# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
from scipy.stats import multivariate_normal
from pgmpy.factors.base import BaseFactor
[docs]
class LinearGaussianCPD(BaseFactor):
r"""
Defines a Linear Gaussian CPD.
The Linear Gaussian CPD makes the following assumptions:
1) The variable is Gaussian/Normally distributed.
2) The mean of the variable depends on the values of the parents and the
intercept term.
3) The variance is independent of other variables.
For example,
.. math::
p(Y|X) = N(0.9 - 2x; 1)
Here, :math:`0.9 - 2x` is the mean of the variable :math:`Y` and the
standard deviation is 1.
In generalized terms, let :math:`Y` be a Gaussian variable with parents
:math:`X_1, X_2, \cdots, X_k`. Assuming linear relationship between Y and
\mathbf{X}, the conditional distribution of Y can be defined as:
.. math:: p(Y |x1, x2, ..., xk) = \mathcal{N}(\beta_0 + x1*\beta_1 + ......... + xk*\beta_k Íž \sigma)
References
----------
.. [1] https://cedar.buffalo.edu/~srihari/CSE574/Chap8/Ch8-PGM-GaussianBNs/8.5%20GaussianBNs.pdf
Parameters
----------
variable: any hashable python object
The variable whose CPD is defined.
beta: list (array-like)
The coefficients corresponding to each of the evidence variable. The first
term of the `beta` array is the intercept term.
std: float
The standard deviation of `variable`.
evidence: iterator (array-like)
List of parents/evidence variables of `variable`. The order in which `evidence`
is specified should match the order of `beta`.
Examples
--------
# To represent the conditional distribution, P(Y| X1, X2, X3) = N(0.2 - 2*x1 + 3*x2 + 7*x3 ; 9.6), we can write:
>>> from pgmpy.factors.continuous import LinearGaussianCPD
>>> cpd = LinearGaussianCPD('Y', [0.2, -2, 3, 7], 9.6, ['X1', 'X2', 'X3'])
>>> cpd.variable
'Y'
>>> cpd.evidence
['x1', 'x2', 'x3']
>>> cpd.beta_vector
[0.2, -2, 3, 7]
"""
def __init__(self, variable, beta, std, evidence=[]):
self.variable = variable
self.beta = np.array(beta)
self.std = std
self.evidence = list(evidence)
self.variables = [variable] + evidence
[docs]
def copy(self):
"""
Returns a copy of the distribution.
Returns
-------
LinearGaussianCPD: copy of the distribution
Examples
--------
>>> from pgmpy.factors.continuous import LinearGaussianCPD
>>> cpd = LinearGaussianCPD('Y', [0.2, -2, 3, 7], 9.6, ['X1', 'X2', 'X3'])
>>> copy_cpd = cpd.copy()
>>> copy_cpd.variable
'Y'
>>> copy_cpd.evidence
['X1', 'X2', 'X3']
"""
copy_cpd = LinearGaussianCPD(
variable=self.variable,
beta=self.beta,
std=self.std,
evidence=list(self.evidence),
)
return copy_cpd
def __str__(self):
mean = self.beta.round(3)
std = round(self.std, 3)
if self.evidence and list(self.beta):
# P(Y| X1, X2, X3) = N(-2*X1_mu + 3*X2_mu + 7*X3_mu; 0.2)
rep_str = "P({node} | {parents}) = N({mu} + {b_0}; {sigma})".format(
node=str(self.variable),
parents=", ".join([str(var) for var in self.evidence]),
mu=" + ".join(
[
f"{coeff}*{parent}"
for coeff, parent in zip(mean[1:], self.evidence)
]
),
b_0=str(mean[0]),
sigma=str(std),
)
else:
# P(X) = N(1, 4)
rep_str = f"P({str(self.variable)}) = N({str(mean[0])}; {str(std)})"
return rep_str
def __repr__(self):
str_repr = self.__str__()
return f"<LinearGaussianCPD: {str_repr} at {hex(id(self))}"
