the :math:WAW parametrization are expressed as an exponential
of the trainable parameters: :math:w_i = \exp(-\theta_i). The Jacobian, which encapsulates the
derivatives of the weights with respect to the parameters can be computed straightforwardly as:
:math:\frac{d w_i}{d\theta_k} = -w_i\delta_{i,k}.

**Example usage:**

>>> dim = 3
>>> embedding = Exp(dim)
>>> parameters = np.array([0.1, 0.2, 0.3])
>>> embedding(parameters)
[0.90483742 0.81873075 0.74081822]

Args:
dim (int): Dimension of the vector of parameters, which is equal to the number of modes
in the GBS distribution
"""

def __init__(self, dim):
"""The simple exponential mapping is a special case where the matrix of feature vectors
is the identity"""
super().__init__(np.eye(dim))


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