sop.OneBody(
    self
    input_size
    hidden_size
    basis_size
    output_size=1
    w_scale=1.0
    b_scale=1.0
    w_dist='uniform'
    b_dist='linspace'
    x0=None
    activation='moderate+silu'
    key=None
    X_out=None
    fix_bias=False
)

Function given by sum of one-body functions

\[ f(q_1, q_2, \ldots, q_f) = \sum_{p=1}^{f} \sum_{\rho_p} W_{\rho_p}^{(p)} \phi_{\rho_p}(w_{\rho_p}^{(p)} q_p+b_{\rho_p}^{(p)}) \]

Attributes

Name Description
q0 Get initial hidden coordinates \(q_0=x_0U\)

Methods

Name Description
export_h5 Export the model to a HDF5 file
import_h5 Import the model from a HDF5 file
mse Mean squared error
mse_force Mean squared error with force
plot_basis Plot distribution of \(\phi\)
show_onebody Visualize one-dimensional cut.
to_nnmpo Convert to NNMPO model

export_h5

sop.OneBody.export_h5(path)

Export the model to a HDF5 file

Parameters

Name Type Description Default
path str path to the HDF5 file required

Examples

import pompon
model = pompon.NNMPO(input_size=3, hidden_size=3, basis_size=5)
model.export_h5("/path/to/model.h5")

See Also

import_h5()

import_h5

sop.OneBody.import_h5(path)

Import the model from a HDF5 file

Parameters

Name Type Description Default
path str path to the HDF5 file required

Returns

Name Type Description
Model Model model instance

Examples

import pompon
model = pompon.NNMPO.import_h5("/path/to/model.h5")

See Also

export_h5()

mse

sop.OneBody.mse(x, y)

Mean squared error

Parameters

Name Type Description Default
x Array input tensor with shape \((D,n)\) where \(D\) is the batch size and \(n\) is the input size. required
y Array output tensor with shape \((D,1)\) required

Returns

Name Type Description
float float mean squared error

mse_force

sop.OneBody.mse_force(x, f)

Mean squared error with force

Parameters

Name Type Description Default
x Array input tensor with shape \((D,n)\) where \(D\) is the batch size and \(n\) is the input size. required
f Array force tensor with shape \((D,n)\) required

plot_basis

sop.OneBody.plot_basis(x)

Plot distribution of \(\phi\)

Parameters

Name Type Description Default
x Array input tensor with shape \((D,n)\) where \(D\) is the batch size and \(n\) is the input size. required

Examples

Code 
import numpy as np
import pompon
x = np.random.rand(10, 3)
model = pompon.NNMPO(input_size=3, hidden_size=3, basis_size=5,
                     activation="gauss", b_scale=1.0, w_scale=1.0)
model.plot_basis(x)

show_onebody

sop.OneBody.show_onebody()

Visualize one-dimensional cut.

Examples

Code 
import pompon
model = pompon.NNMPO(input_size=3, hidden_size=3, basis_size=5)
model.show_onebody()

to_nnmpo

sop.OneBody.to_nnmpo()

Convert to NNMPO model