#pgmpy

[damilola-doodles]

📌Project Title: Dynamic Supply Chain Risk Modeling and Probabilistic Anomaly Detection System. 🔴

ai-ml-ds-supplychain-risk-bayes-006 Filename: dynamic_supply_chain_risk_modeling_and_anomaly_detection.py Timestamp: Mon Jun 02 2025 19:16:16 GMT+0000 (Coordinated Universal Time) Problem Domain:Supply Chain Management, Risk Management, Operations Research, Probabilistic Graphical Models, Anomaly Detection. Project Description:This project develops an advanced system for modeling risks within…

[dammyanimation]

📌Project Title: Dynamic Supply Chain Risk Modeling and Probabilistic Anomaly Detection System. 🔴

ai-ml-ds-supplychain-risk-bayes-006 Filename: dynamic_supply_chain_risk_modeling_and_anomaly_detection.py Timestamp: Mon Jun 02 2025 19:16:16 GMT+0000 (Coordinated Universal Time) Problem Domain:Supply Chain Management, Risk Management, Operations Research, Probabilistic Graphical Models, Anomaly Detection. Project Description:This project develops an advanced system for modeling risks within…

[damilola-ai-automation]

📌Project Title: Dynamic Supply Chain Risk Modeling and Probabilistic Anomaly Detection System. 🔴

ai-ml-ds-supplychain-risk-bayes-006 Filename: dynamic_supply_chain_risk_modeling_and_anomaly_detection.py Timestamp: Mon Jun 02 2025 19:16:16 GMT+0000 (Coordinated Universal Time) Problem Domain:Supply Chain Management, Risk Management, Operations Research, Probabilistic Graphical Models, Anomaly Detection. Project Description:This project develops an advanced system for modeling risks within…

[damilola-warrior-mindset]

📌Project Title: Dynamic Supply Chain Risk Modeling and Probabilistic Anomaly Detection System. 🔴

ai-ml-ds-supplychain-risk-bayes-006 Filename: dynamic_supply_chain_risk_modeling_and_anomaly_detection.py Timestamp: Mon Jun 02 2025 19:16:16 GMT+0000 (Coordinated Universal Time) Problem Domain:Supply Chain Management, Risk Management, Operations Research, Probabilistic Graphical Models, Anomaly Detection. Project Description:This project develops an advanced system for modeling risks within…

[damilola-moyo]

📌Project Title: Dynamic Supply Chain Risk Modeling and Probabilistic Anomaly Detection System. 🔴

ai-ml-ds-supplychain-risk-bayes-006 Filename: dynamic_supply_chain_risk_modeling_and_anomaly_detection.py Timestamp: Mon Jun 02 2025 19:16:16 GMT+0000 (Coordinated Universal Time) Problem Domain:Supply Chain Management, Risk Management, Operations Research, Probabilistic Graphical Models, Anomaly Detection. Project Description:This project develops an advanced system for modeling risks within…

[aibyrdidini]

Bayesian Inference Algorithms.

A Unified Framework for Perception, Reasoning, and Decision-making.

I'll provide you with a simplified example of combining AI with perception (computer vision), reasoning (probabilistic graphical models), and decision-making (Markov decision processes) using Python.

First, let's install the required libraries:

```bash

pip install numpy opencv-python-headless matplotlib pgmpy gym

```

Now, let's create a generic example for each of the three tasks and then combine them.

1. Perception (Computer Vision):

```python

import cv2

import numpy as np

def detect_circles(image):

image = cv2.medianBlur(image, 5)

circles = cv2.HoughCircles(image, cv2.HOUGH_GRADIENT, 1, 20, param1=50, param2=30, minRadius=0, maxRadius=0)

return circles[0] if circles is not None else []

image = cv2.imread("circles.png", cv2.IMREAD_GRAYSCALE)

circles = detect_circles(image)

for circle in circles:

x, y, r = circle

cv2.circle(image, (x, y), r, (0, 255, 0), 2)

cv2.imshow("Detected circles", image)

cv2.waitKey(0)

cv2.destroyAllWindows()

```

2. Reasoning (Probabilistic Graphical Models):

```python

from pgmpy.models import BayesianModel

from pgmpy.estimators import MaximumLikelihoodEstimator, BayesianEstimator

from pgmpy.inference import VariableElimination

model = BayesianModel([('A', 'B'), ('B', 'C'), ('C', 'D')])

model.add_evidence('A', 1)

model.add_cpds(

{'A': np.array([[0.7, 0.3]]),

'B': np.array([[0.9, 0.1], [0.5, 0.5]]),

'C': np.array([[0.8, 0.2], [0.4, 0.6]]),

'D': np.array([[0.6, 0.4], [0.3, 0.7]])})

infer = VariableElimination(model)

query = infer.query(variables=['D'], evidence={'A': 1})

print(query)

```

3. Decision-making (Markov Decision Processes):

```python

import gym

env = gym.make("FrozenLake-v0")

def policy_evaluation(policy, env, theta=0.0001, gamma=1.0):

rewards = np.zeros(env.nS)

while True:

delta = 0

for s in range(env.nS):

v = 0

for a, action_prob in enumerate(policy[s]):

for prob, next_state, reward, done in env.P[s][a]:

v += action_prob * prob * (reward + gamma * rewards[next_state])

delta = max(delta, np.abs(v - rewards[s]))

rewards[s] = v

if delta < theta:

break

return rewards

policy = np.array([[0.0, 1.0, 0.0, 0.0],

[0.0, 0.0, 0.0, 1.0],

[1.0, 0.0, 0.0, 0.0],

[0.0, 0.0, 0.0, 1.0]])

policy_evaluation(policy, env)

```

4. Combining AI, Perception, Reasoning, and Decision-making:

In a real-world application, these tasks would be combined based on the specific use case. For example, a self-driving car would use computer vision to perceive its surroundings, probabilistic graphical models to reason about the state of the world, and Markov decision processes to make decisions.

Due to the complexity of creating a complete example, I'll provide a high-level outline of how these tasks can be combined in a self-driving car scenario:

- Perception:

- Use computer vision techniques to detect and recognize objects, such as cars, pedestrians, and traffic signs.

- Reasoning:

- Use probabilistic graphical models to infer the state of the world, such as the positions and velocities of other vehicles and pedestrians.

- Decision-making:

- Use Markov decision processes to decide on the best actions, such as accelerating, braking, or steering.

This outline is not a complete solution, but it should give you an idea of how AI, perception, reasoning, and decision-making can be combined in a real-world scenario.

RDIDINI PROMPT ENGINEER

[philippinesnewsdigest]

Caloocan police chief placed on ‘administrative relief’

http://dlvr.it/PgmpYS