from typing import List, Dict, Any
from csle_base.json_serializable import JSONSerializable
[docs]class EmpiricalConditional(JSONSerializable):
"""
A DTO representing an empirical conditional distribution
"""
def __init__(self, conditional_name: str, metric_name: str,
sample_space: List[int],
probabilities: List[float]) -> None:
"""
Initializes the DTO
:param conditional_name: the name of the conditional
:param metric_name: the name of the metric
:param sample_space: the sample space (the domain of the distribution)
:param probabilities: the probability distribution
"""
self.conditional_name = conditional_name
self.probabilities = probabilities
assert round(sum(probabilities), 2) == 1
self.metric_name = metric_name
self.sample_space = sample_space
[docs] @staticmethod
def from_dict(d: Dict[str, Any]) -> "EmpiricalConditional":
"""
Converts a dict representation of the DTO into an instance
:param d: the dict to convert
:return: the converted instance
"""
return EmpiricalConditional(
conditional_name=d["conditional_name"], metric_name=d["metric_name"],
sample_space=d["sample_space"], probabilities=d["probabilities"]
)
[docs] def to_dict(self) -> Dict[str, Any]:
"""
:return: a dict representation of the DTO
"""
d: Dict[str, Any] = {}
d["conditional_name"] = self.conditional_name
d["metric_name"] = self.metric_name
d["sample_space"] = self.sample_space
d["probabilities"] = self.probabilities
return d
def __str__(self) -> str:
"""
:return: a string representation of the DTO
"""
return f"conditional_name:{self.conditional_name}, metric_name: {self.metric_name}, " \
f"sample_space: {self.sample_space}, probabilities: {self.probabilities}"
[docs] @staticmethod
def from_json_file(json_file_path: str) -> "EmpiricalConditional":
"""
Reads a json file and converts it to a DTO
:param json_file_path: the json file path
:return: the converted DTO
"""
import io
import json
with io.open(json_file_path, 'r') as f:
json_str = f.read()
return EmpiricalConditional.from_dict(json.loads(json_str))